WM CIO Global Asset Allocation – Investing with UBS WM

Investing with UBS Wealth Management

ab

The concepts behind our solutions

WM CIO Global Asset Allocation

Mads N. S. Pedersen, Group Managing Director, UBS CIO WM Global Investment Office, Head Global Asset Allocation

Christophe de Montrichard, Managing Director, UBS CIO WM Global Investment Office, GAA Head Strategic Asset Allocation

Carolina Moura-Alves, Managing Director, UBS CIO WM Global Investment Office, GAA Head Fixed Income Strategies

Oliver Malitius, Executive Director, UBS CIO WM Global Investment Office, GAA Head Quantitative Strategies

Philipp Schöttler, Executive Director, UBS CIO WM Global Investment Office, GAA Head DM Credit Strategies

Katarina Cohrs, Director, UBS CIO WM Global Investment Office, GAA TAA & Investment Methodologies

For marketing purpose onlyThis publication does not constitute UBS independent research as it has not been drafted in accordance with the statutory regulations regarding the independence of financial research.

Foreword

We face a world in transition. The drivers of global growth are changing, valuations of many assets are high, and we expect to enter an environment of rising US interest rates for the first time in close to a decade.

Successfully guiding portfolios through this world in transition will undoubtedly raise various questions:

How to protect against downside in an increasingly volatile market? How to build effective fixed income portfolios in an environment of rising rates? How to gen-erate strong risk-adjusted performance when little excess return is on offer in tra-ditional markets?

In the following pages we introduce a series of new investment concepts to answer them.

We are not the only ones trying to answer these questions. But by applying the advanced methodologies of some of the most successful risk managers, hedge funds, and endowments for use by private investors, we believe CIO has created a unique set of solutions.

These new investment concepts represent the latest step in on our road to enhance the investment content we provide, and we look forward to engaging with you all to make this content as relevant for your needs in the future.

We hope you find this publication infor-mative and helpful in navigating portfolios through a world in transition.

Mark H. HaefeleGlobal Chief Investment OfficerUBS Wealth Management

Jürg ZeltnerPresidentUBS Wealth Management

Jürg Zeltner Mark H. Haefele

Introduction

Dear reader,

In the Chief Investment Office (CIO) in UBS Wealth Management, our mission is to help our clients preserve and grow their assets. In the CIO Global Asset Alloca-tion team, we believe that clients can best achieve their goals by investing in global portfolios that profit from the only free lunch in finance, diversification.

This document introduces you to investment concepts which, in various ways, take advantage of such global diversification benefits. These concepts form the back-bone of the investment solutions UBS Wealth Manage-ment offers clients in its discretionary and advisory products and mandates. The cutting-edge methodology we employ in our portfolios spans the range of invest-ment management approaches: from defining our tradi-tional Strategic Asset Allocations (SAAs); through systematically managing portfolio risk exposure in Sys-tematic Allocation Portfolios; harvesting liquidity premia in global bond markets to create well-balanced Global Credit Opportunities; to building portfolios inspired by the large university endowments that explic-itly benefit from the greater return potential of private market investment, a concept we call for simplicity’s sake Endowment Style Portfolio.

Our Strategic Asset Allocation concept, with associ-ated portfolios covering various risk levels, is meant for investors who seek the best trade-off between expected return and expected risk via investments in traditional relatively liquid global markets.

Some investors become understandably concerned or nervous when stock markets experience a large correc-tion, though they are comfortable holding equities through smaller drawdowns. The Systematic Alloca-tion Portfolio concept, by drawing on the principles of market momentum and the persistence of trends in asset classes like bonds and equities, tailors to these investors. This concept has historically outperformed traditional SAAs over the last 20 years, and we believe will continue doing so provided the world remains a place where financial markets exhibit persistent trends.

We are aware that other investors focus on income generation, and feel uncomfortable with the volatility associated with equity investments. While we ordinarily advise clients to take advantage of “full” diversification across all main asset classes, we respect this investment

constraint and have developed the Global Credit Opportunities concept in response. We partially offset the lack of equity investment by allocating to a well- diversified set of credit sub-asset classes – across regions, currencies and central banks, credit quality and the liquidity spectrum. These allocations nonetheless come with a cost: lower liquidity than one has in tradi-tional portfolios.

Investors who take a very long-term view and do not require “liquidity,” or the ability to buy and sell individ-ual asset classes or an entire portfolio in any given year, should consider investing according to the principles established by large university endowments, i.e. follow-ing the Endowment Style Portfolio. This concept departs from traditional SAAs by taking large positions in illiquid investments in private markets, real estate, private debt and private equity. It aims at preserving capital and wealth over many generations, and we recommend that the investments be built up over a number of years.

For investors who wish to keep it simple, by having a globally well diversified portfolio with an optimal asset class mix, but don’t wish to run the risk or harvest the benefits of active selection of individual bonds or equi-ties, we have constructed a set of Global Beta Port-folios tracking as closely as possible the individual asset classes on index level.

Thank you and kind regards,

Mads N. S. PedersenHead Global Asset Allocation UBS Wealth Management

Mads N. S. Pedersen

4

Contents

02 Foreword

03 Introduction

05 Strategic Asset Allocation (SAA) Methodology and Portfolios

21 Systematic Allocation Portfolio (SAP)

43 Global Credit Opportunities (GCO) Portfolio

57 Endowment-Style Portfolio (ESP)

77 Global Beta Portfolio (Gl. BP)

SAP-Nr.: 84612EN

April 2016

Strategic Asset Allocation (SAA) Methodology and Portfolios



SAAs: The driving force of our portfolios

ab

Please always read in conjunction with the glossary and the risk information at the end of the document. 7


Mads N. S. Pedersen, Group Managing Director, UBS CIO WM Global Investment Offi ce, Head Global Asset Allocation

Christophe de Montrichard, Managing Director, UBS CIO WM Global Investment Offi ce, GAA Head Strategic Asset Allocation

SAAs: The driving force of our portfolios

ab

SAAs: What they are and why they are importantThe strategic asset allocation (SAA) constitutes the backbone of a long-term investment portfolio. It structures a portfolio at the asset class level to match the specifi c investment objectives and risk tolerance of clients (their Financial Situa-tion and Personality) while off ering them the best risk/return trade-off for the given level of risk accepted. Creating the right portfolio for the long run lies at the heart of how we advise on our clients’ wealth, and the SAA is integral to it. Our SAAs will be the main driver of our portfolios’ performance contributing about 80% to the portfolios’ risk and return over time. By design the bulk of each portfolio is allocated to longer term investments. In this sense, the SAA is even more important than short-term market timing or securities selection, which are also parts of our investment approach. SAAs: What they are based onDiversifi cation is the only free lunch1 in the investment world. A diversifi ed port-folio combines a number of diff erent asset classes with diff erent risk and return characteristics. The asset classes range from government bonds through corpo-rate credit to high yield and emerging market bonds; from developed and emerg-ing market equities to hedge funds and private markets. By constructing appro-priate SAAs one can achieve a better risk/return ratio than would be the case with a narrower portfolio consisting of fewer asset classes – or even just a single asset class. Ultimately, the optimal SAA is one that, relative to others, realizes better returns while bearing less risk.

1 AQR Ilmanen & Villalm “Alpha beyond expected returns” 2012.

UBS Strategic Asset Allocations are an essential part of our disciplined style of managing and growing our clients’ wealth. These SAAs ensure that our clients remain on course to their fi nancial goals and steer clear of common investment dangers by investing in a well- diversifi ed manner. Our SAA methodology is anchored on our experts-based Capital Market Assumptions (CMAs) and the annual review process of our SAAs and CMAs. This method-ology is also the basis for our other CIO solutions such as the Endowment-Style Portfolio (ESP) and the Global Credit Opportunities (GCO) Portfolio which complement our tradi-tional SAAs Portfolios with additional investment concepts and return drivers.

Please always read in conjunction with the glossary and the risk information at the end of the document.

8 Please always read in conjunction with the glossary and the risk information at the end of the document.

SAAs: Begin with the right profi leWhether your primary investment objective is to protect or to grow your wealth, understanding your objectives, investment time horizon, and risk tolerance, is crucial. Hence, we have developed diff erent SAAs which exhibit diff erent levels of risk in order to off er portfolios fi tting diff erent types of clients. SAAs: The fruit of quantitative and qualitative analysisConstructing portfolios involves choosing not only the right asset classes but also the optimal asset weighting to achieve the best possible return for the low-est amount of risk. We base our portfolio building on a solid quantitative meth-odology that combines risk estimations based on factor analysis2 over several business cycles to derive risk estimations including asset class volatilities and correlations. We complement this statistical estimation of risk with return expec-tations for the next single business cycle from our asset class experts. The com-bination of these risk and return elements constitutes our Capital Market Assumptions (CMAs) which represent our expectations of markets over the next 5 to 10 years. This dual approach enables us to apply our seasoned market judgment within a robust quantitative framework, and is a method favored by such leading institutional investors as the Yale Endowment3. Ultimately these quantitative and qualitative inputs enable our Asset Allocation team to devise each of our Strategic Asset Allocations (SAAs).

SAAs: A dynamic processTo ensure reasonable assumptions we review our SAAs and CMAs on an ongoing basis and expect SAA adjustments every 18 to 36 months. One of the elements triggering these adjustments is changes to our Capital Market Assumption (CMAs). These represent our expectation for each asset class of return, volatility and correlations over 5 to 10 years. These are reviewed at least annually or aft er major market adjustments. Hence our SAAs are anchored on our long-term views but also account for structural market adjustments over time (for examplethe “new” interest rate environment post the Global Financial Crisis).

2 Professor Heinz Muller –Consultant from St. Gallen University

3 Annual Report – 2012 The Yale Endowment – Investment Policy

SAAs: How clients can take advantage of themWe believe that achieving superior investment results depends on an SAA at the core of one’s investment portfolio. SAAs can be implemented in the form of a discretionary solution that UBS manages, or/and as part of a set-up in which clients adhere to a certain portfolio structure or plan but direct and decide on the specifi c investments themselves. These portfolios are constructed to provide an expected return in line with each client’s fi nancial aspirations and within a reasonable investment time frame (fi ve to ten years for most clients).

2Please always read in conjunction with the glossary and the risk information at the end of the document.


Asset allocation that meets the challenge of change

Constructing and managing SAAs at UBS is a complex team undertaking. It involves our most experienced strate-gist and asset class experts; and includes our risk offi ce professionals and quantitative portfolio construction spe-cialists. All of them provide their insight to our Global Chief Investment Offi ce (GCIO), where our asset alloca-tion team constructs internationally diversifi ed portfolio strategies tailored to our clients.

Please always read in conjunction with the glossary and the risk information at the end of the document. 9 3Please always read in conjunction with the glossary and the risk information at the end of the document.


SAA construction in detail

Defi ning our SAA approach

Our SAA approach entails a predefi ned asset class allocation toward which the portfolio is to be rebalanced either at regular intervals or when some predefi ned deviation limits are reached. This approach aims to keep the portfolio within a predefi ned risk level while generating returns from both the long-term return expectations of each asset class and from the mean-reverting behavior of asset class performance.

This approach rests on our assumptions of the long-term up-trending performance of those asset classes we select while accepting expected short to mid- term drawdowns and market volatility – the level of return and risk depends on the chosen SAAs. The SAA approach focuses on asset classes (for example US equities) and not single securities (for example, Company XYZ). We fi nd that in the long-term, an asset allocation approach to investing is more predictable and off ers a better risk/return ratio than other approaches, such as shorter-term market timing and security selection. However, these other strategies can also add value to a portfolio in certain cases and we recommend to implement them in conjunction with an SAA, although they should not constitute the main driver of the portfolio’s long-term risk and return.

The art and science of creating SAAs

Constructing an SAA is both an art and a science; it requires a robust quantita-tive framework and seasoned judgment. The quantitative framework supplies a detailed understanding of the behavior of fi nancial markets – how diff erent markets behave diff erently during diff erent economic periods. Our qualitative assessments – i.e. the seasoned judgment provided by our asset class and asset allocation experts – complement this framework by capturing the subtleties, dynamic nature, structural changes and likely future developments of various markets.

Our construction process involves (see fi g. 1):• Defi ning the investment universe• Estimating multi-business-cycle “equilibrium” asset class returns and

a covariance matrix• Estimating single-business-cycle (fi ve-to-ten-year) asset class returns• Consolidating asset class estimates within one quantitative platform• Constructing SAAs based on optimal risk and return trade-off , including

testing portfolios across history and possible future market stress

Figure 1: UBS CIO SAA construction process

Final SAADecision

ConsolidationCovarianceMatrix & ReturnEstimates

Single BusinessCycle ExpectedReturns

InvestmentUniverse –Definitionand Analysis

Multi-BusinessCycle “equilibrium“Covariance Matrix& Returns

For illustrative purposes only.


The three main contributors to SAA construction at UBS

1. A quantitative SAA construction platform

To construct the SAAs we bring together on a quantitative platform quantitative and qualitative estimations for each asset class and across all asset classes. We can then perform various optimizations and simulations which are used as input by the CIO asset allocation experts. The key functions within the quantitative tool include:

a. Time series analyzer – per asset class

b. Capital market assumptions (CMAs)

b(1) Multi-business-cycle “equilibrium” returns and covariance matrix

b(2) Integration of single-business-cycle expected returns (5 year horizon)

c. Optimization and simulation functions

a. Time series analyzerA thorough understanding of the behavior of each market is an essential step in portfolio construction. We need to know how assets behave during diff erent market cycles and shocks, both individually and in relation to each other.

First, we analyze each asset class individually and look, among others, at the following parameters: 1) the return probability distribution, including skewness and kurtosis; 2) the volatility, both up and down; 3) the drawdowns and time under water; 4) the return patterns during fi nancial crises; 5) the rolling returns and volatility during diff erent holding periods; 6) the related risk-adjusted returns; and 7) liquidity constraints.

Second, we analyze the co-movement of asset classes, i.e. their correlations in a covariance matrix. This analysis is performed over diff erent rolling periods (example: rolling three and fi ve-year windows). Furthermore, the change in correlations over time is assessed to determine patterns such as increasing correlations during mar-ket crises. This analysis allows us to determine whether changes in correlation over time are of a cyclical nature or the result of structural changes in the economy and to analyze the eff ects of short-term adverse deviations from “normal” patterns. For examples, see fi g. 2.





b. Capital market assumptions (CMAs)Constructing internally consistent CMAs enables us to bring together many of the risk and return parameters of each asset class used in our SAAs to estimate the over-all expected return and volatility of any given SAA. The CMAs consist of two key elements: 1) the covariance matrices (volatility and correlations) and 2) the expected risk/return premiums (based on risk factors and described in detail in fi g. 3). The CIO CMAs blend are multi-business cycle “equilibrium” estimations with our asset class experts’ single-business-cycle expected returns. Within the quantitative framework several quantitative optimization functions are available based on the CMAs. They are used as inputs by our asset allocation experts to create the UBS SAAs.

The CIO CMAs are reviewed annually or aft er major market adjustments. Hence, annually each CIO asset class expert provides an update of their specifi c asset class long-term return estimate. Also the covariance matrix is recalibrated annually to include the respective asset classes’ latest time series data. The estimates for the money markets will be updated monthly to account for on the ongoing central bank rate adjustments. The update of the CMAs is provided as input for several other parts of the investment process including the Investor Profi le and investment solutions material which display for investors the expected risk and return expected for each CIO SAAs and related investment solutions.

Source: UBS


Figure 2: Time series analysis of Equities (Economic and Monetary Union)

12 Please always read in conjunction with the glossary and the risk information at the end of the document. 6Please always read in conjunction with the glossary and the risk information at the end of the document.


For illustrative purposes only. Source: UBS CIO

Figure 3: CMA construction

b(1) Multi business cycle “equilibrium” covariance matrixThe covariance matrix consists of estimates of volatilities and correlations for each asset class. These estimates are considered “equilibrium” estimates that describe the average behavior of assets over diff erent market cycles. The estimates are based on the longest time series available for each asset class, which is generally more than 20 years and includes three to four business cycles.

The long-term covariance matrix is chosen for two reasons. First, correlation forecasting is prone to estimation errors. For instance, while we know that cor-relations tend to increase during crisis periods and take this into account within our simulation framework, it is diffi cult to predict changing correlation patterns between asset classes. Second, we fi nd that volatilities over a mid to long-term holding period are generally stable; hence we fi nd that the long-term history is a reasonable base case assumption for the forward-looking covariance matrix.

Given that we estimate the covariance of over 100 diff erent asset and sub asset classes, we cannot do a direct historical estimation. Such an approach would necessitate longer time series than are historically available and could result in an inconsistent matrix without the mathematical properties needed for optimiza-tion and/or simulation purposes. To circumvent this we have developed a propri-etary factor approach to our covariance construction in which we model the identifi ed asset classes based on a subset of market factors.

We have a selection of ”factors” that we believe broadly represent the global fi nancial markets. All other asset classes are regressed either directly against this set or indirectly via a layering process. The regression process is iterative to allow the analysis of correlation patterns over diff erent periods and thus take into account any structural changes in the economy (for example, the creation of the Eurozone).


4 Including Prof. Heinz Mueller of the Faculty of Mathematics and Statistics at St. Gallen University, Switzerland



This construction process has been established and is maintained by the UBS quantitative team. The work is done in collaboration with and supervised by leading academics in the fi eld4. The resulting covariance matrix is both fully consis-tent (i.e. positive defi nite) while incorporating as much information as is available for each asset class (use of the longest possible time series per asset class). See fi g. 4 for an illustration.

Source: UBS


Figure 4: Factor regression approach and the resulting correlation matrix

b(2) Experts for each asset class – integration of single-business-cycle expected returnsThe estimated returns for each asset class are based on a combination of the implied “equilibrium” returns and adjustments based on return estimations for the next fi ve to ten years developed by the respective asset class experts within the Asset Allocation team in the CIO WM Global Investment Offi ce. This dual approach provides a robust quantitative framework for forecasting long-term returns while incorporating our assessment of certain imbalances we fi nd in cer-tain markets.



The “equilibrium” approach assumes that fi nancial markets are in balance and that investors will be compensated with returns proportional to the inherent risk of each asset class and to its particular characteristic within the broad invest-ment universe (including correlation, liquidity, etc.). Deriving “equilibrium” assumptions starts with calculating the amount of return investors expect per unit of risk (calculated as volatility) over the long term. This calculation is based on the equity risk premium (ERP), which represents the return above a “risk-free” investment (such as highly rated government bonds) that investors expect over the long term for investing in equities5. The ERP is used as the reference point for a process called “reverse optimization,” which calibrates the risk pre-miums of all other asset classes based on their individual risk, long-term correla-tions (found in b.1), size within the investment universe, and the ERP itself.

In the short to medium term, the ERP may be lower or higher than its “equilibrium” value depending on where one is within the business cycle or current aggregate investor market sentiments (“fear & greed”) among other factors. We take these deviations into account when estimating expected returns over our chosen forward-looking fi ve to ten-year horizon. Furthermore, we estimate the speed and magnitude of the change in this deviation over time as the ERP converges to its long-term level. The assessment of the deviation and conver-gence path of the ERP is based on the medium-term return expectations of our asset class experts. The adjustments from “equilibrium” value that they estimate are done both on the ERP itself and on individual asset classes. Many asset class-specifi c issues come into account within the asset class expert estimations which also justify deviation from “equilibrium” risk premium (for example for corporate bonds: spread compression, default/recovery rates, etc.). A short description of how the fi ve to ten-year expectations are derived is found below in section 2: Asset class experts – expected returns.

The ”risk-free” investment (such as highly rated government bonds) is also used as a building block for the annual calculation of the long-term expected returns. The expected return for each asset class is the sum of the asset class’ expected premium (derived as described above) and the expected ”risk-free” rate. We use highly rated government bonds as a proxy for the ”risk-free” rate. As we do for the ERP, we estimate both an ”equilibrium” value and a development path from the current (i.e. possibly non-”equilibrium”) level to the long-term value of the ”risk-free” rates. The ”risk free” rate is updated monthly to account for any central bank rates adjustments over time. The calculation of each asset class total return is however set annually as described above.

c. Optimization and simulation functionsA proprietary quantitative tool captures all the UBS CMAs and provides port-folio analytical capabilities including optimization, simulation function. This tool (see fi g. 5) enables us to conduct several optimization functions based on the CMAs described above, including the volatility, return and correlation estimates. Hence, we can establish related estimation of the effi cient frontier, in both con-strained and unconstrained portfolios. As previously mentioned, the effi cient frontier is not considered as a fi nal SAA but rather as a key input used by our asset allocation experts to decide fi nal SAA proposals.

5 See Fernandez [2008] and Asness [2011]. The “risk free” rate is a theoretical rate investors could expect with no risk. In practice, how ever, highly rated government rates are used as proxies for this rate and they may include some risk of the government not paying back its debt.



Another important function needed to assess the parameters of each SAA is a forward-looking Monte Carlo simulation engine – a proprietary tool which attempts to simulate multiple diff erent hypothetical market conditions to esti-mate the possible dispersion of performance results (see fi g. 6). It enables us to determine a statistical distribution of the diff erent expected outcomes of an SAA’s performance over time from best to worst, as well as their related devel-opment paths. Based on this simulation tool we can then assess the expected probability distribution for individual SAAs. This approach takes into account not only the specifi c return distribution of each asset class in the portfolio but also their correlations. Furthermore, these simulations can factor-in decreases or increases of correlations among asset classes as seen during certain market periods in order to estimate possible portfolio outcomes during these scenarios.

Source: UBS


Figure 5: Optimization Suite

Source: UBS


Figure 6: Monte Carlo simulation tool



2. Asset class experts – expected returnsAsset class experts within the CIO Global Chief Investment Offi ce calculate the return that each asset class will generate over the next fi ve to ten years. Their estimates are anchored in an “equilibrium” framework that takes into account the likely risk patterns and related returns across asset classes. This assessment is supplemented by business cycle-specifi c evaluations and anticipated develop-ments in yields to determine the likely total return outcomes from both a top-down and a bottom-up approach. The fi nal return estimates also include the multifaceted and asset class-specifi c modeling of each asset class expert. They are derived from a peer-reviewed methodology and a common set of macro-economic expectations for the period. This dual approach (“equilibrium” statisti-cal modeling + asset expert modeling) ensures a consistent approach across asset classes while considering each asset class’ specifi c factors on a forward-looking basis.

We develop a path of expected returns per asset class over the period. For instance, if we expect short-term interest rates to rise, this information is refl ected in the quantitative simulation tool.

Yield surfaces – calculating fi xed income expected returnsThe yield surfaces we develop for each relevant bond currency constitute the core of our approach to estimating the expected return of fi xed income asset classes. They represent our forecast for the “risk free” yield curve (e.g. US Treas-ury, German sovereign, etc.) over the next 10 years, and form the basis of our expected total return calculation for government and credit bonds over the SAA time horizon. Long-term bond returns depend on two main components: 1) the “risk free” rate, i.e. the rate at which highly rated governments borrow (example: US 10-year Treasury bonds); and 2) the credit spread, i.e. the additional compensa-tion investors require to assume the risk of default (and other risks, e.g. liquidity, regulatory, etc.) associated with a given bond issue. These two components are usually described simply as the “risk free” (i.e. government bond) rate of return and the credit risk (i.e. credit spread) rate of return. For any fi nite time horizon, bond returns also depend on changes in the yield surface that lead to both loss/gains and changes in yield levels.

The “risk free“ rate of return depends for the most part on the duration of a comparable “risk free” bond investment and the expected evolution of “risk free” yields, i.e. the yield surface. Specifi cally, the yield surface represents the current “risk free” yield curve, i.e. the current bond yield level for each bond maturity, and our forecast for the next 10 years of the changes to each maturity-specifi c yield. To illustrate, we show below the current yield curve and our expected yield curve in 10 years’ time for both USD and EUR in fi g. 7. We can see the current one-year USD government yield around 0.6%, and we expect it to rise over the next 10 years to about 3%, while the yield of the 10-year US gov-ernment bond is expected to increase from 1.9% to 3.4% over 10 years.



Our forecasts indicate rising government interest rates for both USD and EUR that will stem from the US Federal Reserve and the European Central Bank normalizing their monetary policy. Rising interest rates do not necessarily imply negative returns for high grade and credit sub-asset classes over the medium term, as shown in the table detailing the asset allocations.

We construct the yield surface by combining qualitative inputs from CIO Global Chief Investment Offi ce fi xed income experts with proprietary quantitative mod-els. Our approach uses quantitative models to forecast the short end (money market rate) and the long end of the yield curve (10-year maturity) over a fi ve-to-10-year period. We then derive the yield curve for each year by interpolation. Since our models are designed to describe long-term equilibria yield curves, we refl ect our short-term expectations of yield curve developments through a qualitative overlay.

Using the yield surface (see fi g. 8) we can mechanically derive the expected return for diff erent types of “risk free” bond investments. This approach is suit-able for both “buy and hold to maturity” strategies (e.g. buying and holding a single bond) and for duration-targeted approaches (e.g. purchasing a fund which replicates a bond index that contains bonds with maturities of fi ve to seven years only). In the process we account for the income generation, the roll-down eff ect and the reinvestment of interest.

Ultimately, yield surface return modeling provides a framework for systematically capturing both the evolution of yields and how this aff ects the expected return of diff erent types of “risk free“ bond investments. By breaking down the com-ponents of bond returns and identifying and forecasting their respective return drivers, we can be more confi dent in our return estimate.

-1.0%

-0.5%

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

1 2 3 4 5 6 7 8 9 10

Yie

ld

Maturity

USD Yield Curves

in 10 years

Current

-1.0%

-0.5%

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

1 2 3 4 5 6 7 8 9 10

Yie

ld

Maturity

EUR Yield Curves

in 10 years

Current

Make sure this version is used in all papers with this chart.

Also change yield surface charts to "2016" as already done in ESP paper

Source: UBS, February 2016For illustrative purposes only.

Figure 7: USD and EUR yield curve expectations



We also forecast the course of credit spreads and corporate defaults as well as recovery rates for each credit sub-asset class included in the SAAs. We combine these forecasts with the yield surface for the relevant currency to generate our long-term return expectations for the whole fi xed income investment universe.

3. Asset allocation experts – SAA decision

Building on the quantitative platform described above and its expert assessment, the Global CIO asset allocation team determines the SAA compositions (see table 1 for 2016 allocations). The objective is twofold: Firstly, the SAAs should take advantage of the best possible return drivers and low correlations between asset classes, hence minimizing the expected risks for any given level of expected return. Secondly, the SAAs construction should take into account the uncertainty of forecasting, hence spread risk among risk factors and markets.

The SAA composition considers several factors in the fi nal mix which include:1) optimizations functions including mean-variance, diversifi cation-index and maximum drawdown-based approaches (using both “equilibrium” and expert based return estimates), 2) stochastic simulation (Monte Carlo), and to assess potential “fat-tail” events 3) stress test scenarios (historic and prospective) and4) maximum drawdown and recovery analysis.

Finally, it is important to acknowledge that fi nancial forecasting is an uncertain endeavor with a multitude of input factors that can only be approximately quantifi ed and/or modeled, if they can be at all. Determining the composition of each SAA is therefore not a purely quantitative optimization. The fi nal SAA decision lies with the CIO asset allocation team and is based on the extensive and multifaceted quantitative/qualitative evaluations mentioned above.

USD Yield Surface

in 10 years

2016

CHF Yield Surface

in 10 years

2016

HeaderMulti-Business Cycle "equilibrium" Covariance Matrix & Returns

Investment Universe -Definition and Analysis

Consolidation Covariance Matrix & Return Estimates

Single Business Cycle Expected Returns

Optimization & Final SAA Decision

HeaderMulti-Business Cycle "equilibrium" Covariance Matrix & Returns

Investment Universe -Definition and Analysis

Consolidation Covariance Matrix & Return Estimates

Single Business Cycle Expected Returns

Final SAA Decision

Source: UBS For illustrative purposes only.

Figure 8: USD and CHF yield surfaces



Source: UBS CIO

The above asset classes and allocations are indicative only and can be changed at any time at UBS’s discretion without informing the client. Information valid as of 2016. Please always read in conjunction with the glossary and the risk information at the end of the document.


Table 1: UBS CIO SAAs in USD including Capital Market Assumptions (CMAs)

USD Fixed Income Income Yield Balanced Growth EquitiesFX

Hedged

Expected 5 Yrs

Return p.a.

Expected Volatility

p.a.

LIQUIDITY 5% 5% 5% 5% 5% 5% 0%

Cash USD 5% 5% 5% 5% 5% 5% 2.1% 0.5%

BONDS 95% 69% 50% 33% 17% 5% 0.0% 0.0%

USD high grade bonds 1-3 years 10% 0% 0% 0% 0% 0% 1.8% 1.6%



USD corporate bonds 1-5y 7% 4% 0% 0% 0% 0% 2.5% 3.0%

USD corporate intermediate bonds (IG) 23% 20% 15% 8% 2% 0% 2.7% 4.2%

USD high yield bonds 3% 3% 3% 3% 3% 0% 5.0% 8.8%

EUR high yield bonds 2% 2% 2% 2% 2% X 4.3% 8.5%

EM sovereign bonds (USD) 3% 3% 3% 2% 3% 0% 5.4% 9.1%

EM corporate bonds (USD) 2% 2% 2% 2% 0% 0% 4.8% 9.9%

EQUITIES 10% 25% 42% 62% 90% 0.0% 0.0%

US 0% 5% 12% 20% 32% 44% 7.5% 15.4%

EM 0% 0% 4% 6% 9% 13% 8.5% 24.1%

Eurozone 0% 0% 4% 6% 8% 10% X 10.0% 18.4%

UK 0% 3% 3% 5% 7% 9% X 8.4% 15.0%

Japan 0% 0% 0% 3% 4% 6% X 9.2% 19.8%

Canada 0% 0% 0% 0% 0% 3% X 8.0% 15.0%

Australia 0% 0% 0% 0% 0% 3% X 8.8% 14.9%

Switzerland 0% 2% 2% 2% 2% 2% X 9.1% 14.9%

HEDGE FUNDS 16% 20% 20% 16% 0.0% 0.0%

Hedge Funds 0.0% 16% 20% 20% 16% 0% 5.2% 5.9%

0%

TOTAL 100% 100% 100% 100% 100% 100%

Expected 5 yrs Return p.a. 2.5% 3.6% 4.7% 5.6% 6.6% 7.6%

Expected Volatility p.a. 3.4% 4.1% 6.0% 8.1% 10.7% 13.5%

Sharpe ratio 0.13 0.38 0.43 0.44 0.43 0.41

Systematic Allocation Portfolio (SAP)



Oliver Malitius, Executive Director, UBS CIO WM Global Investment Office, GAA Head Quantitative Strategies

SAP: Systematically driven equity exposure

ab


Systematic Allocation Portfolio: What is the underlying concept and how does it fi t into CIO’s existing portfolios?The Systematic Allocation Portfolio relies exclusively on the proprietary UBS CIO World Equity Market Model to defi ne its underlying equity allocation, which chiefl y determines market risk exposure. This model is an integral part of our Global Tactical Asset Allocation investment process, in which we combine quan-titative and qualitative inputs to derive our six-month investment views. It uses macroeconomic variables and momentum as inputs, combined with proprietary fi ltering techniques2. We have used this model in a live environment since mid-2011, and it leverages the 15+ years of investment experience of its developers. In the context of the Systematic Allocation Portfolio, we design three SAAs with diff erent risk profi les – Defensive, Medium and Dynamic, following the same principles used in defi ning the CIO SAAs.3 We then defi ne the tactical equity allocation according to the signal from the CIO World Equity Market Model.

1 Mark H. Haefele, Mads N. S. Pedersen, and Katarina Cohrs, Global Tactical Asset Allocation (TAA) Methodology, UBS CIO WM Global Investment Offi ce (2015)

2 Matthias W. Uhl, Mads N. S. Pedersen, and Oliver Malitius, What’s in the News? Using News Sentiment Momentum for Tactical Asset Allocation, The Journal of Portfolio Management, Vol. 41, No. 2: pp. 100–112 (2015)

3 Mads N. S. Pedersen, and Christophe de Montrichard, Strategic Asset Allocation (SAA) Methodology, UBS CIO WM Global Investment Offi ce (2014)



Carolina Moura-Alves, Managing Director, UBS CIO WM Global Investment Offi ce, GAA Head Fixed Income Strategies

Oliver Malitius, Executive Director, UBS CIO WM Global Investment Offi ce, GAA Head Quantitative Strategies

SAP: Systematically driven equity exposure

ab


The Systematic Allocation Portfolio uses a quantitative macroeconomic and fi nancial frame-work to determine the portfolio risk level. It translates the signal of the CIO World Equity Market Model1 to make large asset allocation changes, with equity allocation moves ranging from 10% to 40%. The CIO World Equity Market Model is designed to capture market and business cycle trends. It applies the principles of momentum and frequency analysis to mar-ket-price data and key fi nancial and macroeconomic variables in a unique, proprietary way. The portfolio’s risk exposure changes signifi cantly over time, enabling clients to participate fully in strongly up-trending equity markets and to lessen their exposure to equity risk in strongly down-trending and volatile equity markets. The Systematic Allocation Portfolio complements the range of existing CIO Strategic Asset Allocations (SAAs). It is distinguished by how it adheres to the CIO World Equity Market Model’s assessment of the fi nancial market risk environment and allocates a larger part of the risk budget and risk management to the Tactical Asset Allocation (TAA).



Depending on the CIO World Equity Market Model signal, we increase or decrease the Systematic Allocation Portfolio allocation to equities. This increase/decrease is matched by the corresponding decrease/increase in the high grade bond allocation. In other words, if the model indicates rising markets ahead, we buy equities and sell high grade bonds, and vice versa. The main principle behind the Systematic Allocation Portfolio is full participation in strongly up-trending equity markets (high allocation) and low exposure to risk in strongly down-trending and volatile equity markets (low allocation). Historical analysis shows that allocating to equities according to Fig. 1 has delivered risk/return character-istics that outperform static portfolios with no asset allocation changes. During clear economic and equity market trends, the strategy should outperform, e.g. during 2001 the Systematic Allocation Portfolio would have been constantly in low allocation while during 2004 the Systematic Allocation Portfolio would have been constantly in high allocation. However, in years such as 2012 it would have underperformed with an higher-than-average number of signal changes (his-torically, the signal changed 3.7 times on average per year).

Fig. 1: Equity allocation for diff erent Systematic Allocation Portfolios following a three-level approachFig. 1, 8

10%

20% 20%

40%

60%

30%

55%

80%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Defensive Medium Dynamic

Low Medium High

+20%

+30%

+40%

+10%

+15%

+20%

Equity Exposure

Source UBS CIO, for illustrative purposes only

Fig. 2: Historical risk/return of Systematic Allocation Portfolios compared with CIO SAAs

CIO SAA Fixed Income

CIO SAA Income

CIO SAA Yield

CIO SAA Balanced

CIO SAA Growth

CIO SAA Equity

Defensive

Medium

Dynamic

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

9.0%

10.0%

0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 14.0%

Ret

urn

(p

.a.)

Volatility (p.a.) Period: May 2003 to January 2016. USD reference currency portfolios.Source UBS CIO, for illustrative purposes only

Please always read in conjunction with the glossary and the risk information at the end of the document. 25 3



Table 1: Historical risk/return of Systematic Allocation Portfolios compared with CIO SAAs

Portfolios Return p.a. Volatility Max Draw Down

S.A.P. Dynamic 9.4% 8.7% –17%

S.A.P. Medium 8.0% 6.0% –12%

S.A.P. Defensive 6.4% 3.9% –10%

CIO SAA Equity 7.7% 12.8% –45%

CIO SAA Growth 6.8% 10.1% –40%

CIO SAA Balanced 6.1% 7.7% –30%

CIO SAA Yield 5.5% 5.7% –20%

CIO SAA Income 4.9% 4.1% –10%

CIO SAA Fixed Income 4.3% 3.5% –5%

Period: May 2003 to January 2016: USD reference currency portfoliosS.A.P.: Systematic Allocation Portfolio

Fig. 2 and Table 1 compare the performances of the three Systematic Allocation Portfolios with the more static portfolios comprised of the six CIO SAA risk profi les. The Systematic Allocation Portfolios superior risk/return characteristics are to a large extent explained by the fact that markets exhibit strong trends and adhere to refl exive feedback loops4, which are captured, at least partly, by the CIO World Equity Market Model.

Our technology, designed to capture trends, enables us to signifi cantly limit drawdowns, i.e. peak-to-trough declines. Below we illustrate this feature of the model exemplifi ed by the Systematic Allocation Portfolio Medium:

Fig. 3: Reduced drawdown with dynamic equity allocation, Systematic Allocation Portfolio Medium

-30%

-25%

-20%

-15%

-10%

-5%

0%

12/89 12/92 12/95 12/98 12/01 12/04 12/07 12/10 12/13

Static Allocation Systematic Allocation

Fig. 3

Draw Down

Weekly Data: 29.12.1989 – 26.02.2016 Source UBS CIO, for illustrative purposes only

4 George Soros, Financial Markets, The Soros Lectures (2010)



Fig. 3 compares the drawdown of a static allocation (Systematic Allocation Port-folio Medium, medium allocation) with a systematic and dynamic equity allocation for this portfolio. The static allocation has historically suff ered sporadic draw-downs that can approach 30%, while the systematic allocation shows evenly dis-tributed drawdowns and limits a portfolio’s drawdown risk. As a result, the Sys-tematic Allocation Portfolio Medium exhibits historical outperformance and improved risk/return characteristics, e.g. a higher Sharpe ratio, as depicted by Fig. 4. We also considered the standard 60/40 portfolio (60% world government bonds and 40% world equities) in our historical analysis to highlight the benefi t of specifying the static allocation following the principles used in defi ning the CIO SAAs. Over the long period considered, the Systematic Allocation Portfolio Medium clearly outperformed, aft er trading costs, by switching the equity alloca-tion 3.7 times per year on average.

However, the model is not as eff ective or accurate when equity markets exhibit weak trends, be they positive or negative. We think of this trade-off between lack of trend and eff ectiveness as a temporary “insurance” cost the investor must bear: the additional performance generated by clear up-trending and down-trending equity markets comes at the cost of underperformance during weakly trending markets, such as occurred in 2012. By construction, the Sys-tematic Allocation Portfolio is clearly exposed to two types of model risk:

1) Equity market dynamics not captured accurately – e.g. a situation in which some model inputs stop describing market behavior.

2) Trendless equity markets – e.g. frequent up/down movements in stock prices with no clear direction.

In both cases we should expect the Systematic Allocation Portfolio to underper-form the static asset allocation. We mitigate this situation by designing our model on the basis of extensive historical analysis that spans decades, and on the dynamics of equity markets and the macroeconomic variables that infl uence them. We believe equity markets do exhibit trends, as history clearly demon-strates, and that these trends will persist in the future.

Fig. 4: Performance simulation for Medium Systematic Allocation Portfolio USDFig. 4

Weekly Data: 29.12.1989 – 26.02.2016

Total Return

Static S.A.P.

Return p.a.

Volatility

Max Draw down

Alpha p.a.

Switches p.a.

474%

6.9%

6.9%

-30.3%

821%

8.8%

5.8%

-11.6%

1.9%

3.7

Return / Vol. 1.51 0.99

60/40

389%

6.2%

6.4%

-23.0%

0.97

Systematic Allocation Portfolio (S.A.P.): Signal strongly positive (>=25%): 55% equities; Signal negative (<0%): 10% equities; Signal weak positive (0%-25%): 40% equities Costs: 0.3% per 100% Turnover, weekly rebalancing

Index Equity Exposure

0%

20%

40%

60%

80%

100%

100

200

300

400

500

600

700

800

900

1'000

12/89 12/93 12/97 12/01 12/05 12/09 12/13

Equity Exposure

Static Allocation

Systematic Allocation





Systematic Allocation Portfolio: What does the CIO World Equity Market Model capture?Financial market participants oft en quote the colloquial saying “the trend is your friend.” Some regard it as less than serious, but there is indeed truth to it. The reasons are simple: human behavior and the laws of economics. Humans herd by nature, which in a fi nancial market context means that investors tend to buy stocks that have recently gone up in price and sell stocks that have recently gone down. Simply put, investors follow trends – also called momen-tum. Momentum applies to economic activity as well: manufacturing usually changes steadily over time, earnings increase or decrease steadily, and employ-ment rises gradually aft er recessions and step-wise in a recovery. During a typi-cal business cycle, the momentum of several key variables is self-reinforcing. If, for instance, fi nancial conditions improve, corporate bond spreads decline, which makes it cheaper to fi nance company operations and M&A. Company earnings rise, optimism returns, growth gets paid into equities and asset prices tend to go up. This momentum in fi nancial markets usually leads to a stabiliza-tion in the real economy.

The CIO World Equity Market Model is designed to capture market and business cycle trends by applying the principles of momentum and frequency analysis to market-price data and key fi nancial and macroeconomic variables in a unique, proprietary way. The model consists of three primary components: a business cycle component mostly based on US macro data and global corporate earnings; an equity market momentum component that combines momentum signals from a set of industrialized countries (represented in the MSCI World); and a risk component that includes three diff erent market-risk measures. These compo-nents are ultimately aggregated with weightings of 60% and 40% respectively (with the risk signal included in the momentum component) to generate a signal bounded between –100% and +100% (see Appendix for more details).

The basic principle behind the model, shown in Fig. 5, is that it signals an increase in equity allocation when equity markets are trending up and the busi-ness cycle is improving. Conversely, it calls for a lower exposure to equities when equity markets are trending down and the business cycle is worsening.

Fig. 5: Equity allocation managed by the signal of the CIO World Equity Market Model

Fig. 5

-75%

-50%

-25%

0%

25%

50%

75%

1'200

1'700

2'200

2'700

3'200

3'700

12/05 12/07 12/09 12/11 12/13 12/15

Signal World Index

World Market

Low Risk, positive Momentum

High Risk, negative Momentum

Signal

3-level allocation

Equity

High

Medium

Low




Systematic Allocation Portfolio: How does it behave in diff erent market environments?By design, the CIO World Equity Market Model will clearly indicate periods of positive or negative performance in equity markets, provided there is a well-defi ned underlying trend. The model will not be as eff ective in periods with weak trends, whether they are positive or negative. In other words, in equity markets that feature only slightly positive/negative performance and frequent market up/down movements, the model will be less accurate. We think of this trade-off between lack of trend and eff ectiveness as a temporary “insurance” cost the investor must bear: the additional performance generated by clear up-trending and down-trending equity markets comes at the cost of underperfor-mance during weakly trending markets. Strong positive signals correspond to strong positive equity returns at low market volatility in contrast to strong nega-tive signals corresponding to negative equity returns with high market volatility. Weak signals give a mixed performance picture. Fig. 6 illustrates the world equity market’s return per annum and its volatility along the y-axis. The x-axis shows the strength of the CIO World Equity Market Indicator. The further to the left , the more positive the signal and correspondingly higher equity returns in a low volatility environment. The further to the right, the more negative the signal and correspondingly lower equity returns in a high volatility environment. When equity markets are only marginally positive/negative and the signal is rather muted in either direction, as depicted by the red box within the graph, the Sys-tematic Allocation Portfolio is likely to lag more static investment concepts.

Fig. 6: Relationship between CIO World Equity Market Indicator and equity markets

5%

10%

15%

20%

25%

30%

35%

40%

-30%

-20%

-10%

0%

10%

20%

30%

80% 60% 40% 20% 0% -20% -40% -60%

Return World Equity Market Volatility

Fig. 6

positive

Return p.a. Volatility

UBS/CIO World Equity Market Indicator

negative

Based on daily data 1989 - 2016

Strong positive signal – low volatility and high returns – overweight equities

Negative signal – high volatility and low or negative returns – underweight equities





Using quantitative signals to drive equity exposure

Constructing and developing new investment concepts at UBS is a comprehensive team undertaking. The Systematic Alloca-tion Portfolio is an investment concept that applies a quantita-tive TAA to a diversifi ed, multi-asset class portfolio. The strategy is based on a systematic assessment of the risk environment in fi nancial markets and allocates a larger part of the risk budget and risk management to the TAA.

Systematic Allocation Portfolio: Systematically driven equity exposureAt the core of the Systematic Allocation Portfolio is a pre-specifi ed adjustment that determines the equity allocation. Specifi cally, we defi ne three levels of equity allocation: low, medium and high. Stocks are bought/sold against high grade bonds, which means the high-grade bond allocation will be lowest in the high level and highest in the low. The CIO World Equity Market Model signal is used to determine the respective level, as follows:

• Signal negative (< 0%): Low equity allocation• Signal positive (0% <= signal < 25%): Medium equity allocation• Signal strongly positive (>= 25%): High equity allocation

Fig. 7: Applying a three-level approach to the CIO World Equity Market Indicator

-75%

-50%

-25%

0%

25%

50%

75%

0

50

100

150

200

250

300

350

400

450

500

1989 1993 1997 2001 2005 2009 2013

Positive Signals Negative Signals World Equities

Fig. 7

MSCI World Index UBS/CIO World Equity Market Indicator

Medium Allo-

cation

High Equity Allo-

cation

Low Equity Allo-

cation

0%

25%

Weekly Data: 29.12.1989 – 26.02.2016




Systematic Allocation Portfolio: Asset allocation We construct three diff erent types of portfolios: Defensive, Medium and Dynamic. Each corresponds to a diff erent client risk profi le and all three port-folios have three diff erent equity allocation possibilities, as detailed in the diagram below:

We chose a three-level approach based on how the model behaved in diff erent market environments. Our analysis suggests that positive but mediocre signals have a mixed relationship with positive equity market performance. Therefore, we prefer a smaller allocation (medium) if the signal is lower than 25% and only overweight equities fully if the signal exceeds 25% (high). Additionally, we set the overweight/underweight asymmetrically, with a greater underweight than overweight. We chose these asymmetric levels to benefi t explicitly from the drawdown-reduction capability of the model, which we describe in more detail in a later section.

The three allocations are multi-asset class and include high grade bonds, corpo-rate and emerging market bonds, equities and hedge funds. Fig. 9 illustrates in detail the three-level asset allocation for each Systematic Allocation Portfolio:

Fig. 8: Equity allocation for diff erent Systematic Allocation Portfolios depending on three-level approachFig. 1, 8

10%

20% 20%

40%

60%

30%

55%

80%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%


Low Medium High

+20%

+30%

+40%

+10%

+15%

+20%

Equity Exposure





The allocation to equity markets, high grade, corporate and emerging market bonds derives from the corresponding CIO SAA profi les and are revised yearly within the CIO SAA process. Fig. 9 details the two sub-asset classes whose allocation depends on whether the model signals low, medium or high equity allocation: high grade bonds and world equities. We also show how the port-folios in the low allocation remain partly invested (except for Defensive) in the domestic equity market only. The investment in corporate and emerging market bonds remains stable, as does the hedge fund allocation. In addition to USD, we also defi ned Systematic Allocation Portfolios in EUR, CHF and GBP, adjusting the bond and equity allocations accordingly. All asset classes, with the exception of EM equities, are hedged to the reference currency of the portfolio in the low, medium and high allocation.

Systematic Allocation Portfolio: Risk and return analysisTo analyze the risk/return characteristics of the Systematic Allocation Portfolio, we simulated its historical performance, including the purely quantitative TAA as determined by the historical CIO World Equity Market Model signal. We also compared it with a reference portfolio (static allocation), which we defi ne as the asset allocation corresponding to the medium equity allocation. The results in the plot below for the Systematic Allocation Medium portfolio clearly demon-strate both the additional performance contribution and, more importantly, the signifi cant volatility and drawdown reduction brought by the quantitative TAA. We obtained similar results when performing the same historical risk/return analysis for both Systematic Allocation Portfolios Defensive and Dynamic, as well as for the diff erent reference currencies.

Fig. 9: Detailed asset allocation per Systematic Allocation Portfolio USD using three-level approach

Systematic Allocatin Portfolio USD

Equity Allocation Low Medium High Low Medium High Low Medium High

LIQUIDITY 2% 2% 2% 2% 2% 2% 2% 2% 2%

BONDS 88% 68% 58% 78% 48% 33% 68% 28% 8%

USD high grade bonds 1-5 years 18% 8% 3% 20% 8% 20% 10%

USD high grade bonds 5-10 years 38% 28% 23% 32% 14% 7% 40% 10%

USD corporate intermediate bond (IG) 22% 22% 22% 16% 16% 16%

USD high yield bonds 3% 3% 3% 3% 3% 3% 3% 3% 3%

EUR high yield bonds 2% 2% 2% 2% 2% 2% 2% 2% 2% X

EM sovereign bonds (USD) 3% 3% 3% 3% 3% 3% 3% 3% 3%

EM corporate bonds (USD) 2% 2% 2% 2% 2% 2%

EQUITIES 20% 30% 10% 40% 55% 20% 60% 80%

Equities AC World 10% 15% 20% X

Equities USA 10% 10% 10% 18% 18% 20% 30% 30%

Equities Emerging Markets 6% 6% 9% 9%

Equities Eurozone 4% 4% 6% 6% 8% 8% X

Equities United Kingdom 4% 4% 5% 5% 7% 7% X

Equities Japan 3% 3% 4% 4% X

Equities Switzerland 2% 2% 2% 2% 2% 2% X

HEDGE FUNDS 10% 10% 10% 10% 10% 10% 10% 10% 10% X

TOTAL 100% 100% 100% 100% 100% 100% 100% 100% 100%

EQUITY SHIFTS -20% 10% -30% 15% -40% 20%

Defensive Medium Dynamic FX Hedged




This analysis highlights, in particular, the drawdown-reduction capabilities of the Systematic Allocation Portfolio and its suitability for clients who wish to be underinvested in strongly down-trending equity markets, such as occurred dur-ing the 2002–03 and 2008–09 recessions.

Fig. 11 displays in detail a similar ability to reduce drawdowns for all the three risk profi les. Obviously, the CIO World Equity Market Model determines the equity allocation for all risk profi les simultaneously, so the number of switches and the periods each portfolio remains in low, medium and high allocation is the same. On average, the strategy changed the asset allocation 3.7 times per year. In a given year, however, the number of changes depends on the state of the business cycle and the momentum of the equity market. During clear eco-nomic and equity market trends, the strategy can hold a position for a long time, e.g. the Systematic Allocation Portfolio was in low allocation during 2001 and fully invested (high allocation) during 2004. In other years, such as 2012 during the euro crisis, it may change more oft en.

Fig. 10: Performance simulation for Medium Systematic Allocation Portfolio USDFig. 10

Index Equity Exposure

Total Return

Static S.A.P.

Return p.a.

Volatility

Max Draw down

Alpha p.a.

Switches p.a.

474%

6.9%

6.9%

-30.3%

821%

8.8%

5.8%

-11.6%

1.9%

3.7

Return / Vol. 1.51 0.99

Weekly Data: 29.12.1989 – 26.02.2016

60/40

389%

6.2%

6.4%

-23.0%

0.97

Systematic Allocation Portfolio (S.A.P.): Signal strongly positive (>=25%): 55% equities; Signal negative (<0%): 10% equities; Signal weak positive (0%-25%): 40% equities Costs: 0.3% per 100% Turnover, weekly rebalancing

0%

20%

40%

60%

80%

100%

100

200

300

400

500

600

700

800

900

1'000

12/89 12/93 12/97 12/01 12/05 12/09 12/13

Equity Exposure

Static Allocation

Systematic Allocation





Fig. 11: Detailed simulation results for Systematic Allocation Portfolios in USD

Year Static S.A.P. Static S.A.P. Static S.A.P. Switches1990 3.4% 5.5% -1.3% 4.1% -4.4% 2.9% 4 1991 18.3% 15.3% 20.3% 15.2% 21.4% 14.5% 2 1992 9.9% 8.6% 9.3% 6.5% 9.0% 4.9% 7 1993 15.1% 16.3% 18.4% 20.4% 20.1% 22.7% 1 1994 -4.2% -4.6% -3.7% -4.3% -2.8% -3.6% 7 1995 20.3% 20.6% 19.3% 19.9% 19.5% 20.2% 6 1996 10.5% 11.2% 12.8% 13.9% 15.6% 17.0% 3 1997 13.7% 12.9% 13.6% 12.7% 15.3% 14.3% 5 1998 9.3% 10.5% 9.2% 12.3% 11.9% 16.2% 4 1999 8.4% 9.7% 16.3% 18.4% 22.6% 25.6% 5 2000 5.4% 6.6% -0.7% 1.6% -5.5% -2.2% 2 2001 2.9% 7.8% -1.1% 5.6% -5.2% 3.2% - 2002 1.3% 5.9% -5.5% 0.5% -12.3% -4.6% 6 2003 10.9% 11.3% 15.9% 17.4% 19.8% 21.9% 4 2004 6.5% 7.5% 9.0% 10.6% 10.7% 12.9% - 2005 5.3% 6.5% 9.2% 11.1% 11.8% 14.3% 4 2006 8.1% 8.9% 11.2% 12.2% 14.0% 15.2% 4 2007 6.3% 5.9% 7.6% 6.8% 8.8% 7.7% 4 2008 -12.7% -1.6% -23.2% -6.4% -30.7% -9.3% 2 2009 17.1% 17.2% 23.4% 21.3% 26.9% 23.6% 2 2010 8.4% 8.6% 9.2% 9.6% 9.8% 10.2% 4 2011 3.0% 1.7% -0.7% -0.8% -3.2% -3.1% 2 2012 9.1% 9.0% 10.9% 10.6% 11.7% 11.3% 6 2013 5.0% 6.0% 9.8% 11.3% 15.4% 17.6% 6 2014 6.0% 5.0% 6.3% 5.1% 6.8% 5.3% 5 2015 1.1% 1.9% 0.2% 2.6% -0.1% 3.3% 3 2016 -0.6% 0.9% -2.6% 0.2% -4.1% -0.4% -

Total Return 481% 666% 474% 821% 475% 992%mean Return p.a. 6.9% 8.1% 6.9% 8.8% 6.9% 9.5%

Volatility p.a. 4.4% 4.1% 6.9% 5.8% 9.8% 8.1%Sharpe Ratio 1.57 1.96 0.99 1.51 0.70 1.18

Max Draw Down -17.4% -9.2% -30.3% -11.6% -40.1% -16.7%Switches 3.7 3.7 3.7 % High 48% 48% 48%

% Medium 25% 25% 25%% Low 27% 27% 27%

Turnover p.a. 53% 80% 107%Simulation until 26.02.2016 including transaction costs



We also outline the Systematic Allocation Portfolio historical risk/return charac-teristics in the context of CIO SAAs (Fig. 13). The plot shows how the volatility of the Systematic Allocation Portfolio Defensive, Medium and Dynamic resembles that of CIO SAA Income, CIO SAA Balanced and CIO SAA Growth, respectively, even though the constituent asset classes diff er. Specifi cally, the equity allocation of the Systematic Allocation Portfolio follows a three-level rule with a wide range, while the CIO SAA equity allocation is set at a determined level (Fig. 12).

Fig. 12: Equity allocation for Systematic Allocation Portfolio and UBS SAA compared

Fig. 12

Equity Allocation Systematic Allocation Portfolio versus CIO SAA

Defensive CIO SAA Income

0% - 20% - 30% 10%

Medium CIO SAA Balanced

10% - 40% - 55% 42%

Dynamic CIO SAA Growth

20% - 60% - 80% 62%




When interpreting the results of Fig. 13, we should bear in mind an important aspect: the period considered included the global fi nancial crisis of 2008–09. While the Systematic Allocation Portfolios would have been underweight for most of it due to a negative signal, we assume for the purpose of this simulation that the CIO SAAs would have remained unchanged throughout.

As mentioned earlier, the CIO World Equity Market Model can be used to man-age portfolio drawdowns, since it is designed to give a negative signal in strongly down-trending equity markets. We simulated the historical drawdown of the Systematic Allocation Portfolio Medium and compared it with the reference portfolio (static allocation), as defi ned earlier. While the Systematic Allocation Portfolio does not always manage drawdowns better than the reference port-folio, it clearly outperforms it in periods of recession, such as 2002–03 and 2008–09. Remarkably, the drawdowns of the medium Systematic Allocation Portfolio profi le stay in a range of 5% to 12% through diff erent crises, events and recessions. This supports our view that the CIO World Equity Market Indica-tor may be able to capture the next crises in a similar manner.

Fig. 13: Risk/return of Systematic Allocation Portfolios compared with UBS SAA

CIO SAA Fixed Income

CIO SAA Income

CIO SAA Yield

CIO SAA Balanced

CIO SAA Growth

CIO SAA Equity

Defensive

Medium

Dynamic

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

9.0%

10.0%

0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 14.0%

Retu

rn (

p.a

.)

Volatility (p.a.)

Period: May 2003 to January 2016. USD reference currency portfolios.Source UBS CIO, for illustrative purposes only




Fig. 15: Capital allocation and risk contribution for Medium Portfolio USDFig.15

24%

1%

26%

11%

40%

87%

10% 1%

0%

20%

40%

60%

80%

100%

Capital Allocation Risk Contribution

Govern. Bonds Credit Equities Hedge Funds

•  Hedge Funds are diversifying

•  Equities are dominating portfolio risk

•  Credit contribute less risk than capital allocation

•  Gov. Bonds are diversifying

•  Main risk of the portfolio is managed by TAA Model!

Weekly Data: 29.12.1989 – 26.02.2016


Systematic Allocation Portfolio: Managing portfolio risk via equity allocationThe Systematic Allocation Portfolio manages its equity allocation, the main risk contributor, by adhering to the market and business cycle assessment of the CIO World Equity Market Model. It simultaneously manages the portfolio risk level.

Fig. 15 depicts a simplifi ed asset allocation of the Medium profi le, comparing the capital allocation of the individual asset classes in one bar to their risk con-tribution in a second bar. On average, the static Medium profi le with medium equity allocation displayed an annualized volatility of 6.9%. With 40% of the capital invested in equities, 26% in credit, 24% in government bonds and 10% in hedge funds, the portfolio is well diversifi ed.

-30%

-25%

-20%

-15%

-10%

-5%

0%

12/89 12/92 12/95 12/98 12/01 12/04 12/07 12/10 12/13

Static Allocation Systematic Allocation

Fig. 14

Draw Down

Weekly Data: 29.12.1989 – 26.02.2016

Collapse of dot-com bubble

Financial crisis

EURO crisis

Asian crisis Recession on Kuwait crisis

China slow down

Bond market crisis


Fig. 14: Drawdown management of Systematic Allocation Portfolio Medium Portfolio USD



Fig. 16: Mapping UBS risk profi les to Systematic Allocation Portfolios

Fig. 16

Fixed Income

Income

Yield

Balanced

Growth

Equity

UBS CIO SAA Systematic Allocation Portfolio

Dynamic

Medium

Defensive


Analysis of the contribution of the diff erent asset classes to the overall portfolio volatility, however, reveals that capital allocation and the risk contribution of the respective asset classes diverge greatly. The equity allocation clearly dominates the portfolio risk. Hedge funds, credit and government bonds contribute only modestly to it. By actively managing its equity risk, the Systematic Allocation Portfolio addresses almost 90% of its overall risk.

How to map Systematic Allocation Portfolios to UBS client risk profi les?UBS client risk profi les are determined by the ability and willingness of our clients to take risk. Risk is measured in volatility and potential (maximum) drawdown of a certain portfolio. UBS has defi ned six diff erent risk profi les with corresponding SAA portfolios from Equity to Fixed Income with declining vola-tility and drawdown risk (see Fig.2 and Table 1). While we expect the CIO World Equity Market Model to reduce the drawdown risk and volatility of the Systematic Allocation Portfolios, the potential risk of the large equity exposure during “High” allocation mode has to be taken into account as well. We consider the risk profi le of the Systematic Allocation Portfolios to be comparable to (Fig. 16):

Systematic Allocation Portfolio Dynamic has a similar risk profi le to CIO SAA Growth. The equity allocation is similar, and we expect the Systematic Alloca-tion Portfolio Dynamic to generate less volatility and suff er fewer drawdowns due to its lower exposure to equities during crises and recessions.

Systematic Allocation Portfolio Medium has a similar risk profi le to CIO SAA Balanced. The equity allocation of both portfolios is similar. Again, the potential swings in equity allocation for Systematic Allocation Portfolio Medium is larger than for the Balanced risk profi le, but the portfolio risk should be reduced by the systematic equity allocation based on the CIO World Equity Market Model signal.

Systematic Allocation Portfolio Defensive has a similar risk profi le to CIO SAA Yield. Clients with a lower risk profi le should not invest in Systematic Allocation portfolios as the most conservative (Defensive) portfolio may invest up to 30% in equity markets.




Fig. 17: Structure of the CIO World Equity Market ModelFig. 17

Sub-Components Weights

Assessment of momentum across World equity markets

Momentum in Equity Markets

40%

Short/Medium Term Trend

Long Term Trend

Market Risk Indicators

daily

daily

daily

Momentum in Business Cycle

60%

Trend in Global Earnings

Business Activity Indicators

Business Risk Indicators

monthly

weekly

weekly

Assessment of earnings dynamics and business cycle activity driving equity markets

Overall indication for equity market investment:

•  Be fully invested at strong positive signals

•  Be partly invested at weaker signals

•  Underweight equities on negative signals

Current earnings situation

Indication for future earnings

Investors expectations

Short term volatility

Bull-/bear market

Current trend


Appendix: CIO World Equity Market ModelDefi ning our approachThe CIO World Equity Market Model processes market and economic data to generate a signal that recommends ascribing an overweight, neutral or under-weight position to equity investments within a portfolio. The model consists of three primary components: a business cycle component mostly based on US macro data and global corporate earnings; a momentum component that com-bines momentum signals from a set of industrialized countries (represented in the MSCI World); and a risk component that includes three diff erent market-risk measures. These components are ultimately aggregated with weightings of 60% and 40% respectively (with the risk signal included in the momentum component) to generate a signal bounded between –100% and +100%. The model is calibrated to generate approximately three to fi ve signal changes per year, which is roughly in line with the stated objective of postulating a tactical position for the next six months.

Combining trends and business cycle indicatorsWe start with a set of statistically tested quantitative indicators to get an in-depth understanding of current equity market movements and the dynamics of the current business cycle. The design of the CIO World Equity Market Model is based on two simple principles: 1) equity investors co-own the companies they invest in, so the value of the equity investment is directly related to the current and future income stream of the company; 2) beyond business and earnings dynamics, equity markets are oft en aff ected by crises, political change, central bank interventions and many other unpredictable events best captured quanti-tatively in the equity market price momentum.

Thus, we have constructed the CIO World Equity Market Model using two modules: Momentum in Business Cycle and Momentum in World Equity Markets (Fig. 17).



Fig. 18: Business Cycle signals and world equity marketFig. 18

MSCI World Index Business Cycle Signal

Envi

ronm

ent

ok f

or e

quity

in

vest

men

ts En

viro

nmen

t di

ffic

ult

for

equi

ty in

vest

men

ts

Weekly Data: 29.12.1989 – 26.02.2016

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

0

50

100

150

200

250

300

350

400

450

500

1989 1993 1997 2001 2005 2009 2013



The Business Cycle module assesses the business cycle and especially earnings dynamics, which are crucial to equity market performance. The methodology derives from the momentum model described below. The module has three main sections with almost equal weightings. Within “Trend in Global Earnings” we assess the current dynamics in reported (trailing) earnings for all developed country equity markets. The section “Business Activity Indicators” summarizes the signals from a set of leading indicators that have predictive power for earn-ings growth (e.g. Purchasing Manager indices, retail sales, etc.). The last section, “Business Risk Indicators,” captures the perception of market participants on risk and economic developments. We calculate the three components on weekly and monthly data according to its availability and combine them to generate the Business Cycle signal. It combines current earnings trends and business activity with investor expectations about the business cycle (Fig. 18).

A positive signal indicates robust earnings growth, a positive trend for leading macro indicators and a declining risk premium demanded by market partici-pants – a favorable environment for equity investments. A strong negative sig-nal points to a recession, falling earnings and higher risk premiums demanded by investors – an environment in which high grade bonds are preferred and equity markets exhibit high volatility and, most likely, negative returns. In the scenario where Business Cycle signals are weak, the CIO World Equity Market Model signal is likely to be driven by the current momentum of the equity market, which is captured by the momentum sub-model.

The Momentum in World Equity Markets module (Fig. 17) provides us with a deep and comprehensive description of current market movements. The meth-odology behind the calculations of market momentum derives from electronic engineering and frequency analysis. Applying this technology to describe the dynamics of the equity market is a unique, proprietary approach developed in house5. This module has three main sections as well. Short and medium-term fi lters separate daily noise from the underlying market movements of recent

5 Matthias W. Uhl, Mads N. S. Pedersen, and Oliver Malitius, What’s in the News? Using News Sentiment Momentum for Tactical Asset Allocation, The Journal of Portfolio Management, Vol. 41, No. 2: pp. 100–112 (2015)




Fig. 19: Illustration of frequency fi ltering on equity market indexFig. 20

Germany Index Signal per Frequency

Signals transformed … and added up

Example Index: German Equity, 31.8.2010 – 21.9.2011

Example Frequencies: 10-20, 30-60, 50-100 days

1'500

1'600

1'700

1'800

1'900

2'000

2'100

2'200

08/10 10/10 12/10 02/11 04/11 06/11 08/11


days and weeks. The signal from this section is more trading oriented, giving buy and sell recommendations about fi ve to 10 times per year on average. The long-term fi lter works more as a regime indicator and signals periods of bull and bear markets: buy and sell recommendations change one to three times annu-ally on average. Fig. 19 illustrates the eff ect of applying fi ltering technology to a specifi c equity market.

Each equity market in the MSCI World is analyzed with the same set of fre-quency fi lters and lengths of frequencies. We combine the signals from diff er-ent frequencies and fi lters, as illustrated in Fig. 19, and derive an overall trend indicator for each equity market by normalizing the diff erent signals using a roll-ing window of one to two years of past data. The normalized signals now show the same distribution and values between +3 and –3 and are easy to combine to produce the overall trend indicator for a specifi c equity market. The use of a rolling window for the normalization has an additional interesting eff ect: the momentum model “learns.” The rolling normalization helps to adapt to chang-ing volatility regimes in the equity market. With the additional treatment of out-liers, we fi nally get a pure trend signal for each market that ranges between +100% and –100%. We consider momentum in our model as a combination of market trend and market risk. With the help of the third section, we combine these trend fi lter signals with market risk indicators to derive the fi nal momen-tum signal for each market. The short-term market risk indicators react to sud-den market crises and complement the longer-term trend fi lters.

The momentum signal for the world equity market is calculated as an aggrega-tion of each single equity-market momentum signal within the MSCI World Index (Fig. 20). A strong positive signal indicates a strong trend and low risk in most equity markets within MSCI World: it’s a time we want to be invested in equity markets. A strong negative signal points to a negative trend and/or extreme volatility in most equity markets – a period we try to avoid and invest in high grade bonds instead of equities.



Fig. 20: Momentum signals and world equity marketFig. 20

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

0

50

100

150

200

250

300

350

400

450

500

1989 1993 1997 2001 2005 2009 2013


MSCI World Index Momentum Signal

Mo

men

tum

po

siti

ve

Mar

ket

risi

ng

Mo

men

tum

neg

ativ

e M

arke

t vo

lati

le

Weekly Data: 29.12.1979 – 26.02.2016


CIO World Equity Market Model SignalAft er generating the Business Cycle and World Equity Market Momentum signal, we combine them, applying the respective weightings, to obtain the CIO World Equity Market Indicator:

Fig. 21: Total model signals and world equity marketFig. 21

-75%

-50%

-25%

0%

25%

50%

75%

0

50

100

150

200

250

300

350

400

450

500

1989 1993 1997 2001 2005 2009 2013


MSCI World Index UBS/CIO World Equity Market Indicator

Weekly Data: 29.12.1989 – 26.02.2016





Fig. 22: Analyzing world equity market performance at certain TAA signal levels

Fig. 22

Recommendation Signal Return p.a. Volatility

Low allocation

High allocation

% Time

>25%

<0%

48%

27%

13.9%

-6.1%

10.7%

22.5%

MSCI World local

Weekly data from 1989 – 2016

Medium allocation 0% - 25% 25% 3.4% 13.6%


Strong positive signals now stem from the good support provided by the busi-ness cycle and strong momentum in equity markets – a period the Systematic Allocation Portfolio is fully exposed to the equity market (high allocation). Nega-tive signals may stem from the combination of a recession and negative market momentum – when the portfolio risk for the Systematic Allocation Portfolio is reduced signifi cantly (low allocation to equities). During mixed signals, the Sys-tematic Allocation Portfolio has a medium exposure to the equity market.

We analyzed the model’s forecast ability to distinguish between diff erent mar-ket regimes by computing the annualized returns and volatility of the MSCI World Index aft er diff erent model signal ranges. We found, as shown in Fig. 22, that the model distinguishes between periods of low volatility and strong posi-tive returns, moderate volatility and moderate positive returns, and high volatil-ity and negative returns. This analysis justifi es our choice of a three-level approach for the Systematic Allocation Portfolio.

42

Global Credit Opportunities (GCO) Portfolio




Philipp Schöttler, Executive Director, UBS CIO WM Global Investment Office, GAA Head DM Credit Strategies


GCO: Liquidity premia enhance returns

ab


GCO: What is the underlying concept and how does it fi t into CIO’s existing portfolios (SAAs)?If diversifi cation is the only free lunch in the investment world, it makes sense to consider every potential source of return when constructing a portfolio. We act on this observation in developing our CIO SAAs2, which we construct from a broad range of asset classes. We emphasize more-liquid asset classes that we believe suit the vast majority of our clients. However, less-liquid asset classes can provide an additional, diversifying source of return – i.e. the liquidity premium – to investors. Liquidity premia are particularly found in the more niche areas of the credit markets, of which high yield and emerging market bonds are well-known examples. These bond investments by nature generate income. So it is possible to construct a well-balanced, high income-producing portfolio that benefi ts directly from liquidity premia. It should be noted there is a trade-off between high income generation and liquidity of the underlying investments, but not between income and duration (interest rate exposure). Specifi cally, the GCO qualifi es as a low- duration investment, positioned against uncertain interest rate hiking cycles and the volatile fi xed income market regime we are currently experiencing.

GCO: What is the investment universe?The GCO focuses on fi xed income, though it can also invest in hedge funds. The portfolio takes advantage of the full credit spectrum across the capital structure, from investment grade credit to private debt, in various regions worldwide. The allocation to hedge funds plays primarily a diversifi cation role; and it is not expected to have a strong income generation component. By design, the diff er-ent credit sub-asset classes contribute in similar proportions to the portfolio’s overall risk. Additionally, the GCO invests in credit whose liquidity profi le makes it generally compatible with monthly or quarterly liquidity requirements.

The Global Credit Opportunities (GCO) off ers exposure to traditionally illiquid asset classes. It constitutes an alternative Strategic Asset Allocation (SAA) that complements the range of CIO SAAs. It is designed for clients who invest for the long term and seek to capture high recurring income and the additional sources of return present in less-liquid investments1.

1 J. Dick-Nielsen, P. Feldhütter, and D. Lando., Corporate bond liquidity before and aft er the onset of the subprime crisis. Journal of Financial Economics, 2011; Longstaff , Francis A., Portfolio Claustropho-bia: Asset Pricing in Markets with Illiquid Assets, American Economic Review, 2009

2 Mads N. S. Pedersen, and Christophe de Montrichard, Strategic Asset Allocation (SAA) Methodology, UBS CIO WM Global Investment Offi ce

Global Credit Opportunities (GCO) Portfolio




Philipp Schöttler, Executive Director, UBS CIO WM Global Investment Offi ce, GAA Head DM Credit Strategies

Katarina Cohrs, Director, UBS CIO WM Global Investment Offi ce, GAA TAA & Investment Methodologies

GCO: Liquidity premia enhance returns

ab



Global Credit Opportunities (GCO)

To illustrate the interplay between liquidity premia, expected return and credit quality in the diff erent credit sub-asset classes, we have drawn a representative risk/return scatter plot in fi g. 1. It represents the additional return benefi t of capitalizing on liquidity premia:

GCO: How can clients incorporate this portfolio in their overall asset allocation?GCO is designed for clients who want to generate recurring income from their investments, do not require constant and immediate access to the capital they invest, and favor preserving capital over the medium term. As such, it can be viewed as:

• a stand-alone portfolio that provides a source of recurring income to help fund the client’s lifestyle or cover other recurring expenses

• the core allocation in a core-satellite portfolio setup, with the client supple-menting it by investing in other asset classes, e.g. equities

• a satellite in a core-satellite portfolio setup, an illiquid investment that pro-vides recurring income

• a wealth-preservation (“stay rich”) portfolio that can be expected to preserve invested capital’s real value

The risk and return profi le of the GCO is comparable to that of the CIO SAA Yield. In line with our CIO SAA methodology, the GCO can be paired with other asset allocations. There is an important diff erence, however, between the GCO and the CIO SAA Yield: the former focuses on credit and is ill-suited to clients unable to accept the volatility associated with credit crises. We note however that historical simulations show that the GCO recovered its value faster than CIO SAA Yield during the 2008–09 credit crisis, despite its high allocation to less-liquid credit sub-asset classes.

Clients can use the GCO allocation as part of a set-up in which they adhere to a certain portfolio structure or plan but direct/decide on the specifi c invest -ments themselves. The GCO’s relative lack of liquidity places constraints on making investments that have no market price impact, therefore the portfolio

Global Credit Opportunities

Source UBS CIO For illustrative purposes only.

Core Credit

Hedge Funds

ABS/Bank Capital

Private Debt

Building blocks

Figure 1: Representative risk/return scatter plot




implemen tation period can range up to between nine and twelve months. It is a multi-year investment by design, so its performance does not depend on timing the market correctly. Given the unique characteristics and complexity of the credit sub-asset classes that off er higher liquidity premia, specialist fund managers are needed to populate the portfolio. UBS’s open architecture regarding third-party investment fund providers off ers a distinct advantage in implementing the GCO’s asset allocation.

Harvesting liquidity premia in a portfolio context

Constructing and developing new investment concepts at UBS is a comprehen-sive team undertaking. It involves our most experienced strategists and asset class experts; and it includes our risk offi ce professionals and quantitative port-folio construction specialists. The GCO is an investment concept that enables clients to benefi t from existing liquidity premia available in fi nancial markets in a diversifi ed and well-balanced portfolio.

GCO: Begin with the right building blocks

To achieve the dual goals of generating a high amount of income while captur-ing the returns fueled by liquidity premia, we must select the right sub-asset classes as building blocks. Credit, as mentioned above, features prominently. We consider the full spectrum in terms of credit worthiness (rating), location (developed and emerging market), complexity (subordinated and structured credit) and liquidity. At the most liquid end of the spectrum we look at invest-ment grade corporate bonds, and at the illiquid end we consider private debt.

The GCO investment universe covers the whole capital structure except equity capital (see fi g. 2).


Figure 2: Typical capital structure of corporate entities, fi nancial and non-fi nancial



GCO: Asset allocation

At the core of the GCO asset allocation are diff erent credit sub-asset classes, namely, investment grade corporate, high yield corporate, emerging market sov-ereign and emerging market corporate bonds. We invest in these assets in diff er-ent currencies, subject to diff erent central bank policies, as a way of gaining fur-ther diversifi cation. We hedge the currency risk because the volatility associated with currency movements would worsen the GCO’s risk and return characteris-tics. The recent past has demonstrated the eff ect diverging central bank policies (e.g. those of the US Federal Reserve and the European Central Bank) can have on the performance of credit sub-asset classes. We complement our allocation to traditional credit by adding senior secured loans, bank capital and asset-backed securities and private debt. Additionally, we recommend hedge funds. This allocation primarily plays a diversifi cation role – while we capture liquidity premia in it, we do not assume it will generate signifi cant income.

Table 1 provides three examples of a USD-focused GCO asset allocation. They range from one focused on the more-liquid credit segments to one that includes bank capital, asset-backed securities and hedge funds and private debt. It illus-trates the risk-and-return benefi t of adding less-liquid asset classes to the core GCO asset allocation.

Table 1 also displays the estimated yield to maturity (YTM) of each GCO option, which can be considered a proxy for the annual recurring income it generates. For comparison, the current YTM of a comparable risk profi le, in this case the CIO SAA Yield USD, is c. 1.8%, i.e. approximately 3.2%–3.8% lower.

GCO: Risk and return analysis

1. GCO in the context of CIO SAAs To demonstrate how the GCO fi ts in with our existing CIO SAAs in terms of risk and return characteristics, we analyzed historical and forward-looking risk and return estimates. They are shown in fi g. 3 in the context of the risk and return characteristics of CIO SAAs. The plot clearly indicates that GCO’s volatility mir-rors that of the CIO SAA Yield, even though the constituent asset classes diff er (the CIO SAA Yield has an allocation of 25% to equities vs. 0% for the GCO).

USDGlobal Credit Opportunities


with HFs


with HFs & ABS & PD

FX Hedged

Expected 10 Yrs Return

p.a.

Current Yield to Maturity

(YTM)

Expected Volatility

p.a.

LIQUIDITY 5% 5% 5%

Cash USD 5% 5% 5% 2.6% 0.6% 0.5%

BONDS 95% 77% 65% 0.0% 0.0%

USD corporate intermediate bonds (IG) 15% 15% 0% 3.4% 3.0% 4.2%

EUR securitized ABS 0% 0% 5% X 4.2% 1.1% 4.3%

EUR subord. financial bonds 5% 5% 5% X 4.0% 2.5% 10.0%

USD senior loans 15% 15% 15% 6.3% 8.1% 7.4%

EUR senior loans 10% 10% 10% X 6.5% 6.0% 7.1%

US high yield short duration 5% 5% 5% 4.9% 9.1% 7.4%

USD high yield bonds 10% 10% 8% 5.5% 8.5% 8.8%

EUR high yield bonds 10% 7% 7% X 5.0% 5.5% 8.5%

EM sovereign bonds (USD) 10% 5% 5% 5.5% 6.1% 9.1%

EM corporate bonds (USD) 10% 5% 5% 5.2% 6.0% 9.9%

Asia credit (USD) 5% 0% 0% 4.4% 4.8% 7.4%

HEDGE FUNDS 18% 18% 0.0% 0.0%

Hedge Funds 0% 18% 18% 6.1% 5.0% 5.9%PRIVATE MARKETS 12%Private debt 12% 8.5% 10.0% 9.8%

TOTAL 100% 100% 100%

Expected 10 yrs Return p.a. 5.0% 5.2% 5.8%

YTM estimate 6.0% 5.0% 4.3%

Expected Volatility p.a. 5.8% 5.3% 5.9%Sharpe ratio 0.42 0.50 0.55Duration 3.8 2.8 1.7Max. Drawdown -22% -22% -26%

Source UBS CIO, February 2016 – pending QIS update For illustrative purposes only.

Table 1: Examples of USD-focused GCO asset allocation




Comparison of Portfolios

2 Generated by QIS

a

b

c

3 - CIO SAA Yield

4.0%

4.5%

5.0%

5.5%

6.0%

5.0% 5.2% 5.4% 5.6% 5.8% 6.0% 6.2% 6.4%

An

nu

alis

ed E

stim

ated

Ret

urn

%

Annualised Estimated Risk %

a = GCO b = GCO + HF c = GCO + HF + ABS + PD

Portfolio forward-looking estimated risk and return scatter

Data as of 29.02.2016

For illustrative purposes only. Markets are subject to change and returns may vary. See explanation under "Ex-Ante Estimates" at the end of this document. Please note that this page is always to be read in conjunction with the risk information and explanations of terms appended to this presentation.

UPDATE AGAIN


There are two important diff erences between the GCO and the CIO SAAs: 1) the GCO focuses on credit sub-asset classes; and 2) the GCO has lower liquid-ity than the CIO SAAs. To illustrate the GCO’s risk and return behavior better, we have simulated its historical performance over the last 16 years, which includes both the 2008–09 credit crisis and the 2002–03 recession in fi g. 4.

GCO Allocation

Risk & return – LPP vs SAA set

1 Generated by QIS

1

2

34

5

6

1.5%

2.5%

3.5%

4.5%

5.5%

6.5%

7.5%

8.5%

0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 14.0% 16.0%

A nnualised Estimated Risk %

Ann

ualised

Estim

ated

Return %

1 = CIO SAA Fixed Income 2 = CIO SAA Income 3 = CIO SAA Yield 4 = CIO SAA Balanced 5 = CIO SAA Growth 6 = CIO SAA Equities

GCOs Risk p.a Return p.a

GCO 5.8% 5.0%

GCO + HF 5.3% 5.2%

GCO + HF + ABS + PD 5.9% 5.8%

Portfolio forward-looking estimated risk and return scatter

Data as of 29.02.2016

For illustrative purposes only. Markets are subject to change and returns may vary. See explanation under "Ex-Ante Estimates" at the end of this document. Please note that this page is always to be read in conjunction with the risk information and explanations of terms appended to this presentation.

SAAs Risk p.a Return p.a

Fixed Income 3.4% 3.2%

Income 4.1% 4.2%

Yield 6.0% 5.2%

Balanced 8.1% 6.0%

Growth 10.7% 6.9%

Equities 13.5% 7.6%

UPDATE AGAIN


Figure 3: Risk/return comparison



Furthermore, its performance over the past 16 years demonstrates good through-the-cycle risk and return characteristics. Its time to recovery aft er the initial drawdown during the 2008–09 credit crisis would have been six to nine months quicker than the CIO SAA Yield’s, as shown in fi g. 5.

We also show in more detail the recovery dynamics of the GCO allocations during other periods of drawdown, when compared with CIO SAA Yield, which reveals the GCO’s swift er recovery from historical drawdowns in fi g. 6.


3 Generated by QIS

100

120

140

160

180

200

220

240

260

280

300

320

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

USD

GCO GCO + HF GCO + HF + ABS + PD CIO SAA Yield USD

Simulated historical performance

Data as of 29.02.2016. Time horizon: 31.12.1998 - 29.02.2016

For illustrative purposes only. Markets are subject to change and returns may vary. See explanation under "Simulated Historical Performance" at the end of this document. Please note that this page is always to be read in conjunction with the risk information and explanations of terms appended to this presentation.

Historical performance

Risk p.a.

Return p.a.

GCO 6.2% 6.4%

GCO + HF 5.5% 5.7%

GCO + HF + ABS + PD 6.2% 5.7%

CIO SAA Yield USD 5.7% 5.5%

Global Financial Crisis 2008-09

UPDATE AGAIN


Figure 4: Simulated historical performance


4 Generated by QIS

-27.0%

-24.0%

-21.0%

-18.0%

-15.0%

-12.0%

-9.0%

-6.0%

-3.0%

0.0% 2007 2008 2009 2010

Ret

urn

s

GCO GCO + HF + ABS + PD CIO SAA Yield USD

Simulated historic draw downs

·  The chart shows the simulated historic drawdown. It shows the losses that occurred in the portfolio as "valleys", where each loss is the difference between the peak and trough of a valley.

·  The maximum simulated drawdown or loss is the deepest "valley" shown on the chart over the indicated time horizon. The time taken to reach the bottom of the "valley" is known as the drawdown period. The time taken to get back to the original peak or level is known as the drawdown recovery period.

·  The maximum time under the water is the time taken to traverse the "widest" valley from peak to peak.



UPDATE AGAIN


Figure 5: Time under water 2007–2010



Additional analysis we conducted tested the capital-preserving characteristics of the GCO. We estimated its shortfall risk, i.e. the probability in each future period that its projected value will fall short of the initial capital invested (see fi g. 7). Our calculations show that, aft er fi ve years, the shortfall risk is c. 6% and aft er 10 years it is c. 1.5%. We believe this validates the GCO as an investment for clients with a medium-term time horizon.

The GCO seeks to capture existing liquidity premia in credit markets so its liquid-ity is, by design lower, than that of CIO SAAs. We estimate that the liquidity profi le of GCO is compatible with monthly or quarterly liquidity requirements, but it should be noted that an allocation to Private Debt will typically be imple-mented through investment funds with multi-year lockups. The GCO is only suitable for clients who do not need the ability to redeem their portfolio on short notice.


5 Generated by QIS

-7.0%

-6.0%

-5.0%

-4.0%

-3.0%

-2.0%

-1.0%

0.0% 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Ret

urn

s

GCO GCO + HF + ABS + PD CIO SAA Yield USD


·  The chart shows the simulated historic drawdown. It shows the losses that occurred in the portfolio as "valleys", where each loss is the difference between the peak and trough of a valley.

·  The maximum simulated drawdown or loss is the deepest "valley" shown on the chart over the indicated time horizon. The time taken to reach the bottom of the "valley" is known as the drawdown period. The time taken to get back to the original peak or level is known as the drawdown recovery period.

·  The maximum time under the water is the time taken to traverse the "widest" valley from peak to peak.



UPDATE AGAIN


Figure 6: Time under water 2002–2015


6 Generated by QIS

0%

5%

10%

15%

20%

25%

30%

35%

40%

2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

Sho

rtfa

ll Pr

ob

abili

ty

GCO GCO + HF GCO + HF + ABS + PD CIO SAA Yield USD

·  The graph shows the probability in each future period that the projected value will fall short of a desired target value or return.

·  In general, the longer the investment horizon the lower the risk of not reaching a given return target.

·  IMPORTANT: The projections or other information shown regarding the likelihood of various investment outcomes are hypothetical in nature, do not reflect actual investments results and are not guarantees of future results.

Shortfall risk

Data as of 30.09.2015, Time horizon: +10 years, Initial amount: USD 100

For illustrative purposes only. Markets are subject to change and returns may vary. See explanation under "Ex-Ante Estimates" and "Monte Carlo Simulation" at the end of this document. Please note that this page is always to be read in conjunction with the risk information and explanations of terms appended to this presentation.

UPDATE AGAIN


Figure 7: Short fall risk



2. Capturing the liquidity premium – credit asset classes We defi ne the liquidity premium as the additional return investors require to hold assets that cannot be as readily bought and sold as common securities. In other words, given two assets with identical fi nancial market risk and return characteristics (e.g. two corporate bonds with the same rating, issued by com-panies in the same industry and denominated in the same currency), the asset with the lower liquidity should off er a better expected return. Otherwise inves-tors would always prefer to invest in the asset with the lower overall risk. The size of the liquidity premium varies from asset class to asset class, and also over time. Financial markets stress are specifi c periods when we observe increases in liquidity premia.

To illustrate the role of liquidity premia in the valuation of an asset class, we have charted high yield bond yields over time in fi g. 8. We have broken the yield into three components: risk-free (Treasury yield), credit risk (the expected default spread as estimated by the CIO Global Investment Offi ce), and risk premium (composed primarily of liquidity premium but including other market premia as well).

The graph shows how the liquidity premium represents a separate source of return that changes over time and tends to rise meaningfully during fi nancial crises. A similar description applies to the other credit sub-asset classes in the GCO allocation: the main diff erence is that, the lower the liquidity, the higher the liquidity premium component of the expected returns.


Figure 8: US High Yield total bond yield (1998–2014)




GCO construction in detail

Strategic Asset Allocation (SAA) in the context of the GCO

The GCO SAA is based on our fundamental SAA approach which you can read in detail in the initial section of this document – “SAA Methodology”. Our SAA methodology is anchored on our experts-based Capital Market Assumptions (CMAs) and the annual review process of our SAAs and CMAs. This approach is complemented by the GCO’s specifi c focus on capturing liquidity premium within the credit asset classes as explained above.

Tactical Asset Allocation (TAA) in the context of the GCO

The GCO asset allocation combines a very broad variety of credit sub-asset classes, with diff erent risk and return drivers. The relative performance of these diff erent building blocks will vary over the business cycle. For example, there will be instances where, over shorter time horizons, emerging markets (EM) will out/underperform developed markets (DM), investment grade will out/underper-form high yield, etc. Thus, we develop and implement tactical investment views specifi c to the GCO asset allocation, which aim to capture positive relative per-formance within credit sub-asset classes. We follow closely our well-established Global TAA investment process 3 but we defi ne a longer TAA investment horizon and take GCO specifi c asset class restrictions into account. The relative illiquidity of the GCO building blocks means that we set the GCO-specifi c TAA time hori-zon to 12 to 18 months, i.e. longer than the 6 months for the Global TAA. We set the risk budget in the GCO TAA to be identical to our unconstrained Global TAA risk budget.

Tactical investment viewsWhen defi ning the GCO TAA, we consider four main types of tactical invest-ment views:

• Corporate credit quality – e.g. high yield vs. investment grade bonds, US high yield vs. EUR high yield

• Regional exposure – e.g. developed markets vs. emerging markets, Asia vs. global EM

• Interest rate curve exposure – e.g. USA vs. Europe, high yield vs. senior loans• Liquidity and complexity premia – e.g. investment grade corporates (higher

liquidity and lower complexity) vs. bank capital (lower liquidity and higher complexity)

The GCO asset classes – expanding the CIO SAA universe

The core of the GCO allocation is composed of investment grade corporate, high yield corporate, emerging market sovereign and emerging market corpo-rate bonds. All these credit sub-asset classes are bond investments and share some characteristics. However, there are important diff erences among them, which must be kept in mind when defi ning a credit asset allocation.

A bond is a form of lending to a corporation or government-related entity. It gives the investor the right to receive the bond’s face value (also called principal) at maturity plus fi xed or variable interest coupons during its life. Bonds are generally categorized by the creditworthiness of their issuer, to which rating agencies such as Moody’s assign a rating. Generally, the higher the probability of default of the bond issuer, the higher the interest rate off ered by the bond. Within credit, there are main four bond sub-asset classes:

3 Mark H. Haefele, Mads N. S. Pedersen and Katarina Cohrs, Global Tactical Asset Allocation (TAA) Methodology, UBS CIO WM Global Investment Offi ce



a) Investment grade corporates – Issued by developed market corporations with a credit rating from AAA to BBB–

b) High yield corporates – Issued by developed market corporations with a sub-investment grade credit rating of BB+ or worse

c) Emerging market sovereigns – Issued in USD by emerging market govern-ments; they can be investment grade or high yield

d) Emerging market corporates – Issued in USD by corporations located in emerging market countries; they can be investment grade or high yield

Additionally, the GCO sub-asset class universe includes senior secured loans, bank capital, asset-backed securities and private debt. These credit sub-asset classes benefi t from greater liquidity premia than standard bonds and so con-tribute additionally to expected return and portfolio diversifi cation.

1. Senior secured loans – fl oating-rate loans given to high yield corporatesSenior secured loans are a sub-investment grade credit sub-asset class charac-terized by very low interest rate exposure and historically high recovery rates in case of loan default. The loans’ fl oating-rate nature means their performance stems from the yield of the loans and the course of credit spreads rather than changes in Treasury yields. Additionally, their ranking as senior secured in the corporate capital structure protects recoveries in the case of corporate default.

In fi gure 9 we off er a diagram of a typical corporate capital structure, which illustrates the degree of loss protection provided by equity holders and other corporate lenders. Historically, senior secured loans have had higher recovery rates than traditional high yield bonds, as the loans benefi t from their senior secured claim on the corporate assets.

Source UBS CIOFor illustrative purposes only.

Figure 9: Typical corporate capital structure




2. Asset-backed securities – secured by portfolios of debt obligationsAsset-backed securities (ABS) are bonds whose interest coupons and principal payments depend directly on cash generated by a pool of debt obligations. In this sense these bonds are secured by an underlying pool of assets. Typically they are fl oating-rate, which means they have very limited interest rate exposure.

The pools of debt obligations underlying ABS are usually highly diversifi ed and composed of such assets as mortgages, credit card loans, auto loans, equip-ment leases and other consumer loans. The performance of ABS depend on the default rates of the underlying assets and the prevailing short-term interest rate, i.e. Libor.

The ABS bonds are created by prioritizing, or tranching, cash fl ows so that each underlying pool of assets supports bonds with specifi c risks. In other words, the cash fl ows of each pool of assets are typically allocated to securities with three distinct ranks: senior, mezzanine and junior. The senior ABS benefi ts from the loss protection given by the mezzanine and junior ABS, while the junior ABS is the fi rst security to absorb any losses from the underlying pool of assets.

3. Bank capital – subordinated bonds issued by banksBank capital is a credit sub-asset class composed of hybrid fi xed income securi-ties, i.e. bonds that are structured with additional features such as coupon deferral, perpetual maturity and equity conversion triggers. These bonds have a subordinated ranking in the bank’s capital structure, below senior unsecured bonds but above common equity, as depicted in fi g. 10.

Bank capital as a sub-asset class is infl uenced by changes in banking regulation, e.g. Basel III superseding Basel II. This regulation determines the required features of the bonds, e.g. recently issued bank capital securities include conversion to equity or bail-in clauses.


Figure 10: Bank capital



4. Private debt – loans to non-publicly traded companiesPrivate debt is a credit sub-asset class composed of loans to non-publicly traded companies. The loans are negotiated individually and oft en contain specifi c covenants, e.g. equity conversion, payment in kind (PIK), etc. Their ranking in the corporate capital structure can range from senior secured to junior mez-zanine (see fi g. 11).


Figure 11: Private debt

Private debt is a prime example of a sub-asset class with liquidity premia that can be captured. The idiosyncratic nature of private loans, coupled with their illiquidity and complex structure, provide investment opportunities with high recurring income and attractive risk/return characteristics. Typically clients can invest in this sub-asset class through private debt funds that have a lock-up structure of several years (i.e. the capital invested cannot be accessed until the fund matures), periodic capital calls (i.e. clients allocate capital to the fund on a request basis to avoid unnecessary build-ups of uninvested cash in the fund structure) and period disbursements of income generated by the private debt investments to clients.

ab

Endowment-Style Portfolio (ESP)




Andrew Lee, Managing Director, UBS CIO WM Global Investment Office, Deputy Head UHNW & Alternatives

ESP: Investing across the premia

Please always read in conjunction with the glossary and the risk information at the end of the document. 59Please always read in conjunction with the glossary and the risk information at the end of the document.

Endowment-Style Portfolio (ESP) provides enhanced expected volatility-adjusted returns by allocating a high proportion of investor capital to alternative investments (AI), namely hedge funds, private equity and debt, and real assets/estate funds. Alternative investments provide access to return drivers not found in more traditional asset classes. With, for example, 40% in AI, the majority of the expected return of this portfolio comes from alternative returns. ESP asset allocations are expected to outperform traditional strategic asset allocations (SAAs) (see Fig. 1). The “cost” of higher returns is less liquidity in the AI part of the port-folio. At fi rst this may seem undesirable, but it can prevent the common behavioral bias of selling “risky” assets during stressed markets. The ESP is designed for investors with a long-term investment horizon who seek cash fl ow and the “real” (infl ation adjusted) preser-vation of their capital. For those investors who are not willing to commit 40% to alternative investments we also off er ESP with about 20% instead. This might be considered a good beginning that is half way there. As for any strategic solution the Global Asset Allocation team off ers, for UHNW clients, ESPs tailored to their individual needs.

ab





Andrew Lee, Managing Director, UBS CIO WM Global Investment Offi ce, Deputy Head UHNW & Alternatives

ESP: Investing across the premia

ESP: How the “endowment model” fi ts private investorsPrivate investors can take advantage of the same key investment principles put into successful practice by endowment funds to the extent they have comparable investment objectives, universes and time horizons. The “endowment model,” as pioneered by David Swensen, the CIO of Yale’s endowment1, aims to provide recurring income for an institution’s operational needs (in Swensen’s case, Yale University) while preserving the infl ation-adjusted value of the assets granted to the endowment in perpetuity. Many private clients have similar goals – a strategy that ensures their fi nancial legacy while contributing to their spending needs which may include, for example, costs related to private life or public commit-ments for a foundation.

1 Source “Pioneering Portfolio Management” by David Swensen – Yale University



Please always read in conjunction with the glossary and the risk information at the end of the document.2

Endowment funds do not limit their investment universe. They take positions across asset classes worldwide, including in the more niche and less-liquid mar-kets. They use both passive and active money managers, depending on the asset class. This liberty of choice and style is also available to many private investors. With the ESP, UBS provides private investors with a rigorous investment frame-work, portfolio solutions and access to top-notch investment managers who are not readily available to many clients.

Endowment funds are generally created to last forever to sustain the existence of a particular institution. This very long-term time frame calls for portfolio con-struction with diff erent weights and asset classes other than entailed in tradi-tional SAAs, one that focuses more on long-term performance and less on liquid-ity. Such an approach also characterizes many private investors, whose investment horizon tends to be generational, if not multi-generational.

ESP: How they anchor the portfolio’s long-term structureThe ESP, like other CIO SAAs, structure each portfolio at the asset class level to match the specifi c investment objectives and risk tolerance of investors (their fi nancial situation and personality). They do so while off ering investors the best risk and return trade-off for the given level of risk they accept. Our ESP asset allocations themselves chiefl y determine the portfolios’ performance. The skills of the asset manager(s) selected for each asset class constitute a second key factor in performance. This is particularly the case for the ESP given its large allocation to alternative investments (AI) where managers have a large leeway to implement their skill and potentially also generate alpha.

ESP: How they maximize diversifi cation benefi tsOur investment approach, and that of most endowments, capitalizes on the benefi ts of one of fi nance’s few “free lunches“ – i.e. diversifi cation, the ability to improve a portfolio’s risk-adjusted return by allocating one’s investments across imperfectly correlated assets and from liquidity premia which are only available to investors who can truly aff ord to wait before having invested capital returned. The ESP does so by embracing not only traditional asset classes such as public equities and fi xed income. They also take signifi cant positions in alter-native investments, namely hedge funds, private equity, private debt and real assets/estate. They invest across a range of diff erent investment categories, from diff erent underlying entities (companies, governments, etc.), sources of

The ESP asset allocations capture higher expected returns, from alternative investments and liquidity premia, than traditional SAAs do. For example, an ESP Balanced off ers about 1.2% more expected return per annum, i.e. 21% outperformance on initial capital invested, over 10 years. The majority of the ESP’s returns will come from the allocation to alternative investments.

Possible new graphs

1 Generated by IS

ESP -‐ IncomeESP -‐ Y ield

ESP -‐ Bal.ESP -‐ Grow th

ESP -‐ Equities

SAA Income

SAA Y ieldSAA Balanced

SAA Grow thSAA Equities

0%

2%

4%

6%

8%

10%

12%

0% 2% 4% 6% 8% 10% 12% 14% 16%


Ann

ualised

Estim

ated

Return %

ES SAAs vs. Traditional SAAs Return p.a Risk p.a

ESP Income 5.6% 4.7% SAA Income 4.2% 4.1%

ESP Yield 6.4% 6.3% SAA Yield 5.2% 6.0%

ESP Balanced 7.2% 8.1% SAA Balanced 6.0% 8.1%

ESP Growth 8.0% 10.0% SAA Growth 6.9% 10.7%

ESP Equities 9.0% 11.1% SAA Equities 7.6% 13.5%

March Update


Figure 1: Expected Risk and Return




fi nancing (loans, equity, etc.) and various regions of the world, and also to credit quality (high to low creditworthiness) and market types (public, private, etc.). Additionally, greater exposure to AI enables the ESP to profi t from complemen-tary forms of active manager alpha skill. Each asset class represents a distinct mix of these categories, which ultimately are imperfectly correlated – exactly what is needed for true diversifi cation. The ESP is specifi cally designed to allocate capital across this whole spectrum of investments.

ESP: Which additional returns it capturesA major reason for the outstanding performance of certain endowments lies in their ability to capture returns beyond those of traditional public markets (see fi g. 2). To do likewise, the CIO ESP asset allocations have a large allocation to alternative investments (about 40% is our guidance), especially to private mar-kets (private equity, private debt and real assets/estate). The additional returns/premia they benefi t from are:

1) the alternative investment premia; 2) the liquidity premia; and 3) the larger alpha potential within these markets

A fourth potential source of return stems from investing in locked-up invest-ments that, when fear is rampant during major drawdowns, can actually prove benefi cial by limiting one’s tendency for panic selling and by enabling profes-sional discretionary managers to use committed capital to exploit attractive opportunities, such as those that presented themselves during and aft er the 2008–09 and 2001–03 crises.

Alternative Investment Premia: These represent the additional return required by investors to hold assets that are oft en diffi cult to access. For example, while private equity companies are fundamentally similar to their publicly traded equivalents, one cannot easily purchase shares of them with the click of a button in one’s brokerage account. Investments in private companies are complex individual transactions that require investors to devote signifi cant time and eff ort to sourcing, structuring, due diligence, in some cases leverage, management

PublicMarketsPremia

AlternativeInvestments

Premia

LiquidityPremia

Alpha

Return from exposure to public marketsExample: investing in a public US equity market (passive fund on S&P 500 stock index)

Return from exposure to alternative markets (these include hedge funds and private markets)Example: investing in US private companies (diversifi ed set of US private companies)

Return from exposure to less liquid marketsExample: investing in a US Private Equity fund with a 10 year life

Return from the selection skill of the investment managerExample: investing in the best US Private Equity fund


Figure 2: Sources of Return




oversight and, ultimately, exit management, to name just a few elements. To compensate for this work and risk investors demand a premium. “We should be getting an incremental return for that illiquidity – and we call that our illiquidity premium – of a least 3% annually on average over what we expect in publicly traded stocks” Jane Mendillo, CEO of Harvard Management Company (Baron’s, February 8th, 2014).

Liquidity Premium: It consists of the additional return investors require to hold investments not as readily convertible to cash as public securities on major markets. Given two equivalent investments, the less-liquid one should provide a premium because it grants less fl exibility to investors. In some cases transaction costs may increase and in other cases there might be no market to exit these investments prior to the predetermined life of the investment.

Alpha: Alternative investments generally off er a further source of return: higher alpha opportunities, i.e. the possibility of beating the overall market or one’s investment peers. They arise because AI markets are oft en less effi ciently priced and investors in them tend to operate with fewer restrictions. The better fund managers within these asset classes can add value by wisely selecting and man-aging their assets. For example Private Equity managers may add value through their skills in selecting deals, managing them over time and also appropriately applying leveraged fi nancing. Unfortunately, such managers are oft en hard to identify and even harder to access. A benefi t UBS off ers private clients is screen-ing for and providing access to them.

The Endowment Style Portfolio is expected to harvest the additional premia from alternative investments mentioned above. We estimate these additional premia to range from about 3% to 5% depending on the asset classes. This represents the additional return compared to investing the “equivalent” liquid version of these asset classes, for example listed equities compared to private equity. Follow-ing are the additional premia we estimate for each alternative asset class broadly: hedge funds 3%, private real estate 3%, private equity 3% and private debt 5%.

Figure 3 shows that the ESP asset allocations off er a better expected trade-off of volatility and return than traditional SAAs over a forward-looking 10 year hori-zon. Therefore the ESP provides higher risk-adjusted returns (i.e. higher Sharpe ratios) than traditional SAAs without private markets. For a similar volatility level investors can expect about 1.3% additional return p.a. with an allocation of 40% to alternative investments and more if the allocation is higher or if considering the high alpha (active return) potential of alternative investment managers. Vola-tility is used below as a measure the risk level of the diff erent portfolios, however, that is just one of the several measures of risk needed to understand risk within a portfolio. A description of how we derive the long-term expected risk and return assumptions shown in fi g. 3 and of the limits of certain risk measures is explained in the second part of this document. The returns shown in fi g. 3 assume full exposure to each asset class as per the allocation described in the asset alloca-tion table shown in the next section of this document.




ESP: The investor type they are designed forThe ESP is designed for higher returns. ESP investors seek ongoing cash fl ow while wishing to preserve the infl ation-adjusted value of their capital and remain willing to stay invested over the long term. In this concept paper we show the ESP concept applied across fi ve diff erent risk levels. The concept is applicable across all main currencies. The ESP concept and model portfolios are available in fi ve versions (per reference currency). They are spread across the risk spectrum, to cater to investor risk tolerance, as represented by UBS’s Income to Equities investor profi les. UBS off ers, for UHNW clients, ESP tailored to the individual needs. Private investors can use the ESP:

• as a total wealth portfolio that supplies a source of ongoing cash fl ow to fund expenses while ultimately providing capital for a bequest

• as a long-term portfolio to reach investment goals 10 or more years in the future, such as the education of off spring, retirement or a bequest, by rein-vesting any cash fl ow it generates

• as an intergenerational portfolio that secures continuity and capital growth across decades and generations

To capture the additional returns described above, ESP investors need to be will-ing and able to invest a large portion of their portfolio in less-liquid and oft en less-standardized asset classes worldwide. For example, some private equity funds have 10-year investment periods and take positions in private companies that do not have standard disclosure obligations. Our ESP allocates less capital to the less-liquid asset classes than do many endowment funds (for example, the Yale endowment devotes about 75% of its funds to AI) and ensure that ESP investors can liquidate the majority of their portfolio in a short period of time should unforeseen life events unfold. The current allocation to less-liquid asset classes is meant as guidance for wealthy investors however there is oft en no overriding reason for the wealthiest people in the world to hold so much (60% in the ESP) of their net worth in liquid assets.

The less-liquid part of the portfolio may take several quarters, in the case of hedge funds, or several years, in the case of private markets, to liquidate com-pletely (in some cases one may sell sooner, but generally only at a large discount on the secondary market). For that reason, the ESP is suited to long-term investors.

Possible new graphs

1 Generated by IS



ESP -‐ Equities

SAA Income



0%

2%

4%

6%

8%

10%

12%

0% 2% 4% 6% 8% 10% 12% 14% 16%


Ann

ualised

Estim

ated

Return %

ESPs vs. Traditional SAAs Return p.a Risk p.a




ESP Growth 8.0% 10.0% SAA Growth 6.9% 10.7% ESP Equities 9.0% 11.1%

SAA Equities 7.6% 13.5%

March Update

Source: UBSFor illustrative purposes only.

Possible new graphs

1 Generated by IS



ESP -‐ Equities

SAA Income



0%

2%

4%

6%

8%

10%

12%

0% 2% 4% 6% 8% 10% 12% 14% 16%


Ann

ualised

Estim

ated

Return %

ES SAAs vs. Traditional SAAs Return p.a Risk p.a




ESP Growth 8.0% 10.0% SAA Growth 6.9% 10.7%

ESP Equities 9.0% 11.1% SAA Equities 7.6% 13.5%

March Update

Figure 3: Expected Risk and Return




Because of the complex nature of some of the investments and how they need to be managed within a portfolio context, we advise ESP investors to use best in class manager selection capabilities to select and manage their portfolios. For example, investing in private markets requires the time and expertise to select several funds that will maintain a steady level of investment that is diversifi ed across fund “vintages” (see private market description below for more details). As such, UBS’s mandates and open architecture fund platform are ideal for implementing the ESP. They provide access, active portfolio management and ongoing monitoring/evaluation of third-party specialist fund managers around the world for each asset class.

ESP: Implementation and timeline considerationsPrivate market investments, which represent 22% to 40% of the ESP, are com-plicated to implement and manage in a portfolio context. We recommend that investors undertake such investments over several years when establishing the portfolio. This time frame enables them to diversify their capital across diff erent start years (also called vintages), to diversify across investment opportunities, and to wait for access to the best managers, who may not always be open to new investors. During this built-up period, we advise conducting a monthly or quar-terly review with experts in the CIO Global Asset Allocation team and respective CIO asset class specialists. Subsequently, a less-frequent review, quarterly or annually, should suffi ce and include among other topics the rebalancing of the less liquid parts of the portfolio.

Figures 4 and 5 illustrate the expected long-term benefi t of investing in the ESP compared to traditional SAAs. The graphs show the cumulative performance of Balanced and Growth profi le SAAs using Monte Carlo simulation over 20 years assuming one invests 100 million USD today and also the related probability, over time, of not preserving the initial investment amount i.e. the short fall risk. For simulation purposes, in fi g. 4 and 5, we assume full investment exposure to each asset class over time and regular rebalancing to maintain the portfolio near the target SAA weights.


4 Generated by QIS

100

150

200

250

300

350

400

450

500

2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035

USD

SA A -‐ USD Balanced

ESP -‐ USD Balanced

SA A -‐ USD Growth

ESP -‐ USD Growth

50% percentiles

·  The graph shows the probability that the allocation will fall below a particular projected value.




March Update


Figure 4: 50th percentile Monte Carlo simulated portfolio value over 20 year period




Figure 6 shows the Monte Carlo simulated probability of not achieving a posi-tive return overtime, i.e. the shortfall risk, over a 20 year period for diff erent SAAs.


5 Generated by QIS

100

200

300

400

500

600

700

2015 2020 2025 2030 2035 2040 2045 2050

USD




ESP -‐ USD Growth

50% percentiles

·  The graph shows the probability that the allocation will fall below a particular projected value.




March Update


Figure 5: 50th percentile Monte Carlo simulated portfolio value in “real” (infl ation adjusted) term over a multigenerational 35 year period


6 Generated by QIS

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035

Shortfall P

roba

bility




ESP -‐ USD Growth

·  The graph shows the probability in each future period that the projected value will fall short of a desired target value or return.

·  In general, the longer the investment horizon the lower the risk of not reaching a given return target.


Shortfall risk



March Update


Figure 6: Shortfall Risk




Constructing and managing the Endowment-Style Portfolio

Constructing and developing new investment concepts at UBS is a comprehensive team undertaking. It involves our most experienced strategists and asset class experts, and includes our risk offi ce professionals and quantitative portfolio construction specialists. The ESP represents an investment concept that enables investors with a long-term investment horizon to benefi t from the best risk-and-return trade-off via the broadest diversifi cation, and to also benefi t from the extra return that characterizes less-liquid asset classes. Investors gain access to the best-in-class third-party managers oper-ating in the private equity, private debt and real estate markets.

ESP: Begin with the right building blocksTo ensure recurring cash fl ow and “real” (infl ation-adjusted) long-term capital preservation, investors, given today’s historically low interest rate environment, cannot rely solely on the traditional income-generating building blocks of high grade bonds. Furthermore, with interest rates and infl ation expected to increase in the coming years, high grade bonds may not in fact preserve real value nor perform as well as they have in the past. That’s why we construct our ESP to gen-erate income from multiple sources: dividends from public equities, distributions from private equity, rent from real estate, and interest from a diverse set of fi xed income sub-asset classes that range from high grade bonds to private debt (see overview below). To grow the real value of the portfolio, the ESP allocates a con-siderable amount of capital to building blocks whose value typically increases with nominal growth, namely the equity (public and private) and real estate asset classes. Hedge funds are included to provide diversifi cation and growth via the performance of skilled managers, who seek to provide returns with low correla-tions to broader markets.

Figure 7 shows the diff erent building blocks that make up the ESP. Their related primary sources of cash fl ow and general risk and return levels are also detailed.

Equities

Fixed Income High Grade

Fixed Income Investment Grade

Fixed Income Below Invest. Grade

Medium/High return and risk Cash flow from interest/coupon

Medium return and risk Cash flow from interest/coupon

Low return and risk Cash flow from interest/coupon

High return and risk Cash flow from dividends

Private Equities

Private Debt

Real Asset/Estate

Hedge Funds

High return and risk Cash flow from capital gains

Medium/High return and risk Cash flow from interest & capital gains

Low/Medium return and risk Cash flow from income & capital gains

Low/Medium return and risk Cash flow reinvested by manager

Public Markets* Alternative Investments *

* the risk and return levels shown are indicative only and will ultimately depend on exact investments used for implementation

Figure 7: ESP Building Blocks




Asset allocation and risk & return characteristicsThe current set of ESP asset allocations is the result of the extensive work done by the Global Asset Allocation team in collaboration with multiple asset class experts. In table 1 we show how we apply the ESP methodology across the applicable ranges of UBS Investor Profi les (An ESP for other currencies can also be constructed; for more details on ESP methodology, see section below). The key characteristics of the portfolio are explained in the table and text below: 1) their historical and expected volatility and return characteristics, including simulations; 2) an estimate of annual cash fl ow; 3) a description of the risk considerations specifi c to private markets; 4) a description of special perfor-mance considerations for alternative investments; and 5) liquidity levels.

Table 1 shows diff erent ESP asset allocations for diff erent risk levels (risk profi le) and several of our assumptions related to return and risk expectations. In the col-umns on the right we show our Capital Market Assumptions (expected return and volatility estimations) and an indication regarding the liquidity of the diff er-ent asset classes in a normal market environment. We assume full investment exposure to each asset class over time and regular rebalancing to maintain the portfolio near the target SAA weights. We estimated the potential annual cash fl ow from the ESP which ranges from 2.5% to 3.5% depending on the profi le. Naturally, these cash fl ow estimates represent averages over a long-term holding period and may deviate from that year on year. We used the following proxies for cash fl ow estimates: yield to maturity for bonds, dividend for equities, rental income for real estate, none for hedge funds and half of their respective expected return for private equity/debt as a conservative approach. For private markets asset classes our return assumptions are based on an internal rate of return approach and represent the return on invested capital. For Hedge Funds our return assumption implies a tilt to less liquid strategies and managers.

B (Income)

C (Yield)

D (Balanced)

E (Growth)

F (Equities)

FX Hedged

Expected 10 Yrs Return p.a.

Expected Volatility p.a.

LIQUIDITY 3% 3% 3% 3% 3%Cash USD 3% 3% 3% 3% 3% 2.6% 0.5%BONDS 51% 38% 25% 12% 5% 0.0% 0.0%USD high grade bonds 5-7 years 28% 17% 10% 4% 5% 2.8% 4.6%USD corporate intermediate bonds (IG) 13% 11% 6% 0% 0% 3.4% 4.2%USD high yield bonds 3% 3% 3% 3% 0% 5.5% 8.8%EUR high yield bonds 2% 2% 2% 2% X 5.0% 8.5%EM sovereign bonds (USD) 3% 3% 2% 3% 0% 5.5% 9.1%EM corporate bonds (USD) 2% 2% 2% 0% 0% 5.2% 9.9%EQUITIES 6% 19% 32% 45% 52% 0.0% 0.0%US 4% 9% 16% 21% 25% 7.4% 15.4%EM 0% 3% 5% 7% 8% 9.0% 24.1%Eurozone 0% 3% 5% 7% 8% X 9.6% 18.4%UK 2% 2% 4% 5% 6% X 8.3% 15.0%Japan 0% 0% 0% 3% 3% X 8.0% 19.8%Switzerland 0% 2% 2% 2% 2% X 9.0% 14.9%ALTERNATIVE INVESTMENTS 40% 40% 40% 40% 40%HEDGE FUNDS 14% 18% 18% 14% 0.0% 0.0% Hedge Funds 14% 18% 18% 14% 0% 7.0% 6.7%PRIVATE MARKETS 26% 22% 22% 26% 40% 0.0% 0.0% Private real estate 10% 8% 6% 4% 8.9% 9.8% Private equity 3% 6% 15% 32% 12.0% 12.5% Private debt 13% 8% 6% 5% 4% 8.5% 9.0%

TOTAL 100% 100% 100% 100% 100%

Expected 10 yrs Return p.a. 5.6% 6.4% 7.2% 8.0% 9.0%Expected Volatility p.a. 4.7% 6.3% 8.1% 10.0% 11.1%Sharpe ratio 0.65 0.61 0.57 0.55 0.58

The returns shown for Alternative Investments are net of fund manager fees (including management and performance fees)

6%10%

Table 1: ESP Asset Allocations




Regarding the allocation to sub-strategies within each alternative investments asset class we have used the following ranges as guidance for long-term alloca-tion. The use of ranges is important within Alternative Investments given the opportunistic nature of certain strategies which, for example, might only be interesting at certain stages of the business cycle. For hedge funds we recom-mend an approach focused on less liquid strategies and managers including asset backed securities, distressed & restructuring and convertible arbitrage strat-egies. For Real Estate we advise 50–100% in Value Add, 0–50% in Core and 0–50% in Opportunistic strategies while diversifying across region and sectors. For Private Equity we advise 30–90% in Buyouts, 0–50% in Growth and 0–20% in Venture Capital. For Private Debt we advise 30–70% in Senior debt, 0–50% in Mezzanine debt and 0–50% in Distressed debt.

For some investors a commitment of 40% to alternative investments, including circa 25% in private markets, might be undesirable, possibly because private markets are new to their investment universe or because of temporary or perma-nent liquidity needs. For such investors we off er a lower initial target weight to alternatives of around 20% (see table 2). Given the long-term intergenerational nature of the portfolio we might even say that a good beginning is half way there. This naturally reduces the added return benefi t from alternative invest-ments and lowers the return to risk trade off as calculated in the Sharpe ratio. But this is partly mitigated by adjusting the mix in favor of Private Equity.

B (Income)

C (Yield)

D (Balanced)

E (Growth)

F (Equities)

FX Hedged

Expected 10 Yrs Return p.a.

Expected Volatility p.a.

LIQUIDITY 3% 3% 3% 3% 3%Cash USD 3% 3% 3% 3% 3% 2.6% 0.5%BONDS 67% 52% 37% 21% 5% 0.0% 0.0%USD high grade bonds 5-7 years 35% 25% 18% 10% 5% 2.8% 4.6%USD corporate intermediate bonds (IG) 22% 17% 10% 3% 0% 3.4% 4.2%USD high yield bonds 3% 3% 3% 3% 0% 5.5% 8.8%EUR high yield bonds 2% 2% 2% 2% X 5.0% 8.5%EM sovereign bonds (USD) 3% 3% 2% 3% 0% 5.5% 9.1%EM corporate bonds (USD) 2% 2% 2% 0% 0% 5.2% 9.9%EQUITIES 10% 25% 40% 56% 72% 0.0% 0.0%US 5% 12% 18% 28% 35% 7.4% 15.4%EM 0% 4% 6% 9% 10% 9.0% 24.1%Eurozone 0% 4% 6% 8% 9% X 9.6% 18.4%UK 3% 3% 5% 6% 7% X 8.3% 15.0%Japan 0% 3% 3% 4% X 8.0% 19.8%Canada 3% X 8.2% 15.0%Australia 2% X 9.2% 14.9%Switzerland 2% 2% 2% 2% 2% X 9.0% 14.9%ALTERNATIVE INVESTMENTS 20% 20% 20% 20% 20%HEDGE FUNDS 7% 9% 9% 7% 0.0% 0.0% Hedge Funds 7% 9% 9% 7% 0% 7.0% 6.7%PRIVATE MARKETS 13% 11% 11% 13% 20% 0.0% 0.0% Private real estate 3% 2% 2% 2% 2% 8.9% 9.8% Private equity 6% 6% 7% 9% 16% 12.0% 12.5% Private debt 4% 3% 2% 2% 2% 8.5% 9.0%

TOTAL 100% 100% 100% 100% 100%

Expected 10 yrs Return p.a. 5.0% 5.7% 6.5% 7.4% 8.3%Expected Volatility p.a. 4.5% 6.2% 8.1% 10.4% 12.2%Sharpe ratio 0.54 0.51 0.49 0.46 0.47

The returns shown for Alternative Investments are net of fund manager fees (including management and performance fees)

Table 2: Alternative ESPs with 20% Alternative Investments




1. Risk and return characteristics of the ESP To understand how the ESP behaves, we have analyzed both the historical and forward-looking risk-and-return estimates for each asset class as well as for each overall ESP allocation. For simulation purposes we assume full investment expo-sure to each asset class over time and regular rebalancing to maintain the port-folio near the target SAA weights. Our historical simulations are based on the respective representative indexes for each asset class in the ESP. We compared the ESP to traditional SAAs that do not include private markets (PM) to demon-strate the added value of the specifi c ESP features (fi g. 8).


1 Generated by QIS

0

50

100

150

200

250

300

350

400

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

USD

ES SA A -‐ USD Y ield

SA A -‐ USD Y ield

ES SA A -‐ USD Growth


Simulated historical performance


Return Risk

ESP – USD Yield 6.8% 5.9%

SAA – USD Yield 5.5% 5.7%

ESP – USD Growth 7.0% 9.4%

SAA – USD Growth 5.2% 10.6%

March Update


ESP – USD Yield

SAA – USD Yield

ESP – USD Growth

SAA – USD Growth

Figure 8: Cumulative historical performance




Figure 9 shows the simulated historical drawdown of the ESP versus the tradi-tional SAAs for strategies Yield and Growth. It graphs the losses that occurred in the portfolio as “valleys,” with each loss illustrated by the diff erence between the peaks and troughs. The ESP allocations have lower drawdowns versus their respective traditional SAA counterparts.

2. Special risk considerations within less-liquid markets Volatility is a standard industry measure that attempts to capture in a single num-ber an estimate of the risk of an investment. It has certain limitations, especially when applied to private markets asset classes, whose risk it tends to underesti-mate. Reported returns for private markets funds are based on non-traded assets whose valuations are estimated and based on recent transactions. PM volatility estimates stem from these reported valuations, so they are subject to several potential biases, most prominently the fact that prices are based on appraisal and not public quotes and furthermore the fact that fund data collectors may not track all or a representative sample of the fund managers within the PM fund universe. Several PM indices employ techniques to limit these biases. When con-structing an ESP, we consider these limitations by reducing our reliance on traditional mean-variance optimization techniques which are highly sensitive to estimation error of risk-and-return parameters. We consider, among other fac-tors, the character of the underlying holdings, which for example for private equity is an equity stake in a company similar to equivalent public equities. Our allocation depends less on the reported volatility measures of PM than on the type of underlying investments they are exposed to such as equity for private equity.


2 Generated by QIS

-‐45.0%

-‐40.0%

-‐35.0%

-‐30.0%

-‐25.0%

-‐20.0%

-‐15.0%

-‐10.0%

-‐5.0%

0.0%

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Returns



March update


ESP – USD Yield

ESP – USD Growth

SAA – USD Yield

SAA – USD Growth

Figure 9: Simulated historical drawdown




3. Special performance considerations within alternative investmentsTwo of the additional sources of return that alternative investments can capture that traditional investments cannot – the AI premia and the liquidity premia – are accounted for in our capital market assumptions (CMAs, see details below). The third one, the high alpha potential, is not since it represents the skill of the AI man-ager and not an exposure to a certain market. This additional return can be signifi -cant because AI markets are usually less effi cient than large liquid markets. This lack of effi ciency represents both an opportunity and a risk: fund managers can succeed in capitalizing on it or not, and the dispersion in performance between the worst and the best managers can be high, higher than is generally found in the traditional markets.

Figure 10 shows the annual returns of the median, the bottom quartile (25th per-centile) and the top quartile (75th percentile) of active fund managers over 10 years. It illustrates that the diff erence between the top and bottom quartile is about 5% for public equity fund managers but 15%-20% for private equity fund managers. The average market performance is generally higher than the performance shown given the median does not account for fund sizes and many of the smaller funds are found in the bottom quartiles.

SAA constructionDefi ning our approach

The ESP SAAs are based on our fundamental SAA approach which you can read in detail in the initial section of this document – “SAA Methodology”. Our SAA methodology is anchored on our experts-based Capital Market Assumptions (CMAs) and the annual review process of our SAAs and CMAs. This approach is complemented by the ESP’s specifi c focus on capturing alternative and liquidity premia as explained above.

Source “Pioneering Portfolio Management” by David Swensen – Yale UniversityPeriod: 10 years ending June 2012

Figure 10: Annual Returns by Quartile




The ESP asset classes – expanding the CIO SAA universe

The ESP, as do other CIO SAAs, makes use of the equities, fi xed income and hedge fund asset classes. In addition, they also incorporate what are called private markets to a large extent, namely private equity, private debt, and real assets/estate. More generally, the ESP makes a greater allocation to alternative investments (AI) to better diversify the portfolio and capture more of the addi-tional premia discussed above. Below we describe in more detail each of the AI asset classes to understand how they contribute to the ESP. 1. Hedge fundsHedge funds are actively managed investment vehicles with fl exible mandates. Managers invest opportunistically, both long and short, across asset classes, using a wide range of strategies and instruments (such as derivatives) to achieve attractive risk-adjusted returns. Compared to traditional investments, these vehi-cles tend to exhibit reduced liquidity since fund managers need time to execute their strategies and certain underlying investments are potentially less liquid. On average, hedge funds permit monthly or quarterly redemptions and in some cases require initial lockup periods of up to several years. Certain strategies focused on highly liquid underlying asset classes are available through more liquid structures. Hedge funds are also diff erentiated from traditional asset man-agers in that they typically charge both a management fee on invested assets and an incentive fee on gains.

Added value of hedge funds in portfoliosDespite higher fees, lower liquidity and a more complex structure, hedge funds are important tools for investors who want to improve their portfolio return expectations by diversifying through investments that off er a favorable risk-and-return trade-off . Even aft er deducting fees, hedge funds generate higher risk-adjusted returns than many traditional asset classes. They do so by capturing a combination of 1) traditional and alternative investment premia; 2) liquidity premia; and 3) manager alpha. Alternative risk premia represent hedge funds’ ability to harvest various risk premiums in global capital markets (such as volatility premium). Exposure to such sources of return is a key diff erentiator that has enabled hedge funds to generate better risk-adjusted returns historically than traditional asset classes and at the same time protect investors against severe drawdowns in adverse market conditions, smoothing out the overall volatility of their portfolio.

Not all hedge funds are the sameWhile hedge funds share features such as mitigating downside risk and focusing on asymmetric risk-adjusted returns, the strategies they pursue vary in terms of investment approach, asset class focus, exposure levels, leverage ratios and/or risk-return profi les. In general, the hedge fund universe can be broken down into four broad strategy styles: equity hedge, event-driven, relative value and macro trading. Each category comprises several sub-strategies with diff erent approaches, underlying exposures, market beta and liquidity profi les.

Nils Beitlich, Executive Director, UBS CIO WM Global Investment Offi ce, UHNW & Alternatives, Head Hedge Funds

Cyril Demaria, Executive Director, UBS CIO WM Global Investment Offi ce, UHNW & Alternatives, Private Markets Strategist




• Equity-hedge strategies generally invest long and short in mispriced listed equity securities. They seek to generate performance through fundamentally driven security selection. These highly liquid strategies tend to be directional in nature, with a bias towards net long exposure, and thus have a higher degree of market correlation.

• Event-driven strategies invest long and short in equity and debt securities to capitalize on mispricing that result from corporate events such as spinoff s, restructurings, acquisitions and divestitures. This category includes both direc-tional and non-directional strategies with diff erent degrees of liquidity. For instance, debt-focused strategies typically exhibit lower liquidity than most equity-focused event-driven strategies.

• Relative-value strategies seek to capitalize on pricing discrepancies across related fi nancial instruments that should correct over time. They tend to rely on quantitative analysis and are generally non directional, with low market correlation but potentially greater illiquidity.

• Macro and trading strategies invest in instruments across asset classes to anticipate shift s in macroeconomic trends, using either a discretionary approach or a systematic quantitative-driven one. They generally have the most fl exible mandates, tend to be directional in nature and are typically among the most liquid hedge funds.

Choosing the right hedge fund allocationGiven the numerous strategies and styles available, investors can customize their hedge fund exposure to match their return objectives, time horizon, risk profi le and/or liquidity needs. In the context of a portfolio allocation, a balanced portfo-lio across strategies is the best way to add alternate sources of return without signifi cantly changing the portfolio’s sensitivity to market movements. Alloca-tions to hedge funds should emphasize access to manager alpha and alternative premia rather than broad market premia. Traditional assets (such as equities and bonds) can provide low-cost (low-fee) exposure to overall market beta with high liquidity. Hedge funds by contrast should be used as alternate sources of return that enhance diversifi cation, performance and downside protection. The hedge fund allocation in the ESP may be focused on less-liquid strategies to benefi t more from the liquidity premia, a key feature of the ESP.

Fewer liquidity constraints can be rewardingGenerally, adding hedge fund exposure only requires moderate incremental illiquidity. The more illiquidity that investors can accommodate, the more they benefi t from the ability of hedge funds to achieve low correlations and deliver better risk-adjusted returns. Less-liquid strategies such as “relative value“ and fi xed income-focused “event-driven“ typically outperform more-liquid strategies such as “equity hedge“ and “macro trading.“ Long-term investors who can tolerate even greater illiquidity, like endowments and ESP investors, can also access hybrid hedge fund structures. The lockups for these vehicles (on average one to three years) exceed typical hedge fund illiquidity but remain shorter than those of private markets. They enable such funds to target longer-lived instru-ments, less-liquid assets and strategies that take longer to play out, with an expected return profi le between that of hedge funds and private markets funds.

2. Private marketsPrivate markets (PM) are important components of the ESP. These strategies, which include private equity, private debt and real assets/estate, signifi cantly expand the universe of instruments, exposures and sources of return available to investors. PM strategies principally make unlisted equity or debt investments in mostly private businesses and assets. While certain large investors have the staff -ing and expertise to undertake such direct investments in private companies and assets on their own, the investments are typically made through commingled funds managed by third-party fund managers. The funds commonly impose




signifi cant lockup requirements on investor capital to ensure that their managers have the time and opportunity to derive returns from the less-liquid underlying assets. The terms of these partnerships range from seven to as many as fi ft een or more years, depending on the specifi c investment strategy. Private market investments managed in partnerships generally have defi ned investment periods over which investors committed capital is progressively drawdown (i.e. fund manager request transfer of an investor’s commitments as opportunistic arise) and then redistributed back to investors during the life of the fund. This particu-lar feature of PMs requires careful cash fl ow management across a diversifi ed set of investments in order to maintain a constant exposure to these asset classes over time.

2.1 Private EquityPrivate equity investing entails purchasing signifi cant or controlling private (non-traded) equity interests in mostly unlisted businesses. The objective is to exert varying levels of operational and strategic infl uence to generate high risk-adjusted returns. Private equity represents roughly 70% of the private markets investment universe (by amount invested), of which leveraged buyout (LBO) activity constitutes 60% of the volumes invested. The US represents 60% of the market, Europe 25% and the rest of the world 15%.

InvestingPrivate equity investment strategies require longer asset-holding periods, oft en more than fi ve years (in the case of start-ups, for instance, or companies that are restructuring). As such, they are well-suited to investors with a long-term time horizon and limited interim capital requirements, such as endowments, family offi ces and intergenerational family investors. Widely varying fund performance underscores the importance of fund manager selection. Weightings of the vari-ous private equity strategies in a portfolio at any given time are specifi c to each investor. They depend on overall risk appetite, total assets and the capital avail-able for illiquid investments. In portfolios of a certain size, for example, an inves-tor might decide to emphasize specialized strategies such as venture or turna-round capital rather than a more classic allocation to LBO and growth capital.

Key private equity strategiesPrivate equity investment strategies cover the full lifecycle of a given company, from inception (venture capital) to growth, transfer of ownership and even restructuring. Each investment strategy provides incremental value in a private market portfolio, based on an investor’s appetite for risk, return and illiquidity:

i) Venture and growth capital strategies invest in companies in the start-up and expansion phases, off ering exposure to the growth potential of an idea, econ-omy and/or economic sector. They provide diff erentiated risk-return profi les, with venture capital at the higher end of the risk-return spectrum and growth capital at the lower end.

ii) LBO is generally the entry point to the asset class for new investors: it is the most active strategy and the most familiar to investors in private markets. It off ers attractive average risk-adjusted returns, with lower risks associated with fund manager selection than in venture or turnaround strategies, where perfor-mance varies widely.

iii) Distressed, turnaround capital and other niche strategies off er either uncor-related investment niches or contrarian investment theses (turnaround capital in times of crises) that make them attractive from a portfolio-construction perspective.




Additionally, opportunistic strategies include fund and direct secondaries, direct investments, co-investments and pre-IPO / PIPEs.

2.2 Private DebtPrivate-debt investing targets non-traded debt investments in listed or unlisted businesses. The debt is oft en used to fi nance signifi cant corporate changes such as buyouts, acquisitions or restructuring, or to provide fi nancing to under-served segments such as small and mid-size businesses. Fund managers select and mon-itor borrowers to control risks, and they are compensated with varying levels of return that, in some cases, includes equity or equity-like participation. Private debt represents a smaller but growing portion of the overall private markets universe.

InvestingPrivate debt funds typically have seven to 10-year terms, with the diff erent key strategies subject to varying levels of illiquidity and returns. The more illiquid the strategy is, the more likely the total returns are fuelled by capital gains. At the other end of the private debt spectrum, the more liquid the strategy is (and the shorter the duration of the fund), the more likely returns stem from income yield.

StrategiesPrivate debt has historically comprised senior debt, mezzanine debt and dis-tressed debt (loan-to-own) strategies, the last two make use of conversion rights and have equity-like attributes. Mezzanine debt and distressed debt strategies are at the higher end of the risk-return spectrum of private debt, and thrive at specifi c times in economic cycles. The strategies that capitalize most on the illi-quidity of an endowment-like portfolio are:

i) Direct lending is the provision of typically senior secured debt to private com-panies. This strategy is positioned more senior in the capital structure than dis-tressed or mezzanine, and provides funding to certain segments of the market increasingly underserved by traditional lenders and fi nancing sources. In that respect, it compares to traditional publicly traded bank loans, although private lenders originate, perform the due diligence and structure their own credits. Direct lending strategies are typically shorter in duration and provide current yield.

ii) Mezzanine debt strategies target subordinated tranches of debt in private businesses. This form of debt includes a variable compensation mechanism in the form of equity-conversion rights (“equity kicker”), which can be exercised or sold back to the creditor. Mezzanine debt can be found in LBOs (“sponsored deals”) or in private companies willing to structure their balance sheet beyond the usual lending thresholds (“unsponsored deals”). Underlying loan duration is usually fi ve to eight years, and thus tends to maximize the illiquidity premium. Prepayment from creditors is possible but with compensation to the lender.

iii) Distressed debt (“loan-to-own” or “debt-for-control”) strategies seek to gain ownership of an ailing business by leveraging the bankruptcy procedure. By assessing the value of its assets and restructuring its liabilities appropriately, the investor can provide the business with new life through structures that protect against downside while participating signifi cantly in any upside. This strategy is closest to private equity and, notably, turnaround capital. Investment duration typically spans fi ve to eight years and sometimes more, making this investment strategy particularly suitable for investors with a high tolerance for illiquidity.




iv) Additional strategies dedicated to specifi c economic sectors (oil and gas) or assets (infrastructure, real estate or other tangible assets) are increasingly avail-able. Their maturity profi le matches the long-term investment horizon of endow-ment-style portfolios while delivering income and meeting some short-term recurring liquidity requirements.

v) A range of opportunistic private debt strategies, such as non-performing loans, trade fi nance, factoring and other strategies, that depend on idiosyncratic drivers and whose total investment capacity may be more limited.

2.3 Real Assets/EstatePrivate real asset investing targets investments in illiquid tangible assets with the aim of generating yield, capital appreciation or both. Real asset investments include private real estate, infrastructure, farmland, timberland and natural resources. Their appeal lies in their infl ation protection, yield and lower correla-tion to traditional assets. Depending on risk-and-return objectives, investors in real assets will choose to develop new assets or purchase existing ones. Those investing in private real estate and infrastructure target assets with diff ering pro-fi les through either core, value-add or opportunistic approaches.

InvestingPrivate real assets investments typically require the longest capital commitment from investors due to the physical nature of the underlying assets. Fund terms range from seven to fi ft een or more years. Real assets generate returns from a mix of income and capital gains, depending on the underlying assets and approach. They also provide varying degrees of infl ation protection. As with pri-vate equity, some large investors have the scale and expertise to acquire real assets directly. Most investors choose to participate through commingled fund vehicles managed by third-party managers.

Key strategiesInvestors can gain both equity and debt exposure to real assets through a range of strategies. Within Real Estate the broad strategies are Core, Value Add and Opportunistic. Most target private real estate and private infrastructure.

i) Real estate core strategies involve purchasing established assets with stable high-quality tenants that produce a recurring income stream, while value-add strategies focus on improving properties to become more core-like in nature. Opportunistic strategies involve greater development risk or investing in dis-tressed properties.

ii) Value-add strategies involve assets with more volatile revenue streams that require a higher degree of active ownership. Infrastructure core strategies involve assets with long-term concessions, such as toll roads and bridges. Opportunistic strategies involve developing assets from the ground up and feature higher capi-tal appreciation in the opening phase, followed by income later in the project life.

iii) Additionally Real Asset investment strategies include farm- and timberland investing off ers unique return drivers less correlated to other assets. Both have an income and capital appreciation component. Farmland investing is depend-ent on weather conditions, crop yields and land price appreciation.

ab

Global Beta Portfolio (Gl. BP)Gl. BP: Efficiently managed diversification



Navtej Sehmi, Executive Director, UBS CIO WM Global Investment Office, GAA Quantitative Strategist



ab

Global Beta Portfolios are designed for investors seeking a well-diversifi ed and optimal exposure to global fi nancial markets in a cost effi cient way. They do this by investing as closely as possible in the index of each selected asset class (equities, bonds etc.). This type of investment benefi ts from the only free lunch in fi nance, namely diversifi cation and it exclude risks from active security selection. Global Beta Portfolios, as other CIO solutions, are available for diff erent client risk profi les and benefi t from the same “cutting edge” Strategic Asset Allocation (SAA) process1. This includes, in particular, the regular adjust-ments of the strategies, aiming to deliver the best long-term risk and return trade-off to investors within each risk profi le. The core value of this approach comes from the right combination of markets over time and from managing the portfolio effi ciently. Also, for investors open to alternative asset classes CIO also designs Global Beta Portfolios including hedge fund to enhance the portfolio’s diversifi cation.

Why Global Beta is an effi cient investment approachBeta investing is an effi cient investment approach because it is simple, transpar-ent, cost-eff ective and spreads one’s investments across asset classes globally. For each selected asset and sub-asset class, the portfolio invests in all or a rep-resentative sample of the securities of that market, for example by purchasing an Exchange Traded Fund (ETF) on US equities. This approach contrasts with “stock picking,” more generally called active management, i.e. attempting to pick the best securities in each market. By avoiding active management, the beta approach avoids costs and risks that may result in substantial performance deviation from that of the market, whether on the up or down side. Beta invest-ing harvests the long-term market trend return and limits the risks stemming from over exposure to any individual company by being diversifi ed within each market. The markets selected in the Global Beta Portfolio are chosen because our asset class experts expect them to have a positive long-term return and because they each play a role in diversifying the return streams in the overall portfolio. However, within each market (UK equities, for example) certain indi-vidual securities may underperform the market while others perform. While this can be seen as an opportunity to beat the market, by selecting only those securities expected to outperform, there is also a large risk of selecting wrongly. These single security risks are limited in a beta investing approach. In fact over

Global Beta Portfolio (Gl. BP)Gl. BP: Effi ciently managed diversifi cation



Navtej Sehmi, Executive Director, UBS CIO WM Global Investment Offi ce, GAA Quantitative Strategist

Katarina Cohrs, Director, UBS CIO WM Global Investment Offi ce, GAA TAA & Investment Methodologies

1 Mads N. S. Pedersen, and Christophe de Montrichard, Strategic Asset Allocation (SAA) Methodology, UBS CIO WM Global Investment Offi ce


80 Please always read in conjunction with the glossary and the risk information at the end of the document.Please always read in conjunction with the glossary and the risk information at the end of the document.

Global Beta Portfolio (Gl. BP)

2

the last 25 years, none of the asset classes used in the Global Beta solutions have ever “bankrupted,” as measured by their respective indexes, while single securities have done so. Hence, an investor in the Global Beta Portfolio benefi ts from what we estimate is the best mix of diff erent market trends managed in an effi cient way, while avoiding the potential risk associated with active management.

Benefi tting from diversifi cation – the only free lunch in fi nance Global Beta Portfolios invest in an optimally diversifi ed selection of liquid mar-kets across the diff erent equity and fi xed income asset classes. The solutions benefi t from what we consider the last free lunch in fi nance – diversifi cation. For the same risk budget, an investor can extract more return by wisely combin-ing uncorrelated asset classes over time. For example, as shown in fi g. 1 below, our diversifi ed SAA returned 6% p.a. over 10+ years, similar to what US equities returned over the same period, but our diversifi ed portfolio experienced half of the volatility (8% versus 16%) and much lower drawdowns (the maximum dip was about –29% versus –51% respectively).

More fundamentally, this approach exploits the dissimilar market cycle patterns across each asset class. For example, while the US and European markets might grow at roughly the same long-term rate, their cycles are not synchronous; so while one might be receding from its peak, another might still be peaking. All in all, by combining several markets, investors limit the downside eff ect of any indi-vidual market over short-term periods, while still benefi ting from the long-term growth of each. A key value proposition of the Global Beta Portfolio is determin-ing the optimal diversifi cation of asset classes for today – and year aft er year.

Source: UBS


Figure 1: Historical performance of US equities vs. a Global Beta Portfolio

1

50

100

150

200

250

300

1998 2001 2004 2007 2010 2013 2016

Global Beta Portfolio

USA equities

Volatility Return p.a.

8% 6%

16% 6%

Historical performance of US equities vs. a Global Beta Portfolio

Drawdown Global Beta Portfolio -12% US equities -46%

Drawdown Global Beta Portfolio -29% US equities -51%

Portfolio Value

Please always read in conjunction with the glossary and the risk information at the end of the document. 81Please always read in conjunction with the glossary and the risk information at the end of the document. 3


Which markets are considered in the Global Beta PortfolioThe Global Beta solution is designed for full exposure to a carefully selected set of liquid markets. The investment universe includes what we consider the best suited sub asset classes within the equity and fi xed income markets. Within equities, this means exposure to markets in both developed and emerging countries and across a broad set of sectors. This is also true for fi xed income markets, which also include government and agency bonds, corporate bonds and various sub-markets that refl ect the range from high grade (AA to AAA ratings) to high yield bonds. Economically, this provides exposure to a broad set of fundamental economic drivers: varied industries, types of claims on a compa-ny’s revenues/assets (e.g. debt/interest versus capital/dividends), and economic and monetary policies across several geographies.

How we design Global Beta Portfolio asset allocation The SAAs for the Global Beta portfolios are based on our state-of-the-art CIO Strategic Asset Allocation methodology. They emerge from the extensive research of UBS CIO asset allocation strategists, risk management experts and asset class experts. Ultimately, the Global Beta Strategic Asset Allocations are derived from both our extensive quantitative analysis and the qualitative views of the CIO asset allocation team. Our expectations of return and risk for each of the portfolio’s asset classes, what we call our Capital Market Assumptions (CMAs), are fundamental building blocks for our portfolio construction. Our CMAs are updated annually or ad-hoc if necessary; this rhythm allows us to review the Global Beta Portfolios year aft er year for possible adjustments to account for the changing nature of long-term return as discussed in fi g. 2.

0

Back tested models forecast future market movements across the investment horizon

Quantitative approach

Experts for each asset class and region constantly monitor markets

Qualitative approach

CIO dynamic SAAs blend extensive quantitative analysis with the qualitative views of the asset allocation team to offer the best long-term risk and return trade-off within each risk profile

UBS House View

Source: UBS


Figure 2: Review Process



4

How the Global Beta Portfolio changes over timeThe Strategic Asset Allocations of the Global Beta Portfolios follow the CIO SAA methodology and are adjusted periodically to adapt to our changing expecta-tions of long-term returns and risks. We expect to adjust the Global Beta SAAs every 18 to 36 months. Our research shows that even over the very long term (10+ years), market returns can vary signifi cantly for any specifi c asset class. For example, the 10-year rolling return of the broad US equity market has varied between –5% to +20% p.a. over the past 70+ years. This insight among others is a pillar of CIO SAA methodology. The approach specifi cally entails the estima-tion and ongoing review of each asset class’s anticipated future returns and risk over a fi ve to seven-year horizon in order to improve the risk-adjusted return of the portfolios. This approach allows us to account for the currently diverging business cycle dynamics or monetary policies across regions. So while high qual-ity bonds have historically been a great investment (returning around 6% return p.a. for US high grade bonds), this is unlikely to be the case in the future. We expect about half of that return from this asset class over the next fi ve years, and even near-zero long-term returns for Swiss government bonds, for exam-ple. These considerations are a good example of how our long-term market views have motivated the restructuring of our SAAs over the past few years to adapt them to the challenging fi xed income environment.

Global Beta Portfolio as one comprehensive managed solutionThe effi ciency of the Global Beta portfolio stems not only from investing in a passive fashion in each asset class, using ETFs for example, but also from imple-menting Global Beta as single multi-asset class managed solution. Investors benefi t in a number of ways from having the Global Beta managed as a single solution. First, investing passively is not a trivial endeavor, even if done via pas-sively managed funds. Not all passive investment funds are the same; for exam-ple, depending on the fund’s market replication technique, investors may be taking other risks (such as counter party risk for swap-based replications) rather than just market risk. Further, for some markets, tailored passive instruments may be needed in the portfolio. Hence, having the skill to research and select or create the right passive instruments for the portfolio is essential. Second, a key feature of the Global Beta solution is its ongoing rebalancing to the long-term target weights to keep the portfolio aligned to the long-term strategies and to the investor risk profi le. This is necessary, since as time passes, the portfolio’s asset allocation moves away from its target weights and the target weights themselves are also adjusted over time. This process, while mathematically sim-ple, does take time and requires ongoing monitoring of the positions. Third, to improve risk-adjusted return, the Global Beta Portfolio hedges foreign exchange risk to a large extent. We fi nd that the risk of foreign exchange exposure is not well compensated over the longer term, but we still recommend investing in foreign assets, hence we generally hedge away the foreign exchange risk. Oper-ationally, hedging can be cumbersome to execute, so investors can benefi t from having this feature managed for them. Finally, the managed Global Beta Port-folios off er a consistent and aggregated view of the portfolio positions, perfor-mance and other valuable metrics; a consolidated report provides this overview.

Table 1 and 2, for illustrative purposes, are the UBS CIO SAAs used for the Global Beta Portfolios in USD as reference currency – including the Capital Market Assumptions (CMAs) for each SAA and per asset class. We show our six diff erent SAAs which align to six diff erent risk levels in order to cater to diff erent investor preferences. Each columns show the allocation for one of the SAA at the asset class level and also at the sub-asset class level.



Hedged

Expected 5 Yrs

Return p.a.

Expected Volatility

p.a.

LIQUIDITY 5% 5% 5% 5% 5% 5% 0%

Cash USD 5% 5% 5% 5% 5% 5% 2.1% 0.5%

BONDS 95% 79% 63% 46% 27% 5% 0.0% 0.0%










EQUITIES 16% 32% 49% 68% 90% 0.0% 0.0%

US 0% 8% 14% 23% 34% 44% 7.5% 15.4%

EM 0% 0% 5% 7% 10% 13% 8.5% 24.1%

Eurozone 0% 0% 6% 8% 10% 10% X 10.0% 18.4%

UK 0% 5% 5% 6% 8% 9% X 8.4% 15.0%

Japan 0% 0% 0% 3% 4% 6% X 9.2% 19.8%

Canada 0% 0% 0% 0% 0% 3% X 8.0% 15.0%

Australia 0% 0% 0% 0% 0% 3% X 8.8% 14.9%

Switzerland 0% 3% 2% 2% 2% 2% X 9.1% 14.9%

HEDGE FUNDS 0.0% 0.0%

Hedge Funds 0.0% 0% 5.2% 5.9%

0%

TOTAL 100% 100% 100% 100% 100% 100%



Sharpe ratio 0.13 0.33 0.39 0.41 0.41 0.41

Table 1: Global Beta Portfolio – Strategic Asset Allocations USD

Table 2: Global Beta Portfolio – Strategic Asset Allocations USD including Hedge Funds


Hedged

Expected 5 Yrs

Return p.a.

Expected Volatility

p.a.

LIQUIDITY 5% 5% 5% 5% 5% 5% 0%

Cash USD 5% 5% 5% 5% 5% 5% 2.1% 0.5%

BONDS 95% 69% 50% 33% 17% 5% 0.0% 0.0%










EQUITIES 10% 25% 42% 62% 90% 0.0% 0.0%

US 0% 5% 12% 20% 32% 44% 7.5% 15.4%

EM 0% 0% 4% 6% 9% 13% 8.5% 24.1%

Eurozone 0% 0% 4% 6% 8% 10% X 10.0% 18.4%

UK 0% 3% 3% 5% 7% 9% X 8.4% 15.0%

Japan 0% 0% 0% 3% 4% 6% X 9.2% 19.8%

Canada 0% 0% 0% 0% 0% 3% X 8.0% 15.0%

Australia 0% 0% 0% 0% 0% 3% X 8.8% 14.9%

Switzerland 0% 2% 2% 2% 2% 2% X 9.1% 14.9%

HEDGE FUNDS 16% 20% 20% 16% 0.0% 0.0%

Hedge Funds 0.0% 16% 20% 20% 16% 0% 5.2% 5.9%

0%

TOTAL 100% 100% 100% 100% 100% 100%



Sharpe ratio 0.13 0.38 0.43 0.44 0.43 0.41

Source: UBS CIO

The above asset classes and allocations are indicative only and can be changed at any time at UBS’s discretion without informing the client. Please always read in conjunction with the glossary and the risk information at the end of the document.





6

SAA construction

Defi ning our SAA approach

The Global Beta Portfolio SAAs are based on our fundamental SAA approach which you can read in detail in the initial section of this document – “SAA Methodology”. Our SAA methodology is anchored on our experts-based Capi-tal Market Assumptions (CMAs) and the annual review process of our SAAs and CMAs.


Alternative Investments – Investments not attributable to the traditional asset classes such as equities and bonds.

Asset Allocation – Composition of a portfolio by currency and asset class.

Asset Class – Any collection of assets that react in a unique way to the funda-mental drivers of the economy. The most important asset classes are equity, fi xed income, money market investment, hedge funds and real estate.

Correlation – The statistical measure of the linear relationship between two series of fi gures (e.g. performance of a security and the overall market). A posi-tive correlation means that as one variable increases, so does the other. A nega-tive correlation means that as one variable increases, the other decreases. By defi nition, the scale of correlation ranges from +1 (perfectly positive) to –1 (per-fectly negative). A correlation of 0 indicates that there is no linear relationship between the two variables.

Diversifi cation – A strategy of spreading an investment over diff erent assets to reduce portfolio risk.

Drawdown – The peak-to-trough decline during a specifi c period of an invest-ment or fund. It is usually quoted as the percentage between the peak and the trough.

Equity Risk Premia – The additional return an investor expects to off set the additional risk associated with investing in equities as opposed to investing in a riskless asset (typically investment grade government bonds).

Excess Kurtosis of Returns – Excess kurtosis gives a measure of “peakedness” and heaviness of tails in a return distribution. A distribution with positive excess kurtosis will have the appearance of a distinct peak near the mean that declines rapidly with fat tails. Negative excess kurtosis distributions have most of their values around the mean, giving a fl atter top rather than a sharp peak with thin tails. A standard normal distribution has an excess Kurtosis of zero.

Hedge Funds – Private collective investment vehicles active in global capital markets that are oriented toward absolute returns and aim to achieve capital appreciation. Hedge funds use a variety of investment techniques, are lightly regulated and oft en accept only a limited number of investors to ensure that their investment strategy remains fl exible. They are categorized not only accord-ing to the asset classes in which they invest (equities or bonds) or their geo-graphic or thematic orientation, but also in terms of their strategies (e.g. arbi-trage, macro, event-driven or opportunistic).

High Yield Bond – A bond rated BB+ or below by the leading rating agencies, or one of comparable quality. Because of their inferior credit quality, high-yield bonds off er a higher return than paper with a better credit rating and involve higher risks. The chief issuers of high-yield bonds are companies and emerging markets.

Historical Stress Test – An analysis of the impact an extreme event would have on the value of a portfolio. It involves estimating how a portfolio would have performed during extreme market movements in the past.

Glossary

86 Please always read in conjunction with the glossary and the risk information at the end of the document.Please always read in conjunction with the glossary and the risk information at the end of the document. 8

Maximum Drawdown – Greatest loss suff ered from a (local) peak to the next (local) trough in investment value during the investment period.

Monte Carlo Simulation – A method for estimating the range of possible per-formance of a portfolio. It is based on estimates of expected returns, expected risks and the correlation between all the assets or asset classes included in port-folio. Several thousand simulations are performed to give a statistical distribution

Private Equity – Privately negotiated transactions in public or private companies with a view to increasing their value and directly infl uencing the timing of the exit from the investment. Private equity fi rms can invest in companies at various stages of their development, from fi nancing startups (venture capital) and rapidly growing businesses (growth capital) to supplying capital for the leveraged buy-outs of mature businesses.

Risk – Exposure to damage or fi nancial loss, e.g. a fall in the price of a security, or insolvency on the part of a creditor. Financial market theory measures the risk of an investment or portfolio by the degree of expected return fl uctuations.

Sharpe Ratio – A measure of the risk-adjusted performance of a portfolio derived by dividing the excess return (over cash) by the standard deviation of the portfolio’s performance.

Skewness of Returns – A measure of how symmetrical a returns distribution is about its mean. A standard normal distribution is perfectly symmetrical and has a skewness of zero. Positive skewness indicates a distribution with an asymmet-ric tail extending toward more positive values. Negative skewness indicates a distribution with an asymmetric tail extending towards more negative values.

Vintage – The year in the private equity & debt industries in which a fund is launched; these funds have defi ned investment periods, aft er which any remain-ing invested capital is redistributed to investors.

Volatility – A measure of the fl uctuations in the rate of return of a security within a specifi c period. Usually stated as an annualized standard deviation.

879

Risk information

This publication is intended for information and marketing purposes only. It is not to be regarded as investment research, a sales prospectus, an off er or solicitation of an off er to buy or sell any product or other specifi c service. Although all information and opinions expressed in this publication were obtained from sources believed to be reliable and in good faith, neither representation nor warranty, express or implied, is made as to its accuracy or completeness.

All information, including without limitation benchmarks, asset classes, asset allocation and investment instruments, and opinions indicated, are subject to change without notice. UBS retains the right to change the range of services, the products and the prices at any time without prior notice. The general explanations included in this publication cannot address all of your personal investment objectives, your fi nancial situation as well as your fi nancial needs. Certain products and services are subject to legal restrictions and cannot be off ered worldwide on an unrestricted basis.

Except where explicitly stated, UBS AG (“UBS”) does not provide legal or tax advice and this publication does not constitute such advice.

Approved and issued by UBS, this publication may not be reproduced or copies circulated without prior authority of UBS.

© UBS 2016. The key symbol and UBS are among the registered and unregistered trademarks of UBS. All rights reserved.

ab

Documents

WM CIO Global Asset Allocation – Investing with UBS WM