Alternative Risk Premia Interest rates - Carry · 2020. 7. 22. · algorithm11, based on the Ward linkage methodology12. As ARPs mainly carry specific risks – even within the same

Alternative Risk Premia June 2020

Guillaume Monarcha | PhD, Head of Research

Orion ARP Indices – June 2020

Source: Orion Financial Partners

Performance summary

▪ The ARP industry posted an average return of -0.1% in

June, bringing the average YtD performance at -5%, from

-23.4% for equity short volatility to +10.6% for commodity

trend.

▪ Performances were quite homogenous across the various

asset classes (from -0.3% for equity and FX ARPs to +0.3%

for commodity ones) over the month.

▪ Within the equity space, low risk and quality premia

recorded poor monthly returns (-1.5% on average, below

their 5th percentile) while momentum and value did barely

better (-0.5% and -0.8%). Trading strategies posted a

monthly figure of -0.3%, implying a continuing

deterioration of YtD drawdowns (from -14.7% for trend

strategies to -23.4% for short vol).

▪ Regarding the other asset classes:

– interest rates risk premia displayed flat monthly

returns across the various strategies

– trend strategies suffered on both commodities (-1.4%)

and FX markets (-1.27%)

– conversely, commodity carry and short volatility did

particularly well (+1.5% and +1.6%).

-0.76%

-1.52%

-1.60%

-0.50%

-0.72%

1.31%

-0.64%

-0.81%

1.62%

-0.29%

-1.40%

1.46%

0.06%

-0.28%

-0.09%

-0.05%

-0.01%

0.03%

0.14%

-0.45%

-1.27%

-2.5% 0.0% 2.5%

Equity - Value

Equity - Quatity

Equity - Low risk

Equity - Momentum

Equity - Trend

Equity - Mean-rev.

Equity - Short vol.

Commodity - Carry

Commodity - Carry (trd)

Commodity - Liquidity

Commodity - Trend

Commodity - Short vol.

Interest rates - Carry

Interest rates - Momentum

Interest rates - Trend

Interest rates - Short vol.

FX - Carry

FX - Value

FX - Mean-rev.

FX - Short vol.

FX - Trend

FOCUS | The building blocks of alternative risk premia allocations – part 1: strategy clustering

Alternative Risk Premia | June 2020 Orion Financial Partners | p.2

Focus | The building blocks of alternative risk premia

allocations – part 1: strategy clustering

Summary

- During the recent crisis, alternative risk premia

(ARP) funds displayed particularly poor figures.

- We can reasonably assume that their

underperformance is linked to strong

imbalances in the design of their portfolios.

- These imbalances are intricately linked with the

core building blocks of sound ARP allocations,

namely: strategy clustering, performance

evaluation, tactical allocation, and portfolio

construction.

- In this publication, we focus on strategy

clustering.

Key takeaways

- Within each asset-class, we identify between 4

and 5 strategy clusters, without any common

structure.

- Intra asset-class strategy structures appear to be

heterogeneous.

- There is no clear segmentation between trading

(dynamic bias) and academic (long/short,

market neutral) strategies.

- Within a multi-asset context, the various

strategies are not necessarily clustered

according to their underlying asset-class.

- Short volatility strategies appear to be isolated

from the other ones, whatever the underlying

asset-class considered.

Implications for investors

- The specificity of short volatility strategies must

be carefully considered for the implementation

of balanced ARP allocations.

- Equal-weighting or risk-based allocation

methodologies are not appropriated when

applied to strategy buckets defined from

providers classifications.

1 From a sample of 27 ARP funds. 2 According to our global ARP index. 3 From an average YtD performance of -23.4% for equity short volatility, up to +10.6% YtD for commodity trend ARPs.

- Ad hoc strategy clustering based on the

underlying asset-classes may be a significant

source of imbalance for multi-asset ARP

allocations.

- More globally, investors should go beyond

correlation/covariance figures and conduct a

deep cluster analysis of their risk premia

portfolio to avoid irrecoverable imbalances

during market stress episodes.

Introduction

During the recent crisis, alternative risk premia

(ARP) funds displayed particularly poor figures: -10%

on average between February 19th and March 23rd,

ranging from -20% to -4.1% for individual funds1. As of

June 30, most of these drawdowns were still running

and even worsened (-11.5% on average, -29% for the

worst performing fund), while equity markets

recovered a significant part of their losses (current

drawdown of respectively -8.4% vs. -33.9% at the

height of the crisis for the S&P500 index, and -16.4%

vs. -38.2% for the Euro Stoxx 50). These deceptive

figures – combined with a mitigated average

performance in recent years – have led to significant

redemptions, estimated at 21.6% (YtD) on average for

the 27 funds of our sample.

However, the performances of ARP funds and those

of ARP indices offered by investment banks have been

significantly disconnected during that period. Despite

the severe losses suffered by certain strategies, the

ARP industry recorded an average performance of -

5.1%2 YtD, with significant dispersion, both between3

and within the various strategies4. How can we explain

such a sharp discrepancy? A first hypothesis could be

linked to the risk budget of ARP funds. Seterus paribus,

higher target volatility would imply proportional losses.

However, the average volatility of funds since the start

of the year (9%) is in line with that of our overall ARP

4 See the cross-dispersion charts in the strategy reports, in the following pages.


index (9.5%), which excludes this assumption as their

average loss is twice more important. On the other

hand, this discrepancy could come from the significant

underperformance of the strategies implemented by

the managers compared to those developed by ARP

providers. However, this hypothesis is unlikely since (i)

the probability that the strategies developed by fund

managers systematically underperform those

developed by investment banks is a priori low, and (ii)

some funds precisely invest in the latter. Another

assumption could be related to loosing directional bets

initiated before the market downturn (e.g. net long

equity), in the form of tactical overlays on top of the

risk premia allocations. It could explain both the

significant drawdowns, and the poor recovery implied

by stop loss policies. If this assumption sounds

appealing and might be true for some managers, one

can however hardly assume that most of the 27 funds

of our sample implemented similar tactical overlays. As

a last explanation, we can reasonably assume that this

global underperformance of ARP funds is linked to

strong imbalances in the design of their portfolios,

which could be attributable to:

- the increased concentration in allocations driven

by the search for yield, through the significant

overweighting of trading strategies (short

volatility especially) in a context of below

average performance for some academic ARPs

since 2010 (e.g. the equity value premium),

- a poor definition of strategy clusters, thus

implying “unwanted” portfolio concentration,

- inappropriate portfolio construction processes

relying on risk-based allocations techniques5.

While these approaches are well suited for

portfolios of traditional asset classes – which are

mainly exposed to systematic risks – they are

poorly appropriated for ARP portfolios6. Indeed,

while their risk structure mainly carries specific

risks in normal market environment7, it can

distort heavily in stressed market context,

implying a sudden predominance of common

(systematic) risk factors8, hence leading to

5 Such as equal risk contribution, risk parity, or minimum volatility. 6 Or more generally for portfolios of alternative investment vehicles.

unwanted risk concentration at the portfolio

level.

Given both the strong heterogeneity of the

individual strategies, these imbalances are not

straightforward to handle. They are intricately linked

with the core building blocks of sound ARP allocations,

namely strategy clustering, performance evaluation,

tactical allocation, and portfolio construction.

However, many investors and fund managers only

focus on portfolio construction, generally based on

risk-based allocation methodologies applied to

diversified risk premia portfolios. Their approach then

relies on 3 strong assumptions: the persistence of the

risk premia performances across market cycles, the low

correlation levels between the various risk premia

strategies, and the homogeneity of investment

strategies that are designed to harvest the same risk

premia. Yet, several elements show that these

assumptions are not necessarily checked. First, recent

studies have shown the significant sensitivity of certain

ARP strategies to specific macroeconomic variables

(growth, inflation, risk aversion…), thus questioning the

“all-weather” behavior of ARP strategies. Second,

there are many empirical evidences that quantitative

and alternative investment strategies might exhibit

significant “hidden” commonalities, especially during

stressed market environments. Third, the whole

structure of the ARP industry cannot be apprehended

from the qualitative descriptions provided by

investment banks alone, as strategies can be highly

heterogenous even when they are designed to capture

the same risk premia. This last point suggest that

strategy clustering based on ad hoc classification might

be misleading, therefore resulting significant

imbalances at the portfolio level.

To clarify these issues, we will detail the core

building blocks of ARP allocations in a series of focus,

starting with strategy clustering in this publication.

The challenge of strategy clustering

Strategy clustering is the backbone of ARP

allocation. It basically consists in classifying single risk

7 Therefore, leading to low covariance levels. 8 And a significant increase in the correlation levels.


premia into strategy clusters. This first step is

necessary for:

performance evaluation, based on specific

benchmarks for each strategy cluster,

tactical allocation, that relies on the

identification of the main macroeconomic

drivers of the performance of each strategy

cluster,

constructing balanced portfolios, both between

and within the various clusters.

Common belief is that classifying the ARP universe

is straightforward, as they are a priori based on well-

known academic risk factors. However, reality is a bit

more nuanced. First, providers’ classifications are not

necessarily homogenous. This is especially the case for

trading ARPs that represent 50% of the global offering.

Unlike academic ARPs, trading ones are based on

banks’ proprietary trading strategies (short volatility,

mean reversion) or designed to replicate hedge fund

ones (trend following). Their classification is therefore

more provider specific9, than in the case of academic

premia. Second, different ARP strategies are not

necessarily orthogonal. They can exhibit similar

quantitative features (correlation, extreme risk

exposure…), especially when their respective

definitions rely on similar economic criteria. For

instance, within the equity space, this is the case for

quality, carry, and profitability premia whose definition

both encompass measures of the underlying company

profitability. Furthermore, differences in the

implementation process can lead to substantially

different outcomes for similar ARPs provided by

different banks. These differences can be found at

various steps, from factor definition, to portfolio

construction, model parametrization, stock ranking

methodology, rebalancing rules and frequencies…

Mapping the ARP universe: a quantitative clustering

approach

9 Identical strategy labels may be attributed to strategies relying on significantly different implementation processes, depending on the provider. 10 The extensive review of clustering options being beyond the scope of this paper. 11 Note that, for validation purpose, we found similar results using the K-means clustering approach.

We propose a 3-step clustering approach to classify

the various ARP strategies, based on their statistical

(thus objective) properties, rather than on providers’

classification. In that part, we have arbitrarily chosen

sound methodologic options for illustration purpose

only, that can however be largely discussed and

improved10.

Step 1 simply consists in a segmentation of the ARP

universe based on the underlying asset-class of each

individual strategy. This ad hoc classification step relies

on the assumption that each ARP carry risks that are

specific to its underlying asset class, independently

from its investment strategy (value, carry, short

volatility…).

Step 2. Within each asset class, we identify the main

strategy clusters from a hierarchical clustering

algorithm11, based on the Ward linkage methodology12.

As ARPs mainly carry specific risks – even within the

same strategy – characterizing them by their

covariance structure could lead to misleading

classification. Therefore, we characterize each

individual risk premia by a feature vector composed of

their individual exposures to the underlying latent risk

factors13.

Step 3. In a multi-asset / multi-strategy perspective,

we apply a hierarchical clustering on the whole asset-

based clusters identified in step 2. To this aim, we first

define a benchmark for each cluster, as the average

normalized returns of the single ARPs that compose it.

We then apply the methodology detailed in step 2 on

cluster benchmarks to classify the whole ARP universe.

12 This methodology is designed to minimize the variance within the clusters. We here consider Euclidian distance measure. 13 Common risk factors are extracted from the covariance structure through PCA decomposition. We determine the optimal number of latent factors combining elbow analysis and the Bai and Ng (2002) IC criteria. Factor exposures are estimated from an OLS regression.


Results

We applied our 3-step approach to a set of 285

mono asset-class and mono strategy ARPs, provided by

10 investment banks, for which data are available from

February 2007 to May 2020.

Step 1: asset-based ad hoc clustering

We first divide our ARP universe in 4 top clusters:

equities (123 ARPs), interest rates (48), commodities

(45), and currencies (53).

Step 2: identification of strategy clusters within each

asset-class

Within the equity space, our quantitative

classification is broadly in line with the ad-hoc

classification based on disclosed strategies. We identify

5 clusters (chart 1, detailed clustering in appendix A),

respectively gathering:

– short volatility and mean-reversion strategies

– trend

– value and carry

– quality, profitability, and momentum

– low volatility14

Chart 1: Clustering of equity-based ARP strategies


This result gives a first insight in terms of portfolio

construction, at two levels. First, certain risk premia

appear to carry similar underlying features, like quality

14 In our database, we have grouped the strategies labelled low volatility, low risk, and low beta, under the low volatility label.

and profitability, short-volatility and mean-reversion,

or, more surprisingly, quality and momentum. Second,

equity-based ARPs are divided in 2 macro clusters, the

first one gathering trading and value strategies vs. all

other academic strategies for the second one.

Regarding FX strategies, we obtain 4 clusters: value

and mean-reversion, short volatility, trend, and carry

(chart 2, detailed clustering in appendix B). As in the

case of equity-based strategies, value strategies

appear to be closer to trading strategies (short-

volatility and mean-reversion), than to academic

(carry) ones.

Chart 2: Clustering of FX-based ARP strategies


The hierarchical structure of strategies based on

interest rates shows that the short-volatility cluster

form an isolated branch, while the second one mainly

gathers carry strategies, with more specific clusters

(chart 3, detailed clustering in appendix C). In details:

academic carry strategies (split in 2 clusters

with respectively US and global geographic

focus)

carry (trading), momentum, and trend

carry and short volatility on the other hand.

This distinction within carry strategies highlights the

heterogeneity in the implementation processes of the

various ARP providers, both between academic

(long/short) and trading ARPs (dynamic exposures),

and within academic ones.


Chart 3: Clustering of ARP strategies based on interest rates


Finally, we identify 5 clusters for commodity-based

strategies (chart 4, detailed clustering in appendix D).

There again, carry strategies are split depending on

their nature – academic vs. trading – or depending on

the definition of carry itself (liquidity oriented vs. curve

carry).

We can draw several conclusions from this intra

asset-class analysis. First, we identify between 4 and 5

clusters within each asset-class, top clusters gathering

from 1 to 4 single clusters. Intra asset-class

classification structures appear to be heterogeneous.

This heterogeneity between intra asset-class

classification is implied by obvious differences, that

come (i) from differences in the definition of certain

factors (especially value and carry) depending on the

underlying asset-class, or (ii) from the divergence in

their global trend (for trend and momentum

strategies). Second, there is no clear segmentation

between trading (dynamic bias) and academic

(long/short, market neutral) carry strategies. They

therefore appear to be highly heterogenous within the

commodity and interest-rate spaces.

To sum up, strategy structures within each asset-

class do not appear to be balanced nor homogenous,

implying that equal-weighting allocation schemes are

not appropriated for the implementation of allocations

focused on specific asset-class. This result particularly

holds for equity-based strategies.

15 As that was the case in March this year.

Chart 4: Clustering of commodity-based ARP strategies


Step 3: multi-asset, multi-strategy hierarchical

clustering

The hierarchical structure of the asset-based

clusters identified above is illustrated on chart 5. The

first important result is that, conversely to common

practice in ARP portfolio construction, the various

strategies are not clustered depending on their

underlying asset-class, meaning that asset-specific

risks are not predominant in their whole risk structure.

Therefore, allocations based on asset-class buckets at

the top level (e.g. X% allocated in equity-based ARPs,

Y% in commodity ones…) may be a significant source of

imbalance. Second, in line with intra asset-class results,

short volatility strategies appear to be isolated from

the other ones. One can assume that this result is

linked to common behavior in cross-asset volatility,

especially during market stress episodes15. Third, in the

“all but short-volatility” branch, we identify 5 different

clusters, either gathering ARPs based on the same

underlying asset-class (commodity, interest rates),

either based on similar strategies (trend).


Chart 5: Hierarchical clustering of the ARP universe


Chart 6, through the representation of cluster

exposures to the first 3 common risk factors16,

highlights the complexity of the ARP risk puzzle that

drives industry classification. As illustrated by the

heterogeneity of the exposure to the first principal

component (X-axis), risk diversification between short

volatility (highest exposures) and other strategies

confirms previous results. Among non-volatility

strategies, diversification can be reached through

asset-class diversification (commodity-based ARPs vs.

other asset-classes along the 3rd axis, i.e. circle width,

or FX-based ARPs vs. interest rates ones along the 2nd

axis) or through strategy diversification (trend vs. carry

along the Y-axis).

Overall, several important points spread out from

this illustrative clustering analysis. First, the specificity

of short volatility strategies must be carefully

considered for the implementation of balanced

allocations. Second, investors should conduct deep

analysis of their risk premia portfolio, both across and

within the various strategy clusters, to avoid

irrecoverable imbalances during market stress

episodes. These findings are in line with our hypothesis

that, during the recent market stress, several ARP

16 Extracted from a PCA analysis on cluster benchmarks.

funds were significantly hit by significant imbalances in

their allocation, even if other factors – like bad tactical

overlays – may also have played significant role.


Chart 6: Exposures of the various cluster benchmarks to the first 3 principal components

Note: the width of the circles is determined by the exposure to the 3rd principal component Source: Orion Financial Partners

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

12.0

-15.0 -10.0 -5.0 0.0 5.0 10.0 15.0

Exp

osu

re t

o t

he

2nd

pri

nci

pal

co

mp

on

ent

Exposure to the 1st principal component

Equity - Low volatility Equity Momentum/Quality/Profitability Equity - Value/Carry

Equity - Trend Equity - Short-vol/Mean-reversion FX - Carry

FX - Trend FX - Short-volatility FX Value/Mean-reversion

Rates - Short-vol Rates - Trend/Momentum/Carry(trd) Rates - Carry/Vol

Rates - Carry-US Rates - Carry-global Com - Carry(aca)

Com - Short-vol Com - Trend Com - Carry(trd)

Com - Liquidity/Carry


Appendix A – Detailed hierarchical clustering of equity-based ARP strategies



Appendix B – Detailed hierarchical clustering of FX-based ARP strategies



Appendix C – Detailed hierarchical clustering ARP strategies based on interest rates



Appendix D – Detailed hierarchical clustering of commodity-based ARP strategies



Equity Risk Premia

Performance and risk metrics | June 2020

1At the end of the month. 2Average volatility of individual ARPs (since 2010, EWMA, 63 trading days, λ=0.94). 3Average return / average volatility (since 2010). 4Average return / average maximum drawdown.


Average/min/max of monthly performances

Current vs. min/max historical volatility

Average/min/max cross-sectional dispersion

Note: average intra-index 6 months cross-sectional dispersion, annualized.

Average/min/max volatility

Note: the grey area represents the min/max volatility levels of the various indices. EWMA volatility (63 trading days, λ=0.94).

Number of

underlying

risk premia1

Monthly

performance

Percentile

rankYtD Volatil ity2

Information

ratio3

Maximum

drawdownCalmar ratio4 Current

drawdown

Value 18 -0.8% 17.2% -9.9% 5.5% -0.27 -20.8% -0.07 -20.8%

Europe 7 -1.1% 12.9% -6.7% 5.4% -0.21 -18.3% -0.06 -18.0%

USA 5 -1.0% 19.9% -13.4% 5.3% -0.46 -30.0% -0.08 -30.0%

Quality 12 -1.5% 2.7% 2.5% 4.7% 0.65 -4.2% 0.73 -1.5%

USA 3 -1.0% 9.1% -1.2% 4.1% 0.14 -9.2% 0.06 -5.9%

Europe 4 -2.6% 0.5% 5.0% 5.1% 0.96 -6.0% 0.81 -2.6%

Low risk 15 -1.6% 5.9% -4.1% 5.3% 0.65 -7.8% 0.44 -6.3%

USA 3 -2.4% 1.6% -5.6% 4.8% 0.51 -9.3% 0.26 -9.2%

Europe 6 -1.2% 10.8% -3.4% 5.4% 0.72 -7.0% 0.56 -5.1%

Momentum 14 -0.5% 26.3% 3.2% 6.3% 0.35 -13.2% 0.17 -5.6%

Europe 6 -1.7% 8.1% 5.6% 6.5% 0.64 -9.7% 0.43 -1.8%

USA 3 0.0% 44.6% -4.6% 6.0% -0.07 -22.2% -0.02 -22.2%

Trading risk premia

Trend 4 -0.7% 29.6% -14.7% 6.0% 0.23 -22.9% 0.08 -22.9%

Mean-reversion 8 1.3% 84.9% -17.1% 8.2% 0.25 -22.1% 0.10 -20.9%

Short volatil ity 40 -0.6% 15.1% -23.4% 8.5% 0.47 -26.2% 0.12 -24.7%

Academic risk premia


Commodity Risk Premia










Number of

underlying

risk premia1

Monthly

performance

Percentile


Information

ratio3

Maximum


drawdown

Carry 8 -0.8% 16.7% 1.7% 6.8% -0.01 -7.9% 0.01 -5.0%

Trading risk premia

Carry 9 1.6% 86.0% 8.1% 4.0% 0.76 -4.6% -0.85 -3.1%

Liquidity 6 -0.3% 23.7% 2.5% 3.7% 0.29 -4.4% -0.19 -1.1%

Trend 5 -1.4% 17.7% 10.6% 8.3% 0.10 -22.4% -0.06 -12.8%

Short volatil ity 9 1.5% 75.8% -9.0% 0.0% -0.63 -17.7% 0.24 -15.2%



Interest Rate Risk Premia










Number of

underlying

risk premia1

Monthly

performance

Percentile


Information

ratio3

Maximum


drawdown

Carry 11 0.1% 44.6% -0.4% 8.0% 0.34 -2.0% -0.20 -0.8%

Momentum 4 -0.3% 28.0% 2.9% 2.6% 0.90 -7.1% -0.40 -0.6%

Trading risk premia

Trend 8 -0.1% 39.2% 5.3% 3.9% 1.04 -6.6% -0.56 -0.1%

Short volatil ity 16 0.0% 33.9% -1.8% 4.3% -0.54 -8.4% 0.17 -5.9%



FX Risk Premia










Number of

underlying

risk premia1

Monthly

performance

Percentile


Information

ratio3

Maximum


drawdown

Carry 16 0.0% 47.3% -4.2% 6.0% 0.01 -17.8% 0.00 -6.0%

Value 4 0.0% 41.4% 1.3% 4.1% 0.64 -7.5% -0.33 -0.9%

Trading risk premia

Mean-reversion 4 0.1% 43.0% -1.1% 3.9% -0.04 -8.0% 0.01 -1.9%

Short volatil ity 13 -0.5% 16.1% -3.9% 5.6% -0.02 -9.9% 0.01 -4.4%

Trend 8 -1.3% 12.4% -2.4% 3.4% -0.53 -14.4% 0.18 -14.4%



Strategy definitions

These definitions are taken from G. Monarcha (2019), “An Introduction to Alternative Risk Premia”, Alternative Investment Analyst Review, Q1 2019, CAIA.

We distinguish two types of ARPs in bank’s offering. Academic ARPs are based on factors that have been well documented in the academic literature, and that

essentially involve the trading of listed and liquid products. Trading ARPs encompass a set of systematic and rule-based quantitative investment strategies, that

alternately aim to replicate hedge fund strategies (trend following, M&A…) or to exploit market anomalies, whose economic rationale may be hard to apprehend.

Unlike academic ARPs, trading ARPs are mostly backed by applied research, academic research being limited by data availability (especially in the case they rely

on the trading of OTC derivatives) or by the lack of theoretical foundations. They also differ from academic ARPs in their construction process. They are not

necessarily market neutral and may be based on a more discretionary stock selection process.

Risk premia Economic intuition Implementation Asset class References

Aca

dem

ic r

isk

pre

mia

Value

Benefit from the price convergence between undervalued and overvalued assets. The

relative value of a security is evaluated by an economic measure (price to book ratio for

the shares, PPP for the currencies ...)

Buy undervalued securities, sell overvalued securities.

Equities, rates, credit, FX,

commodities.

Fama et French (1992, 1993); Asness, Frazzini

(2013); Asness, Moskowitz, Pedersen

(2013).

Momentum

The momentum premium is based on a behavioral bias: demand for securities with

the best recent performance tends to be larger than demand for securities with

weaker recent performance.

Long positions in the best-performing stocks, short

positions in the least performing stocks.


commodities.

Jagadeesh et Titman (1993); Carhart (1997); Rouwenhorst (1998);

Moskowitz and Grinblatt (1999);

Asness, Moskowitz, Pedersen (2013).

Low risk, low beta, low volatility…

According to the CAPM theory, investors who cannot use leverage are forced to allocate their assets in a non-optimal

manner, over-allocating to riskier stocks. This generates a market anomaly that, overall, is beneficial to the least risky securities (less susceptible to market

corrections).

Long positions on the least risky stocks, short positions

on the riskiest stocks.


commodities.

Ang, Hodrick, Xing, Zhang (2006); Ang,

Hodrick, Xing, Zhang (2009); Frazzini and

Pedersen (2014).

Carry Benefit from the yield differential (rates, coupons, dividends, etc.) between similar

assets.

Long positions in high yield securities, short on low

yielding ones.


commodities.

Koijen, Moskowitz, Pedersen, Vrugt (2016);

Gorton, Hayashi, Rouwenhorst (2012); Brooks, Moskowitz

(2017).

Quality

Benefit from the outperformance of companies that show superior quality, in

terms of profitability, dividend distribution, credit quality, governance ...

Long high quality compagnies, short low

quality compagnies. Equities.

Greenblatt (2006); Asness, Frazzini,

Pedersen (2013); Novy-Marx (2014).

Trad

ing

Ris

k P

rem

ia

Short volatility

The structural demand for protection implies a structural difference between the levels of

implied (higher) volatility and realized volatility.

Short straddle, delta hedged by a long position in the

underlying market.


commodities.

Coval, Shumway (2001); Ang, Israelov,

Sullivan, Tummala (2018)

Volatility carry Profit from the teem structure of the

volatility curve.

Short volatility future (delta hedged) when the curve is in contango. Reverse position

when the curve is in backwardation.

Volatility

Mean reversion

Exploit short-term market overreaction, generally measured by the difference

between short (daily) realized volatility and longer-term volatility (one or two weeks).

Long or short position in the underlying index in order to

replicate the market sensitivity (delta) induced by

a variance swap.


commodities, volatility.

Poterba, Summers (1988)

Trend following / absolute momentum

Exploit trends in asset prices, similar CTA strategies.

Long positioning on securities with positive trend

and/or short ones with negative trend. Directional

strategy (long or short bias).


commodities, volatility.

Moskowitz, Ooi, and Pedersen (2012); Fung,

Hsieh (2001)

Directional versions of academic ARPs

Cf. academic risk premia.

Long and/or short positions on the securities from the

investment universe, defined by their exposure level to the underlying risk

factor.


commodities.

Cf. the references of academic risk premia.

Source: G. MONARCHA (2019), “An Introduction to Alternative Risk Premia”, Alternative Investment Analyst Review, Q1 2019.


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