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Generalised Mean Variance Analysis and Robust Portfolio Construction February 2006 Steve Wright Tel 44 20 7568
1874
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Institutional fund management
— Low frequency noisy non stationary data
— Dominance of subjective analysis
— Regulatory and client pressure for accountability.
— Interactive tools add structure to the process
Mean Variance Analysis for portfolio diversification
— Widely accepted theory
— With practical problems
– Recommendations are sensitive to small changes in user inputs
– Out of sample performance of the risk models can be an issue
– Hence portfolio churn and lack of confidence in the process
A robust extension of Mean Variance analysis
Business Context
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The investment process can be complex
Global Valuation
Research Analysts
Portfolio Construction
(Portfolio) Trading
Risk & Return modelling
Company Database
Company preferences
Ex ante statistics
Returns
Regional Preferences
Economics
Asset class Preferences
Sector Preferences
Strategy
Style Preferences
Ex post statistics
Debt Markets
Transaction cost control
FX Markets
Futures Markets
Third Party Data Providers
Mandates and Constraints
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Mutually consistent across portfolios (more risk = more equity)
Simple and understandable by an intelligent non specialist.
Stable enough to avoid generating excessive volume of trades.
Reliable enough to have acceptable out of sample behaviour.
Capable of reflecting all likely practical operating constraints
Transparent enough to be monitored easily
A starting point for discussion of the key issues
An ideal investment process
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A typical Asset Allocation Toolbox results screen
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The effect of correlation matrix volatility
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The perfect substitute problem
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Is risk volatility or lack of diversification
German_Bond_3_5_year_maturity
US_Bond_3_5_year_maturity
Japanese_Bond_3_5_year_maturity
German_MSCI_Equity
Japanese_MSCI_Equity
American_MSCI_Equity
Minimum Risk Maximum Return
German_Bond_3_5_year_maturity
US_Bond_3_5_year_maturity
Japanese_Bond_3_5_year_maturity
German_MSCI_Equity
Japanese_MSCI_Equity
American_MSCI_Equity
Minimum Risk Maximum Return
Rank Optimisation
Standard MV
Optimisation
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State Preference Theory -- 1
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State Preference Theory -- 2
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Conclusions
Forecast and portfolio construction is not an exact science. It is an art with no black and white answers. An interactive tools allow you to get a feel for these uncertainties.
Probability based methods can improve your forecast construction by bringing statistical rigour to qualitative analysis and making the results of quantitative analysis more intuitive and realistic.
We cannot eliminate risk, but we can improve the stability, reliability and relevance of the measures used
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Disclaimer
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