Optimal Decentralized ALM © Michael W. Brandt 2008
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Transcript of Optimal Decentralized ALM © Michael W. Brandt 2008
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Optimal Decentralized ALM
Jules H. van Binsbergen, Stanford University
Michael W. Brandt, Duke University and NBER
Ralph S.J. Koijen, University of Chicago
Rotman ICPM Forum, TorontoJune 3-4 2008
© Michael W. Brandt, 2008 All rights reserved without exception
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Motivation
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Optimal Decentralized ALM© Michael W. Brandt 2008 *
Decentralized investment management
• Institutions decentralize investment decisions along asset classes
– Example
– Why? Division of labor
Specialization ) Generating value (i.e., lower transaction cost or positive alpha) within an asset class requires specialized skill
CIO
Fixed Income Portfolio Manager
Equities Portfolio Manager
Motivation
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Optimal Decentralized ALM© Michael W. Brandt 2008 *
Misalignment of objectives
1. Suboptimal diversification
– Joint optimization by CIO over all assets dominates best combination of portfolios optimized by portfolio managers within asset-classes
– Sharpe (1981) and Elton and Gruber (2004)
2. Different risk preferences
– Portfolio managers take more or less risk than CIO desires
– CIO does not generally know the managers’ risk preferences
– van Binsbergen, Brandt, and Koijen (2008)
3. Mismatch risk between assets and liabilities
– Portfolio managers do not consider liabilities in their optimization
– Main motivation for this project
Motivation
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Optimal Decentralized ALM© Michael W. Brandt 2008 *
Benchmarking
• Performance benchmarks are commonly used to evaluate and compensate portfolio managers
– Emphasis is on measuring effort or skill
– Benchmarks are taken as exogenously given (e.g., cash or index)
• We examine to what extent optimally designed benchmarks can alleviate the misalignment induced by decentralization
• To be realistic, we focus on
– Benchmarks that are tradable portfolios and can be matched by the portfolio managers (i.e., no cross-benchmarking)
– Benchmarks that do not depend on unknown quantities
– Unconditional benchmarks
Motivation
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Optimal Decentralized ALM© Michael W. Brandt 2008 *
Objective of our study
• Quantify in an intuitive way the economic cost of decentralization
– How much active skill do delegated portfolio managers have to have in order to justify decentralization?
• Show how to construct benchmarks that perfectly align objectives and achieve the same outcome as if the investment process was centralized and the CIO had the skills of the portfolio managers
– Full benefits of diversification
– Optimal mismatch risk
– Optimal alpha overlay
Motivation
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Optimal Decentralized ALM© Michael W. Brandt 2008 *
Objective of our study (cont)
• Show how to operationalize our approach
– Our optimally constructed benchmarks depend on the portfolio managers’ risk tolerances and active skill levels
– Three possibilities Take an ex-ante stance on both sets of parameters Construct an empirical cross-sectional distribution and
incorporate the resulting “parameter uncertainty” Limit the role of both sets of parameters through constraints
Motivation
Integrated and fully operational approach for decentralized liability driven investment (LDI) management
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Problem setup
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Optimal Decentralized ALM© Michael W. Brandt 2008 *
Pension fund
• CIO
– Liabilities
Exogenous with Treasury-like dynamics
– Assets
Centralized portfolio management (7 assets)– Fixed income indices (Aaa, Baa, and Treasuries)– Equity indices (Growth, Intermediate, Value)– Cash
Decentralized portfolio management (3 assets)– Fixed income manager
– Indices + orthogonal alpha technology
– Equities manager– Indices + orthogonal alpha technology
– Cash
– Preferences = power utility over AT/LT
Problem setup
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Optimal Decentralized ALM© Michael W. Brandt 2008 *
Portfolio managers
• Fixed income manager (4 assets)
– Indices (Aaa, Baa, Treasuries)
– Independent alpha technology
– No cash position
– Preferences = power utility over A1T/B1T
• Equities manager (4 assets)
– Indices (Growth, Intermediate, Value)
– Independent alpha technology
– No cash position
– Preferences = power utility over A2T/B2T
• Two types of benchmarks (cash or optimally chosen)
Problem setup
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Cost of decentralization
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Optimal Decentralized ALM© Michael W. Brandt 2008
Decomposition
*
Cost of decentralization
Cost of Decentralization
Suboptimal Diversification
Asset/Liability Mismatch
Alpha
=
+
-
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Optimal Decentralized ALM© Michael W. Brandt 2008
Suboptimal diversification
• Cash benchmarks
• No alpha technologies
• Portfolio managers have relative risk aversion of 10
*
Cost of decentralization
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Optimal Decentralized ALM© Michael W. Brandt 2008
• CIO’s optimal allocation to the 6 risky assets
• Note
– No liability hedging with ° = 1 (log utility)
– Full liability immunization as ° ! 1
*
Centralized ALM
Cost of decentralization
max SR weights liability hedging weights
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Optimal Decentralized ALM© Michael W. Brandt 2008 *
Centralized ALM (cont)
Cost of decentralization
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Optimal Decentralized ALM© Michael W. Brandt 2008
• Both portfolio managers maximize their (absolute) SR
with
which includes their alpha technologies (technically ¤ ¤C)
• CIO invests optimally in the 2 managed portfolios and cash
*
Decentralized ALM with cash benchmarks
Cost of decentralization
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Decentralized ALM with cash benchmarks (cont)
Cost of decentralization
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Optimal Decentralized ALM© Michael W. Brandt 2008
Cost of decentralization
• How much alpha do the portfolio managers have to add for the CIO to be indifferent between centralized and decentralized ALM?
*
Cost of decentralization
IR
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Optimal benchmarks for delegated ALM
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Optimal Decentralized ALM© Michael W. Brandt 2008
Optimal benchmarks
• Response of the portfolio managers to benchmark with weights ¯i
• Note
– Benchmarks are ineffective with ° = 1 (log utility)
– Tracking error volatility ! 0 as ° ! 1
*
Optimal benchmarks for decentralized ALM
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Optimal Decentralized ALM© Michael W. Brandt 2008
Optimal benchmarks (cont)
• Understanding how portfolio managers respond to benchmarks, the CIO’s optimal benchmark choice is
where xiC is the CIO’s optimal allocation to the portfolio manager’s
assets including the manager’s alpha technology
• These benchmarks induce the first-best solution– Full benefits of diversification– Optimal mismatch risk– Optimal alpha overlay
*
Optimal benchmarks for decentralized ALM
Optimal benchmarks achieve the same outcome as if the investment process was centralized and the CIO had the
skills of the portfolio managers
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Optimal Decentralized ALM© Michael W. Brandt 2008
Optimal benchmarks (cont)
*
Optimal benchmarks for decentralized ALM
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Optimal Decentralized ALM© Michael W. Brandt 2008
Optimal benchmarks (cont)
*
Optimal benchmarks for decentralized ALM
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Practical implementation
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Optimal Decentralized ALM© Michael W. Brandt 2008 *
Unknown quantities
• The optimal benchmarks depend on two unknown quantities
– Portfolio managers’ risk tolerance
– Portfolio managers’ active skill (IC)
• Unknown quantities can be dealt with the same way as they usually are in portfolio choice problems
– “Plug-in” = pick values and proceed as if they known
– Bayesian = construct a subjective cross-sectional distribution of risk tolerance and active skill levels (be careful, they are likely highly correlated) and then integrate out the unknown quantities
Practical implementation
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Optimal Decentralized ALM© Michael W. Brandt 2008 *
Empirical solution
• Looking at past returns on active managers through the lens of a structural model of delegated portfolio management (like ours), we can learn a lot about managers’ risk preferences and skill
) Koijen (2008)
• Intuition
– Structural models predict how much beta exposure and active risk managers take on as a function of their risk aversion and skill
– Beta exposure and active risk can be measured fairly accurately (especially when compared to historical alpha estimates)
– These estimates can then be inverted to risk aversion and skill
Practical implementation
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Empirical solution (cont)
• E.g., cross-sectional distribution of relative risk aversion of U.S. mutual fund managers
Practical implementation
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Extensions and conclusions
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Extensions
• Long-only constraints
• Risk constraints at the portfolio manager level
• Alternative CIO preferences (van Binsbergen and Brandt, 2007)
• Other suggestions?
Extension and conclusions
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Optimal Decentralized ALM© Michael W. Brandt 2008 *
Conclusions
• Three contributions
– Quantify in an intuitive way the economic costs of decentralization
– Show how to construct benchmarks that perfectly align objectives and achieve the same outcome as if the investment process was centralized and the CIO had the skills of the portfolio managers
– Show how to operationalize our approach
• Integrated and fully operational approach for decentralized ALM
Extension and conclusions