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Asset Allocation

Paper Session

Sunday, Jan. 8, 2017 6:00 PM – 8:00 PM

Sheraton Grand Chicago, Missouri
Hosted By: American Finance Association
  • Chair: Francisco Gomes, London Business School

Portfolio Choice With Model Misspecification: A Foundation for Alpha and Beta Portfolios

Paolo Zaffaroni
,
Imperial College London
Raman Uppal
,
EDHEC Business School

Abstract

Our objective is to formalize the effect of model misspecification on mean-variance portfolios and to show how asset-pricing theory and asymptotic analysis can be used to provide powerful solutions to mitigate it. In particular we show how to design mean-variance portfolios that perform well out of sample in the presence of model misspecification. Our key insight is that, instead of treating misspecification directly in the mean-variance portfolio, it is better to first decompose the portfolio into two components, and to then treat misspecification in the two components separately using different methods. The starting point of our analysis is the Arbitrage Pricing Theory (APT). We first extend the APT to show that it can capture not just small pricing errors that are independent of factors, but also large pricing errors from mismeasured or missing factors. Then, we decompose the mean-variance portfolio into two components that correspond to the two components of returns in the APT: an “alpha” portfolio that depends only on pricing errors and a “beta” portfolio that depends on factor risk premia. For the alpha portfolio, we treat misspecification by imposing the APT restriction on alphas, which serves both as an identification condition and a shrinkage constraint, achieving substantial improvement in the precision of the estimated pricing errors; for the beta portfolio, we treat misspecification using asymptotic analysis: as the number of assets increases, the weights of the alpha portfolio dominate those of the beta portfolio, providing an expression for mean-variance portfolio weights that is immune to beta misspecification. Finally, we demonstrate that our approach leads to significant improvement in out-of-sample performance.

On the Asset Allocation of a Default Pension Fund

Magnus Dahlquist
,
Stockholm School of Economics and CEPR
Ofer Setty
,
Tel Aviv University
Roine Vestman
,
Stockholm University

Abstract

We characterize the optimal default fund in a defined contribution (DC) pension plan. Using detailed data on individuals and their holdings inside and outside the pension system, we find substantial heterogeneity among default investors in terms of labor income, financial wealth, and stock market participation. We build a life-cycle consumption-savings model incorporating a DC pension account and realistic investor heterogeneity. We examine the optimal asset allocation for different realized equity returns and investors and compare it with age-based investing. The optimal asset allocation leads to less inequality in pensions while it moderates the risks through active rebalancing.

Information Aggregation and Asset Prices in Large Markets With Institutional Investors

Matthijs Breugem
,
Frankfurt School of Finance and Management
Adrian Buss
,
INSEAD

Abstract

We study the joint determination of endogenous information acquisition and equilibrium asset prices in a rational expectation equilibrium model with a continuum of asset managers who care about their performance relative to a benchmark and have CRRA preferences. In the presence of benchmarking, managers are less willing to deviate from the benchmark and, thus, to speculate based on private information, such that less of a manager’s private information gets incorporated into prices. As benchmarking also reduces the fraction of managers that endogenously decide to acquire private information, prices are substantially less informative in the presence of institutional investors. The benchmark asset is therefore perceived to be more risky, leading to a decline in price, which can dominate the positive price effect stemming from the managers’ excess demand due to index-hedging, and a substantial increase in return volatility.
Discussant(s)
Svetlana Bryzgalova
,
Stanford University
Clemens Sialm
,
University of Texas-Austin and NBER
Andrea Buffa
,
Boston University
JEL Classifications
  • G1 - Asset Markets and Pricing