On Portfolio Optimization: Forecasting Covariances and Choosing the Risk Model

60 Pages Posted: 20 Apr 1999 Last revised: 12 Oct 2010

See all articles by Louis K.C. Chan

Louis K.C. Chan

University of Illinois at Urbana-Champaign - Department of Finance

Jason J. Karceski

LSV Asset Management

Josef Lakonishok

University of Illinois at Urbana-Champaign; National Bureau of Economic Research (NBER)

Multiple version iconThere are 2 versions of this paper

Date Written: March 1999

Abstract

We evaluate the performance of different models for the covariance structure of stock returns, focusing on their use for optimal portfolio selection. Comparisons are based on forecasts of future covariances as well as the out-of-sample volatility of optimized portfolios from each model. A few factors capture the general covariance structure but adding more factors does not improve forecast power. Portfolio optimization helps for risk control, but the different covariance models yield similar results. Using a tracking error volatility criterion, larger differences appear, with particularly favorable results for a heuristic approach based on matching the benchmark's attributes.

Suggested Citation

Chan, Louis K.C. and Karceski, Jason J. and Lakonishok, Josef, On Portfolio Optimization: Forecasting Covariances and Choosing the Risk Model (March 1999). NBER Working Paper No. w7039. Available at SSRN: https://ssrn.com/abstract=156690

Louis K.C. Chan (Contact Author)

University of Illinois at Urbana-Champaign - Department of Finance ( email )

1206 South Sixth Street
Champaign, IL 61820
United States
217-333-6391 (Phone)
217-244-3102 (Fax)

Jason J. Karceski

LSV Asset Management ( email )

155 N Wacker Dr.
Chicago, IL 60654
United States
352-246-7674 (Phone)

Josef Lakonishok

University of Illinois at Urbana-Champaign ( email )

1206 South Sixth Street
Champaign, IL 61820
United States
217-333-7185 (Phone)
217-244-3102 (Fax)

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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