Working Paper: NBER ID: w7039
Authors: Louis K.C. Chan; Jason Karceski; Josef Lakonishok
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.
Keywords: portfolio optimization; covariance forecasting; risk management
JEL Codes: G11; G12
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| market capitalizations, book-to-market ratios (G32) | future return covariance (G17) |
| forecasting models (C53) | optimization outcomes (C61) |
| portfolio optimization (G11) | portfolio volatility (G17) |
| model complexity (C52) | optimization results (C61) |
| benchmark attributes (C52) | optimal portfolio performance (G11) |