Covariance Estimation in Dynamic Portfolio Optimization: A Realized Single Factor Model
Lada M. Kyj
Humboldt University of Berlin; Quantitative Products Laboratory
Rice University - Jesse H. Jones Graduate School of Business
Rice University - George R. Brown School of Engineering
July 9, 2009
AFA 2010 Atlanta Meetings Paper
Realized covariance estimation for large dimension problems is little explored and poses challenges in terms of computational burden and estimation error. In a global minimum volatility setting, we investigate the performance of covariance conditioning techniques applied to the realized covariance matrices of the 30 DJIA stocks. We find that not only is matrix conditioning necessary to deliver the benefits of high frequency data, but a single factor model, with a smoothed covariance estimate, outperforms the fully estimated realized covariance in one-step ahead forecasts. Furthermore, a mixed-frequency single-factor model - with factor coefficients estimated using low-frequency data and variances estimated using high-frequency data performs better than the realized single-factor estimator. The mixed-frequency model is not only parsimonious but it also avoids estimation of high-frequency covariances, an attractive feature for less frequently traded assets. Volatility dimension curves reveal that it is difficult to distinguish among estimators at low portfolio dimensions, but for well-conditioned estimators the performance gain relative to the benchmark 1/N portfolio increases with N.
Number of Pages in PDF File: 38
Keywords: Factor Model, Realized Covariance, Volatilty Timing
JEL Classification: C14, G11, G12
Date posted: March 22, 2009 ; Last revised: October 17, 2009
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