Directional Variance Adjustment: improving covariance estimates for high-dimensional portfolio optimization
Robust and reliable covariance estimates play a decisive role in financial and many other applications. An important class of estimators is based on Factor models. Here, we show by extensive Monte Carlo simulations that covariance matrices derived from the statistical Factor Analysis model exhibit a systematic error, which is similar to the well-known systematic error of the spectrum of the sample covariance matrix. Moreover, we introduce the Directional Variance Adjustment (DVA) algorithm, which diminishes the systematic error. In a thorough empirical study for the US, European, and Hong Kong market we show that our proposed method leads to improved portfolio allocation.
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- Fama, Eugene F & French, Kenneth R, 1992. " The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-65, June.
- Fan, Jianqing & Fan, Yingying & Lv, Jinchi, 2008. "High dimensional covariance matrix estimation using a factor model," Journal of Econometrics, Elsevier, vol. 147(1), pages 186-197, November.
- Shanken, Jay, 1992. "On the Estimation of Beta-Pricing Models," Review of Financial Studies, Society for Financial Studies, vol. 5(1), pages 1-33.
- Campbell, Rachel A.J. & Forbes, Catherine S. & Koedijk, Kees G. & Kofman, Paul, 2008. "Increasing correlations or just fat tails?," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 287-309, March.
- Pagan, Adrian R. & Schwert, G. William, 1990. "Testing for covariance stationarity in stock market data," Economics Letters, Elsevier, vol. 33(2), pages 165-170, June.
- Loretan, Mico & Phillips, Peter C. B., 1994.
"Testing the covariance stationarity of heavy-tailed time series: An overview of the theory with applications to several financial datasets,"
Journal of Empirical Finance,
Elsevier, vol. 1(2), pages 211-248, January.
- Ledoit, Olivier & Wolf, Michael, 2003.
"Improved estimation of the covariance matrix of stock returns with an application to portfolio selection,"
Journal of Empirical Finance,
Elsevier, vol. 10(5), pages 603-621, December.
- Olivier Ledoit & Michael Wolf, 2001. "Improved estimation of the covariance matrix of stock returns with an application to portofolio selection," Economics Working Papers 586, Department of Economics and Business, Universitat Pompeu Fabra.
- Ravi Jagannathan & Tongshu Ma, 2003.
"Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps,"
Journal of Finance,
American Finance Association, vol. 58(4), pages 1651-1684, 08.
- Ravi Jagannathan & Tongshu Ma, 2002. "Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps," NBER Working Papers 8922, National Bureau of Economic Research, Inc.
- Donald Rubin & Dorothy Thayer, 1982. "EM algorithms for ML factor analysis," Psychometrika, Springer, vol. 47(1), pages 69-76, March.
- Roll, Richard & Ross, Stephen A, 1980. " An Empirical Investigation of the Arbitrage Pricing Theory," Journal of Finance, American Finance Association, vol. 35(5), pages 1073-1103, December.
- K. Jöreskog, 1967. "Some contributions to maximum likelihood factor analysis," Psychometrika, Springer, vol. 32(4), pages 443-482, December.
- Schäfer Juliane & Strimmer Korbinian, 2005. "A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 4(1), pages 1-32, November.
- Michael E. Tipping & Christopher M. Bishop, 1999. "Probabilistic Principal Component Analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(3), pages 611-622.
- Longin, Francois, 2005. "The choice of the distribution of asset returns: How extreme value theory can help?," Journal of Banking & Finance, Elsevier, vol. 29(4), pages 1017-1035, April.
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