RFS Advance Access published online on October 28, 2005
Review of Financial Studies, doi:10.1093/rfs/hhj002
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* To whom correspondence should be addressed. We investigate the implications of time-varying expected return and volatility on asset allocation in a high-dimensional setting. We propose a DFMSV model that allows the first two moments of returns to vary over time for a large number of assets. We then evaluate the economic significance of the DFMSV model by examining the performance of various dynamic portfolio strategies chosen by mean-variance investors in a universe of 36 stocks. We find that the DFMSV dynamic strategies significantly outperform various benchmark strategies out of sample. This outperformance is robust to different performance measures, investors objective functions, time periods, and assets.
Article
Asset Allocation with a High Dimensional Latent Factor Stochastic Volatility Model*
1 Tulane University
Yufeng Han, E-mail: yhan{at}tulane.edu
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Abstract
* I am grateful to Kerry Back, Suman Banerjee, David Bates, Nai-fu Chen, Shane Corwin, Cheol Eun, Heber Farnsworth, Frank Fehle, Jeff Fleming, Thomas George, Shingo Goto, John Hund, Narayanan Jayaraman, Christopher Jones, Praveen Kumar, David Lesmond, Marc Lipson, Greg Niehaus, Thomas Noe, John Scruggs, Paul Schultz, Ken Singleton (the editor), Jason Smith, Christopher Stivers, Sheri Tice, Sergey Tsyplakov, Anand Vijh, Yihong Xia, Guofu Zhou, seminar participants at the 2003 AFA meetings, Georgia Institute of Technology, Tulane University, University of California at Irvine, University of Georgia, University of Houston, University of Iowa, University of Notre Dame, University of South Carolina and Washington University in St. Louis for helpful comments, and especially to Siddhartha Chib for his advice and encouragement, and an anonymous referee for insightful suggestions that have substantially improved the article. This paper was previously circulated under the title "The Economic Value of Volatility Modeling: Asset Allocation with a High Dimensional Dynamic Latent Factor Multivariate Stochastic Volatility Model."
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