Estimating Flexible, Fat-Tailed Conditional Asset Return Distributions

44 Pages Posted: 7 Jan 2011 Last revised: 10 Oct 2012

Craig A. Friedman

TIAA-CREF

Yangyong Zhang

Standard & Poor's - Quantitative Analytics

Wenbo Cao

Standard & Poor's - Quantitative Analytics

Date Written: October 9, 2012

Abstract

We provide a new alternative to thin-tailed regression models by introducing robust numerical methods, based on the minimum relative U−entropy (MRUE) principle, to estimate heteroskedastic, fat-tailed, flexible univariate probability density functions, conditioned on a number of explanatory variables. We benchmark our method against state-of-the-art asset return models on the 30 constituents of the Dow 30 and find that our models outperform the benchmarks out-of-sample.

Keywords: Minimum Relative U−Entropy, Conditional Probability Distribution, Fat-tailed, Power-Law Distribution, Heteroskedastic, Financial Data, Asset Returns

Suggested Citation

Friedman, Craig A. and Zhang, Yangyong and Cao, Wenbo, Estimating Flexible, Fat-Tailed Conditional Asset Return Distributions (October 9, 2012). Available at SSRN: https://ssrn.com/abstract=1735522 or http://dx.doi.org/10.2139/ssrn.1735522

Craig A. Friedman (Contact Author)

TIAA-CREF ( email )

730 3rd Ave
New York, NY 10017
United States

Yangyong Zhang

Standard & Poor's - Quantitative Analytics ( email )

55 Water Street
New York, NY 10041
United States

Wenbo Cao

Standard & Poor's - Quantitative Analytics ( email )

55 Water Street
New York, NY 10041
United States

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