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Estimating Univariate Distributions Via Relative Entropy Minimization: Case Studies On Financial And Economic Data

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  • CRAIG FRIEDMAN

    (Standard & Poor's, 55 Water Street, 46th Floor, New York, NY 10041, USA)

  • YANGYONG ZHANG

    (Standard & Poor's, 55 Water Street, 46th Floor, New York, NY 10041, USA)

  • JINGGANG HUANG

    (Standard & Poor's, 55 Water Street, 46th Floor, New York, NY 10041, USA)

Abstract

We use minimum relative entropy (MRE) methods to estimate univariate probability density functions for a varied set of financial and economic variables, including S&P500 index returns, individual stock returns, power price returns and a number of housing-related economic variables. Some variables have fat tail distributions, others have finite support. Some variables have point masses in their distributions and others have multimodal distributions. We indicate specifically how the MRE approach can be tailored to the stylized facts of the variables that we consider and benchmark the MRE approach against alternative approaches. We find, for a number of variables, that the MRE approach outperforms the benchmark methods.

Suggested Citation

  • Craig Friedman & Yangyong Zhang & Jinggang Huang, 2010. "Estimating Univariate Distributions Via Relative Entropy Minimization: Case Studies On Financial And Economic Data," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 13(01), pages 163-193.
    • Handle: RePEc:wsi:ijtafx:v:13:y:2010:i:01:n:s0219024910005723
    DOI: 10.1142/S0219024910005723
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    References listed on IDEAS

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    1. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers Archive 1488, Iowa State University, Department of Economics.
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