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Modeling Maximum Entropy Distributions for Financial Returns by Moment Combination and Selection

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  • Yi-Ting Chen

Abstract

In empirical finance, conditional distributions of financial returns are often established by specifying the standardized error distributions of GARCH-type models. In this article, we apply the maximum entropy (MaxEnt) approach and propose a moment combination and selection method to explore this distribution-building problem. We demonstrate that this framework is useful for unifying and comparing existing distribution specifications, generating more suitable distribution spec-ifications, and shedding light on the roles of different moments in the distribution-building process. We also show the applicability of our method to real data by means of an empirical study on stock index returns.

Suggested Citation

  • Yi-Ting Chen, 2015. "Modeling Maximum Entropy Distributions for Financial Returns by Moment Combination and Selection," Journal of Financial Econometrics, Oxford University Press, vol. 13(2), pages 414-455.
    • Handle: RePEc:oup:jfinec:v:13:y:2015:i:2:p:414-455.
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbt007
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    Cited by:

    1. Domenico Di Gangi & Fabrizio Lillo & Davide Pirino, 2015. "Assessing systemic risk due to fire sales spillover through maximum entropy network reconstruction," Papers 1509.00607, arXiv.org, revised Jul 2018.

    More about this item

    Keywords

    GARCH-type models; maximum entropy; moment combination; moment selection; standardized error distribution;
    All these keywords.

    JEL classification:

    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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