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Modeling multivariate parametric densities of financial returns (in Russian)

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  • Alexey Balaev

    (Higher School of Economics, Moscow, Russia)

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    Abstract

    This paper compares several bivariate conditional density parameterizations for stock market returns in terms of in-sample fit and out-of-sample predictive ability for the whole conditional density. We consider Skew-Normal, Skew-Student, Skew-GED and Gram-Charlier densities. We focus on the ability of these density specifications to capture asymmetry and so called 'multivariate tails'. Using a test based on Kullback-Leibler information criterion we conduct pairwise comparisons of estimated conditional density models in sample and out of sample. The models are ranked according to their quality of fit and predictive ability. We discuss the causes behind superiority of this or that density specification.

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    File URL: http://quantile.ru/09/09-AB.pdf
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    Bibliographic Info

    Article provided by Quantile in its journal Quantile.

    Volume (Year): (2011)
    Issue (Month): 9 (July)
    Pages: 39-60

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    Handle: RePEc:qnt:quantl:y:2011:i:9:p:39-60

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    Web page: http://quantile.ru/

    Related research

    Keywords: conditional density; Gram-Charlier expansion; skewed distribution; quality of fit; predictive ability;

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    Cited by:
    1. Balaev , Alexey, 2011. "Multivariate skewed t-distribution with degrees of freedom vector and its application to financial modeling," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 23(3), pages 79-97.

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