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A Generalized Asymmetric Student-t Distribution with Application to Financial Econometrics

Author

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  • Dongming Zhu
  • John Galbraith

Abstract

This paper proposes a new class of asymmetric Student-t (AST) distributions, and investigates its properties, gives procedures for estimation, and indicates applications in financial econometrics. We derive analytical expressions for the cdf, quantile function, moments, and quantities useful in financial econometric applications such as the expected shortfall. A stochastic representation of the distribution is also given. Although the AST density does not satisfy the usual regularity conditions for maximum likelihood estimation, we establish consistency, asymptotic normality and efficiency of ML estimators and derive an explicit analytical expression for the asymptotic covariance matrix. A Monte Carlo study indicates generally good finite-sample conformity with these asymptotic properties. Le présent document propose une nouvelle catégorie de distributions asymétriques suivant la loi t de Student (Asymmetric Student-t Distribution - AST). Il en examine les propriétés, suggère des procédures d'estimation et propose des applications dans le domaine de l'économétrie financière. Nous établissons des expressions analytiques pour la fonction de distribution cumulative, la fonction quantile, les moments et les quantités, ces aspects étant utiles dans certaines applications liées à l'économétrie financière, par exemple l'estimation du manque à gagner prévu. Nous mettons aussi de l'avant une représentation stochastique de la distribution. Même si la densité suivant la loi t de Student ne répond pas aux conditions habituelles de régularité pour l'estimation du maximum de vraisemblance, nous établissons néanmoins la consistance, la normalité asymptotique et l'efficacité des estimateurs du maximum de vraisemblance et arrivons à une expression analytique explicite en ce qui concerne la matrice de covariance asymptotique. Une étude selon la méthode Monte Carlo indique généralement une bonne conformité des échantillons finis avec ces propriétés asymptotiques.

Suggested Citation

  • Dongming Zhu & John Galbraith, 2009. "A Generalized Asymmetric Student-t Distribution with Application to Financial Econometrics," CIRANO Working Papers 2009s-13, CIRANO.
  • Handle: RePEc:cir:cirwor:2009s-13
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    File URL: http://www.cirano.qc.ca/files/publications/2009s-13.pdf
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    References listed on IDEAS

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    1. Philip Hans Franses & Marco van der Leij & Richard Paap, 2008. "A Simple Test for GARCH Against a Stochastic Volatility Model," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 6(3), pages 291-306, Summer.
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    3. Dima Alberg & Haim Shalit & Rami Yosef, 2008. "Estimating stock market volatility using asymmetric GARCH models," Applied Financial Economics, Taylor & Francis Journals, vol. 18(15), pages 1201-1208.
    4. Panayiotis Theodossiou, 1998. "Financial Data and the Skewed Generalized T Distribution," Management Science, INFORMS, vol. 44(12-Part-1), pages 1650-1661, December.
    5. Kjersti Aas & Ingrid Hobaek Haff, 2006. "The Generalized Hyperbolic Skew Student's t-Distribution," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 4(2), pages 275-309.
    6. M. C. Jones & M. J. Faddy, 2003. "A skew extension of the "t"-distribution, with applications," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 159-174.
    7. John Galbraith & Dongming Zhu, 2009. "Forecasting Expected Shortfall With A Generalized Asymmetric Student-T Distribution," Departmental Working Papers 2009-01, McGill University, Department of Economics.
    8. Luc Bauwens & Sébastien Laurent, 2002. "A New Class of Multivariate skew Densities, with Application to GARCH Models," Computing in Economics and Finance 2002 5, Society for Computational Economics.
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    11. Mittnik, Stefan & Paolella, Marc S., 2003. "Prediction of Financial Downside-Risk with Heavy-Tailed Conditional Distributions," CFS Working Paper Series 2003/04, Center for Financial Studies (CFS).
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    More about this item

    Keywords

    asymmetric distribution; expected shortfall; maximum likelihood estimation; distribution asymétrique; manque à gagner prévu; estimation du maximum de vraisemblance;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions

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