A GENERALIZED ASYMMETRIC STUDENT-T DISTRIBUTION WITH APPLICATION TO FINANCIAL ECONOMETRICS
John Galbraith and
Dongming Zhu ()
Departmental Working Papers from McGill University, Department of Economics
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.
JEL-codes: C13 C16 (search for similar items in EconPapers)
Pages: 37 pages
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Journal Article: A generalized asymmetric Student-t distribution with application to financial econometrics (2010)
Working Paper: A Generalized Asymmetric Student-t Distribution with Application to Financial Econometrics (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:mcl:mclwop:2009-02
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