Stochastic volatility model with leverage and asymmetrically heavy-tailed error using GH skew Student's t-distribution

Jouchi Nakajima and Yasuhiro Omori ()

No CARF-F-199, CARF F-Series from Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo

Abstract: Bayesian analysis of a stochastic volatility model with a generalized hyperbolic (GH) skew Student?s t-error distribution is described where we first consider an asymmetric heavy-tailness as well as leverage effects. An efficient Markov chain Monte Carlo estimation method is described exploiting a normal variance-mean mixture representation of the error distribution with an inverse gamma distribution as a mixing distribution. The proposed method is illustrated using simulated data, daily TOPIX and S&P500 stock returns. The model comparison for stock returns is conducted based on the marginal likelihood in the empirical study. The strong evidence of the leverage and asymmetric heavy-tailness is found in the stock returns. Further, the prior sensitivity analysis is conducted to investigate whether obtained results are robust with respect to the choice of the priors.

Pages: 26 pages
Date: 2009-12
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Citations: View citations in EconPapers (3)

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Related works:
Journal Article: Stochastic volatility model with leverage and asymmetrically heavy-tailed error using GH skew Student’s t-distribution (2012)
Working Paper: Stochastic Volatility Model with Leverage and Asymmetrically Heavy-Tailed Error Using GH Skew Student?s t-Distribution (2010)
Working Paper: Stochastic Volatility Model with Leverage and Asymmetrically Heavy-tailed Error Using GH Skew Student's t-distribution (2010)
Working Paper: Stochastic Volatility Model with Leverage and Asymmetrically Heavy-Tailed Error Using GH Skew Student's t-Distribution (2009)
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