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

Jouchi Nakajima and Yasuhiro Omori ()

Computational Statistics & Data Analysis, 2012, vol. 56, issue 11, 3690-3704

Abstract: A 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-tailed error and leverage effects. An efficient Markov chain Monte Carlo estimation method is described that exploits a normal variance-mean mixture representation of the error distribution with an inverse gamma distribution as the mixing distribution. The proposed method is illustrated using simulated data, daily S&P500 and TOPIX stock returns. The models for stock returns are compared based on the marginal likelihood in the empirical study. There is strong evidence in the stock returns high leverage and an asymmetric heavy-tailed distribution. Furthermore, a prior sensitivity analysis is conducted whether the results obtained are robust with respect to the choice of the priors.

Keywords: Generalized hyperbolic skew Student’s t-distribution; Markov chain Monte Carlo; Mixing distribution; State space model; Stochastic volatility; Stock returns (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (49)

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Related works:
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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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:11:p:3690-3704

DOI: 10.1016/j.csda.2010.07.012

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