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

Author

Listed:
  • Jouchi Nakajima

    (Department of Statistical Science, Duke University and Bank of Japan)

  • Yasuhiro Omori

    (Faculty of Economics, 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.

Suggested Citation

  • Jouchi Nakajima & Yasuhiro Omori, 2009. "Stochastic volatility model with leverage and asymmetrically heavy-tailed error using GH skew Student's t-distribution," CARF F-Series CARF-F-199, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    • Handle: RePEc:cfi:fseres:cf199
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