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

  • Jouchi Nakajima

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

  • Yasuhiro Omori

    (Faculty of Economics, University of Tokyo)

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.

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Paper provided by Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo in its series CARF F-Series with number CARF-F-199.

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Length: 26 pages
Date of creation: Dec 2009
Date of revision:
Handle: RePEc:cfi:fseres:cf199
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