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

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  • Nakajima, Jouchi
  • Omori, Yasuhiro

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.

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Bibliographic Info

Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 56 (2012)
Issue (Month): 11 ()
Pages: 3690-3704

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Handle: RePEc:eee:csdana:v:56:y:2012:i:11:p:3690-3704

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Web page: http://www.elsevier.com/locate/csda

Related research

Keywords: Generalized hyperbolic skew Student’s t-distribution; Markov chain Monte Carlo; Mixing distribution; State space model; Stochastic volatility; Stock returns;

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References

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Citations

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Cited by:
  1. Xiuping Mao & Esther Ruiz & Helena Veiga, 2013. "One for all : nesting asymmetric stochastic volatility models," Statistics and Econometrics Working Papers ws131110, Universidad Carlos III, Departamento de Estadística y Econometría.
  2. Tsunehiro Ishihara & Yasuhiro Omori, 2009. "Efficient Bayesian estimation of a multivariate stochastic volatility model with cross leverage and heavy-tailed errors," CARF F-Series CARF-F-198, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
  3. Joshua C.C. Chan, 2013. "Moving Average Stochastic Volatility Models with Application to Inflation Forecast," CAMA Working Papers 2013-31, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  4. Takahashi, Makoto & Omori, Yasuhiro & Watanabe, Toshiaki, 2013. "News impact curve for stochastic volatility models," Economics Letters, Elsevier, vol. 120(1), pages 130-134.
  5. Joshua C.C. Chan & Angelia L. Grant, 2014. "Issues in Comparing Stochastic Volatility Models Using the Deviance Information Criterion," CAMA Working Papers 2014-51, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  6. Cabral, Celso Rômulo Barbosa & da-Silva, Cibele Queiroz & Migon, Helio S., 2014. "A dynamic linear model with extended skew-normal for the initial distribution of the state parameter," Computational Statistics & Data Analysis, Elsevier, vol. 74(C), pages 64-80.
  7. Deschamps, Philippe J., 2012. "Bayesian estimation of generalized hyperbolic skewed student GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3035-3054.
  8. Felicia Ramona Birău, 2012. "Stochastic Volatility Models For Financial Time Series Analysis," Anale. Seria Stiinte Economice. Timisoara, Faculty of Economics, Tibiscus University in Timisoara, vol. 0, pages 472-475, November.
  9. Kastner, Gregor & Frühwirth-Schnatter, Sylvia, 2014. "Ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC estimation of stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 408-423.

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