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A threshold stochastic volatility model with explanatory variables

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  • Han Li
  • Kai Yang
  • Dehui Wang

Abstract

In this paper, we introduce a threshold stochastic volatility model with explanatory variables. The Bayesian method is considered in estimating the parameters of the proposed model via the Markov chain Monte Carlo (MCMC) algorithm. Gibbs sampling and Metropolis–Hastings sampling methods are used for drawing the posterior samples of the parameters and the latent variables. In the simulation study, the accuracy of the MCMC algorithm, the sensitivity of the algorithm for model assumptions, and the robustness of the posterior distribution under different priors are considered. Simulation results indicate that our MCMC algorithm converges fast and that the posterior distribution is robust under different priors and model assumptions. A real data example was analyzed to explain the asymmetric behavior of stock markets.

Suggested Citation

  • Han Li & Kai Yang & Dehui Wang, 2019. "A threshold stochastic volatility model with explanatory variables," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 73(1), pages 118-138, February.
    • Handle: RePEc:bla:stanee:v:73:y:2019:i:1:p:118-138
    DOI: 10.1111/stan.12143
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    Cited by:

    1. Kai Yang & Qingqing Zhang & Xinyang Yu & Xiaogang Dong, 2023. "Bayesian inference for a mixture double autoregressive model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 77(2), pages 188-207, May.
    2. Xinyang Wang & Dehui Wang & Kai Yang, 2021. "Integer-valued time series model order shrinkage and selection via penalized quasi-likelihood approach," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(5), pages 713-750, July.
    3. Yang, Kai & Yu, Xinyang & Zhang, Qingqing & Dong, Xiaogang, 2022. "On MCMC sampling in self-exciting integer-valued threshold time series models," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
    4. Eric A. Beutner & Yicong Lin & Andre Lucas, 2023. "Consistency, distributional convergence, and optimality of score-driven filters," Tinbergen Institute Discussion Papers 23-051/III, Tinbergen Institute.
    5. Hashem Zarafat & Sascha Liebhardt & Mustafa Hakan Eratalay, 2022. "Do ESG Ratings Reduce the Asymmetry Behavior in Volatility?," JRFM, MDPI, vol. 15(8), pages 1-32, July.

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