Bayesian estimation for a univariate threshold stochastic volatility model with explanatory variables}%uthorCHN{杨凯ffil{1},王德辉$^st$

YANG Kai; WANG Dehui; LI Han

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Bayesian estimation for a univariate threshold stochastic volatility model with explanatory variables}%uthorCHN{杨凯ffil{1},王德辉$^st$

YANG Kai 1,WANG Dehui 2 * #,LI Han 1

1.Institute of Mathematics, Jilin University, Changchun 130012

2.College of Science,GuiZhou University, GuiYang 550025

*Correspondence author

#Submitted by

Subject:

Funding: Specialized Research Fund for the Doctoral Program of HigherEducation （No.No. 20110061110003）, Program for New Century ExcellentTalents in University （No.NCET-08-237）, Scientific Research Fund ofJilin University （No.No. 201100011）

Opened online:26 November 2015

Accepted by: none

Citation: YANG Kai,WANG Dehui,LI Han.Bayesian estimation for a univariate threshold stochastic volatility model with explanatory variables}%uthorCHN{杨凯ffil{1},王德辉$^st$[OL]. [26 November 2015] http://en.paper.edu.cn/en_releasepaper/content/4663917

leftskip=0pt ightskip=0pt plus 0cmIn this paper, we consider the Bayesian estimationfor a univariate threshold stochastic volatility model with explanatory variables via MCMC algorithms.Gibbs sampling and Metropolis-Hastings sampling methods are used for drawing the posterior samples of the parameters and the latent variables. In simulation study, the accuracy of the MCMC algorithm, sensitivity of the algorithm for model assumptions and the robust for the priors are considered. Simulation results indicate that our MCMC algorithms convergence fast and are robust for different priors and model assumptions. A real data application was analyzed to explain the asymmetric behavior of stock markets.

Keywords:Threshold stochastic volatility model; Bayesian estimation; Gibbs sampling; Metropolis-Hastings sampling; Markov Chain Monte Carlo

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