A threshold stochastic volatility model with explanatory variables

作者：Li, Han; Yang, Kai; Wang, Dehui^*

来源：Statistica Neerlandica, 2019, 73(1): 118-138.

DOI：10.1111/stan.12143

• Bayesian estimation
• Gibbs sampling
• Markov chain Monte Carlo
• Metropolis-Hastings sampling
• threshold stochastic volatility model

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.

• 出版日期2019-2
• 单位吉林大学; 长春工业大学