跳跃随机波动的阈值模型风险值的贝叶斯估计

Bayesian Estimation on Jump Stochastic VolatilityThreshold Model of VaR

文章基于一类跳跃随机波动的阈值模型风险值估计贝叶斯分析,在给定先验分布下,以马尔科夫链蒙特卡洛方法估计模型中的未知参数,并给出了MCMC模拟算法,进而讨论了风险值的预测。根据模拟结果,我们得知,如果没有考虑金融时间序列的外生冲击导致的跳跃行为,将会高估风险值,因此考虑跳跃行为后,将增加风险值估计的精度。

This paper develops a class of jump stochastic volatility threshold model of VaR Estimation from a Bayesian viewpoint.Bayesian inferences of the unknown parameters are obtained with respect to a subjective prior distribution via Markov chain Monte Carlo（MCMC） method,MCMC algorithm and the value at risk（VaR） predictive are also developed.Based on simulation,if the jump is not Considered,the value at risk is overestimated.The precision of value at risk estimation is increased.