中文摘要：

考虑正负资产收益对分位数冲击的不对称性提出间接TARCH-CAViaR模型.CAViaR一般模型中递归分位回归方程的非线性和非连续可微性是参数估计的一个难题,基于含有尺度参数的不对称拉普拉斯分布作为误差过程,指出将尺度参数固定为常数会导致不对称拉普拉斯分布随机变量的方差存在最小正值的限制,与实际金融数据分布不符;进而提出采用贝叶斯分析和马尔科夫链蒙特卡罗模拟方法,估计间接TARCH-CAViaR模型的参数,并可获得尺度参数的合理估计.实证研究中对上证指数的市场风险演化模式进行了测算,动态分位检验、后验测试表明模型VaR预测效果理想,消息冲击曲线表明上海股市好坏消息对市场风险的冲击是不对称的,且这种影响作用在不同置信水平市场风险中表现得有显著差异.

英文摘要：

This paper gives a new model called indirect TARCH-CAViaR model that can explain the asynanetric impact of price information on the quantiles of returns. The nonlinearity and discrete gradient inherited in CAViaR model is a conundrum for parameter estimation. We take the asymmetric Laplace distribution with scale parameter as the error process; indicate the variance has a minimum positive value when the scale parameter is a constant, conflicting with the distribution of real financial data. Further we estimate the parameters of indirect TARCH-CAViaR model base on Bayesian framework and Markov chain Monte Carlo method. The optimal scale parameter can also be obtained by Markov chain Monte Carlo method. In the empirical study, the dynamic evolution pattern of Shanghai Composite Index is measured by our model. The dynamic quantile test and the back testing also support that the new model performs well in VaR forecasting. It is indicated the significant being of asymmetric impact of bad or good price news on the market risk under different confidence levels, and the coefficients of impact diversify strongly with various confidence levels.