上证综指收益的风险价值测度——基于高斯混合自回归模型的研究

Value at Risk Measurement for the Return of Shanghai Composite Index: Based on Gaussian Mixture Autoregressive Model

本文将高斯混合自回归模型(GMAR)引入到风险价值(VaR)的计算上,并将其用于计算上证综指收益的VaR,将所得结果与GARCH类模型进行比较分析发现,GMAR能捕捉剧烈波动的条件异方差性,但对于较小的波动无法捕捉,因此所得VaR曲线较GARCH类模型平坦。而在不同的显著性水平下,GMAR模型预测VaR的能力都显著优于GARCH类模型。

The paper introduced Gaussian Mixture Autoregressive Model (GMAR) to the calculation of Value-at-Risk (VaR), calculated the VaR of the return of Shanghai Stock Exchange (SSE) composite index. By comparing the results with that of Generalized Auto-Regressive Conditional Hetero-skedasticity (GARCH), it is found that GMAR can capture heteroscedasticity's sharp fluctuations, but not the minor ones, so that the VaR curve of GMAR is smoother than GARCH's. Besides, under different significance levels, GMAR's capability of predicting VaR is obviously better than that of GARCH.