长记忆条件下中国股市VaR的估计

本文首先对上证综合指数、深圳成份指数、香港恒生指数进行了一个长记忆性检验,在收益波动率序列中我们发现了高度显著的长记忆性。然后我们用GARCH(1,1)、FIGARCH(1,d,1)和FIEGARCH (1,d,1)模型计算各指数在三个置信水平下的VaR值。实证结果表明在估计95％置信度下的VaR值时基于GED分布的FIGARCH(1,d,1)模型表现最佳。

Estimating Value at Risk in Chinese Stock Market Under the Condition of Long Memory

In this paper, a long memory test is applied first to detect the existence of long memory in volatility for Shanghai Stock Index, Shenzhen Component index and Hongkong Hangseng Index. Strong evidence of long memory in volatility is found. Then the GARCH (1, 1), FIGARCH (1, d, 1) and FIEGARCH (1, d, 1) models are applied to three market indices to assess each model in estimating VaR at various confidence levels. The empirical results show that Fractionally integrated GARCH with GED error model performs the best in estimating five percent VaR.