中文摘要：

金融实践中，金融资产回报不仅具有厚尾性、波动的异方差性两大特点，而且其波动表现出明显的长期记忆性。本文利用FIGARCH模型处理波动异方差性和长期记忆性、EVT-POT方法捕捉回报分布厚尾的优势，提出了能反映厚尾性、异方差性和长期记忆性的金融风险度量模型——基于EVT-POT-FIGARCH的动态VaR模型，并用中国股票市场的沪深300指数和上证综合指数的每日收盘价进行实证分析。结果表明，模型能较好地处理这两个指数回报序列的三大特点．更准确地度量其VaR风险。

英文摘要：

There are two well-known facts about financial equity return series- heteroscedasticity resulting in the well-recognized phenomenon of volatility clustering, and fat-tails of the return distribution. In addition, recent studies show strong evidence that lots of financial equity return series exhibit long memory behavior in volatility. Considering these three features, this article constructs a dynamic VaR risk measure based on the EVT-POT-FIGARCH model for financial equity return series. The combination can make full use of the advantages of FIGARCH model and EVT-POT method from the following two points. On the one hand, as one of the GARCH model relatives, FIGARCH model not only deals with the equity retum＇s heteroscedasticity but also recovers the long memory in volatility. On the other hand, the application Of EVT （Extreme Value Theory） is effective in tracking extreme losses in the study of risk measurement. One of its methods-POT （the Peaks over Threshold） is able to capture the fat tails of the equity return distribution that showing clear non-normal behavior. Then the VaR risk measure based on the EVT-POT-FIGARCH model is applied on daily returns of the Shanghai composite index and the Shanghai and Shenzhen 300 index in Chinese stock market. The empirical analysis indicates that the risk measure can deal with these two index returns＇ three waits and describes their dynamic VaR risk more exactly. The results show that this risk measure performs higher hit rate than the dynamic VaR model based on GARCH-N and the outcomes are more rational than the static VaR model based on EVT-POT. The VaR risk measure based on the EVT-POT-FIGARCH model is of certain value for evaluating financial VaR risk. Our work again proves that there is apparent long memory property in Chinese stock market volatility.