【摘要】 为了准确描述金融收益率序列的波动率聚集,异方差、厚尾等特性,研究我国证券市场的时变风险,本文利用极值理论和GARCH模型在处理金融数据上的优点,构造了基于GARCH-EVT的条件VaR模型,并对沪市综合指数收益率进行实证研究,结果表明,上海股市存在ARCH效应,收益率序列具有较强的自相关性;以GARCH模型为基础的条件极值方法比GARCH-正态和GARCH-t模型更好地消除了厚尾性对估计结果的影响,能更准确地捕捉风险的时变特性.更多还原

【Abstract】 Aimed at describing some characteristics of financial return sequence such as volatility cluster and conditional heteroscedasticity,we propose a method for estimating VaR and related risk measures describing the tail of the conditional distribution of a heteroscedastic financial return series. GARCH models to estimate the current volatility and the extreme value theory (EVT) for estimating the tail of the innovation distribution of the GARCH model are combined. The proposed method was used to estimate VaR and conditional expected shortfalls. The backtesting of Shanghai stock market shows that there is serious ARCH effect on return rate of the stock market and GARCH-EVT model gives better estimates than GARCH-Normal and GARCH-t models.更多还原