统计研究

随着风险度量一致性原则的提出,研究发现金融机构广泛采用的VaR模型存在严重不足,尤其针对分布具有厚尾特征的极端金融风险无法有效度量。本文采用极值理论(EVT)解决VaR方法的尾部度量不足问题,利用CVaR-EVT和BMM模型分析美国、香港股票市场和我国沪深两市指数18年的日收益数据,研究发现:(1)在95%置信区间及点估计中,分位数为99%的CVaR-EVT所揭示的极端风险优于VaR的估计值,且BMM方法为实施长期极端风险管理提供了有力的决策依据,其回报率受分段时区的影响,期间越长,风险估计值越高;(2)模型采用ML和BS方法统计估值显示,我国股票市场极端风险尾部估计值高于香港和美国市场,但是,国内市场逐步稳定,并呈现出跟进国际市场且差距缩小的发展趋势。

The Basel Accord Ⅱ has suggested that Value-at-Risk ( VaR) is the advanced measure model of Risk management and it is widely used by the financial institutions and regulation bodies worldwide. Howerver,VaR doesn't satisfy the coherent risk measure,especially it couldn't be used to measure the fat-tail distribution of risk such as the operational risk and extreme financial risks. So,this paper takes the Extreme Value Theory(EVT)and Conditional Value at Risk (CVaR),and return level of Block Maxima Models(BMM) comparing with VaR to measure extreme quantile estimate of financial risk on Standard and Poor'scomposite index (SPI),Hang Seng index(HSI),Shanghai composite index(SHI) and Shenzhen sub-index ( SZI) in stock markets. The findings are: (1) The estimate values of CVaR-EVT at 99% quantile on Point estimate and 95% maximum likelihood(ML) and bootstrap(Bs) confidence intervals are better than VaR by using Peak Over Threshold (POT),while BMM shows that the higher the return level (daily loss) is,the longer the period of block is; and (2) The Extreme risk in Chinese stock market is far higher than international stock markets,but it is becoming stable and convergent as its counterparts by using ML and BS to estimate the daily loss of models from 1991 to 2008.