Modeling Extreme Risk in Stock Markets：The Influence of Data Dependence and Choice of Optimal Threshold Level on Extreme Value Models

• Main Authors: Anderson, 李坤峰
• Other Authors: 李政峰
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/94340679393918205019

碩士 === 國立高雄應用科技大學 === 金融資訊研究所 === 95 === Value at Risk is a widespread tool of risk management recently. It is a value that measures the worst loss of asset under the particular confidence level and possessed of period. Moreover, it is a quantile describing the tail of distribution of financial return series in statistics. In empirical literature, most of financial data have some properties such as fat tails and volatility clustering. Thus, estimating Value at Risk by conventional method may underestimate the quantile as a result of fat tails. We estimate Value at Risk in stock market by using extreme value theory combine with time series models and thereby compared the performance of the conditional Value at Risk with unconditional Value at Risk. In addition, we investigate optimal threshold level among past experience, method argued by hall and method proposed by Danielsson. Then we experiment backtesting on Value at Risk estimator and evaluate efficiency of estimator by LR statistic. We backtest mentioned above on eleven stock indexes：Dow Jones industrial average, Nasdaq, S&P 500, Nikkei 225, Hang Seng index, A-Share, SSE A-Share,FTSE 100,CAC 40, DAX and KOSPI index. Our finding reveals that conditional Value at Risk fitted ARMA(p,q)-GARCH(1,1) performs better than unconditional Value at Risk, and extreme value theory performed better than traditional method. Furthermore, our empirical result displays the performance of conditional Value at Risk which threshold level is decided by Hall1990 and Danielsson1997, indeed improve on model which threshold level is decided by past experience.