Estimating the Density Tail Index for Financial Time Series
通过蒙特卡洛模拟发现，基于独立同分布假设的尾部指数估计精度在金融数据（存在长程依赖）中被严重高估，并展示了用股票收益率数据得到的修正结论。
The tail index of a density has been widely used as an indicator of the probability of getting a large deviation in a random variable. Most of the theory underlying popular estimators of it assume that the data are independently and identically distributed (i.i.d.). However, many recent applications of the estimator have been to financial data, and such data tend to exhibit long-range dependence. We show, via Monte Carlo simulations, that conventional measures of the precision of the estimator, which are based on the i.i.d. assumption, are greatly exaggerated when such dependent data are used. This conclusion also has implications for estimates of the likelihood of getting some extreme values, and we illustrate the changed conclusions one would get using equity return data.