|
|||||
|
|
Nonparametric inferences for kurtosis and conditional kurtosis |
|
| Authors: | Xiao-heng Xie You-hua He |
| Institution: | Department of Mathematics, College of Sciences, Shanghai University, Shanghai 200444, P. R. China |
| Abstract: | Under the assumption of strictly stationary process, this paper proposes a nonparametric model to test the kurtosis and conditional kurtosis for risk time series. We apply this method to the daily returns of S&P500 index and the Shanghai Composite Index, and simulate GARCH data for verifying the efficiency of the presented model. Our results indicate that the risk series distribution is heavily tailed, but the historical information can make its future distribution light-tailed. However the far future distribution's tails are little affected by the historical data. |
| Keywords: | conditional probability density function (PDF) kernel estimate kurtosis conditional kurtosis heavy tail |
| 本文献已被 CNKI 维普 万方数据 SpringerLink 等数据库收录！ | |
| 点击此处可从《上海大学学报(英文版)》浏览原始摘要信息 | |
| 点击此处可从《上海大学学报(英文版)》下载免费的PDF全文 | |