中文摘要：

在严格地静止的过程的假设下面，这篇论文建议一个非参量的模型为风险时间系列测试峭度和有条件的峭度。我们把这个方法用于 S&P500 索引和上海复合指数的每日的回来，并且为验证介绍模型的效率模仿 GARCH 数据。我们的结果显示风险系列分发重重地被跟踪，但是历史的信息能使它的未来分发跟踪光。然而，远未来分发的尾巴是历史的数据影响的很少。

英文摘要：

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.