Nonparametric inferences for kurtosis and conditional kurtosis  

作　　者：谢潇衡 何幼桦 

机构地区：[1]Department of Mathematics,College of Sciences,Shanghai University

出　　处：《Journal of Shanghai University(English Edition)》2009年第3期225-232,共8页上海大学学报（英文版）

基　　金：supported by the National Natural Science Foundation of China (Grant No.60773081);the Key Project of Shanghai Municipality (Grant No.S30104)

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.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.

关 键 词：conditional probability density function （PDF） kernel estimate KURTOSIS conditional kurtosis heavy tail 

分 类 号：F224[经济管理—国民经济]