运筹与管理2018，Vol.27Issue(4)：153-161,9.DOI:10.12005/orms.2018.0097
基于ARMA-GARCH-SN模型的沪深300股指期货日内波动率研究与预测
Volatility Research and Forecast on CSI 300 Index Futures by Using the ARMA-GARCH-SN Model
摘要
Abstract
By using the high frequency data(two deals per second)of five trading days in CSI 300 index futures, this paper mainly studies the intraday volatility characteristics of the CSI 300 stoch index futures and forecasts the intraday volatility.The results show that the intraday yield of high frequency stoch index futures has obvious volatility aggregation and AutoRegressive Conditional Heteroscedasticity(ARCH)effect,but there is no peah and fat-tailed phenomenon.The distribution of the return series accords with a shewed normal distribution.So the optimal ARMA-GARCH-SN model is established, and the adequacy of the model fit is verified.The fitting results show that ARMA(1,2)-GARCH(1,1)-SN can well describe the characteristics of ultrahigh frequency intraday volatility of CSI 300 index futures,the impact on the conditional variance has a strong persistence,and long memory effect is found in the intraday volatility.Finally,two steps of the return rate and the volatility are predicted by the bootstrap method.We also use the rolling regression forecasting method to do the in-sample pre-diction.The prediction results show that the volatility prediction can reflect the intraday volatility characteristics of the stoch index futures commendably.关键词
高频数据/ARMA-GARCH-SN模型/沪深300股指期货/日内模式/预测Key words
high-frequency data/ARMA-GARCH-SN model/CSI 300 index futures/intraday pattern/forecast分类
管理科学引用本文复制引用
王苏生,王俊博,许桐桐,余臻..基于ARMA-GARCH-SN模型的沪深300股指期货日内波动率研究与预测[J].运筹与管理,2018,27(4):153-161,9.基金项目
深圳市科技创新委员会知识创新计划项目"基于高频数据的证券市场动力学及其应用研究"资助(JCYJ20140417173156101) （JCYJ20140417173156101）
