用GARCH模型预测股票指数波动率

用GARCH模型预测股票指数波动率

目录

Abstract .......................................................................................................................................................

1.引言 .........................................................................................................................................................

2.数据 .........................................................................................................................................................

3.方法 .........................................................................................................................................................

3.1．模型的条件平均 ...............................................................................................................................

3.2. 模型的条件方差 ................................................................................................................................

3.3 预测方法 .............................................................................................................................................

3.4 业绩预测评价 .....................................................................................................................................

4.实证结果和讨论 .....................................................................................................................................

5.结论 ......................................................................................................................................................... References...................................................................................................................................................

Abstract

This paper is designed to make a comparison between the daily conditional variance through seven GRACH models. Through this comparison, to test whether advanced GARCH models are outperforming the standard GARCH models in predicting the variance of stock index. The database of this paper is the statistics of 21 stock indices around the world from 1 January to 30 November 2013. By forecasting one –step-ahead conditional variance within different models, then compare the results within multiple statistical tests. Throughout the tests, it is found that the standard GARCH model outperforms the more advanced GARCH models, and recommends the best one-step-ahead method to forecast of the daily conditional variance. The results are to strengthen the performance evaluation criteria choices; differentiate the market condition and the data-snooping bias.

This study impact the data-snooping problem by using an extensive cross-sectional data establish and the advanced predictive ability test. Furthermore, it includes a 13 years’ period sample set, which is relatively long for the unpredictability forecasting studies. It is part of the earliest attempts to inspect the impact of the market condition on the forecasting performance of GARCH models. This study allows for a great choice of parameterization in the GARCH