Financial Forecasting: Comparative Performance of Volatility Models in Chinese Stock Markets

摘要：

This paper presents empirical tests and comparisons of GARCH family models and nonparametric models for predicting the volatility of Chinese stock markets. Since the volatility of financial asset returns often exhibits asymmetry, fat-tails and long-range memory property in the stock market, nonparametric models maybe have better performance. By the criteria of mean absolute forecast error (MAE), mean squared error (RMSE) and the hit rate (HR), empirical results show that support vector machine (SVM), a new nonparametric tool for regression estimation, outperforms GARCH family models (GARCH, EGARCH, FIGARCH), moving average and neural network in improving predictive accuracy.

关键词： Volatility forecasting GARCH family models Moving average model Neural network Support vector machine

作者: Jingfeng Xu Jian Liu Haijian Zhao

作者单位: China Institute for Actuarial Science Central University of Finance and Economics Beijing, China School of Banking and Finance University of International Business and Economics Beijing, China Center for Combinatorics Nankai University Tianjin China

会议类型: 国际会议

会议名称: The Fourth International Joint Conference on Computational Science and Optimization(第四届计算科学与优化国际大会 CSO 2011)

会议地点: 昆明、丽江

会议语种:英文

页码: 1220-1225

在线出版日期: 2011-04-15（万方平台首次上网日期，不代表论文的发表时间）