遗传门限GARCH模型及其应用研究

Research on Genetic Threshold GARCH Model and Its Application

在金融时间序列中,GARCH模型能够较好地描叙其异方差性,而门限自回归(TAR)模型能准确地刻画序列的非线性规律。结合两者优点建立了门限GARCH模型,并利用遗传算法,选用2003年1月2日到2011年1月10日共1 945个上市日上证综合指数进行了实证分析。由实证分析结果中与GARCH模型比较发现,门限GARCH模型拟合及预测精度在处理数据上有优势,更适合描叙非线性规律;而且,由于随机性的存在,使得所建模的模型不一,丰富多变,便于决策者从中选取合适的模型进行时序分析和金融解释。

In the analysis of financial time series,the GARCH Model is suitable for depicting the conditional heteroscedasticity and the threshold model(threshold autoregressive or threshold ARMA model) is able to quite accurately describe the non-linear rules of series.The GATCH model in this paper is built by the combination of the advantages of these two with the choice of genetic algorithms.By the comparison of the results of empirical research of the Shanghai Composite Index through a selected total of 1945 launch dates from January 2nd,2003 to January 10th,2011,this paper has discovered that the threshold GARCH model fitting and prediction accuracy is of a slight advantage in the processing of data and therefore is quite suitable for depicting non-linear laws.In addition,as the existence of random makes the constructed model different,rich and varied,it is easy for the decision-maker to select the appropriate model to perform time series analysis and financial explanation.