具有定期变化扰动的GARCH(1,1)模型参数评估
首发时间:2013-04-15
摘要:现实中的金融时间序列存在严重的波动集聚性,即大幅波动往往集中在某一时段,小幅波动往往集中在另一时段。而GARCH类模型能较好地描述金融时间序列波动的动态变化特征,捕捉其聚类和异方差现象。本文首先构建最大似然函数的变形,然后在此基础上来评估带有指数为 > 0定期变化分布扰动的GARCH(1,1)模型参数,最后进一步证明所构造评估的可信性和渐近正态分布性。
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PARAMETERS ESTIMATION OF GARCH(1,1) PROCESS WITH THE ERRORS HAVING REGULARLY VARYING DISTRIBUTION
Abstract:In practice, Financial Time Series have serious volatility cluster, that is large volatility tend to be concentrated in a certain period of time, and small volatility tend to be concentrated in another period of time. While GARCH models can well describe the dynamic changes of the volatility of financial time series, and capture the cluster and heteroscedasticity phenomena. At the beginning of this paper, the modification of maximum likelihood function has been constructed as the theoretical basis of this study. Secondly, estimate parameters of GARCH(1,1) model with residuals having regularly varying distributions with index > 0. Finally, the consistency and asymptotic normality of the estimates constructed are further proved.
Keywords: GARCH process errors regularly varying distribution parameter estimation
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