GARCH模型优化的自适应进化计算方法

A. Swain
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引用次数: 0

摘要

本文采用一种有效的基于混合进化计算(EC)的方法对GARCH(1,1)模型的参数估计进行了研究。通过最大化非线性对数似然函数来估计这些参数。此外,在本研究中,通过与MATLAB、EViews、Excel等现成软件包中常用的Marquardt、BHHH等优化方法的对比研究,验证了基于EC方法的有效性。本文考虑了两种基于ec的方法,一种是快速进化规划方法,另一种是混合进化计算方法,以供探索和后续讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Self-Adaptive Evolutionary Computation Methods for GARCH Model Optimization
This paper deals with the parameter estimation of popular GARCH(1,1) model using an effective hybrid evolutionary computation (EC) based method. These parameters are estimated by maximizing the nonlinear log-likelihood function. Further, in this study, the effectiveness of the EC based methods is verified through a comparative study with that of popular optimization methods such as Marquardt, BHHH etc. incorporated in the ready made software packages like MATLAB, EViews and Excel. Here, two EC-based methods, one a fast evolutionary programming method and the other one, a hybrid evolutionary computation method are considered for exploration and subsequent discussion.
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