GARCH模型中基于卡尔曼滤波的似然函数数值优化

Q3 Mathematics Mathematical Modeling and Computing Pub Date : 2022-01-01 DOI:10.23939/mmc2022.03.599

M. Benmoumen

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引用次数: 0

摘要

在这项工作中，我们提出了一种新的GARCH(p,q)模型参数估计算法。结果表明，该算法在估计给定模型的真实参数值方面是非常可靠的。它结合了极大似然法、卡尔曼滤波算法和模拟退火(SA)方法，不需要对初始值做任何假设。仿真结果表明，该算法是可靠的，具有良好的应用前景。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Numerical optimization of the likelihood function based on Kalman filter in the GARCH models

In this work, we propose a new estimate algorithm for the parameters of a GARCH(p,q) model. This algorithm turns out to be very reliable in estimating the true parameter’s values of a given model. It combines maximum likelihood method, Kalman filter algorithm and the simulated annealing (SA) method, without any assumptions about initial values. Simulation results demonstrate that the algorithm is liable and promising.

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来源期刊

Mathematical Modeling and Computing Computer Science-Computational Theory and Mathematics

CiteScore

1.60

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0.00%

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