Markov switching component GARCH model: Stability and forecasting

被引：6

作者：

Alemohammad, N. ^{[1
]}

Rezakhah, S. ^{[1
]}

Alizadeh, S. H. ^{[2
]}

机构：

[1] Amirkabir Univ Technol, Fac Math & Comp Sci, Tehran, Iran

[2] Islamic Azad Univ, Qazvin Branch, Dept Comp Engn & Informat Technol, Qazvin, Iran

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2016年 / 45卷 / 15期

关键词：

Bayesian inference; Component GARCH models; Forecasting; GARCH models; Griddy Gibbs sampling; Markov process; Stability; 60J10; 62M10; 62F15; VOLATILITY;

D O I：

10.1080/03610926.2013.841934

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

This paper introduces an extension of the Markov switching GARCH model where the volatility in each state is a convex combination of two different GARCH components with time varying weights. This model has the dynamic behavior to capture the variants of shocks. The asymptotic behavior of the second moment is investigated and an appropriate upper bound for it is evaluated. Using the Bayesian method via Gibbs sampling algorithm, a dynamic method for the estimation of the parameters is proposed. Finally, we illustrate the efficiency of the model by simulation and also by considering two different set of empirical financial data. We show that this model provides much better forecasts of the volatility than the Markov switching GARCH model.

引用

页码：4332 / 4348

页数：17

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