Comparative analysis of three MCMC methods for estimating GARCH models

GARCH model have been considered as an important and widely employed tool to analyse and forecast variance of the financial market. This study develops three MCMC methods, namely adaptive random walk Metropolis, Hamiltonian Monte Carlo, and Independence Chain Metropolis-Hastings algorithms. It is us...

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Bibliographic Details
Published in:IOP conference series. Materials Science and Engineering 2018-09, Vol.403 (1), p.12061
Main Author: Nugroho, D B
Format: Article
Language:English
Subjects:
Algorithms
Autoregressive models
Chains
Economic forecasting
Monte Carlo simulation
Random walk
Securities markets
Stochastic models
Variance analysis
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Summary:GARCH model have been considered as an important and widely employed tool to analyse and forecast variance of the financial market. This study develops three MCMC methods, namely adaptive random walk Metropolis, Hamiltonian Monte Carlo, and Independence Chain Metropolis-Hastings algorithms. It is used to estimate GARCH (1,1) under Normal and Student-t distributions for conditional return distribution. Results on real financial market data indicate that the best method is the approach based on the Independence Chain Metropolis-Hastings algorithm.
ISSN:1757-8981
1757-899X
1757-899X
DOI:10.1088/1757-899X/403/1/012061