On a dynamic mixture GARCH model

Xixin CHENG, Philip L. H. YU, Wai Keung LI

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

This paper proposes a new mixture GARCH model with a dynamic mixture proportion. The mixture Gaussian distribution of the error can vary from time to time. The Bayesian Information Criterion and the EM algorithm are used to estimate the number of parameters as well as the model parameters and their standard errors. The new model is applied to the S&P500 Index and Hang Seng Index and compared with GARCH models with Gaussian error and Student's t error. The result shows that the IGARCH effect in these index returns could be the result of the mixture of one stationary volatility component with another non‐stationary volatility component. The VaR based on the new model performs better than traditional GARCH‐based VaRs, especially in unstable stock markets. Copyright © 2008 John Wiley & Sons, Ltd.
Original languageEnglish
Pages (from-to)247-265
JournalJournal of Forecasting
Volume28
Issue number3
DOIs
Publication statusPublished - Apr 2009

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GARCH Model
Mixture Model
Volatility
Mixture Distribution
Bayesian Information Criterion
EM Algorithm
Standard error
Stock Market
Gaussian distribution
Proportion
Unstable
Vary
Model
Estimate
GARCH model
Students
Volatility components

Citation

Cheng, X., Yu, P. L. H., & Li, W. K. (2009). On a dynamic mixture GARCH model. Journal of Forecasting, 28(3), 247-265. doi: 10.1002/for.1093

Keywords

  • Mixture time series
  • GARCH
  • Statistical arbitrage