On a mixture GARCH time-series model

Zhiqiang ZHANG, Wai Keung LI, Kam Chuen YUEN

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23 Citations (Scopus)

Abstract

Recently, there has been a lot of interest in modelling real data with a heavy‐tailed distribution. A popular candidate is the so‐called generalized autoregressive conditional heteroscedastic (GARCH) model. Unfortunately, the tails of GARCH models are not thick enough in some applications. In this paper, we propose a mixture generalized autoregressive conditional heteroscedastic (MGARCH) model. The stationarity conditions and the tail behaviour of the MGARCH model are studied. It is shown that MGARCH models have tails thicker than those of the associated GARCH models. Therefore, the MGARCH models are more capable of capturing the heavy‐tailed features in real data. Some real examples illustrate the results. Copyright © 2006 Blackwell Publishing Ltd.
Original languageEnglish
Pages (from-to)577-597
JournalJournal of Time Series Analysis
Volume27
Issue number4
DOIs
Publication statusPublished - Jul 2006

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Heteroscedastic Model
Conditional Model
Time Series Models
Time series
Tail
Tail Behavior
Heavy-tailed Distribution
Stationarity
Time series models
Conditional model
Modeling

Citation

Zhang, Z., Li, W. K., & Yuen, K. C. (2006). On a mixture GARCH time-series model. Journal of Time Series Analysis, 27(4), 577-597. doi: 10.1111/j.1467-9892.2006.00467.x

Keywords

  • GARCH
  • MGARCH
  • Tochastic difference equation
  • Tail behaviour
  • Volatility clustering