Joint modeling of cointegration and conditional heteroscedasticity with applications

Heung WONG, Wai Keung LI, Shiqing LING

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

A cointegrated vector AR-GARCH time series model is introduced. Least squares estimator, full rank maximum likelihood estimator (MLE), and reduced rank MLE of the model are presented. Monte Carlo experiments are conducted to illustrate the finite sample properties of the estimators. Its applicability is then demonstrated with the modeling of international stock indices and exchange rates. The model leads to reasonable financial interpretations. Copyright © 2005 The Institute of Statistical Mathematics.
Original languageEnglish
Pages (from-to)83-103
JournalAnnals of the Institute of Statistical Mathematics
Volume57
Issue number1
DOIs
Publication statusPublished - Mar 2005

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Conditional Heteroscedasticity
Joint Modeling
Cointegration
Maximum Likelihood Estimator
Reduced Rank
Stock Index
GARCH Model
Monte Carlo Experiment
Least Squares Estimator
Exchange rate
Time Series Models
Estimator
Modeling
Model
Interpretation

Citation

Wong, H., Li, W. K., & Ling, S. (2005). Joint modeling of cointegration and conditional heteroscedasticity with applications. Annals of the Institute of Statistical Mathematics, 57(1), 83-103. doi: 10.1007/BF02506881

Keywords

  • Cointegration
  • Full rank maximum likelihood estimator
  • Least squares estimator
  • Partially nonstationary
  • Reduced rank MLE
  • Vector AR-GARCH model