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Joint modeling of cointegration and conditional heteroscedasticity with applications

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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.

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Authors and Affiliations

  1. Department of Applied Mathematics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China

    Heung Wong

  2. Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong, China

    W. K. Li

  3. Department of Mathematics, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China

    Shiqing Ling

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  1. Heung Wong
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  2. W. K. Li
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  3. Shiqing Ling
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Wong, H., Li, W.K. & Ling, S. Joint modeling of cointegration and conditional heteroscedasticity with applications. Ann Inst Stat Math 57, 83–103 (2005). https://doi.org/10.1007/BF02506881

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  • DOI: https://doi.org/10.1007/BF02506881

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