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Long Memory and Asymmetry for Matrix-Exponential Dynamic Correlation Processes

Author

Listed:
  • Asai Manabu

    (Faculty of Economics, Soka University, 1-236 Tangi-machi, Hachioji, Tokyo 192-8577, Japan)

  • So Mike K.P.

    (Department of Information Systems, Business Statistics and Operations Management, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong)

Abstract

We propose a fractionally integrated matrix-exponential dynamic conditional correlation (FIEDCC) model to capture the asymmetric effects and long- and short-range dependence of a correlation process. We also propose employing an inverse Wishart distribution for the disturbance of a covariance structure, which gives an alternative interpretation for a multivariate t conditional distribution. Using the inverse Wishart distribution, we present a three-step procedure to obtain initial values for estimating a high-dimensional conditional covariance model with a multivariate t distribution. We investigated the finite-sample properties of the ML estimator. Empirical results for nine assets from chemical firms, banks, and oil and gas producers in the US indicate that the new FIEDCC model outperforms the other dynamic correlation models for the AIC and BIC and for forecasting value-at-risk thresholds. Furthermore, the new FIEDCC model captures the stronger connection among the nine assets for the period right after the global financial crisis.

Suggested Citation

  • Asai Manabu & So Mike K.P., 2015. "Long Memory and Asymmetry for Matrix-Exponential Dynamic Correlation Processes," Journal of Time Series Econometrics, De Gruyter, vol. 7(1), pages 1-26, January.
  • Handle: RePEc:bpj:jtsmet:v:7:y:2015:i:1:p:26:n:2
    DOI: 10.1515/jtse-2013-0012
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    References listed on IDEAS

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    Cited by:

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    2. Asai, Manabu & McAleer, Michael, 2015. "Forecasting co-volatilities via factor models with asymmetry and long memory in realized covariance," Journal of Econometrics, Elsevier, vol. 189(2), pages 251-262.
    3. Ilya Archakov & Peter Reinhard Hansen & Asger Lunde, 2020. "A Multivariate Realized GARCH Model," Papers 2012.02708, arXiv.org.
    4. Aida Karmous & Heni Boubaker & Lotfi Belkacem, 2021. "Forecasting Volatility for an Optimal Portfolio with Stylized Facts Using Copulas," Computational Economics, Springer;Society for Computational Economics, vol. 58(2), pages 461-482, August.
    5. Ilya Archakov & Peter Reinhard Hansen, 2021. "A New Parametrization of Correlation Matrices," Econometrica, Econometric Society, vol. 89(4), pages 1699-1715, July.
    6. Karmous, Aida & Boubaker, Heni & Belkacem, Lotfi, 2019. "A dynamic factor model with stylized facts to forecast volatility for an optimal portfolio," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).

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