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Multivariate Fractionally Integrated APARCH Modeling of Stock Market Volatility: A multi-country study

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  • Christian Conrad

    ()
    (University of Heidelberg, Department of Economics)

  • Menelaos Karanasos

    ()
    (Brunel University, Dept. of Economics and Finance)

  • Ning Zeng

    (Brunel University, Dept. of Economics and Finance)

Abstract

Tse (1998) proposes a model which combines the fractionally integrated GARCH formulation of Baillie, Bollerslev and Mikkelsen (1996) with the asymmetric power ARCH speci¯cation of Ding, Granger and Engle (1993). This paper analyzes the applicability of a multivariate constant conditional correlation version of the model to national stock market returns for eight countries. We ¯nd this multivariate speci¯cation to be generally applicable once power, leverage and long-memory e®ects are taken into consideration. In addition, we ¯nd that both the optimal fractional di®erencing parameter and power transformation are remarkably similar across countries. Out-of-sample evidence for the superior forecasting ability of the multivariate FIAPARCH framework is provided in terms of forecast error statistics and tests for equal forecast accuracy of the various models.

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Bibliographic Info

Paper provided by University of Heidelberg, Department of Economics in its series Working Papers with number 0472.

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Length: 31 pages
Date of creation: Jul 2008
Date of revision: Jul 2008
Handle: RePEc:awi:wpaper:0472

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Keywords: Asymmetric Power ARCH; Fractional integration; Stock returns; Volatility forecast evaluation;

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Cited by:
  1. Kumar, Dilip, 2014. "Long range dependence in the high frequency USD/INR exchange rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 134-148.
  2. Charfeddine, Lanouar & Ajmi, Ahdi Noomen, 2013. "The Tunisian stock market index volatility: Long memory vs. switching regime," Emerging Markets Review, Elsevier, vol. 16(C), pages 170-182.
  3. Conrad, Christian & Rittler, Daniel & Rotfuß, Waldemar, 2010. "Modeling and Explaining the Dynamics of European Union Allowance Prices at High-Frequency," Working Papers 0497, University of Heidelberg, Department of Economics.
  4. Walid Chkili & Shawkat Hammoudeh & Duc Khuong Nguyen, 2014. "Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory," Working Papers 2014-389, Department of Research, Ipag Business School.
  5. Menelaos Karanasos & Alexandros Paraskevopoulos & Faek Menla Ali & Michail Karoglou & Stavroula Yfanti, 2014. "Modelling Returns and Volatilities During Financial Crises: a Time Varying Coefficient Approach," Papers 1403.7179, arXiv.org.
  6. Pawel Janus & Siem Jan Koopman & Andr� Lucas, 2011. "Long Memory Dynamics for Multivariate Dependence under Heavy Tails," Tinbergen Institute Discussion Papers 11-175/2/DSF28, Tinbergen Institute.
  7. Walid Chkili & Chaker Aloui & Duc Khuong Nguyen, 2014. "Instabilities in the relationships and hedging strategies between crude oil and US stock markets: do long memory and asymmetry matter?," Working Papers 2014-549, Department of Research, Ipag Business School.
  8. Pawel Janus & Siem Jan Koopman & Andr� Lucas, 2011. "Long Memory Dynamics for Multivariate Dependence under Heavy Tails," Tinbergen Institute Discussion Papers 11-175/2/DSF28, Tinbergen Institute.
  9. M. Karanasos & S. Schurer, 2006. "Is the relationship between ination and its uncertainty linear?," Computing in Economics and Finance 2006 463, Society for Computational Economics.
  10. Dimitriou, Dimitrios & Kenourgios, Dimitris, 2013. "Financial crises and dynamic linkages among international currencies," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 26(C), pages 319-332.
  11. Dimitriou, Dimitrios & Kenourgios, Dimitris & Simos, Theodore, 2013. "Global financial crisis and emerging stock market contagion: A multivariate FIAPARCH–DCC approach," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 46-56.
  12. Rittler, Daniel, 2012. "Price discovery and volatility spillovers in the European Union emissions trading scheme: A high-frequency analysis," Journal of Banking & Finance, Elsevier, vol. 36(3), pages 774-785.

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