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Student-t distribution based VAR-MGARCH: an application of the DCC model on international portfolio risk management

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  • Yuan-Hung Hsu Ku

Abstract

Significant second-moment transmission effects and obvious time-varying patterns of correlation coefficients among major equity and currency markets in the US, Japan and the UK are found to exist. Such observations inspire the time-varying setting of dynamic conditional correlation coefficients in MGARCH models. On the other hand, the multivariate Student-t distribution is suitable for analysing the visible leptokurtosis that is common in financial markets. Both are important for international portfolio risk management. Thus, a comparison on the hedging efficiency of hypothetical portfolios consisting of stock and currency future positions is conducted in order to justify the multivariate Student-t distribution based on the DCC-MGARCH model.

Suggested Citation

  • Yuan-Hung Hsu Ku, 2008. "Student-t distribution based VAR-MGARCH: an application of the DCC model on international portfolio risk management," Applied Economics, Taylor & Francis Journals, vol. 40(13), pages 1685-1697.
    • Handle: RePEc:taf:applec:v:40:y:2008:i:13:p:1685-1697
    DOI: 10.1080/00036840600892894
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    Cited by:

    1. Julyerme M. Tonin & Carlos M. R. Vieira & Rui M. de Sousa Fragoso & João G. Martines Filho, 2020. "Conditional correlation and volatility between spot and futures markets for soybean and corn," Agribusiness, John Wiley & Sons, Ltd., vol. 36(4), pages 707-724, October.
    2. Lahrech, Abdelmounaim & Sylwester, Kevin, 2013. "The impact of NAFTA on North American stock market linkages," The North American Journal of Economics and Finance, Elsevier, vol. 25(C), pages 94-108.
    3. Mensi, Walid & Vo, Xuan Vinh & Kang, Sang Hoon, 2021. "Precious metals, oil, and ASEAN stock markets: From global financial crisis to global health crisis," Resources Policy, Elsevier, vol. 73(C).
    4. Julien Chevallier, 2012. "Time-varying correlations in oil, gas and CO 2 prices: an application using BEKK, CCC and DCC-MGARCH models," Applied Economics, Taylor & Francis Journals, vol. 44(32), pages 4257-4274, November.
    5. Nicolas Koch, 2014. "Dynamic linkages among carbon, energy and financial markets: a smooth transition approach," Applied Economics, Taylor & Francis Journals, vol. 46(7), pages 715-729, March.
    6. Yildirim, Ramazan & Masih, Mansur, 2018. "Investigating International Portfolio Diversification Opportunities for the Asian Islamic Stock Market Investors," MPRA Paper 90281, University Library of Munich, Germany.
    7. Park, Beum-Jo, 2022. "The COVID-19 pandemic, volatility, and trading behavior in the bitcoin futures market," Research in International Business and Finance, Elsevier, vol. 59(C).
    8. Yildirim, Ramazan & Masih, A. Mansur M., 2014. "The Effect of Recent Financial Crisis over Global Portfolio Diversification Opportunities – Empirical Evidence A Comparative Multivariate GARCH-DCC, MODWT and Wavelet Correlation Analysis," MPRA Paper 58269, University Library of Munich, Germany.
    9. BONGA-BONGA, Lumengo & NLEYA, Lebogang, 2018. "Assessing Portfolio Market Risk in the BRICS Economies: Use of Multivariate GARCH Models," Economia Internazionale / International Economics, Camera di Commercio Industria Artigianato Agricoltura di Genova, vol. 71(2), pages 87-128.
    10. Hung, Jui-Cheng & Yi-Hsien Wang, & Chang, Matthew C. & Shih, Kuang-Hsun & Hsiu-Hsueh Kao,, 2011. "Minimum variance hedging with bivariate regime-switching model for WTI crude oil," Energy, Elsevier, vol. 36(5), pages 3050-3057.
    11. A. Maghyereh & B. Awartani, 2012. "Return and volatility spillovers between Dubai financial market and Abu Dhabi Stock Exchange in the UAE," Applied Financial Economics, Taylor & Francis Journals, vol. 22(10), pages 837-848, May.
    12. Rui Li & Saralees Nadarajah, 2020. "A review of Student’s t distribution and its generalizations," Empirical Economics, Springer, vol. 58(3), pages 1461-1490, March.

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