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Contagion determination via copula and volatility threshold models

Author

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  • Veni Arakelian
  • Petros Dellaportas

Abstract

We develop threshold models that allow volatilities and copula functions or their association parameters to change across time. The number and location of the thresholds is assumed unknown. We use a Markov chain Monte Carlo strategy combined with Laplace estimates that evaluate the required marginal densities for a given model. We apply our methodology to financial time series, emphasizing the ability to improve estimates of risk characteristics, as well as measuring financial contagion by inspecting simultaneous changes of dependence and volatility structures.

Suggested Citation

  • Veni Arakelian & Petros Dellaportas, 2012. "Contagion determination via copula and volatility threshold models," Quantitative Finance, Taylor & Francis Journals, vol. 12(2), pages 295-310, October.
    • Handle: RePEc:taf:quantf:v:12:y:2012:i:2:p:295-310
    DOI: 10.1080/14697680903410023
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    Cited by:

    1. Raffaella Calabrese & Johan A. Elkink & Paolo S. Giudici, 2017. "Measuring bank contagion in Europe using binary spatial regression models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(12), pages 1503-1511, December.
    2. Guidolin, Massimo & Hansen, Erwin & Pedio, Manuela, 2019. "Cross-asset contagion in the financial crisis: A Bayesian time-varying parameter approach," Journal of Financial Markets, Elsevier, vol. 45(C), pages 83-114.
    3. Nina Tessler & Itzhak Venezia, 2022. "A multicountry measure of comovement and contagion in international markets: definition and applications," Review of Quantitative Finance and Accounting, Springer, vol. 58(4), pages 1307-1330, May.
    4. Xiaoning Kang & Xinwei Deng & Kam‐Wah Tsui & Mohsen Pourahmadi, 2020. "On variable ordination of modified Cholesky decomposition for estimating time‐varying covariance matrices," International Statistical Review, International Statistical Institute, vol. 88(3), pages 616-641, December.
    5. Holger Fink & Yulia Klimova & Claudia Czado & Jakob Stober, 2016. "Regime switching vine copula models for global equity and volatility indices," Papers 1604.05598, arXiv.org.
    6. Aristeidis Samitas & Elias Kampouris & Zaghum Umar, 2022. "Financial contagion in real economy: The key role of policy uncertainty," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 1633-1682, April.
    7. Thijs Markwat, 2014. "The rise of global stock market crash probabilities," Quantitative Finance, Taylor & Francis Journals, vol. 14(4), pages 557-571, April.
    8. Ye, Wuyi & Li, Mingge & Wu, Yuehua, 2022. "A novel estimation of time-varying quantile correlation for financial contagion detection," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    9. Holger Fink & Yulia Klimova & Claudia Czado & Jakob Stöber, 2017. "Regime Switching Vine Copula Models for Global Equity and Volatility Indices," Econometrics, MDPI, vol. 5(1), pages 1-38, January.

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