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Extreme Risk Value and Dependence Structure of the China Securities Index 300

Author

Listed:
  • Terence Tai-Leung Chong

    (The Chinese University of Hong Kong)

  • Yue Ding

    (The Chinese University of Hong Kong)

  • Tianxiao Pang

    (School of Mathematical Sciences, Zhejiang University)

Abstract

A time-varying copulas–conditional value at risk (CVaR) model is estimated to analyze the extreme risk value and dependence structure of the China Securities Index 300 (CSI 300) and index futures portfolios. The goodness-of-fit test as well as the in-sample and out-of-sample tests show that time-varying copulas outperform constant copulas. Specifically, the Student's t, normal, Plackett, and the rotated Gumbel copulas outperform the rotated Clayton copulas.

Suggested Citation

  • Terence Tai-Leung Chong & Yue Ding & Tianxiao Pang, 2017. "Extreme Risk Value and Dependence Structure of the China Securities Index 300," Economics Bulletin, AccessEcon, vol. 37(1), pages 520-529.
    • Handle: RePEc:ebl:ecbull:eb-16-00292
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    References listed on IDEAS

    as
    1. Creal, Drew & Koopman, Siem Jan & Lucas, André, 2011. "A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(4), pages 552-563.
    2. Lorán Chollete & Andréas Heinen & Alfonso Valdesogo, 2009. "Modeling International Financial Returns with a Multivariate Regime-switching Copula," Journal of Financial Econometrics, Oxford University Press, vol. 7(4), pages 437-480, Fall.
    3. Patton, Andrew J., 2012. "A review of copula models for economic time series," Journal of Multivariate Analysis, Elsevier, vol. 110(C), pages 4-18.
    4. Douglas Rivers & Quang Vuong, 2002. "Model selection tests for nonlinear dynamic models," Econometrics Journal, Royal Economic Society, vol. 5(1), pages 1-39, June.
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    More about this item

    Keywords

    CVaR model; Time-varying copulas;

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables

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