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Dynamic Copulas for Monotonic Dependence Change in Time Series

Author

Listed:
  • Antoine Bergeron

    (Université de Sherbrooke)

  • Pierre Dutilleul

    (McGill University)

  • Carole Beaulieu

    (Université de Sherbrooke)

  • Taoufik Bouezmarni

    (Université de Sherbrooke)

Abstract

A particular class of dynamic bivariate copulas, monotonically increasing or decreasing, is studied for modeling dependence in a time series. As increasing or decreasing functions of time, the copula parameters are estimated via their own parameters. The method of Inference Functions for Margins (IFM), adapted from the static case, is applied for this purpose. Simulations are used to assess the detectability of an increase or a decrease in dependence over time for five copula functions. In an application to wheat prices (source: Food and Agriculture Organization), information criteria are used to select the best copula function, and the dynamic copulas are shown to represent an improvement over static copulas for several of the time series.

Suggested Citation

  • Antoine Bergeron & Pierre Dutilleul & Carole Beaulieu & Taoufik Bouezmarni, 2022. "Dynamic Copulas for Monotonic Dependence Change in Time Series," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(2), pages 683-693, November.
    • Handle: RePEc:spr:sankhb:v:84:y:2022:i:2:d:10.1007_s13571-022-00281-6
    DOI: 10.1007/s13571-022-00281-6
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    References listed on IDEAS

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    1. Nasri, Bouchra R. & Rémillard, Bruno N. & Bouezmarni, Taoufik, 2019. "Semi-parametric copula-based models under non-stationarity," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 347-365.
    2. Chen, Xiaohong & Fan, Yanqin, 2006. "Estimation and model selection of semiparametric copula-based multivariate dynamic models under copula misspecification," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 125-154.
    3. Joe, Harry, 2005. "Asymptotic efficiency of the two-stage estimation method for copula-based models," Journal of Multivariate Analysis, Elsevier, vol. 94(2), pages 401-419, June.
    4. Rémillard, Bruno & Papageorgiou, Nicolas & Soustra, Frédéric, 2012. "Copula-based semiparametric models for multivariate time series," Journal of Multivariate Analysis, Elsevier, vol. 110(C), pages 30-42.
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