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Time varying vine copulas for multivariate returns (in Russian)

Author

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  • Oleg Groshev

    (Barclays Capital, Moscow, Russia)

Abstract

We analyze the multivariate distribution of financial returns using time varying conditional vine copulas. We present the d-Stage Maximum Likelihood (dSML) estimator which is shown to be not only consistent and asymptotically normal, but also more computationally attractive than the standard ML or Patton's 2SML. Using dSML, we fit vine copulas to returns of a portfolio on emerging market currencies.

Suggested Citation

  • Oleg Groshev, 2014. "Time varying vine copulas for multivariate returns (in Russian)," Quantile, Quantile, issue 12, pages 53-67, February.
    • Handle: RePEc:qnt:quantl:y:2014:i:12:p:53-67
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    References listed on IDEAS

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    More about this item

    Keywords

    multidimensional time series; vine copulas; dSML estimator; computational efficiency;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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