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Modeling Dependence Structure and Forecasting Market Risk with Dynamic Asymmetric Copula

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  • Mario Cerrato
  • John Crosby
  • Minjoo Kim
  • Yang Zhao

Abstract

We investigate the dynamic and asymmetric dependence structure between equity portfolios from the US and UK. We demonstrate the statistical significance of dynamic asymmetric copula models in modelling and forecasting market risk. First, we construct “high-minus-low" equity portfolios sorted on beta, coskewness, and cokurtosis. We find substantial evidence of dynamic and asymmetric de- pendence between characteristic-sorted portfolios. Second, we consider a dynamic asymmetric copula model by combining the generalized hyperbolic skewed t copula with the generalized autoregressive score (GAS) model to capture both the multivariate non-normality and the dynamic and asymmetric dependence between equity portfolios. We demonstrate its usefulness by evaluating the forecasting performance of Value-at-Risk and Expected Shortfall for the high-minus-low portfolios. From back- testing, we find consistent and robust evidence that our dynamic asymmetric copula model provides the most accurate forecasts, indicating the importance of incorporating the dynamic and asymmetric dependence structure in risk management.

Suggested Citation

  • Mario Cerrato & John Crosby & Minjoo Kim & Yang Zhao, 2015. "Modeling Dependence Structure and Forecasting Market Risk with Dynamic Asymmetric Copula," Working Papers 2015_15, Business School - Economics, University of Glasgow.
  • Handle: RePEc:gla:glaewp:2015_15
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    1. Fabrizio Cipollini & Robert F. Engle & Giampiero M. Gallo, 2017. "Copula–Based vMEM Specifications versus Alternatives: The Case of Trading Activity," Econometrics, MDPI, vol. 5(2), pages 1-24, April.
    2. Yuri Salazar Flores & Adán Díaz-Hernández, 0. "Counterdiagonal/nonpositive tail dependence in Vine copula constructions: application to portfolio management," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 0, pages 1-33.
    3. Yuri Salazar Flores & Adán Díaz-Hernández, 2021. "Counterdiagonal/nonpositive tail dependence in Vine copula constructions: application to portfolio management," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(2), pages 375-407, June.

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    More about this item

    Keywords

    asymmetry; tail dependence; dependence dynamics; dynamic skewed t copulas; VaR and ES forecasting;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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