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Multivariate Dynamic Copula Models: Parameter Estimation and Forecast Evaluation

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  • Aepli, Matthias D.

    ()

  • Frauendorfer, Karl

    ()

  • Fuess, Roland

    ()

  • Paraschiv, Florentina

    ()

Abstract

This paper introduces multivariate dynamic copula models to account for the time-varying dependence structure in asset portfolios. We firstly enhance the flexibility of this structure by modeling regimes with multivariate mixture copulas. In our second approach, we derive dynamic elliptical copulas by applying the dynamic conditional correlation model (DCC) to multivariate elliptical copulas. The best-ranked copulas according to both in-sample fit and out-of-sample forecast performance indicate the importance of accounting for time-variation. The superiority of multivariate dynamic Clayton and Student-t models further highlight that positive tail dependence as well as the capability of capturing asymmetries in the dependence structure are crucial features of a well-fitting model for an equity portfolio.

Suggested Citation

  • Aepli, Matthias D. & Frauendorfer, Karl & Fuess, Roland & Paraschiv, Florentina, 2015. "Multivariate Dynamic Copula Models: Parameter Estimation and Forecast Evaluation," Working Papers on Finance 1513, University of St. Gallen, School of Finance.
  • Handle: RePEc:usg:sfwpfi:2015:13
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    File URL: http://ux-tauri.unisg.ch/RePEc/usg/sfwpfi/WPF-1513.pdf
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    References listed on IDEAS

    as
    1. Dean Fantazzini, 2008. "Dynamic Copula Modelling for Value at Risk," Frontiers in Finance and Economics, SKEMA Business School, vol. 5(2), pages 72-108, October.
    2. D. Guegan & J. Zhang, 2010. "Change analysis of a dynamic copula for measuring dependence in multivariate financial data," Quantitative Finance, Taylor & Francis Journals, vol. 10(4), pages 421-430.
    3. Lorán Chollete & Andréas Heinen & Alfonso Valdesogo, 2009. "Modeling International Financial Returns with a Multivariate Regime-switching Copula," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 7(4), pages 437-480, Fall.
    4. Nikolay Nenovsky & S. Statev, 2006. "Introduction," Post-Print halshs-00260898, HAL.
    5. Hamilton, James D., 1988. "Rational-expectations econometric analysis of changes in regime : An investigation of the term structure of interest rates," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 385-423.
    6. HEINEN, Andréas & VALDESOGO, Alfonso, 2009. "Asymmetric CAPM dependence for large dimensions: the Canonical Vine Autoregressive Model," CORE Discussion Papers 2009069, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    7. Ostap Okhrin & Yarema Okhrin & Wolfgang Schmid, 2009. "Properties of Hierarchical Archimedean Copulas," SFB 649 Discussion Papers SFB649DP2009-014, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    8. Brandt, Michael W. & Kang, Qiang, 2004. "On the relationship between the conditional mean and volatility of stock returns: A latent VAR approach," Journal of Financial Economics, Elsevier, vol. 72(2), pages 217-257, May.
    9. Rodriguez, Juan Carlos, 2007. "Measuring financial contagion: A Copula approach," Journal of Empirical Finance, Elsevier, vol. 14(3), pages 401-423, June.
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    More about this item

    Keywords

    Multivariate dynamic copulas; regime-switching copulas; dynamic conditional correlation (DCC) model; forecast performance; tail dependence.;

    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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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