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Asymmetric CAPM dependence for large dimensions: the Canonical Vine Autoregressive Model

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Author Info

  • HEINEN, Andréas

    ()
    (Departamento de Estadistica, Universidad Carlos III de Madrid, Spain)

  • VALDESOGO, Alfonso

    ()
    (CREA, University of Luxembourg, Luxembourg)

Abstract

We propose a new dynamic model for volatility and dependence in high dimensions, that allows for departures from the normal distribution, both in the marginals and in the dependence. The dependence is modeled with a dynamic canonical vine copula, which can be decomposed into a cascade of bivariate conditional copulas. Due to this decomposition, the model does not suffer from the curse of dimensionality. The canonical vine autoregressive (CAVA) captures asymmetries in the dependence structure. The model is applied to 95 S&P500 stocks. For the marginal distributions, we use non-Gaussian GARCH models, that are designed to capture skewness and kurtosis. By conditioning on the market index and on sector indexes, the dependence structure is much simplified and the model can be considered as a non-linear version of the CAPM or of a market model with sector effects. The model is shown to deliver good forecasts of Value-at-Risk.

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Bibliographic Info

Paper provided by Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) in its series CORE Discussion Papers with number 2009069.

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Date of creation: 01 Nov 2009
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Handle: RePEc:cor:louvco:2009069

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Related research

Keywords: asymmetric dependence; high dimension; multivariate copula; multivariate GARCH; Value-at-Risk;

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Cited by:
  1. David E. Allen & Mohammad A. Ashraf & Michael McAleer & Robert J. Powell & Abhay K. Singh, 2013. "Financial dependence analysis: applications of vine copulas," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 67(4), pages 403-435, November.
  2. Min, Aleksey & Czado, Claudia, 2014. "SCOMDY models based on pair-copula constructions with application to exchange rates," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 523-535.
  3. David E Allen & Michael McAleer & Robert J Powell & Abhay K Singh, 2013. "Nonparametric Multiple Change Point Analysis of the Global Financial Crisis," KIER Working Papers 866, Kyoto University, Institute of Economic Research.
  4. Nikoloulopoulos, Aristidis K. & Joe, Harry & Li, Haijun, 2012. "Vine copulas with asymmetric tail dependence and applications to financial return data," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3659-3673.
  5. Luca Riccetti, 2013. "A copula–GARCH model for macro asset allocation of a portfolio with commodities," Empirical Economics, Springer, vol. 44(3), pages 1315-1336, June.
  6. Zhun Peng, 2011. "L'analyse dynamique des dépendances," Post-Print dumas-00651795, HAL.

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