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Forecasting the Risk of Speculative Assets by Means of Copula Distributions

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  • Benjamin Beckers
  • Helmut Herwartz
  • Moritz Seidel
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    Abstract

    The GARCH(1,1) model and its extensions have become a standard econometric tool for modeling volatility dynamics of financial returns and port-folio risk. In this paper, we propose an adjustment of GARCH implied conditional value-at-risk and expected shortfall forecasts that exploits the predictive content of uncorrelated, yet dependent model innovations. The adjustment is motivated by non-Gaussian characteristics of model residuals, and is implemented in a semiparametric fashion by means of conditional moments of simulated bivariate standardized copula distributions. We conduct in-sample forecasting comparisons for a set of 18 stock market indices. In total, four competing copula-GARCH models are contrasted against each other on the basis of their one-step ahead forecasting performance. With regard to forecast unbiasedness and precision, especially the Frank-GARCH models provide most conservative risk forecasts and out-perform all rival models.

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    File URL: http://www.diw.de/documents/publikationen/73/diw_01.c.417667.de/dp1282.pdf
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    Bibliographic Info

    Paper provided by DIW Berlin, German Institute for Economic Research in its series Discussion Papers of DIW Berlin with number 1282.

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    Length: 35 p.
    Date of creation: 2013
    Date of revision:
    Handle: RePEc:diw:diwwpp:dp1282

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    Keywords: copula distributions; expected shortfall; GARCH; model selection; non-Gaussian innovations; risk forecasting; value-at-risk;

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