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Robust Estimation in VaR Modelling - Univariate Approaches using Bounded Innovation Propagation and Regression Quantiles Methodology

  • Ewa Ratuszny

    ()

    (Warsaw School of Economics)

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    In the paper we present robust estimation methods based on bounded innovation propagation filters and quantile regression, applied to measure Value at Risk. To illustrate advantage connected with the robust methods, we compare VaR forecasts of several group of instruments in the period of high uncertainty on the financial markets with the ones modelled using traditional quasi-likelihood estimation. For comparative purpose we use three groups of tests i.e. based on Bernoulli trial models, on decision making aspect, and on the expected shortfall.

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    Article provided by CEJEME in its journal Central European Journal of Economic Modelling and Econometrics.

    Volume (Year): 5 (2013)
    Issue (Month): 1 (March)
    Pages: 35-63

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    Handle: RePEc:psc:journl:v:5:y:2013:i:1:p:35-63
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    8. Jacek Osiewalski & Anna Pajor, 2010. "Bayesian Value-at-Risk for a Portfolio: Multi- and Univariate Approaches Using MSF-SBEKK Models," Central European Journal of Economic Modelling and Econometrics, CEJEME, vol. 2(4), pages 253-277, September.
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