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Likelihood-based scoring rules for comparing density forecasts in tails

  • Cees Diks

    ()

    (Department of Quantitive Economics - University of Amsterdam)

  • Valentyn Panchenko

    ()

    (Faculty of Business - University of New South Wales)

  • Dick van Dijk

    ()

    (Erasmus University Rotterdam - Erasmus University Rotterdam)

We propose new scoring rules based on conditional and censored likelihood for assessing the predictive accuracy of competing density forecasts over a specific region of interest, such as the left tail in financial risk management. These scoring rules can be interpreted in terms of Kullback-Leibler divergence between weighted versions of the density forecast and the true density. Existing scoring rules based on weighted likelihood favor density forecasts with more probability mass in the given region, rendering predictive accuracy tests biased towards such densities. Using our novel likelihood-based scoring rules avoids this problem.

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Paper provided by HAL in its series Post-Print with number peer-00834423.

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Date of creation: 15 Jun 2011
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Publication status: Published, Journal of Econometrics, 2011
Handle: RePEc:hal:journl:peer-00834423
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