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Partial Likelihood-Based Scoring Rules for Evaluating Density Forecasts in Tails

  • Dijk, D. van

    ()

    (Erasmus Universiteit Rotterdam)

  • Diks, C.G.H.

    ()

    (Universiteit van Amsterdam)

  • Panchenko, V.

    ()

    (University of New South Wales)

We propose new scoring rules based on partial likelihood for assessing the relative out-of-sample predictive accuracy of competing density forecasts over a specific region of interest, such as the left tail in financial risk management. By construction, existing scoring rules based on weighted likelihood or censored normal likelihood favor density forecasts with more probability mass in the given region, rendering predictive accuracy tests biased towards such densities. Our novel partial likelihood-based scoring rules do not suffer from this problem, as illustrated by means of Monte Carlo simulations and an empirical application to daily S\&P 500 index returns.

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Paper provided by Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance in its series CeNDEF Working Papers with number 08-03.

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Date of creation: 2008
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Handle: RePEc:ams:ndfwpp:08-03
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Dept. of Economics and Econometrics, Universiteit van Amsterdam, Roetersstraat 11, NL - 1018 WB Amsterdam, The Netherlands

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