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

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Author Info

  • Cees Diks

    ()
    (University of Amsterdam)

  • Valentyn Panchenko

    ()
    (University of New South Wales)

  • Dick van Dijk

    ()
    (Erasmus University Rotterdam)

Abstract

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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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 08-050/4.

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Date of creation: 20 May 2008
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Handle: RePEc:dgr:uvatin:20080050

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Web page: http://www.tinbergen.nl

Related research

Keywords: density forecast evaluation; scoring rules; weighted likelihood ratio scores; partial likelihood; risk management;

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Citations

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Cited by:
  1. Anne-Sofie Jore & James Mitchell & Shaun P. Vahey, 2008. "Combining forecast densities from VARs with uncertain instabilities," Working Paper 2008/01, Norges Bank.
  2. Geweke, John & Amisano, Gianni, 2010. "Comparing and evaluating Bayesian predictive distributions of asset returns," International Journal of Forecasting, Elsevier, vol. 26(2), pages 216-230, April.
  3. Ardia, David & Lennart, Hoogerheide & Nienke, Corré, 2011. "Stock index returns’ density prediction using GARCH models: Frequentist or Bayesian estimation?," MPRA Paper 28259, University Library of Munich, Germany.
  4. Hoogerheide, Lennart F. & Ardia, David & Corré, Nienke, 2012. "Density prediction of stock index returns using GARCH models: Frequentist or Bayesian estimation?," Economics Letters, Elsevier, vol. 116(3), pages 322-325.

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