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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

    ()
    (School of Economics, University of New South Wales)

  • Dick van Dijk

    ()
    (Econometric Institute, 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 likelihoodbased 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 School of Economics, The University of New South Wales in its series Discussion Papers with number 2008-10.

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Length: 31 pages
Date of creation: May 2008
Date of revision:
Handle: RePEc:swe:wpaper:2008-10

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

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References

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Citations

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Cited by:
  1. 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.
  2. 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.
  3. Anne Sofie Jore & James Mitchell & Shaun Vahey, 2008. "Combining Forecast Densities from VARs with Uncertain Instabilities," Reserve Bank of New Zealand Discussion Paper Series DP2008/18, Reserve Bank of New Zealand.
  4. 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.

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