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

Cees Diks (), Valentyn Panchenko and Dick van Dijk ()
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Cees Diks: University of Amsterdam

No 08-050/4, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: This discussion paper led to a publication in the 'Journal of Econometrics' , 2011, 163, 215-230. 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.

Keywords: density forecast evaluation; scoring rules; weighted likelihood ratio scores; partial likelihood; risk management (search for similar items in EconPapers)
JEL-codes: C12 C22 C52 C53 (search for similar items in EconPapers)
Date: 2008-05-20
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Working Paper: Partial Likelihood-Based Scoring Rules for Evaluating Density Forecasts in Tails (2008) Downloads
Working Paper: Partial Likelihood-Based Scoring Rules for Evaluating Density Forecasts in Tails (2008) Downloads
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