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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)

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

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Find related papers by JEL classification:
C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications

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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. John Geweke & Gianni Amisano, 2008. "Comparing and evaluating Bayesian predictive distributions of asset returns," Working Paper Series 969, European Central Bank. [Downloadable!]
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