Post by Sapphire Capital on Jul 23, 2008 21:23:08 GMT 4
Partial Likelihood-Based Scoring Rules for Evaluating Density Forecasts in Tails
CEES G.H. DIKS
University of Amsterdam - Faculty of Economics and Business (FEB)
VALENTYN PANCHENKO
University of New South Wales
thingy J.C. VAN DIJK
Econometric Institute - Erasmus University Rotterdam
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May 19, 2008
Tinbergen Institute Discussion Paper No. 2008-050/4
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.
papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1135531_code356671.pdf?abstractid=1135531&mirid=3
CEES G.H. DIKS
University of Amsterdam - Faculty of Economics and Business (FEB)
VALENTYN PANCHENKO
University of New South Wales
thingy J.C. VAN DIJK
Econometric Institute - Erasmus University Rotterdam
--------------------------------------------------------------------------------
May 19, 2008
Tinbergen Institute Discussion Paper No. 2008-050/4
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.
papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1135531_code356671.pdf?abstractid=1135531&mirid=3