Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/87234
Authors: 
Diks, Cees
Panchenko, Valentyn
Sokolinskiy, Oleg
van Dijk, Dick
Year of Publication: 
2013
Series/Report no.: 
Tinbergen Institute Discussion Paper 13-061/III
Abstract: 
This paper develops a testing framework for comparing the predictive accuracy of copula-based multivariate density forecasts, focusing on a specific part of the joint distribution. The test is framed in the context of the Kullback-Leibler Information Criterion, but using (out-of-sample) conditional likelihood and censored likelihood in order to focus the evaluation on the region of interest. Monte Carlo simulations document that the resulting test statistics have satisfactory size and power properties in small samples. In an empirical application to daily exchange rate returns we find evidence that the dependence structure varies with the sign and magnitude of returns, such that different parametric copula models achieve superior forecasting performance in different regions of the support. Our analysis highlights the importance of allowing for lower and upper tail dependence for accurate forecasting of common extreme appreciation and depreciation of different currencies.
Subjects: 
Copula-based density forecast
Kullback-Leibler Information Criterion
out-of-sample forecast evaluation
JEL: 
C12
C14
C32
C52
C53
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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