Comparing the Accuracy of Copula-Based Multivariate Density Forecasts in Selected Regions of Support

Cees Diks, Valentyn Panchenko, Oleg Sokolinskiy, and Dick van Dijk
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Cees Diks: University of Amsterdam, the Netherlands
Oleg Sokolinskiy,: Rutgers Business School, United States

No 13-061/III, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: This discussion paper resulted in a publication in the 'Journal of Economic Dynamics and Control' , 2014, 48, 79-94.

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.

Keywords: Copula-based density forecast; Kullback-Leibler Information Criterion; out-of-sample forecast evaluation (search for similar items in EconPapers)
JEL-codes: C12 C14 C32 C52 C53 (search for similar items in EconPapers)
Date: 2013-04-19
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Persistent link: https://EconPapers.repec.org/RePEc:tin:wpaper:20130061

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