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Business Analytics

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

Dr Valentyn Panchenko, School of Economics, UNSW

9th Mar 2012  11:00 am - Room 498, Merewether Building (H04)

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, and using (out-of-sample) conditional likelihood and censored likelihood in order to restrict the evaluation to the region of interest. Monte Carlo simulations show 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 copula 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.