Out-of-sample comparison of copula specifications in multivariate density forecasts
We introduce a statistical test for comparing the predictive accuracy of competing copula specifications in multivariate density forecasts, based on the Kullback-Leibler information criterion (KLIC). The test is valid under general conditions on the competing copulas: in particular it allows for parameter estimation uncertainty and for the copulas to be nested or non-nested. Monte Carlo simulations demonstrate that the proposed test has satisfactory size and power properties in finite samples. Applying the test to daily exchange rate returns of several major currencies against the US dollar we find that the Student-t copula is favored over Gaussian, Gumbel and Clayton copulas.
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