Out-of-sample comparison of copula specifications in multivariate density forecasts
AbstractWe 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: 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's t copula is favored over Gaussian, Gumbel and Clayton copulas. This suggests that these exchange rate returns are characterized by symmetric tail dependence.
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Bibliographic InfoPaper provided by Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance in its series CeNDEF Working Papers with number 08-10.
Date of creation: 2008
Date of revision:
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- Diks, Cees & Panchenko, Valentyn & van Dijk, Dick, 2010. "Out-of-sample comparison of copula specifications in multivariate density forecasts," Journal of Economic Dynamics and Control, Elsevier, vol. 34(9), pages 1596-1609, September.
- Cees Diks & Valentyn Panchenko & Dick van Dijk, 2008. "Out-of-sample Comparison of Copula Specifications in Multivariate Density Forecasts," Tinbergen Institute Discussion Papers 08-105/4, Tinbergen Institute.
- Cees Diks & Valentyn Panchenko & Dick van Dijk, 2008. "Out-of-sample comparison of copula specifications in multivariate density forecasts," Discussion Papers 2008-23, School of Economics, The University of New South Wales.
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-01-03 (All new papers)
- NEP-ECM-2009-01-03 (Econometrics)
- NEP-FOR-2009-01-03 (Forecasting)
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