A Multivariate Quantile Predictor |
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| Authors: | Jan G De Gooijer Ali Gannoun Dawit Zerom |
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| Institution: | 1. Department of Quantitative Economics , University of Amsterdam , Amsterdam, The Netherlands j.g.degooijer@uva.nl;3. Laboratoire de Probabilités et Statistique , Université Montpellier II , Montpellier, France;4. Department of Finance and Management Science , University of Alberta, School of Business , Edmonton, Alberta, Canada |
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| Abstract: | ABSTRACT We introduce a nonparametric quantile predictor for multivariate time series via generalizing the well-known univariate conditional quantile into a multivariate setting for dependent data. Applying the multivariate predictor to predicting tail conditional quantiles from foreign exchange daily returns, it is observed that the accuracy of extreme tail quantile predictions can be greatly improved by incorporating interdependence between the returns in a bivariate framework. As a special application of the multivariate quantile predictor, we also introduce a so-called joint-horizon quantile predictor that is used to produce multi-step quantile predictions in one-go from univariate time series realizations. A simulation example is discussed to illustrate the relevance of the joint-horizon approach. |
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| Keywords: | Conditional quantile Joint-horizon prediction Kernel Multivariate Single-horizon prediction Time series |
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