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G-2013-85

Forecasting Time Series with Multivariate Copulas

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In this paper we present a forecasting method for time series using copula-based models for multivariate time series. We study how the performance of the predictions evolves when changing the strength of the different possible dependencies and compare it with a univariate version of our forecasting method introduced recently by Sokolinskiy & Van Dijk. Moreover, we also study the influence of the marginal distribution with the help of a new performance measure and lastly we look at the impact of the dependence structure on the predictions performance. We also give an example of practical implementation with financial data.

, 20 pages

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