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Forecasting time series with multivariate copulas

Forecasting time series with multivariate copulas AbstractIn this paper we present a forecasting method for time series using copula-based models for multivariatetime series. We study how the performance of the predictions evolves when changing the strength ofthe different possible dependencies, as well as the structure of the dependence. We also look at the impactof the marginal distributions. The impact of estimation errors on the performance of the predictions is alsoconsidered. In all the experiments, we compare predictions from our multivariate method with predictionsfrom the univariate version which has been introduced in the literature recently. To simplify implementation,a test of independence between univariate Markovian time series is proposed. Finally, we illustrate themethodology by a practical implementation with financial data. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Dependence Modeling de Gruyter

Forecasting time series with multivariate copulas

Dependence Modeling , Volume 3 (1): 1 – May 28, 2015

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Publisher
de Gruyter
Copyright
© 2015 Clarence Simard, Bruno Rémillard
ISSN
2300-2298
eISSN
2300-2298
DOI
10.1515/demo-2015-0005
Publisher site
See Article on Publisher Site

Abstract

AbstractIn this paper we present a forecasting method for time series using copula-based models for multivariatetime series. We study how the performance of the predictions evolves when changing the strength ofthe different possible dependencies, as well as the structure of the dependence. We also look at the impactof the marginal distributions. The impact of estimation errors on the performance of the predictions is alsoconsidered. In all the experiments, we compare predictions from our multivariate method with predictionsfrom the univariate version which has been introduced in the literature recently. To simplify implementation,a test of independence between univariate Markovian time series is proposed. Finally, we illustrate themethodology by a practical implementation with financial data.

Journal

Dependence Modelingde Gruyter

Published: May 28, 2015

References