Forecasting time series with multivariate copulas

Clarence Simard; Bruno Rémillard

Dependence Modeling (2015)

  • Volume: 3, Issue: 1, page 59-82, electronic only
  • ISSN: 2300-2298

Abstract

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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, as well as the structure of the dependence. We also look at the impact of the marginal distributions. The impact of estimation errors on the performance of the predictions is also considered. In all the experiments, we compare predictions from our multivariate method with predictions from 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 the methodology by a practical implementation with financial data.

How to cite

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Clarence Simard, and Bruno Rémillard. "Forecasting time series with multivariate copulas." Dependence Modeling 3.1 (2015): 59-82, electronic only. <http://eudml.org/doc/271015>.

@article{ClarenceSimard2015,
abstract = {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, as well as the structure of the dependence. We also look at the impact of the marginal distributions. The impact of estimation errors on the performance of the predictions is also considered. In all the experiments, we compare predictions from our multivariate method with predictions from 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 the methodology by a practical implementation with financial data.},
author = {Clarence Simard, Bruno Rémillard},
journal = {Dependence Modeling},
keywords = {Copulas; time series; forecasting; realized volatility; copulas},
language = {eng},
number = {1},
pages = {59-82, electronic only},
title = {Forecasting time series with multivariate copulas},
url = {http://eudml.org/doc/271015},
volume = {3},
year = {2015},
}

TY - JOUR
AU - Clarence Simard
AU - Bruno Rémillard
TI - Forecasting time series with multivariate copulas
JO - Dependence Modeling
PY - 2015
VL - 3
IS - 1
SP - 59
EP - 82, electronic only
AB - 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, as well as the structure of the dependence. We also look at the impact of the marginal distributions. The impact of estimation errors on the performance of the predictions is also considered. In all the experiments, we compare predictions from our multivariate method with predictions from 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 the methodology by a practical implementation with financial data.
LA - eng
KW - Copulas; time series; forecasting; realized volatility; copulas
UR - http://eudml.org/doc/271015
ER -

References

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