Dynamic Copulas for Monotonic Dependence Change in Time Series

Antoine Bergeron, Pierre Dutilleul (), Carole Beaulieu and Taoufik Bouezmarni
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Antoine Bergeron: Université de Sherbrooke
Pierre Dutilleul: McGill University
Carole Beaulieu: Université de Sherbrooke
Taoufik Bouezmarni: Université de Sherbrooke

Sankhya B: The Indian Journal of Statistics, 2022, vol. 84, issue 2, No 10, 683-693

Abstract: Abstract A particular class of dynamic bivariate copulas, monotonically increasing or decreasing, is studied for modeling dependence in a time series. As increasing or decreasing functions of time, the copula parameters are estimated via their own parameters. The method of Inference Functions for Margins (IFM), adapted from the static case, is applied for this purpose. Simulations are used to assess the detectability of an increase or a decrease in dependence over time for five copula functions. In an application to wheat prices (source: Food and Agriculture Organization), information criteria are used to select the best copula function, and the dynamic copulas are shown to represent an improvement over static copulas for several of the time series.

Keywords: Time-series dependence modeling; Dynamic versus static bivariate copulas; Inference functions for margins; Properties of estimators; 62H05; 62M10; 91B84 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s13571-022-00281-6

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