In financial researches and among risk management practitioners the analysis of multiple time-series is often conducted in a non-linear context. In addition, capturing the quantile conditional dependence structure could prove of interest in order to measure financial contagion risk. We propose a 3-stage estimation copula-based method applied to non-linear quantile dependence analysis of timeseries vectors. This method aims to analyze the serial and cross-section dependence of time-series given specified quantiles, reducing the computational complexity. To the best of our knowledge, this is the first approach that combines the conditional quantile dependence analysis of multiple time-series with non-linear modeling by means of copula functions. Finally, we examine the conditional quantile behavior of real financial time-series with a non-linear copula quantile VAR model.

Copula quantile dependence for the analysis of multiple time series

Giorgia Rivieccio
Membro del Collaboration Group
;
Giovanni De Luca
Membro del Collaboration Group
2017

Abstract

In financial researches and among risk management practitioners the analysis of multiple time-series is often conducted in a non-linear context. In addition, capturing the quantile conditional dependence structure could prove of interest in order to measure financial contagion risk. We propose a 3-stage estimation copula-based method applied to non-linear quantile dependence analysis of timeseries vectors. This method aims to analyze the serial and cross-section dependence of time-series given specified quantiles, reducing the computational complexity. To the best of our knowledge, this is the first approach that combines the conditional quantile dependence analysis of multiple time-series with non-linear modeling by means of copula functions. Finally, we examine the conditional quantile behavior of real financial time-series with a non-linear copula quantile VAR model.
978-84-697-4075-0
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11367/65917
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