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Copula quantile regression for analysis of multiple time series

Oberseminar Finanz- und Versicherungsmathematik


Copula quantile regression for analysis of multiple time series

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 time-series vectors. This method aims to analyse 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 modelling by means of copula functions. Finally, we examine the conditional quantile behaviour of financial time-series with a non-linear copula quantile VAR model. The talk is based on joint work with Giovanni De Luca.