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
