Abstract
Understanding the complex nature of financial markets is still a great challenge. In particular, a challenge is to understand the underlying mechanisms of influence that operate in financial markets. A method is discussed to estimate how a company is influenced by an economic sectors after identifying the partial dependence structure of each asset with the assets of the sector excluding the influence of the market. An effective way used to capture the dependence structure of a multivariate time-series is the copula function. However the variety of copula functions and ease of estimation dramatically reduce when the dimension of the multivariate time-series increases. On the other hand, bivariate copula functions are popular and effective in capturing the dependence structure of a 2-dimensional continuous random vector. A simple strategy to continue to use bivariate copula functions for modelling multivariate time-series is the recourse to the vine copulas. The procedure provides a picture of the relative influence of the economic sectors on each stock in terms of Kendall’s t and tail dependence.