Using the concepts of copula we can represent and interpret the dependence structure presented in random vectors with clarity, particularly in bivariate vectors. In bivariate analysis, the role of both heterogeneous tail-dependence coefficient and homogenous tail- dependence coefficient are to study a measure of dependence when variables reach extreme values. We find expressions for the heterogeneous tail-dependence coefficients from the conditional cumulative distribution function and prove that the homoge- neous tail-dependence coefficients of a skewed normal distribution are equal to zero. Using the concepts of copula and the total tail dependence, we study the dependence structure between the following variables: (i) log- return of interpolated rates for the 1-year and 2-year fixed term structure; (ii) log-return of interpolated rate for the 1-year and log- return for the Bo- vespa index; e (iii) log-return of interpolated rate for the 1-year fixed term structure and log-return of expected PTAX, 6 months ahead.