TY - DATA T1 - Maximum likelihood estimation of skew-t copulas with its applications to stock returns PY - 2018/05/16 AU - Toshinao Yoshiba UR - https://tandf.figshare.com/articles/journal_contribution/Maximum_likelihood_estimation_of_skew-_i_t_i_copulas_with_its_applications_to_stock_returns/6276209 DO - 10.6084/m9.figshare.6276209.v1 L4 - https://ndownloader.figshare.com/files/11484179 KW - Skew-t distribution KW - copula KW - maximum likelihood estimation KW - tail asymmetry KW - tail dependence KW - generalized hyperbolic distribution KW - 62E17 KW - 62H10 KW - 62H20 KW - 65C60 N2 - The multivariate Student-t copula family is used in statistical finance and other areas when there is tail dependence in the data. It often is a good-fitting copula but can be improved on when there is tail asymmetry. Multivariate skew-t copula families can be considered when there is tail dependence and tail asymmetry, and we show how a fast numerical implementation for maximum likelihood estimation is possible. For the copula implicit in a multivariate skew-t distribution, the fast implementation makes use of (i) monotone interpolation of the univariate marginal quantile function and (ii) a re-parametrization of the correlation matrix. Our numerical approach is tested with simulated data with data-driven parameters. A real data example involves the daily returns of three stock indices: the Nikkei225, S&P500 and DAX. With both unfiltered returns and GARCH/EGARCH filtered returns, we compare the fits of the Azzalini–Capitanio skew-t, generalized hyperbolic skew-t, Student-t, skew-Normal and Normal copulas. ER -