We develop a new methodology that measures conditional dependency. We achieve this by using copula functions that link marginal distributions, here chosen to obey a GARCH-type model with time-varying skewness and kurtosis. We apply this model to daily returns of stock-market indices. We find...

Modeling the dependency between stock market returns is a difficult task when returns follow a complicated dynamics. It is not easy to specify the multivariate distribution relating two or more return series. In this paper, a methodology based on fitting ARIMA, GARCH and ARMA-GARCH models and...

We propose a new class of observation-driven time-varying parameter models for dynamic volatilities and correlations to handle time series from heavy-tailed distributions. The model adopts generalized autoregressive score dynamics to obtain a time-varying covariance matrix of the multivariate...