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...

Most multivariate variance or volatility models suffer from a common problem, the “curse of dimensionality”. For this reason, most are fitted under strong parametric restrictions that reduce the interpretation and flexibility of the models. Recently, the literature has focused on...

This work brings together two distinct pieces of evidence concerning, at the macro level, international distributions of incomes and their dynamics, and, at the micro level, the size distributions of firms and the properties of their growth rates. First, our empirical analysis provides a new...

This work brings together two distinct pieces of evidence concerning, at the macro level, international distributions of incomes and their dynamics, and, at the micro level, the size distributions of firms and the properties of their growth rates. First, our empirical analysis provides a new...

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...

Qualitative and quantitative properties of the Cornish-Fisher-Expansion in the context of Delta-Gamma-Normal approaches to the computation of Value at Risk are presented. Some qualitative deficiencies of the Cornish-Fisher-Expansion - the monotonicity of the distribution function as well as...