We study the possibility for international diversification of catastrophe risk by the insurance sector. Adopting the argument that large insurance losses may be a `globalizing factor' for the industry, we study the dependence of geographically distant insurance markets via equity returns. In...

We propose a new class of observation driven time series models referred to as Generalized Autoregressive Score (GAS) models. The driving mechanism of the GAS model is the scaled score of the likelihood function. This approach provides a unified and consistent framework for introducing...

We develop new multi-factor copula models with time-varying dependence structures via factor loadings with observation-driven dynamics. The new models are highly flexible, scalable to high dimensions, and ensure positivity of covariance and correlation matrices. The model retains a closed-form...

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...