Abstract In this paper we analyse the properties of hierarchical Archimedean copulas. This class is a generalisation of the Archimedean copulas and allows for general non-exchangeable dependency structures. We show that the structure of the copula can be uniquely recovered from all bivariate...

Abstract There is an increasing demand for models of multivariate time-series with time-varying and non-Gaussian dependencies. The available models suffer from the curse of dimensionality or from restrictive assumptions on the parameters and distributions. A promising class of models is that of...

Abstract Copulae became an extremely popular tool in different areas of research. Since the first applications in risk management in the late 90th, they attracted many other quantitatively oriented sciences like biostatistics, hydrology and finance. The main reason originates in the  Sklar...

Purpose – The purpose of this paper is to assess the losses of weather‐related insurance at different regional levels. The possibility of spatial diversification of insurance is explored by estimating the joint occurrence on unfavorable weather conditions in different locations, looking...

This study proposes a novel framework for the joint modelling of commodity forward curves. Its key contribution is twofold. First, we introduce a family of dynamic conditional correlation models based on hierarchical Archimedean copulae (HAC-DCC), which are flexible but parsimonious instruments...

Modelling portfolio credit risk is one of the crucial challenges faced by financial services industry in the last few years. We propose the valuation model of collateralized debt obligations (CDO) based on hierarchical Archimedean copulae (HAC) with up to three parameters, with default...

One of the main goals in non-life insurance is to estimate the claims reserve distribution. A generalized time series model, that allows for modeling the conditional mean and variance of the claim amounts, is proposed for the claims development. On contrary to the classical stochastic reserving...