Forecasting temperature in time and space is an important precondition for both the design of weather derivatives and the assessment of the hedging effectiveness of index based weather insur-ance. In this article, we show how this task can be accomplished by means of Kriging techniques....

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...

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...

On the temperature derivative market, modeling temperature volatility is an important issue for pricing and hedging. In order to apply pricing tools of nancial mathematics, one needs to isolate a Gaussian risk factor. A conventional model for temperature dynamics is a stochastic model with...

In this paper we provide a method for estimating multivariate distributions defined through hierarchical Archimedean copulas. In general, the true structure of the hierarchy is unknown, but we develop a computationally efficient technique to determine it from the data. For this purpose we...

This study proposes a novel framework for the joint modelling of commodity forward curves. Its key contribution is twofold. First, dynamic correlation models are applied in this context as part of the modelling scheme. Second, we introduce a family of dynamic conditional correlation models based...

This paper aims at explanation of the R-package HAC, which provides user friendly methods for dealing with high-dimensional hierarchical Archimedean copulae (HAC). A computationally ecient estimation procedure allows to recover the structure and the parameters of HACs from data. In addition,...

We introduce the notion of realized copula. Based on assumptions of the marginal distri- butions of daily stock returns and a copula family, realized copula is dened as the copula structure materialized in realized covariance estimated from within-day high-frequency data. Copula parameters are...

We introduce the notion of realized copula. Based on assumptions of the marginal distributions of daily stock returns and a copula family, realized copula is defined as the copula structure materialized in realized covariance estimated from within-day highfrequency data. Copula parameters are...