In general, risk of an extreme outcome in financial markets can be expressed as a function of the tail copula of a high-dimensional vector after standardizing marginals. Hence it is of importance to model and estimate tail copulas. Even for moderate dimension, nonparametrically estimating a tail...

Recently there has been an increasing interest in applying elliptical distributions to risk management. Under weak conditions, Hult and Lindskog (2002) showed that a random vector with an elliptical distribution is in the domain of attraction of a multivariate extreme value distribution. In this...

Empirical volatility changes in time and exhibits tails, which are heavier than normal. Moreover, empirical volatility has - sometimes quite substantial - upwards jumps and clusters on high levels. We investigate classical and nonclassical stochastic volatility models with respect to their...

Rising poverty and inequality increases the risk of social instability in countries all around the world. For measuring poverty and inequality there exists a variety of statistical indicators. Estimating these indicators is trivial as long as the income variable is measured on a metric scale....

The transformation of area aggregates between non-hierarchical area systems is a standard problem of official statistics. We introduce a new method which is based on kernel density estimates. It is a modification of the SEM algorithm proposed by Gross et al. (2016), which was used for the...

Map-based regional analysis is interested to detect areas with a large concentration of certain populations. Here kernel density estimates (KDE) offer advantages over classical choropleth maps. However, kernel density estimation needs exact geo-coordinates. In a recent paper Groß et al. (2017)...