In this paper we propose a subsampling estimator for the distribution of statistics diverging at either known rates when the underlying time series in strictly stationary abd strong mixing. Based on our results we provide a detailed discussion how to estimate extreme order statistics with...

A transformation kernel density estimator that is suitable for heavy-tailed distributions is discussed. Using a truncated Beta transformation, the choice of the bandwidth parameter becomes straightforward. An application to insurance data and the calculation of the value-at-risk are presented.

Taking into account that one of the most important factors which have caused the financial crisis was the bad risk management practices in banks we want to confirm the need to develop more efficient risk management practices. The fact that return distributions are characterized by time varying...

The paper suggests a nonlinear and multivariate time series model framework that enables the study of simultaneity in returns and in volatilities, as well as asymmetric effects arising from shocks and an outside stock exchange. Using daily data 2000-2006 for the Baltic state stock exchanges and...

The objective of this paper is to determine how relative market and credit risk changes among European sectors during times of extreme market fluctuations. Ten sectors comprising the S&P Euro index are compared prior to and during the Global Financial Crisis (GFC). Market risk is measured using...

In this article, we formulate a time-scale decomposition of an international version of the CAPM that accounts for both market and exchange-rate risk. In addition, we derive an analytical formula for time-scale value at risk and marginal value at risk (VaR) of a portfolio. We apply our...

There exists a wide variety of models for return, and the chosen model determines the tool required to calculate the value at risk (VaR). This paper introduces an alternative methodology to model-based simulation by using a Monte Carlo simulation of the Dirichlet process. The model is...

Over the past decade value at risk (VaR) has become the most widely used technique for the quantification of market-risk exposure. VaR is a measure of the potential loss that may occur from adverse moves in market prices (interest rates, exchange rates, equity prices and so forth). The capacity...

This paper compares two types of volatility models for returns, ARCH-type and stochastic volatility (SV) models, both from a theoretical and an empirical point of view. In particular a GARCH(1,1) model, an EGARCH(1,1) model and a log-normal AR(1) stochastic volatility model are considered. The...