This paper studies the performance of nonparametric quantile regression as a tool to predict Value at Risk (VaR). The approach is flexible as it requires no assumptions on the form of return distributions. A monotonized double kernel local linear estimator is applied to estimate moderate (1%)...

Der Value at Risk ist die am stärksten verbreitete Kennzahl zur Bestimmung des Risikos bei Finanzinstituten. Für diese gibt es bezüglich Theorie, Simulation und empirischer Anwendung bereits ein breites Spektrum an Literatur. Im Rahmen dieser Arbeit werden verschiedene Methoden zur Schätzung...

The increasing exposure to renewable energy has amplified the need for risk management in electricity markets. Electricity price risk poses a major challenge to market participants. We propose an approach to model and fore- cast electricity prices taking into account information on renewable...

This appendix extends simulation and empirical results reported in Mancini and Trojani (2010). It discusses the choice of the robustness tuning constants; describes the unconditional, independence and conditional coverage tests for VaR forecast evaluation; provides additional Monte Carlo...