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Anwendung der Extremwerttheorie zur Quantifizierung von Marktpreisrisiken – Test der Relevanz anhand vergangener Extrembelastungen von DAX und MSCI Europe

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Pohl, M. Anwendung der Extremwerttheorie zur Quantifizierung von Marktpreisrisiken – Test der Relevanz anhand vergangener Extrembelastungen von DAX und MSCI Europe. Credit and Capital Markets – Kredit und Kapital, 44(2), 243-278. https://doi.org/10.3790/kuk.44.2.243
Pohl, Michael "Anwendung der Extremwerttheorie zur Quantifizierung von Marktpreisrisiken – Test der Relevanz anhand vergangener Extrembelastungen von DAX und MSCI Europe" Credit and Capital Markets – Kredit und Kapital 44.2, 2011, 243-278. https://doi.org/10.3790/kuk.44.2.243
Pohl, Michael (2011): Anwendung der Extremwerttheorie zur Quantifizierung von Marktpreisrisiken – Test der Relevanz anhand vergangener Extrembelastungen von DAX und MSCI Europe, in: Credit and Capital Markets – Kredit und Kapital, vol. 44, iss. 2, 243-278, [online] https://doi.org/10.3790/kuk.44.2.243

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Anwendung der Extremwerttheorie zur Quantifizierung von Marktpreisrisiken – Test der Relevanz anhand vergangener Extrembelastungen von DAX und MSCI Europe

Pohl, Michael

Credit and Capital Markets – Kredit und Kapital, Vol. 44 (2011), Iss. 2 : pp. 243–278

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Prof. Dr. Michael Pohl, Steinbeis-Hochschule Berlin, Lehrstuhl für Wealth Management und Banking, Gürtelstraße 29A/30, D-10247 Berlin.

Abstract

Application of the Extreme Value Theory for the Quantification of Market Price Risks – Empirical Relevance Test Using Extreme Losses of DAX and MSCI Europe

The Extreme Value Theory is an approach designed with the objective to quantify risks which occur with a very low probability. The empirical application of the Extreme Value Theory in terms of the Peaks Over Threshold (POT)-Method to the index declines of the DAX and the MSCI Europe on 11.9.01, 21.1.08 and 16.10.08 in this paper shows that the quality of risk assessment highly depends on the underlying data source. As the analysis shows the resulting risk level during the considered days is clearly linked to the applied threshold. Nevertheless it is shown that the POT-Method beats the assumption of normal distribution and GARCH models with normally distributed and t-distributed innovations – especially after periods of high market volatility – concerning the goodness of risk quantification for the examined events.