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Estimating Value-at-Risk Using Neural Networks

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Informationssysteme in der Finanzwirtschaft

Zusammenfassung

There are various statistical techniques to estimate the market risk of a portfolio. This will most frequently be done by identifying market risk factors and modelling the impact of changes in market risk factors on the portfolio value. One increasingly popular measure for portfolio risk is the so called Value-at-Risk (VaR). This paper aims to show how VaR estimates can be obtained using a neural network approach. Results on a US-Dollar portfolio are compared to different VaR models.

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Author information

Authors and Affiliations

  1. Technische Universität Dresden, Lehrstuhl für BWL - Finanzwirtschaft, >Momrnsenstraße 13, 01062, Dresden, Deutschland

    Hermann Locarek-Junge

  2. Technische Universität Dresden, Lehrstuhl für Statistik, 93053, >Momrnsenstraße 13, 01062, Dresden, Deutschland

    Ralf Prinzler

Authors
  1. Hermann Locarek-Junge
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  2. Ralf Prinzler
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Editor information

Editors and Affiliations

  1. Fachbereich Wirtschaftswissenschaften BWL-Wirtschaftsinformatik, Justus-Liebig-Universität Gießen, Licher Str. 70, D-35394, Gießn, Deutschland

    Christof Weinhardt

  2. Seminar für Bankwirtschaft, Ludwig-Maximilians-Universität München, Ludwigstr. 28, D-80539, München, Deutschland

    Hermann Meyer zu Selhausen

  3. Fachbereich Wirtschaftswissenschaften Lehrstuhl für Risikomanagement, Justus-Liebig-Universität Gießen, Licher Str. 74, D-35394, Gießen, Deutschland

    Martin Morlock

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© 1998 Springer-Verlag Berlin Heidelberg

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Locarek-Junge, H., Prinzler, R. (1998). Estimating Value-at-Risk Using Neural Networks. In: Weinhardt, C., Selhausen, H.M.z., Morlock, M. (eds) Informationssysteme in der Finanzwirtschaft. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-60327-3_28

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  • DOI: https://doi.org/10.1007/978-3-642-60327-3_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-64355-2

  • Online ISBN: 978-3-642-60327-3

  • eBook Packages: Springer Book Archive

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