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A News-Based Approach for Computing Historical Value-at-Risk

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Management Intelligent Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 171))

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Abstract

Within the field of finance, Value-at-Risk (VaR) is a widely adopted tool to assess portfolio risk. When calculating VaR based on historical stock return data, the data could be sensitive to outliers caused by seldom occurring news events in the sampled period. Using a data set of news events, of which the irregular events are identified using a Poisson distribution, we research whether the VaR accuracy can be improved by considering news events as additional input in the calculation. Our experiments show that when a rare event occurs, removing the event-generated noise from the stock prices for a small, optimized time window can improve VaR predictions.

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Author information

Authors and Affiliations

  1. Erasmus University Rotterdam, P.O. Box 1738, NL-3000 DR, Rotterdam, The Netherlands

    Frederik Hogenboom, Michael de Winter, Flavius Frasincar & Alexander Hogenboom

Authors
  1. Frederik Hogenboom
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  2. Michael de Winter
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  3. Flavius Frasincar
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  4. Alexander Hogenboom
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Corresponding author

Correspondence to Frederik Hogenboom .

Editor information

Editors and Affiliations

  1. Dept. Computer Science & A.I., University of Granada, Granada, 18071, Spain

    Jorge Casillas

  2. Dept. of Management, University of Granada, Granada, 18071, Spain

    Francisco J. Martínez-López

  3. Department of Computing Science, and Control, Universidad Salamanca, Plaza de la Merced, s/n s/n, Salamanca, 37008, Spain

    Juan Manuel Corchado Rodríguez

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Hogenboom, F., de Winter, M., Frasincar, F., Hogenboom, A. (2012). A News-Based Approach for Computing Historical Value-at-Risk. In: Casillas, J., Martínez-López, F., Corchado Rodríguez, J. (eds) Management Intelligent Systems. Advances in Intelligent Systems and Computing, vol 171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30864-2_27

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  • DOI: https://doi.org/10.1007/978-3-642-30864-2_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30863-5

  • Online ISBN: 978-3-642-30864-2

  • eBook Packages: EngineeringEngineering (R0)

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