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

  • Frederik Hogenboom
  • Michael de Winter
  • Flavius Frasincar
  • Alexander Hogenboom
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 171)

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.

Keywords

Mean Square Error Stock Price Mean Absolute Percentage Error News Event Technical Indicator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Frederik Hogenboom
    • 1
  • Michael de Winter
    • 1
  • Flavius Frasincar
    • 1
  • Alexander Hogenboom
    • 1
  1. 1.Erasmus University RotterdamRotterdamThe Netherlands

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