Event-Based Historical Value-at-Risk

Frederik Hogenboom, M de Winter, M Jansen, Alexander Hogenboom, Flavius Frasincar, U Kaymak

Research output: Chapter/Conference proceedingConference proceedingAcademicpeer-review

3 Citations (Scopus)


Value-at-Risk (VaR) is an important tool to assess portfolio risk. When calculating VaR based on historical stock return data, we hypothesize that this historical data is sensitive to outliers caused by news events in the sampled period. In this paper, we research whether the VaR accuracy can be improved by considering news events as additional input in the calculation. This involves processing the historical data in order to reflect the impact of news on the stock returns. Our experiments show that when an event occurs, removing the noise (that is caused by an event) from the measured stock prices for a small time window can improve VaR predictions.
Original languageEnglish
Title of host publicationIEEE Computational Intelligence for Financial Engineering & Economics 2012 (CIFEr 2012)
Place of PublicationNew York City, New York, USA
Number of pages7
Publication statusPublished - 29 Mar 2012

Research programs

  • EUR ESE 32


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