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. © 2012 Springer-Verlag.
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CITATION STYLE
Hogenboom, F., De Winter, M., Frasincar, F., & Hogenboom, A. (2012). A news-based approach for computing historical value-at-risk. In Advances in Intelligent Systems and Computing (Vol. 171 AISC, pp. 283–292). Springer Verlag. https://doi.org/10.1007/978-3-642-30864-2_27
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