Event-based historical value-at-risk
| Autor: | Alexander Hogenboom, Milan Jansen, Michael de Winter, Uzay Kaymak, Frederik Hogenboom, Flavius Frasincar |
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| Přispěvatelé: | Information Systems IE&IS, Erasmus School of Economics, Erasmus MC other, Econometrics |
| Jazyk: | angličtina |
| Rok vydání: | 2012 |
| Předmět: | |
| Zdroj: | Proceedings of IEEE Computational Intelligence for Financial Engineering & Economics 2012 (CIFEr 2012), March 29-30, 2012, New York City, 1-7 STARTPAGE=1;ENDPAGE=7;TITLE=Proceedings of IEEE Computational Intelligence for Financial Engineering & Economics 2012 (CIFEr 2012), March 29-30, 2012, New York City CIFEr IEEE Computational Intelligence for Financial Engineering & Economics 2012 (CIFEr 2012), 164-170 STARTPAGE=164;ENDPAGE=170;TITLE=IEEE Computational Intelligence for Financial Engineering & Economics 2012 (CIFEr 2012) |
| DOI: | 10.1109/CIFEr.2012.6327787 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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