Event-based historical value-at-risk

F.P. Hogenboom; Michael Winter; A.C. Hogenboom; Milan Jansen; F. Frasincar; U. Kaymak

doi:10.1109/CIFEr.2012.6327787

Event-based historical value-at-risk

F.P. Hogenboom
, Michael Winter
, A.C. Hogenboom
, Milan Jansen
, F. Frasincar
, U. Kaymak

Information Systems IE&IS

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

3 Citations (Scopus)

Abstract

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 language	English
Title of host publication	Proceedings of IEEE Computational Intelligence for Financial Engineering & Economics 2012 (CIFEr 2012), March 29-30, 2012, New York City
Place of Publication	Piscataway
Publisher	IEEE Press
Pages	1-7
DOIs	https://doi.org/10.1109/CIFEr.2012.6327787
Publication status	Published - 2012
Event	conference; CIFEr 2012; 2012-03-30; 2012-03-30 - Duration: 30 Mar 2012 → 30 Mar 2012

Conference

Conference	conference; CIFEr 2012; 2012-03-30; 2012-03-30
Period	30/03/12 → 30/03/12
Other	CIFEr 2012

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10.1109/CIFEr.2012.6327787

Cite this

@inproceedings{056ef9fed13043308c86a4b95634c60e,

title = "Event-based historical value-at-risk",

abstract = "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.",

author = "F.P. Hogenboom and Michael Winter and A.C. Hogenboom and Milan Jansen and F. Frasincar and U. Kaymak",

year = "2012",

doi = "10.1109/CIFEr.2012.6327787",

language = "English",

pages = "1--7",

booktitle = "Proceedings of IEEE Computational Intelligence for Financial Engineering \& Economics 2012 (CIFEr 2012), March 29-30, 2012, New York City",

publisher = "IEEE Press",

note = "conference; CIFEr 2012; 2012-03-30; 2012-03-30 ; Conference date: 30-03-2012 Through 30-03-2012",

}

Hogenboom, FP, Winter, M, Hogenboom, AC, Jansen, M, Frasincar, F & Kaymak, U 2012, Event-based historical value-at-risk. in Proceedings of IEEE Computational Intelligence for Financial Engineering & Economics 2012 (CIFEr 2012), March 29-30, 2012, New York City. IEEE Press, Piscataway, pp. 1-7, conference; CIFEr 2012; 2012-03-30; 2012-03-30, 30/03/12. https://doi.org/10.1109/CIFEr.2012.6327787

Event-based historical value-at-risk. / Hogenboom, F.P.; Winter, Michael; Hogenboom, A.C. et al.
Proceedings of IEEE Computational Intelligence for Financial Engineering & Economics 2012 (CIFEr 2012), March 29-30, 2012, New York City. Piscataway: IEEE Press, 2012. p. 1-7.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Event-based historical value-at-risk

AU - Hogenboom, F.P.

AU - Winter, Michael

AU - Hogenboom, A.C.

AU - Jansen, Milan

AU - Frasincar, F.

AU - Kaymak, U.

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

U2 - 10.1109/CIFEr.2012.6327787

DO - 10.1109/CIFEr.2012.6327787

M3 - Conference contribution

SP - 1

EP - 7

BT - Proceedings of IEEE Computational Intelligence for Financial Engineering & Economics 2012 (CIFEr 2012), March 29-30, 2012, New York City

PB - IEEE Press

CY - Piscataway

T2 - conference; CIFEr 2012; 2012-03-30; 2012-03-30

Y2 - 30 March 2012 through 30 March 2012

ER -

Event-based historical value-at-risk

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