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

被引：0

作者：

Hogenboom, Frederik ^{[1
]}

de Winter, Michael ^{[1
]}

Frasincar, Flavius ^{[1
]}

Hogenboom, Alexander ^{[1
]}

机构：

[1] Erasmus Univ, NL-3000 DR Rotterdam, Netherlands

来源：

MANAGEMENT INTELLIGENT SYSTEMS | 2012年 / 171卷

关键词：

PUBLIC INFORMATION; STOCK; VOLATILITY;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：283 / 292

页数：10

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