Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/288827 
Year of Publication: 
2020
Citation: 
[Journal:] Journal of Asset Management [ISSN:] 1479-179X [Volume:] 21 [Issue:] 5 [Publisher:] Palgrave Macmillan UK [Place:] London [Year:] 2020 [Pages:] 428-446
Publisher: 
Palgrave Macmillan UK, London
Abstract: 
Enhanced machine learning methods provide an encouraging alternative to forecast asset prices by extending or generalizing the possible model specifications compared to conventional linear regression methods. Even if enhanced methods of machine learning in the literature often lead to better forecasting quality, this is not clear for small asset classes, because in small asset classes enhanced machine learning methods may potentially over-fit the in-sample data. Against this background, we compare the forecasting performance of linear regression models and enhanced machine learning methods in the market for catastrophe (CAT) bonds. We use linear regression with variable selection, penalization methods, random forests and neural networks to forecast CAT bond premia. Among the considered models, random forests exhibit the highest forecasting performance, followed by linear regression models and neural networks.
Subjects: 
CAT bond
Machine learning
Linear regression
Risk premium
JEL: 
C45
C58
G12
G17
G22
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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