Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/288827 
Erscheinungsjahr: 
2020
Quellenangabe: 
[Journal:] Journal of Asset Management [ISSN:] 1479-179X [Volume:] 21 [Issue:] 5 [Publisher:] Palgrave Macmillan UK [Place:] London [Year:] 2020 [Pages:] 428-446
Verlag: 
Palgrave Macmillan UK, London
Zusammenfassung: 
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.
Schlagwörter: 
CAT bond
Machine learning
Linear regression
Risk premium
JEL: 
C45
C58
G12
G17
G22
Persistent Identifier der Erstveröffentlichung: 
Creative-Commons-Lizenz: 
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Dokumentart: 
Article
Dokumentversion: 
Published Version

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