Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/287105 
Year of Publication: 
2021
Citation: 
[Journal:] Review of Derivatives Research [ISSN:] 1573-7144 [Volume:] 25 [Issue:] 2 [Publisher:] Springer US [Place:] New York, NY [Year:] 2021 [Pages:] 109-136
Publisher: 
Springer US, New York, NY
Abstract: 
The calibration of financial models is laborious, time-consuming and expensive, and needs to be performed frequently by financial institutions. Recently, the application of artificial neural networks (ANNs) for model calibration has gained interest. This paper provides the first comprehensive empirical study on the application of ANNs for calibration based on observed market data. We benchmark the performance of the ANN approach against a real-life calibration framework that is in action at a large financial institution. The ANN based calibration framework shows competitive calibration results, roughly four times faster with less computational efforts. Besides speed and efficiency, the resulting model parameters are found to be more stable over time, enabling more reliable risk reports and business decisions. Furthermore, the calibration framework involves multiple validation steps to counteract regulatory concerns regarding its practical application.
Subjects: 
Deep learning
Derivatives
Model calibration
Interest rate term structure
Global optimizer
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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