Deep calibration of financial models: turning theory into practice

Artikel

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Review of Derivatives Research ; ISSN: 1573-7144 ; Volume: 25 ; Year: 2021 ; Issue: 2 ; Pages: 109-136 ; New York, NY: Springer US

Classification and Topics

• Classification: Wirtschaft
• Subject: Deep learning ; Derivatives ; Model calibration ; Interest rate term structure ; Global optimizer

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Büchel, Patrick ; Kratochwil, Michael ; Nagl, Maximilian ; Rösch, Daniel
• Event: Veröffentlichung
• (who): Springer US
• (where): New York, NY
• (when): 2021

Further information

• DOI: doi:10.1007/s11147-021-09183-7
• Last update: 10.03.2025, 11:42 AM CET