Deep calibration of financial models: turning theory into practice

Patrick Büchel (), Michael Kratochwil (), Maximilian Nagl () and Daniel Rösch ()
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Patrick Büchel: Commerzbank AG
Michael Kratochwil: Dr. Nagler & Company GmbH
Maximilian Nagl: Universtät Regensburg, Chair of Statistics and Risk Management
Daniel Rösch: Universtät Regensburg, Chair of Statistics and Risk Management

Review of Derivatives Research, 2022, vol. 25, issue 2, No 1, 109-136

Abstract: 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.

Keywords: Deep learning; Derivatives; Model calibration; Interest rate term structure; Global optimizer (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11147-021-09183-7

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