Deep calibration of financial models: turning theory into practice - Patrick Büchel; Michael Kratochwil; Maximilian Nagl; Daniel Rösch (Review of Derivatives Research, 2021)
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: 330; Deep learning; Derivatives; Model calibration; Interest rate term structure; Global optimizer