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Deep calibration of financial models: turning theory into practice

Author

Listed:
  • 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)

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.

Suggested Citation

  • Patrick Büchel & Michael Kratochwil & Maximilian Nagl & Daniel Rösch, 2022. "Deep calibration of financial models: turning theory into practice," Review of Derivatives Research, Springer, vol. 25(2), pages 109-136, July.
    • Handle: RePEc:kap:revdev:v:25:y:2022:i:2:d:10.1007_s11147-021-09183-7
    DOI: 10.1007/s11147-021-09183-7
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    References listed on IDEAS

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    Cited by:

    1. Daniele Maria Di Nosse & Federico Gatta, 2024. "A Multi-step Approach for Minimizing Risk in Decentralized Exchanges," Papers 2406.07200, arXiv.org, revised Jun 2024.

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