Deep Calibration of Financial Models: Turning Theory Into Practice

29 Pages Posted: 24 Sep 2020

See all articles by Patrick Büchel

Patrick Büchel

affiliation not provided to SSRN

Michael Kratochwil

Dr. Nagler & Company Gmbh

Maximilian Nagl

University of Regensburg

Daniel Roesch

University of Regensburg

Date Written: August 10, 2020

Abstract

The calibration of financial models is a laborious, time-consuming and expensive task, which needs to be performed frequently by financial institutions. Recently, the application of artificial neural networks (ANNs) for 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 against a real-life calibration framework. We show that the results of an ANN based calibration framework are very competitive and derive guidelines for its practical implementation to enhance and accelerate managerial decisions. Furthermore, we show that our calibrated parameters are more stable over time, enabling more reliable risk reports and business decisions.

Keywords: Decision Support, Global Optimization, Deep Learning, OR in Banking, Model Calibration

JEL Classification: C23, G21, G33, E43

Suggested Citation

Büchel, Patrick and Kratochwil, Michael and Nagl, Maximilian and Roesch, Daniel, Deep Calibration of Financial Models: Turning Theory Into Practice (August 10, 2020). Available at SSRN: https://ssrn.com/abstract=3667070 or http://dx.doi.org/10.2139/ssrn.3667070

Patrick Büchel

affiliation not provided to SSRN

Michael Kratochwil

Dr. Nagler & Company Gmbh ( email )

Maximilianstrasse 47
Munich, 80538
Germany

Maximilian Nagl (Contact Author)

University of Regensburg ( email )

93040 Regensburg
D-93040 Regensburg, 93053
Germany

Daniel Roesch

University of Regensburg ( email )

Chair of Statistics and Risk Management
Faculty of Business, Economics and BIS
Regensburg, 93040
Germany

HOME PAGE: http://www-risk.ur.de/

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
145
Abstract Views
529
rank
223,331
PlumX Metrics