Deep Calibration with Artificial Neural Network: A Performance Comparison on Option-Pricing Models

Abstract

This article explores artificial neural network (ANN) as a model-free solution for a calibration algorithm of option-pricing models. The authors construct ANNs to calibrate parameters for two well-known GARCH-type option-pricing models: Duan’s GARCH and the classical tempered stable GARCH models that significantly improve upon the limitation of the Black–Scholes model but have suffered from computation complexity. To mitigate this technical difficulty, the authors train ANNs with a dataset generated by the Monte Carlo simulation (MCS) method and apply them to calibrate optimal parameters. The performance results indicate that the ANN approach consistently outperforms MCS and takes advantage of faster computation times once trained. The Greeks of options are also discussed.