Deep Calibration With Artificial Neural Network: A Performance Comparison on Option Pricing Models
This work addresses a technical bottleneck for financial practitioners using advanced option pricing models, though it is incremental as it applies existing ANN methods to a known problem.
This paper tackles the computational complexity of calibrating GARCH-type option pricing models by using an Artificial Neural Network (ANN) as a model-free solution, achieving faster computation times and outperforming Monte Carlo Simulation methods in performance.
This paper explores Artificial Neural Network (ANN) as a model-free solution for a calibration algorithm of option pricing models. We construct ANNs to calibrate parameters for two well-known GARCH-type option pricing models: Duan's GARCH and the classical tempered stable GARCH that significantly improve upon the limitation of the Black-Scholes model but have suffered from computation complexity. To mitigate this technical difficulty, we train ANNs with a dataset generated by 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.