# Deep Calibration With Artificial Neural Network: A Performance Comparison on Option Pricing Models

arXiv: https://arxiv.org/abs/2303.08760 · HTML: https://mlsanity.com/paper/2303.08760/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 37 references that are in the map.
- First posted on arXiv 15 Mar 2023.

## Builds on (most cited first, 3 of 3)

- [Neural networks for option pricing and hedging: a literature review](https://mlsanity.com/paper/1911.05620/) (2019)
- [Deep calibration of rough stochastic volatility models](https://mlsanity.com/paper/1810.03399/) (2018)
- [Deep Calibration of Interest Rates Model](https://mlsanity.com/paper/2110.15133/) (2021)

## Built on by (most cited first, 1 of 1)

- [Integrating LSTM Networks with Neural Levy Processes for Financial Forecasting](https://mlsanity.com/paper/2512.07860/) (2025)

## Abstract (arXiv)

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.