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.

翻译：本文探索了人工神经网络（ANN）作为一种无模型解决方案，用于期权定价模型的校准算法。我们构建了人工神经网络来校准两个著名的GARCH类期权定价模型中的参数：Duan's GARCH和经典稳定GARCH，这些模型显著改进了Black-Scholes模型的局限性，但一直存在计算复杂度高的问题。为缓解这一技术难题，我们使用蒙特卡洛模拟（MCS）方法生成的数据集训练人工神经网络，并将其应用于校准最优参数。性能结果表明，人工神经网络方法在训练后始终优于MCS方法，并且具有更快的计算速度优势。本文还讨论了期权的希腊值。