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：本文探讨了人造神经网络(ANN)作为选项定价模型校准算法的无模式解决方案。我们建造了ANN,用于校准两个众所周知的GARCH型选项定价模型的参数:Duan的GARCH和古典的温和稳定的GRCH,这两个模型大大改进了黑雪模型的局限性,但又受到计算复杂性的影响。为了减轻这一技术困难,我们用蒙特卡洛模拟(MCS)方法生成的数据集对ANN进行了培训,并将其用于校准最佳参数。性能结果显示,ANN方法始终优于MSS,在培训后利用更快的计算时间。还讨论了各种选项的希腊人。</s>