The calibration of financial models is laborious, time-consuming and expensive, and needs to be performed frequently by financial institutions. Recently, the application of artificial neural networks (ANNs) for model calibration has gained interest. This paper provides the first comprehensive empirical study on the application of ANNs for calibration based on observed market data. We benchmark the performance of the ANN approach against a real-life calibration framework that is in action at a large financial institution. The ANN based calibration framework shows competitive calibration results, roughly four times faster with less computational efforts. Besides speed and efficiency, the resulting model parameters are found to be more stable over time, enabling more reliable risk reports and business decisions. Furthermore, the calibration framework involves multiple validation steps to counteract regulatory concerns regarding its practical application.

金融模型的校准费时费力，成本高昂，需要金融机构频繁进行。最近，人工神经网络（ANN）在模型校准中的应用引起了人们的兴趣。本文提供了第一个基于观察到的市场数据应用人工神经网络进行校准的综合实证研究。我们将 ANN 方法的性能与大型金融机构正在实施的现实校准框架进行基准测试。基于 ANN 的校准框架显示了具有竞争力的校准结果，大约快四倍，计算量更少。除了速度和效率之外，我们发现生成的模型参数随着时间的推移更加稳定，从而实现更可靠的风险报告和业务决策。此外，