论文标题
一致的重新校准模型和深层校准
Consistent Recalibration Models and Deep Calibration
论文作者
论文摘要
已经引入了一致的重新校准模型（CRC），以捕获必要的一般性衍生物价格术语结构的动态特征。已经提出了几种方法来解决这个问题，但是所有这些方法，包括CRC模型，主要是由于存在复杂的漂移项或一致性条件而遭受数值造成的。我们通过机器学习技术克服了这个问题，该技术允许将关键的漂移术语存储在神经网络类型功能中。这产生了可以有效模拟的首次动态术语结构模型。
Consistent Recalibration models (CRC) have been introduced to capture in necessary generality the dynamic features of term structures of derivatives' prices. Several approaches have been suggested to tackle this problem, but all of them, including CRC models, suffered from numerical intractabilities mainly due to the presence of complicated drift terms or consistency conditions. We overcome this problem by machine learning techniques, which allow to store the crucial drift term's information in neural network type functions. This yields first time dynamic term structure models which can be efficiently simulated.
