Machine learning for multiple yield curve markets: fast calibration in the Gaussian affine framework
This work addresses calibration issues for financial practitioners in interest rate markets, though it is incremental as it adapts existing machine learning techniques to a specific domain.
The paper tackled the challenging calibration problem in multiple yield curve markets by applying Gaussian process regression, achieving very good results for single curve markets but encountering many challenges in multi-curve markets within a Vasicek framework.
Calibration is a highly challenging task, in particular in multiple yield curve markets. This paper is a first attempt to study the chances and challenges of the application of machine learning techniques for this. We employ Gaussian process regression, a machine learning methodology having many similarities with extended Kalman filtering - a technique which has been applied many times to interest rate markets and term structure models. We find very good results for the single curve markets and many challenges for the multi curve markets in a Vasicek framework. The Gaussian process regression is implemented with the Adam optimizer and the non-linear conjugate gradient method, where the latter performs best. We also point towards future research.