Abstract
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.
Highlights
It is the aim of this paper to apply machine learning techniques to the calibration of bond prices in multi curve markets in order to predict the term structure of basic instruments
The co-existence of different yield curves associated to different tenors is a phenomenon in interest rate markets which originates with the 2007–2009 financial crisis
Following (Rasmussen and Williams, 2006, Chapter 2 and 5) we provide a brief introduction to Gaussian process regression (GPR)
Summary
It is the aim of this paper to apply machine learning techniques to the calibration of bond prices in multi curve markets in order to predict the term structure of basic instruments. In multi curve markets one term structure is present but multiple yield curves for different lengths of the future investment period are given in the market and have to be calibrated. This is a very challenging task, see Eberlein et al (2019) for an example using Levy processes. We choose Gaussian process regression (GPR) as our machine learning approach which ensures fast calibration; see De Spiegeleer et al (2018) This is a non-parametric Bayesian approach to regression and is able to capture non-linear relationships between variables. We place ourselves in the context of the Vasicek model, which is a famous affine model, see Filipovic (2009) and Keller-Ressel et al (2018) for a guide to the literature and details
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