Machine Learning for Multiple Yield Curve Markets: Fast Calibration in the Gaussian Affine Framework

Sandrine Gümbel and Thorsten Schmidt
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Sandrine Gümbel: Department of Mathematical Stochastics, University of Freiburg, Ernst-Zermelo Str. 1, 79104 Freiburg im Breisgau, Germany
Thorsten Schmidt: Department of Mathematical Stochastics, University of Freiburg, Ernst-Zermelo Str. 1, 79104 Freiburg im Breisgau, Germany

Risks, 2020, vol. 8, issue 2, 1-18

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 Kálmán filtering, 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 Vasi?ek 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.

Keywords: Vasi?ek model; single-curve markets; affine models; multi-curve markets; machine learning; Gaussian process regression; filtering; Adam optimizer; conjugate gradient method; term structure models (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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