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

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ID: 109992
2020
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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 Vasiček 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.
Reference Key
gümbel2020risksmachine Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Sandrine Gümbel;Thorsten Schmidt;Gümbel, Sandrine;Schmidt, Thorsten;
Journal risks
Year 2020
DOI
10.3390/risks8020050
URL
https://doi.org/10.3390/risks8020050##versions-div
Keywords
Machine learning filtering vasiček model single-curve markets affine models multi-curve markets gaussian process regression adam optimizer conjugate gradient method term structure models

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