Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/258004 
Year of Publication: 
2020
Citation: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 8 [Issue:] 2 [Article No.:] 50 [Publisher:] MDPI [Place:] Basel [Year:] 2020 [Pages:] 1-18
Publisher: 
MDPI, Basel
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 Vasiécek 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.
Subjects: 
Vasiécek model
single-curve markets
affine models
multi-curve markets
machine learning
Gaussian process regression
filtering
Adam optimizer
conjugate gradient method
term structure models
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Appears in Collections:

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.