Deep arbitrage-free learning in a generalized HJM framework via arbitrage-regularization

Open access
Date
2020-06Type
- Journal Article
Abstract
A regularization approach to model selection, within a generalized HJM framework, is introduced, which learns the closest arbitrage-free model to a prespecified factor model. This optimization problem is represented as the limit of a one-parameter family of computationally tractable penalized model selection tasks. General theoretical results are derived and then specialized to affine term-structure models where new types of arbitrage-free machine learning models for the forward-rate curve are estimated numerically and compared to classical short-rate and the dynamic Nelson-Siegel factor models. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000412725Publication status
publishedExternal links
Journal / series
RisksVolume
Pages / Article No.
Publisher
MDPISubject
arbitrage-regularization; bond pricing; model selection; deep learning; dynamic PCAOrganisational unit
03845 - Teichmann, Josef / Teichmann, Josef
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