Deep arbitrage-free learning in a generalized HJM framework via arbitrage-regularization
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Date
2020-06
Publication Type
Journal Article
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yes
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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.
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published
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Journal / series
Volume
8 (2)
Pages / Article No.
40
Publisher
MDPI
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Edition / version
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Software
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Subject
arbitrage-regularization; bond pricing; model selection; deep learning; dynamic PCA
Organisational unit
03845 - Teichmann, Josef / Teichmann, Josef