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Deep Arbitrage-Free Learning in a Generalized HJM Framework via Arbitrage-Regularization
Version 1
: Received: 28 February 2020 / Approved: 2 March 2020 / Online: 2 March 2020 (01:15:00 CET)
A peer-reviewed article of this Preprint also exists.
Kratsios, A.; Hyndman, C. Deep Arbitrage-Free Learning in a Generalized HJM Framework via Arbitrage-Regularization. Risks 2020, 8, 40. Kratsios, A.; Hyndman, C. Deep Arbitrage-Free Learning in a Generalized HJM Framework via Arbitrage-Regularization. Risks 2020, 8, 40.
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.
Keywords
Arbitrage-Regularization; Bond Pricing; Model Selection; Deep Learning; Dynamic PCA
Subject
Computer Science and Mathematics, Applied Mathematics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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