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Deep Arbitrage-Free Learning in a Generalized HJM Framework via Arbitrage-Regularization

Anastasis Kratsios () and Cody Hyndman ()
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Anastasis Kratsios: Department of Mathematics, ETH Zürich, 8092 Zürich, Switzerland
Cody Hyndman: Department of Mathematics and Statistics, Concordia University, 1455 De Maisonneuve Blvd. W., Montréal, QC H3G 1M8, Canada

Risks, 2020, vol. 8, issue 2, 1-30

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 (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 M2 M4 K2 (search for similar items in EconPapers)
Date: 2020
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Handle: RePEc:gam:jrisks:v:8:y:2020:i:2:p:40-:d:349565