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

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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)

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.

Suggested Citation

  • Anastasis Kratsios & Cody Hyndman, 2020. "Deep Arbitrage-Free Learning in a Generalized HJM Framework via Arbitrage-Regularization," Risks, MDPI, vol. 8(2), pages 1-30, April.
    • Handle: RePEc:gam:jrisks:v:8:y:2020:i:2:p:40-:d:349565
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    References listed on IDEAS

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