Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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.

Journal reference: Risks 2020, 8, 40
DOI: 10.3390/risks8020040

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.

Subject Areas

Arbitrage-Regularization; Bond Pricing; Model Selection; Deep Learning; Dynamic PCA

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