Preprint
Article

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

This version is not peer-reviewed.

Submitted:

28 February 2020

Posted:

02 March 2020

You are already at the latest version

A peer-reviewed article of this preprint also exists.

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 open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.

Downloads

170

Views

175

Comments

0

Subscription

Notify me about updates to this article or when a peer-reviewed version is published.

Email

Prerpints.org logo

Preprints.org is a free preprint server supported by MDPI in Basel, Switzerland.

Subscribe

© 2025 MDPI (Basel, Switzerland) unless otherwise stated