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

Clicks: 120
ID: 116647
2020
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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.
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kratsios2020risksdeep Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Anastasis Kratsios;Cody Hyndman;Kratsios, Anastasis;Hyndman, Cody;
Journal risks
Year 2020
DOI
10.3390/risks8020040
URL
https://www.mdpi.com/2227-9091/8/2/40/htm
Keywords
Deep learning model selection arbitrage-regularization bond pricing dynamic pca

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