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Deep Arbitrage-Free Learning in a Generalized HJM Framework via Arbitrage-Regularization
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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.
Title: Deep Arbitrage-Free Learning in a Generalized HJM Framework via Arbitrage-Regularization
Description:
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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