Deep Arbitrage-Free Learning in a Generalized HJM Framework via Arbitrage-Regularization
Anastasis Kratsios () and
Cody Hyndman ()
Additional contact information
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
Risks, 2020, vol. 8, issue 2, 1-30
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 (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 M2 M4 K2 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:gam:jrisks:v:8:y:2020:i:2:p:40-:d:349565
Access Statistics for this article
Risks is currently edited by Prof. Dr. J. David Cummins
More articles in Risks from MDPI, Open Access Journal
Bibliographic data for series maintained by XML Conversion Team ().