Next Article in Journal
How Do Health, Care Services Consumption and Lifestyle Factors Affect the Choice of Health Insurance Plans in Switzerland?
Next Article in Special Issue
Machine Learning for Multiple Yield Curve Markets: Fast Calibration in the Gaussian Affine Framework
Previous Article in Journal
A Tail Dependence-Based MST and Their Topological Indicators in Modeling Systemic Risk in the European Insurance Sector
Previous Article in Special Issue
Neural Networks for the Joint Development of Individual Payments and Claim Incurred
Open AccessArticle

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

1
Department of Mathematics, ETH Zürich, 8092 Zürich, Switzerland
2
Department of Mathematics and Statistics, Concordia University, 1455 De Maisonneuve Blvd. W., Montréal, QC H3G 1M8, Canada
*
Author to whom correspondence should be addressed.
Risks 2020, 8(2), 40; https://doi.org/10.3390/risks8020040
Received: 28 February 2020 / Revised: 9 April 2020 / Accepted: 17 April 2020 / Published: 23 April 2020
(This article belongs to the Special Issue Machine Learning in Finance, Insurance and Risk Management)
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. View Full-Text
Keywords: arbitrage-regularization; bond pricing; model selection; deep learning; dynamic PCA arbitrage-regularization; bond pricing; model selection; deep learning; dynamic PCA
Show Figures

Figure 1

MDPI and ACS Style

Kratsios, A.; Hyndman, C. Deep Arbitrage-Free Learning in a Generalized HJM Framework via Arbitrage-Regularization. Risks 2020, 8, 40.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop