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

Clicks: 246
ID: 116647
2020
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #45 of 89 articles by views in risks

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
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.
Reference Key
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

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.