Efficient computation of mean reverting portfolios using cyclical coordinate descent

Théophile Griveau-Billion and Ben Calderhead

Papers from arXiv.org

Abstract: The econometric challenge of finding sparse mean reverting portfolios based on a subset of a large number of assets is well known. Many current state-of-the-art approaches fall into the field of co-integration theory, where the problem is phrased in terms of an eigenvector problem with sparsity constraint. Although a number of approximate solutions have been proposed to solve this NP-hard problem, all are based on relatively simple models and are limited in their scalability. In this paper we leverage information obtained from a heterogeneous simultaneous graphical dynamic linear model (H-SGDLM) and propose a novel formulation of the mean reversion problem, which is phrased in terms of a quasi-convex minimisation with a normalisation constraint. This new formulation allows us to employ a cyclical coordinate descent algorithm for efficiently computing an exact sparse solution, even in a large universe of assets, while the use of H-SGDLM data allows us to easily control the required level of sparsity. We demonstrate the flexibility, speed and scalability of the proposed approach on S\&P$500$, FX and ETF futures data.

Date: 2019-05
New Economics Papers: this item is included in nep-bec
References: Add references at CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/1905.05841 Latest version (application/pdf)

Related works:
Journal Article: Efficient computation of mean reverting portfolios using cyclical coordinate descent (2021)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1905.05841

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().