Network risk parity: graph theory-based portfolio construction

Article


Ciciretti, V. and Pallotta, A. 2024. Network risk parity: graph theory-based portfolio construction. Journal of Asset Management. 25 (2), pp. 136-146. https://doi.org/10.1057/s41260-023-00347-8
TypeArticle
TitleNetwork risk parity: graph theory-based portfolio construction
AuthorsCiciretti, V. and Pallotta, A.
Abstract

This study presents network risk parity, a graph theory-based portfolio construction methodology that arises from a thoughtful critique of the clustering-based approach used by hierarchical risk parity. Advantages of network risk parity include: the ability to capture one-to-many relationships between securities, overcoming the one-to-one limitation; the capacity to leverage the mathematics of graph theory, which enables us, among other things, to demonstrate that the resulting portfolios is less concentrated than those obtained with mean-variance; and the ability to simplify the model specification by eliminating the dependency on the selection of a distance and linkage function. Performance-wise, due to a better representation of systematic risk within the minimum spanning tree, network risk parity outperforms hierarchical risk parity and other competing methods, especially as the number of portfolio constituents increases.

KeywordsPortfolio construction; Graph theory; Hierarchical clustering; Eigenvalues
Sustainable Development Goals9 Industry, innovation and infrastructure
Middlesex University ThemeCreativity, Culture & Enterprise
PublisherSpringer
JournalJournal of Asset Management
ISSN1470-8272
Electronic1479-179X
Publication dates
Online20 Feb 2024
PrintMar 2024
Publication process dates
Submitted31 May 2023
Accepted11 Dec 2023
Deposited17 Jul 2024
Output statusPublished
Publisher's version
License
File Access Level
Open
Copyright Statement

© The Author(s) 2024
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/

Digital Object Identifier (DOI)https://doi.org/10.1057/s41260-023-00347-8
Web of Science identifierWOS:001168964900001
Related Output
Is supplemented byhttps://link.springer.com/article/10.1057/s41260-023-00347-8#Sec14
LanguageEnglish
Permalink -

https://repository.mdx.ac.uk/item/10443q

Download files


Publisher's version
s41260-023-00347-8.pdf
License: CC BY 4.0
File access level: Open

  • 28
    total views
  • 17
    total downloads
  • 4
    views this month
  • 0
    downloads this month

Export as

Related outputs

Should you use GARCH models for forecasting volatility? A comparison to GRU neural networks
Pallotta, A. and Ciciretti, V. 2023. Should you use GARCH models for forecasting volatility? A comparison to GRU neural networks. Studies in Nonlinear Dynamics & Econometrics. https://doi.org/10.1515/snde-2022-0025