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Partial correlation financial networks

Partial correlation financial networks
Partial correlation financial networks
Correlation based networks have been a popular way of inferring a financial network due to the simplicity of construction and the ease of interpretability. However two variables which share a common cause can be correlated, leading to the inference of spurious relationships. To solve this we can use partial correlation. In this paper we construct both correlation and partial correlation networks from S&P500 returns and compare and contrast the two. Firstly we show that the partial correlation networks have a smaller and much less variable intensity than the correlation networks, but in fact are less stable. We look at the centrality of the various sectors in the graph using degree centrality and eigenvector centrality, finding that sector centralities move together during the 2009 market crash and that the financial sector generally has a higher mean centrality over most of the dataset. Exploring the use of these centrality measures for portfolio construction, we shown there is mild correlation between the in-sample centrality and the out of sample Sharpe ratio but there is negative correlation between the in-sample centrality and out of sample risk. Finally we use a community detection method to study how the networks reflect the underlying sector structure and study how stable these communities are over time.
Correlation network, Covariance estimation, Financial networks, Partial correlation, Portfolio optimization
1-19
Millington, Tristan
53030837-7d43-4389-b676-1dcdabeff250
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Millington, Tristan
53030837-7d43-4389-b676-1dcdabeff250
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f

Millington, Tristan and Niranjan, Mahesan (2020) Partial correlation financial networks. Applied Network Science, 5 (1), 1-19, [11]. (doi:10.1007/s41109-020-0251-z).

Record type: Article

Abstract

Correlation based networks have been a popular way of inferring a financial network due to the simplicity of construction and the ease of interpretability. However two variables which share a common cause can be correlated, leading to the inference of spurious relationships. To solve this we can use partial correlation. In this paper we construct both correlation and partial correlation networks from S&P500 returns and compare and contrast the two. Firstly we show that the partial correlation networks have a smaller and much less variable intensity than the correlation networks, but in fact are less stable. We look at the centrality of the various sectors in the graph using degree centrality and eigenvector centrality, finding that sector centralities move together during the 2009 market crash and that the financial sector generally has a higher mean centrality over most of the dataset. Exploring the use of these centrality measures for portfolio construction, we shown there is mild correlation between the in-sample centrality and the out of sample Sharpe ratio but there is negative correlation between the in-sample centrality and out of sample risk. Finally we use a community detection method to study how the networks reflect the underlying sector structure and study how stable these communities are over time.

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More information

Accepted/In Press date: 8 January 2020
Published date: 6 February 2020
Keywords: Correlation network, Covariance estimation, Financial networks, Partial correlation, Portfolio optimization

Identifiers

Local EPrints ID: 437273
URI: http://eprints.soton.ac.uk/id/eprint/437273
PURE UUID: 7543e320-aebb-43c5-9275-2fb21c9b87da
ORCID for Mahesan Niranjan: ORCID iD orcid.org/0000-0001-7021-140X

Catalogue record

Date deposited: 23 Jan 2020 17:34
Last modified: 28 Apr 2022 04:14

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Contributors

Author: Tristan Millington
Author: Mahesan Niranjan ORCID iD

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