Centrality-Based Equal Risk Contribution Portfolio

Shreya Patki, Roy H. Kwon () and Yuri Lawryshyn
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Shreya Patki: Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada
Roy H. Kwon: Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada
Yuri Lawryshyn: Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada

Risks, 2024, vol. 12, issue 1, 1-17

Abstract: This article combines the traditional definition of portfolio risk with minimum-spanning-tree-based “interconnectedness risk” to improve equal risk contribution portfolio performance. We use betweenness centrality to measure an asset’s importance in a market graph (network). After filtering the complete correlation network to a minimum spanning tree, we calculate the centrality score and convert it to a centrality heuristic. We develop an adjusted variance–covariance matrix using the centrality heuristic to bias the model to assign peripheral assets in the minimum spanning tree higher weights. We test this methodology using the constituents of the S&P 100 index. The results show that the centrality equal risk portfolio can improve upon the base equal risk portfolio returns, with a similar level of risk. We observe that during bear markets, the centrality-based portfolio can surpass the base equal risk portfolio risk.

Keywords: networks; portfolio optimization; equal risk portfolio; asset allocation; centrality; market graph (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2024
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