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Centrality-Based Equal Risk Contribution Portfolio

Author

Listed:
  • 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
    Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON M5S 3G8, Canada)

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.

Suggested Citation

  • Shreya Patki & Roy H. Kwon & Yuri Lawryshyn, 2024. "Centrality-Based Equal Risk Contribution Portfolio," Risks, MDPI, vol. 12(1), pages 1-17, January.
    • Handle: RePEc:gam:jrisks:v:12:y:2024:i:1:p:8-:d:1311950
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    References listed on IDEAS

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    1. Vladimir Boginski & Sergiy Butenko & Oleg Shirokikh & Svyatoslav Trukhanov & Jaime Gil Lafuente, 2014. "A network-based data mining approach to portfolio selection via weighted clique relaxations," Annals of Operations Research, Springer, vol. 216(1), pages 23-34, May.
    2. Cho, Younghwan & Song, Jae Wook, 2023. "Hierarchical risk parity using security selection based on peripheral assets of correlation-based minimum spanning trees," Finance Research Letters, Elsevier, vol. 53(C).
    3. Li, Bo & Li, Xiangfa & Teo, Kok Lay & Zheng, Peiyao, 2022. "A new uncertain random portfolio optimization model for complex systems with downside risks and diversification," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    4. Gian Paolo Clemente & Rosanna Grassi & Asmerilda Hitaj, 2021. "Asset allocation: new evidence through network approaches," Annals of Operations Research, Springer, vol. 299(1), pages 61-80, April.
    5. Boginski, Vladimir & Butenko, Sergiy & Pardalos, Panos M., 2005. "Statistical analysis of financial networks," Computational Statistics & Data Analysis, Elsevier, vol. 48(2), pages 431-443, February.
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