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Network analysis of pension funds investments

Author

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  • Herteliu, Claudiu
  • Levantesi, Susanna
  • Rotundo, Giulia

Abstract

In this paper, we analyze the Italian pension funds and their declared benchmarks, which are market indexes. Within this perspective, the amounts invested in accord to the declared benchmarks can be analyzed like as a portfolio of benchmarks. We aim at understanding whether the pension funds investments are in line with the optimal portfolios which can be built through the declared benchmarks. To achieve the results, we set up a portfolio optimization problem building two networks of pension funds: one based on the (Pearson) correlation, and the other measuring the tail correlation. For each network, we use the local clustering coefficients to describe the level of connectivity, and we insert it in the risk function. This approach allows us to consider the network measures directly in the portfolio optimization model. We compare the results with the classical Markowitz setting, and we find a new efficient frontier overperforming the Markowitz one. A comparison among the performances of pension funds and their declared portfolio of benchmarks is also reported.

Suggested Citation

  • Herteliu, Claudiu & Levantesi, Susanna & Rotundo, Giulia, 2021. "Network analysis of pension funds investments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 579(C).
  • Handle: RePEc:eee:phsmap:v:579:y:2021:i:c:s037843712100412x
    DOI: 10.1016/j.physa.2021.126139
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    References listed on IDEAS

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    1. Mcassey, Michael P. & Bijma, Fetsje, 2015. "A clustering coefficient for complete weighted networks," Network Science, Cambridge University Press, vol. 3(2), pages 183-195, June.
    2. Fenghua Wen & Xin Yang & Wei‐Xing Zhou, 2019. "Tail dependence networks of global stock markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(1), pages 558-567, January.
    3. Conlon, T. & Ruskin, H.J. & Crane, M., 2007. "Random matrix theory and fund of funds portfolio optimisation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(2), pages 565-576.
    4. Han, Yingying & Gong, Pu & Zhou, Xiang, 2016. "Correlations and risk contagion between mixed assets and mixed-asset portfolio VaR measurements in a dynamic view: An application based on time varying copula models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 940-953.
    5. Grassi, Rosanna, 2010. "Vertex centrality as a measure of information flow in Italian Corporate Board Networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(12), pages 2455-2464.
    6. Li, Yan & Jiang, Xiong-Fei & Tian, Yue & Li, Sai-Ping & Zheng, Bo, 2019. "Portfolio optimization based on network topology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 671-681.
    7. Fei Ren & Ya-Nan Lu & Sai-Ping Li & Xiong-Fei Jiang & Li-Xin Zhong & Tian Qiu, 2017. "Dynamic Portfolio Strategy Using Clustering Approach," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-23, January.
    8. Anna D’Arcangelis & Giulia Rotundo, 2015. "Mutual funds relationships and performance analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(4), pages 1573-1584, July.
    9. 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.
    10. Guercio, Diane Del & Tkac, Paula A., 2002. "The Determinants of the Flow of Funds of Managed Portfolios: Mutual Funds vs. Pension Funds," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 37(4), pages 523-557, December.
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