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Portfolio optimization with idiosyncratic and systemic risks for financial networks

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  • Yajie Yang
  • Longfeng Zhao
  • Lin Chen
  • Chao Wang
  • Jihui Han

Abstract

In this study, we propose a new multi-objective portfolio optimization with idiosyncratic and systemic risks for financial networks. The two risks are measured by the idiosyncratic variance and the network clustering coefficient derived from the asset correlation networks, respectively. We construct three types of financial networks in which nodes indicate assets and edges are based on three correlation measures. Starting from the multi-objective model, we formulate and solve the asset allocation problem. We find that the optimal portfolios obtained through the multi-objective with networked approach have a significant over-performance in terms of return measures in an out-of-sample framework. This is further supported by the less drawdown during the periods of the stock market fluctuating downward. According to analyzing different datasets, we also show that improvements made to portfolio strategies are robust.

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  • Yajie Yang & Longfeng Zhao & Lin Chen & Chao Wang & Jihui Han, 2021. "Portfolio optimization with idiosyncratic and systemic risks for financial networks," Papers 2111.11286, arXiv.org.
    • Handle: RePEc:arx:papers:2111.11286
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    Cited by:

    1. Roman Mestre, 2023. "Stock profiling using time–frequency-varying systematic risk measure," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-29, December.

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