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Model-Free Market Risk Hedging Using Crowding Networks

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  • Vadim Zlotnikov
  • Jiayu Liu
  • Igor Halperin
  • Fei He
  • Lisa Huang

Abstract

Crowding is widely regarded as one of the most important risk factors in designing portfolio strategies. In this paper, we analyze stock crowding using network analysis of fund holdings, which is used to compute crowding scores for stocks. These scores are used to construct costless long-short portfolios, computed in a distribution-free (model-free) way and without using any numerical optimization, with desirable properties of hedge portfolios. More specifically, these long-short portfolios provide protection for both small and large market price fluctuations, due to their negative correlation with the market and positive convexity as a function of market returns. By adding our long-short portfolio to a baseline portfolio such as a traditional 60/40 portfolio, our method provides an alternative way to hedge portfolio risk including tail risk, which does not require costly option-based strategies or complex numerical optimization. The total cost of such hedging amounts to the total cost of rebalancing the hedge portfolio.

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  • Vadim Zlotnikov & Jiayu Liu & Igor Halperin & Fei He & Lisa Huang, 2023. "Model-Free Market Risk Hedging Using Crowding Networks," Papers 2306.08105, arXiv.org.
    • Handle: RePEc:arx:papers:2306.08105
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    References listed on IDEAS

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    1. Vipul Satone & Dhruv Desai & Dhagash Mehta, 2021. "Fund2Vec: Mutual Funds Similarity using Graph Learning," Papers 2106.12987, arXiv.org.
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