Model-Free Market Risk Hedging Using Crowding Networks
Vadim Zlotnikov Jiayu Liu Igor Halperin Fei He Lisa Huang
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.