Estimation of Optimal Hedge Ratio: A Wild Bootstrap Approach

Phong Minh Nguyen, Darren Henry, Jae H. Kim and Sisira Colombage ()
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Phong Minh Nguyen: Institute of Innovation, Science, and Sustainability, Federation University Australia, Ballarat, VIC 3350, Australia
Jae H. Kim: Business School, La Trobe University, Bundoora, VIC 3086, Australia
Sisira Colombage: Institute of Innovation, Science, and Sustainability, Federation University Australia, Ballarat, VIC 3350, Australia

JRFM, 2024, vol. 17, issue 7, 1-26

Abstract: This paper proposes a new approach to estimating the minimum variance hedge ratio (MVHR) based on the wild bootstrap and evaluates the approach using a spectrum of conservative to aggressive alternative hedging strategies associated with the percentiles of the MVHR’s bootstrap distribution. This approach is suggested to be more informative and effective relative to the conventional method of hedging solely based on a single-point estimate. Furthermore, the percentile-based MVHRs are robust to influential outliers, non-normality, and unknown forms of heteroskedasticity. The bootstrap percentile-based hedging strategies’ effectiveness is compared with those from the naïve method and the asymmetric DCC-GARCH model for a range of financial assets and commodities. The bootstrap percentile-based hedging technique is identified to outperform its alternatives in terms of hedging effectiveness, downside risk, and return variability, suggesting its superiority to other methods in both the literature and in practice.

Keywords: minimum variance hedge ratio; wild bootstrap; DCC-GARCH; hedging effectiveness; heteroskedasticity; stochastic dominance (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2024
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