Nonparametric Kernel Method to Hedge Downside Risk

Jinbo Huang, Ashley Ding and Yong Li

International Review of Finance, 2019, vol. 19, issue 4, 929-944

Abstract: We propose a nonparametric kernel estimation method (KEM) that determines the optimal hedge ratio by minimizing the downside risk of a hedged portfolio, measured by conditional value‐at‐risk (CVaR). We also demonstrate that the KEM minimum‐CVaR hedge model is a convex optimization. The simulation results show that our KEM provides more accurate estimations and the empirical results suggest that, compared to other conventional methods, our KEM yields higher effectiveness in hedging the downside risk in the weather‐sensitive markets.

Date: 2019
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https://doi.org/10.1111/irfi.12257

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International Review of Finance is currently edited by Bruce D. Grundy, Naifu Chen, Ming Huang, Takao Kobayashi and Sheridan Titman

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