Nonparametric kernel method to hedge downside risk

Jinbo Huang, Ashley Ding, Yong Li

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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.
Original languageEnglish
Pages (from-to)929-944
Number of pages16
JournalInternational Review of Finance
Issue number4
Early online date14 Mar 2019
Publication statusPublished - 1 Dec 2019


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