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Nonparametric Kernel Method to Hedge Downside Risk

We propose a nonparametric kernel estimation method (KEM) that deter-mines the optimal hedge ratio by minimizing the downside risk of a hedgedportfolio, measured by conditional value-at-risk (CVaR). We also demonstratethat the KEM minimum-CVaR hedge model is a convex optimization. Thesimulation results show that our KEM provides more accurate estimationsand the empirical results suggest that, compared to other conventionalmethods, our KEM yields higher effectiveness in hedging the downside riskin the weather-sensitive markets.