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Nonparametric Kernel Method to Hedge Downside Risk
Author(s) -
Huang Jinbo,
Ding Ashley,
Li Yong
Publication year - 2019
Publication title -
international review of finance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.489
H-Index - 18
eISSN - 1468-2443
pISSN - 1369-412X
DOI - 10.1111/irfi.12257
Subject(s) - downside risk , cvar , hedge , econometrics , portfolio , nonparametric statistics , expected shortfall , kernel (algebra) , mathematics , economics , financial economics , biology , ecology , combinatorics
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.