This paper investigated the performance of non-parametric option hedging methods in the Chinese market,in which investors minimized their single-period mean-squared hedging errors.Experiments were conducted using SSE(Shanghai stock exchange)50 ETF(exchange traded fund)options.It was proposed the use of feed-forward neural networks and linear regression for model mapping from option-observable variables to hedg-ing strategies.Results showed that non-parametric methods significantly outperformed the benchmark parametric models with hedging errors reduced by over 10％due to the fact that non-parametric models could capture the leverage effect in the SSE 50 ETF option market.
维普
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