研究了非参数期权对冲这种风险管理方法在我国市场上的表现. 投资者期望在日间 交易过程中最小化单期的均方对冲误差, 提出了使用包括前馈人工神经网络和线性回归在 内的非参数对冲方法, 构造了从期权可观测变量到对冲策略的模型. 2017—2023 年的上证 (Shanghai stock exchange, SSE) 50 交易基金 (exchange traded fund, ETF) 期权数据的实 证结果表明, 非参数模型相较于基准参数模型可以降低超过 10% 的对冲误差, 其原因在于非 参数模型能够捕捉到 SSE 50 ETF 期权中表现出的杠杆效应.

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 hedging 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.