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基于线性回归和神经网络的期权对冲方法:以SSE 50 ETF期权为例

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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.
Non-parametric option hedging:evidence derived from SSE 50 ETF options
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.

option hedgingmachine learninglinear regressionleverage effect

王伟冠、丁静、刘鑫

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上海大学经济学院,上海 200444

期权对冲 机器学习 线性回归 杠杆效应

2024

上海大学学报(自然科学版)
上海大学

上海大学学报(自然科学版)

CSTPCD北大核心
影响因子:0.579
ISSN:1007-2861
年,卷(期):2024.30(6)