一种混合建模方法及其在ETF期权定价中的应用

科学合理的交易型开放式指数基金（ETF）期权定价有利于充分发挥其风险对冲功能，也是一个需要准确掌握市场规律并兼顾经济学意义的复杂建模过程。本文提出了一种新的混合建模方法，将嵌套长短时记忆神经网络模型（NLSTM）与Heston模型结合，实现ETF期权定价偏差的动态修正，并基于华夏上证50ETF、嘉实沪深300ETF和华泰柏瑞沪深300ETF的高频期权数据，实验验证了所提方法的有效性。研究结果表明，不同类型ETF期权价格的波动特征差异显著，无论是基于BS定价模型还是Heston定价模型都难以准确刻画ETF期权价格的复杂变化规律。通过将NLSTM神经网络模型与Heston模型结合，能够有效地捕捉不同类型ETF期权的动态变化规律，从而提升ETF期权定价的准确性。

A Hybrid Modeling Framework and Its Application for Exchange Traded Fund Options Pricing

The scientific and reasonable exchange traded fund (ETF) options price contributes to implementing risk hedging function. This complex modeling process needs to consider the economic significance and accurately grasp the market rules. The issue of pricing ETF options is studied and a hybrid ETF options model is proposed. It combines the Nested-LSTM neural network model and the Heston model for the modeling, and dynamically corrects the option pricing deviation. The high-frequency data of ChinaAMC China 50 ETF, Harvest SZSE SME-CHINEXT 300 ETF and Huatai-PB CSI 300 ETF are taken as examples to verify the effectiveness of the proposed model. The experiment results show that the volatility characteristics of different types of ETF options prices are significantly different. Therefore, neither the Black-Scholes model nor the Heston model can be adapted to handle complex variation rules of ETF option prices accurately. By introducing Nested-LSTM neural network model into the Heston model, the proposed model can effectively capture the dynamic change rules of different types of ETF options, thus improving the estimation accuracy of ETF option prices effectively.