Research On ETF Option Pricing Based On LSTM Hybrid Neural Network Model

With the increasing improvement of China’s financial market system,the financial market has become an important place for price discovery and risk management,especially as financial derivatives provide flexible and varied risk management methods,providing important guarantees for the stable and orderly development of the financial market.As an important component of financial derivatives,ETF options have greatly promoted the development of China’s financial market.On the one hand,ETF options can enrich market investment strategies,improve the liquidity of underlying assets,and hedge against asset price fluctuations.On the other hand,ETF options have leverage properties,and if used improperly,they can amplify losses and exacerbate market volatility.Therefore,as a double-edged sword,accurate pricing is the prerequisite and foundation for leveraging the functions of ETF option hedging and risk hedging in the ETF option trading process.How to establish an accurate and efficient ETF option pricing model that takes into account market rules and economic significance is of great significance for the stable development of the financial market.Option pricing models are mainly divided into two parts:parameterized models and non parameterized models,each with its own advantages and disadvantages.Among them,traditional parameterized option pricing models have strict economic logic,but their assumptions are often too ideal.When the assumptions do not match the actual market situation,the pricing effect of the model is not good.Although non parametric neural network models have strong nonlinear fitting and computational abilities,the model itself lacks rigorous economic derivation process,resulting in a lack of economic meaning in the model.Combining traditional parameterized option pricing models with non parameterized neural network models not only has rigorous economic implications,but also is compatible with strong nonlinear fitting capabilities,which can maximize the advantages of each model.Therefore,this article applies the hybrid modeling concept to organically combine the Heston model in parameterized models with the LSTM neural network model in non parameterized models,and introduces an improved particle swarm optimization algorithm to optimize the parameters of the LSTM neural network model,proposing a new IPSO-LSTM-Heston hybrid neural network option pricing model.To verify the pricing effectiveness of the hybrid model,this article sets up multiple control models and conducts empirical analysis based on daily frequency data of Huaxia Shanghai Stock Exchange 50 ETF options,Jiashi Shanghai Shenzhen 300 ETF options,and Huatai Bairui Shanghai Shenzhen 300 ETF options.This article analyzes the pricing errors within and outside the sample of option data,and the main conclusions are as follows:(1)A single neural network model lacks stability in the pricing effect within and outside the sample.(2)The improved particle swarm optimization algorithm has a significant optimization effect on the parameters of the LSTM neural network model.After introducing the improved particle swarm algorithm,the pricing accuracy of the LSTM hybrid neural network model has been greatly improved.(3)The IPSO-LSTM Heston hybrid neural network model proposed in this paper is generally superior to other models in terms of pricing effect.This model can not only capture the dynamic characteristics of ETF option data,but also integrate the advantages of the tightness of the traditional model pricing process and the nonlinear fitting ability of neural network model.It has good robustness and applicability while reducing pricing errors.