This paper introduces IPSO algorithm to adaptively match the hyperparameter of LSTM neural network,and proposes a new IPSO-LSTM-Heston option pricing model combined with Heston model.In order to test the pricing effect,an empirical analysis is carried out based on the high-frequency data of 50ETF option.The results show that:IPSO algorithm has excellent global optimization ability and convergence speed,which can greatly improve the pricing efficiency of LSTM hybrid neural network model.By combining the LSTM neural network model optimized by IPSO algorithm with Heston model,the model proposed in this paper can not only capture the dynamic characteristics of high-frequency data,but also give full play to the nonlinear fitting ability of neural network model and the tightness of traditional model pricing process,so as to significantly improve the pricing accuracy while reducing the pricing error of the model.
Option PricingParticle Swarm OptimizerLSTM Neural NetworkHybrid Neural Network Model
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