Prediction Research on Implied Volatility Surface of Shanghai Stock Exchange 50 ETF Options——Based on Integrated GRU Neural Network Incorporating Prior Financial Knowledge
Based on the research framework of Zheng et al,which incorporates prior financial knowledge into the design and training of neural networks,an integrated GRU neural network model for predicting implicit vola-tility surfaces is proposed.The model uses an activation function that includes a volatility smile,and incorpo-rates financial conditions such as arbitrage,left and right boundaries,and asymptotic slopes into the training process of the neural network.Empirical analysis was conducted using trading data from the Shanghai 50 ETF options from February 9,2015 to March 31,2023.The empirical results show that compared to the SSVI model and the benchmark neural network model,the integrated GRU model has the highest prediction accuracy among all models with an average absolute percentage error of 8.56 on the training set and 11.17 on the test set,while satisfying embedded financial conditions.
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