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Options, serving as a crucial financial instrument, are used by investors tomanage and mitigate their investment risks within the securities market.Precisely predicting the present price of an option enables investors to makeinformed and efficient decisions. In this paper, we propose a machine learningmethod for forecasting the prices of SPY (ETF) option based on gated recurrentunit (GRU) and self-attention mechanism. We first partitioned the raw datasetinto 15 subsets according to moneyness and days to maturity criteria. For eachsubset, we matched the corresponding U.S. government bond rates and ImpliedVolatility Indices. This segmentation allows for a more insightful explorationof the impacts of risk-free rates and underlying volatility on option pricing.Next, we built four different machine learning models, including multilayerperceptron (MLP), long short-term memory (LSTM), self-attention LSTM, andself-attention GRU in comparison to the traditional binomial model. Theempirical result shows that self-attention GRU with historical data outperformsother models due to its ability to capture complex temporal dependencies andleverage the contextual information embedded in the historical data. Finally,in order to unveil the "black box" of artificial intelligence, we employed theSHapley Additive exPlanations (SHAP) method to interpret and analyze theprediction results of the self-attention GRU model with historical data. Thisprovides insights into the significance and contributions of different inputfeatures on the pricing of American-style options.

American Option Pricing using Self-Attention GRU and Shapley Value Interpretation