American Option Pricing using Self-Attention GRU and Shapley Value
  Interpretation
Yanhui Shen
Options, serving as a crucial financial instrument, are used by investors to
manage and mitigate their investment risks within the securities market.
Precisely predicting the present price of an option enables investors to make
informed and efficient decisions. In this paper, we propose a machine learning
method for forecasting the prices of SPY (ETF) option based on gated recurrent
unit (GRU) and self-attention mechanism. We first partitioned the raw dataset
into 15 subsets according to moneyness and days to maturity criteria. For each
subset, we matched the corresponding U.S. government bond rates and Implied
Volatility Indices. This segmentation allows for a more insightful exploration
of the impacts of risk-free rates and underlying volatility on option pricing.
Next, we built four different machine learning models, including multilayer
perceptron (MLP), long short-term memory (LSTM), self-attention LSTM, and
self-attention GRU in comparison to the traditional binomial model. The
empirical result shows that self-attention GRU with historical data outperforms
other models due to its ability to capture complex temporal dependencies and
leverage the contextual information embedded in the historical data. Finally,
in order to unveil the "black box" of artificial intelligence, we employed the
SHapley Additive exPlanations (SHAP) method to interpret and analyze the
prediction results of the self-attention GRU model with historical data. This
provides insights into the significance and contributions of different input
features on the pricing of American-style options.