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LSTM-based option pricing: evaluating model performance through input variation and SHAP interpretation
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The rapid growth in options trading volume highlights the need for accurate
pricing models in financial markets. Traditional models like Black-Scholes provide a
foundational approach but often have limitation due to their assumptions of constant
volatility and normally distributed returns, which do not align with real world data.
These limitations result in pricing inaccuracies, creating a demand for more flexible
and robust models.
Machine learning models, particularly LSTMs, present a strong alternative by
capturing complex financial patterns without relying on strict assumptions. However, their predictive power can make them black boxes, making it difficult to understand their decision-making processes. This lack of interpretability limits their
use in finance, where transparency is critical.
To address these challenges, this study applies an LSTM model along with SHAP
to create a transparent and accurate approach for option pricing. Four models were
developed for call and put options, progressively adding features: traditional BlackScholes variables, GARCH volatility, Black-Scholes prices and a range of market
indicators like technical indicators, trading volume, the S&P 500 index and the
VIX. The models’ performance was evaluated in different market conditions and
SHAP analysis provided insights into feature importance.
The results show that historical volatility is consistently the most significant factor in predicting option prices. The inclusion of GARCH volatility and technical
indicators improves model performance, particularly for high-strike prices. SHAP
analysis highlights the relative importance of features, confirming that while technical indicators are relevant under specific conditions, volatility remains the dominant
factor. Model 4, which includes all features, demonstrates the best overall performance, making it the preferred approach for accurate and interpretable option
pricing. This work contributes to Explainable AI in finance, offering a transparent,
data-driven solution for option valuation.
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Citação
Delgado, Francisco R. (2024). “LSTM-based option pricing: evaluating model performance through input variation and SHAP interpretation”. Dissertação de Mestrado. Universidade de Lisboa. Instituto Superior de Economia e Gestão
Editora
Instituto Superior de Economia e Gestão