Reliable option pricing through deep learning: An anomaly score-based approach

We propose a neural-network variant integrating the Isolation Forest anomaly detection algorithm into its loss function. By incorporating anomaly scores as weights—effectively treating them as inverse measures of data reliability—the model suppresses outlier impact, yielding modest but consistent accuracy gains. Using KOSPI 200 option price data from 2019 to 2023, our experiments show that this anomaly-based approach enhances predictive accuracy by an average of 4.77% on the test set compared to a baseline neural network. Moreover, performance gains are generally observed across various market conditions, including different moneyness states, trading volumes, and time to maturity. Analysis of the identified anomalies reveals that trading volume and time to maturity are key factors strongly associated with irregularities in option data. Option moneyness also contributes to these irregularity patterns, particularly with other market conditions or at extreme levels. In contrast, interest rates show a less direct impact on anomaly scores in our dataset. These findings are broadly consistent with established market regularities, suggesting the anomaly detector's effectiveness in capturing characteristics of market inefficiencies or challenging pricing conditions. Overall, the proposed methodology contributes to the development of a more robust option pricing framework by better reflecting actual market dynamics. It shows potential during periods of heightened volatility, offering useful insights for further academic and practical applications.