Can Machine Learning Algorithms Outperform Traditional Models for Option Pricing?

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Highly relevant for quantitative trading desks involved in derivatives pricing, volatility trading, and options market-making. The findings demonstrate that ML models (especially CatBoost) can achieve near-perfect pricing accuracy (R² > 0.999) on real market data while offering faster prediction times than traditional models. This is directly applicable to real-time option valuation, hedging strategy construction, and arbitrage detection. However, the single-stock limitation and lack of temporal validation reduce immediate production readiness.

Moderate complexity. The models used (MLP, Random Forest, CatBoost) are well-supported by scikit-learn and CatBoost libraries. Hyperparameter tuning via RandomizedSearchCV and BayesSearchCV is standard. The Heston model implementation uses QuantLib's AnalyticHestonEngine. Data preprocessing (yield curve interpolation, bid-ask midpoint calculation) adds some complexity but is well-documented. No custom architectures or distributed computing required.

All code is available on GitHub including data generation, model implementation, and evaluation scripts. Real market data sourced from Yahoo Finance API and U.S. Treasury Par Yield Curve. Heston parameters are explicitly stated. However, the exact GitHub URL is not provided in the extract, and the real market dataset (1,087 AAPL contracts) may not be fully reproducible due to time-dependent market data.