Predicting implied volatility using deep learning for option pricing

This study investigates the efficacy of a hybrid deep learning–ensemble methodology for enhancing the accuracy of option pricing by leveraging forecasted implied volatility. Employing a comprehensive, multi-year dataset of Tesla, Inc. options data—including spot price, strike price, the full suite of Greeks, and time to maturity—sourced from the Wharton OptionMetrics directory, we develop a Long Short-Term Memory neural network to forecast future implied volatility. The LSTM model is rigorously benchmarked against traditional statistical moving-average approaches, demonstrating a significant reduction in predictive error relative to conventional benchmarks. Thereafter, the forecasted volatility is incorporated as an additional input feature within a Random Forest regression framework, alongside original market variables, to predict option prices. This hybrid model is trained and validated on historical Tesla options data and subsequently evaluated within a live, real-time trading environment to assess practical performance under market conditions. Empirical findings indicate that the integration of deep learning-derived volatility forecasts materially enhances pricing precision when compared to ensemble models lacking such forecasts. These results underscore the potential of combining sequence-based neural forecasting with ensemble regression techniques to advance quantitative option pricing methodologies and inform more robust risk management and trading strategies.