The economic value of equity implied volatility forecasting with machine learning

We evaluate the importance of nonlinear and interactive effects in implied volatility innovation forecasting by comparing the performance of machine learning models that can search for interactive effects relative to classical ones that cannot, measuring the economic significance of these predictions in cross-sectional and time series pricing tests of delta-hedged option returns. Machine learning models offer superior out of sample performance. Since the predictive variables are the same across all models, these performance differences likely capture the value of nonlinear and interactive effects in implied volatility forecasts. Our results are robust to look-ahead bias and model overfitting.