The Economic Value of Equity Implied Volatility Forecasting with Machine Learning

Abstract

We evaluate the importance of nonlinear interactions in volatility forecasting by comparing the predictive power of decision tree ensemble models relative to classical ones for normalized at-the-money implied volatility innovations. We measure the economic significance of these predictions in cross-sectional and time series pricing tests of delta-hedged option returns. Classification tree ensembles outperform a multinomial logit classifier by 0.35% to 0.46% monthly abnormal returns in delta-hedged option portfolio sorts on volatility innovation forecast data, while regression tree ensembles outperform OLS and LASSO models by 0.03% to 0.14%. Since the predictive variables are the same across all models, these performance differences likely capture the value of nonlinear interactions in implied volatility forecasts. Our results are robust to look-ahead bias and model over-fitting.