The Economic Value of Equity Implied Volatility Forecasting with Machine Learning
59 Pages Posted: 15 Oct 2020
Date Written: September 1, 2020
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.
Keywords: Volatility Forecasting, Options, Return Predictability, Machine Learning
JEL Classification: G12, G13
Suggested Citation: Suggested Citation