The Economic Value of Equity Implied Volatility Forecasting with Machine Learning

59 Pages Posted: 15 Oct 2020

See all articles by Paul Borochin

Paul Borochin

University of Miami - Department of Finance

Yanhui Zhao

University of Wisconsin - Whitewater - College of Business and Economics

Date Written: September 1, 2020

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.

Keywords: Volatility Forecasting, Options, Return Predictability, Machine Learning

JEL Classification: G12, G13

Suggested Citation

Borochin, Paul and Zhao, Yanhui, The Economic Value of Equity Implied Volatility Forecasting with Machine Learning (September 1, 2020). Available at SSRN: https://ssrn.com/abstract=3684864 or http://dx.doi.org/10.2139/ssrn.3684864

Paul Borochin (Contact Author)

University of Miami - Department of Finance ( email )

P.O. Box 248094
Coral Gables, FL 33124-6552
United States

Yanhui Zhao

University of Wisconsin - Whitewater - College of Business and Economics ( email )

Whitewater, WI 53190
United States

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
85
Abstract Views
310
rank
330,646
PlumX Metrics