We evaluate the performance of several linear and nonlinear machine learning models in forecasting the realized volatility (RV) of ten global stock market indices in the period from January 2000 to December 2021. We train models using a dataset which includes past values of the RV and additional...

We evaluate the performance of several linear and nonlinear machine learning (ML) models in forecasting the realized volatility (RV) of ten global stock market indices in the period from January 2000 to December 2021. We train models using a dataset that includes past values of the RV and...

We pit individual theoretical predictors of the equity premium against a variety of data-driven statistical methods. Theoretically motivated predictive regressions outperform conventional penalised regressions but have similar out-of-sample R2 and lower economic gains relative to more agnostic...