A Forest Full of Risk Forecasts for Managing Volatility

Research output: Working paperPreprintAcademic


We propose a hybrid approach to modeling stock return volatility in a large panel of stocks that combines the machine learning principle random forest with ordinary least squares regression models. Our model's time-varying parameters are assumed to be data-driven functions of idiosyncratic stock information and changing market conditions. The empirical analysis demonstrates our model’s superior risk forecasting performance across multiple forecast horizons and 186 S&P 500 constituents, resulting in significantly higher utility for volatility-managed investments. Furthermore, this superior forecast performance is observed uniformly across firm characteristics.
Original languageEnglish
Publication statusPublished - 13 Jul 2022

Bibliographical note

JEL Classification: C32, C53, C55, C58, G17

Erasmus Sectorplan

  • Sectorplan SSH-Breed


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