A forest full of risk forecasts for managing volatility

Abstract

We propose a heterogeneous autoregressive (HAR) model with time-varying parameters in the form of a local linear random forest. In contrast to conventional random forests that approximate the volatility nonparametrically using local averaging, the building blocks of our forest are HAR panel models. The local HAR panel models cover the established linear relationship in realized variances while the trees model nonlinearities and interaction effects. Our approach allows the model coefficients to depend on idiosyncratic stock information and overall changing market conditions. We observe superior risk forecasting performance of the HAR forest across multiple forecast horizons and across 186 S&P 500 constituents. This leads to significantly higher utility for volatility managed portfolios. Superior forecast performance is especially pronounced for firms with high leverage.