A Forest Full of Risk Forecasts for Managing Volatility

34 Pages Posted: 22 Jul 2022 Last revised: 25 Jul 2022

See all articles by Onno Kleen

Onno Kleen

Erasmus University Rotterdam (EUR) - Erasmus School of Economics (ESE)

Anastasija Tetereva

Erasmus University Rotterdam (EUR) - Erasmus School of Economics (ESE)

Date Written: July 13, 2022

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.

Keywords: Volatility forecasting, local linear random forest, pooled estimation

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

Suggested Citation

Kleen, Onno and Tetereva, Anastasija, A Forest Full of Risk Forecasts for Managing Volatility (July 13, 2022). Available at SSRN: https://ssrn.com/abstract=4161957 or http://dx.doi.org/10.2139/ssrn.4161957

Onno Kleen

Erasmus University Rotterdam (EUR) - Erasmus School of Economics (ESE) ( email )

P.O. Box 1738
3000 DR Rotterdam, NL 3062 PA
Netherlands

Anastasija Tetereva (Contact Author)

Erasmus University Rotterdam (EUR) - Erasmus School of Economics (ESE) ( email )

Burg. Oudlaan 50
tetereva@ese.eur.nl
Rotterdam, 9008
Netherlands

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