A Forest Full of Risk Forecasts for Managing Volatility

33 Pages Posted: 1 Mar 2023 Last revised: 21 Jun 2023

See all articles by Onno Kleen

Onno Kleen

Erasmus University Rotterdam (EUR) - Erasmus School of Economics (ESE); Tinbergen Institute

Anastasija Tetereva

Erasmus University Rotterdam (EUR) - Erasmus School of Economics (ESE); Tinbergen Institute

Date Written: November 20, 2022

Abstract

We propose a novel approach to cross-sectional forecasting of stock return volatility, by utilizing a heterogeneous autoregressive (HAR) model with time-varying parameters in the form of a local linear forest. Unlike traditional random forests, which approximate volatility nonparametrically through local averaging, the foundation of our forest is composed of HAR panel models. These local models capture established linear relationships in realized variances, while the trees are utilized to model nonlinearities and interactions. This approach allows the model coefficients to be driven by both idiosyncratic stock information and changing market conditions. Our 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 performance of the HAR forest is observed uniformly across firm characteristics.

Keywords: Risk management, volatility forecasting, local linear forest, firm characteristics, 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 (November 20, 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

Tinbergen Institute ( email )

Burg. Oudlaan 50
Rotterdam, 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

Tinbergen Institute ( email )

Burg. Oudlaan 50
Rotterdam, 3062 PA
Netherlands

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