A Practical Guide to Harnessing the HAR Volatility Model

57 Pages Posted: 8 May 2019 Last revised: 11 Aug 2021

See all articles by Adam Clements

Adam Clements

Queensland University of Technology - School of Economics and Finance; Queensland University of Technology - QUT Centre for Data Science

Daniel P. A. Preve

Singapore Management University

Date Written: August 2, 2021

Abstract

The standard heterogeneous autoregressive (HAR) model is perhaps the most popular benchmark model for forecasting return volatility. It is often estimated using raw realized variance (RV) and ordinary least squares (OLS). However, given the stylized facts of RV and well-known properties of OLS, this combination should be far from ideal. The aim of this paper is to investigate how the predictive accuracy of the HAR model depends on the choice of estimator, transformation, or combination scheme made by the market practitioner. In an out-of-sample study, covering the S&P 500 index and 26 frequently traded NYSE stocks, it is found that simple remedies systematically outperform not only standard HAR but also state of the art HARQ forecasts.

Keywords: Volatility forecasting, realized variance, HAR, HARQ, robust regression, weighted least squares, Box-Cox transformation, forecast comparisons, QLIKE, MSE, VaR, model confidence set

JEL Classification: C22, C51, C52, C53, C58

Suggested Citation

Clements, Adam and Preve, Daniel P. A., A Practical Guide to Harnessing the HAR Volatility Model (August 2, 2021). Journal of Banking and Finance, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3369484 or http://dx.doi.org/10.2139/ssrn.3369484

Adam Clements (Contact Author)

Queensland University of Technology - School of Economics and Finance ( email )

GPO Box 2434
2 George Street
Brisbane, Queensland 4001
Australia

Queensland University of Technology - QUT Centre for Data Science ( email )

2 George Street
Brisbane, Queensland 4000
Australia

HOME PAGE: http://https://research.qut.edu.au/qutcds/

Daniel P. A. Preve

Singapore Management University ( email )

90 Stamford Road
Singapore, 178903
Singapore

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