A Practical Guide to Harnessing the HAR Volatility Model

33 Pages Posted: 8 May 2019 Last revised: 16 May 2019

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: May 14, 2019

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. One goal of this paper is to investigate how the predictive accuracy of the HAR model depends on the choice of estimator, transformation, and forecasting scheme made by the market practitioner. Another goal is to examine the effect of replacing its high-frequency data based volatility proxy (RV) with a proxy based on free and publicly available low-frequency data (logarithmic range). In an out-of-sample study, covering three major stock market indices over 16 years, it is found that simple remedies systematically outperform not only standard HAR but also state of the art HARQ forecasts, and that HAR models using logarithmic range can often produce forecasts of similar quality to those based on RV.

Keywords: Volatility forecasting, realized variance, HAR model, HARQ model, robust regression, Box-Cox transformation, forecast comparisons, QLIKE loss, 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 (May 14, 2019). 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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