Publication Type
Journal Article
Version
acceptedVersion
Publication Date
12-2021
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, HARHARQ, Robust regression, Weighted least squares, Box-Cox transformation, Forecast comparisons, QLIKE, MSE, VaR, Model confidence set
Discipline
Econometrics
Research Areas
Econometrics
Publication
Journal of Banking and Finance
Volume
133
First Page
1
Last Page
16
ISSN
0378-4266
Identifier
10.1016/j.jbankfin.2021.106285
Publisher
Elsevier
Citation
1
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.