A practical guide to harnessing the HAR volatility model
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...
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sg-smu-ink.soe_research-34882021-09-28T07:33:00Z A practical guide to harnessing the HAR volatility model CLEMENTS, Adam PREVE, Daniel P. A. 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. 2021-12-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/2489 info:doi/10.1016/j.jbankfin.2021.106285 https://ink.library.smu.edu.sg/context/soe_research/article/3488/viewcontent/SSRN.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Volatility forecasting Realized variance HARHARQ Robust regression Weighted least squares Box-Cox transformation Forecast comparisons QLIKE MSE VaR Model confidence set Econometrics |
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Volatility forecasting Realized variance HARHARQ Robust regression Weighted least squares Box-Cox transformation Forecast comparisons QLIKE MSE VaR Model confidence set Econometrics |
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Volatility forecasting Realized variance HARHARQ Robust regression Weighted least squares Box-Cox transformation Forecast comparisons QLIKE MSE VaR Model confidence set Econometrics CLEMENTS, Adam PREVE, Daniel P. A. A practical guide to harnessing the HAR volatility model |
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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. |
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text |
| author |
CLEMENTS, Adam PREVE, Daniel P. A. |
| author_facet |
CLEMENTS, Adam PREVE, Daniel P. A. |
| author_sort |
CLEMENTS, Adam |
| title |
A practical guide to harnessing the HAR volatility model |
| title_short |
A practical guide to harnessing the HAR volatility model |
| title_full |
A practical guide to harnessing the HAR volatility model |
| title_fullStr |
A practical guide to harnessing the HAR volatility model |
| title_full_unstemmed |
A practical guide to harnessing the HAR volatility model |
| title_sort |
practical guide to harnessing the har volatility model |
| publisher |
Institutional Knowledge at Singapore Management University |
| publishDate |
2021 |
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https://ink.library.smu.edu.sg/soe_research/2489 https://ink.library.smu.edu.sg/context/soe_research/article/3488/viewcontent/SSRN.pdf |
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