A practical guide to harnessing the HAR volatility model

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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Bibliographic Details
Main Authors: CLEMENTS, Adam, PREVE, Daniel P. A.
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2021
Subjects:
Volatility forecasting
Realized variance
HARHARQ
Robust regression
Weighted least squares
Box-Cox transformation
Forecast comparisons
QLIKE
MSE
VaR
Model confidence set
Econometrics
Online Access:https://ink.library.smu.edu.sg/soe_research/2487
https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=3486&context=soe_research
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Institution: Singapore Management University
Language: English
Description
Summary: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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