Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in Quantitative Finance on 02/06/2022, available online: http://www.tandfonline.com/10.1080/14697688.2022.2076606
Accepted author manuscript, 1.75 MB, PDF document
Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License
Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - A Generalized Heterogeneous Autoregressive Model using the Market Index
AU - Hizmeri, Rodrigo
AU - Izzeldin, Marwan
AU - Nolte, Ingmar
AU - Pappas, Vasileios
N1 - This is an Accepted Manuscript of an article published by Taylor & Francis in Quantitative Finance on 02/06/2022, available online: http://www.tandfonline.com/10.1080/14697688.2022.2076606
PY - 2022/8/31
Y1 - 2022/8/31
N2 - This paper introduces a novel class of volatility forecasting models that incorporate market realized (co)variances and semi(co)variances within the framework of a heterogeneous autoregressive (HAR) model. Our empirical analysis shows statistically and economically significant forecasting gains. For our most parsimonious market-HAR specification, stock volatility forecasting is improved by 9.80% points. Using a mixed sampling frequency market-HAR variant with low (high) sampling frequency for the stock (market) improves forecasting by a further 6.90% points. Our paper also develops noise-robust estimators to facilitate the use of realized semi(co)variances at high sampling frequencies.
AB - This paper introduces a novel class of volatility forecasting models that incorporate market realized (co)variances and semi(co)variances within the framework of a heterogeneous autoregressive (HAR) model. Our empirical analysis shows statistically and economically significant forecasting gains. For our most parsimonious market-HAR specification, stock volatility forecasting is improved by 9.80% points. Using a mixed sampling frequency market-HAR variant with low (high) sampling frequency for the stock (market) improves forecasting by a further 6.90% points. Our paper also develops noise-robust estimators to facilitate the use of realized semi(co)variances at high sampling frequencies.
KW - Realized volatility
KW - Microstructure noise
KW - Pre-averaged estimators
KW - Semi-variance
KW - Semi-covariance
KW - Volatility forecasting
U2 - 10.1080/14697688.2022.2076606
DO - 10.1080/14697688.2022.2076606
M3 - Journal article
VL - 22
SP - 1513
EP - 1534
JO - Quantitative Finance
JF - Quantitative Finance
SN - 1469-7688
IS - 8
ER -