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  • SSRN-id3342090

    Rights statement: This is the author’s version of a work that was accepted for publication in International Journal of Forecasting. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Forecasting, 35, 2019 DOI: 10.1016/j.ijforecast.2019.04.017

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A novel cluster HAR-type model for forecasting realized volatility

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A novel cluster HAR-type model for forecasting realized volatility. / Yao, Xingzhi; Izzeldin, Marwan; Li, Zhenxiong.

In: International Journal of Forecasting, Vol. 35, 01.10.2019, p. 1318-1331.

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Yao, Xingzhi ; Izzeldin, Marwan ; Li, Zhenxiong. / A novel cluster HAR-type model for forecasting realized volatility. In: International Journal of Forecasting. 2019 ; Vol. 35. pp. 1318-1331.

Bibtex

@article{4ff44389a9b246df9f1a4e1ab902dd9f,
title = "A novel cluster HAR-type model for forecasting realized volatility",
abstract = "This paper proposes a cluster HAR-type model that adopts the hierarchical clustering technique to form the cascade of heterogeneous volatility components. In contrast to the conventional HAR-type models, the proposed cluster models are based on the relevant lagged volatilities selected by the cluster group Lasso. Our simulation evidence suggests that the cluster group Lasso dominates other alternatives in terms of variable screening and that the cluster HAR serves as the top performer in forecasting the future realized volatility. The forecasting superiority of the cluster models are also demonstrated in an empirical application where the highest forecasting accuracy tends to be achieved by separating the jumps from the continuous sample path volatility process.",
keywords = "Heterogeneous autoregressive model, Clustering, Lasso, Realized volatility, Volatility forecast",
author = "Xingzhi Yao and Marwan Izzeldin and Zhenxiong Li",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in International Journal of Forecasting. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Forecasting, 35, 2019 DOI: 10.1016/j.ijforecast.2019.04.017",
year = "2019",
month = oct,
day = "1",
doi = "10.1016/j.ijforecast.2019.04.017",
language = "English",
volume = "35",
pages = "1318--1331",
journal = "International Journal of Forecasting",
issn = "0169-2070",
publisher = "Elsevier Science B.V.",

}

RIS

TY - JOUR

T1 - A novel cluster HAR-type model for forecasting realized volatility

AU - Yao, Xingzhi

AU - Izzeldin, Marwan

AU - Li, Zhenxiong

N1 - This is the author’s version of a work that was accepted for publication in International Journal of Forecasting. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Forecasting, 35, 2019 DOI: 10.1016/j.ijforecast.2019.04.017

PY - 2019/10/1

Y1 - 2019/10/1

N2 - This paper proposes a cluster HAR-type model that adopts the hierarchical clustering technique to form the cascade of heterogeneous volatility components. In contrast to the conventional HAR-type models, the proposed cluster models are based on the relevant lagged volatilities selected by the cluster group Lasso. Our simulation evidence suggests that the cluster group Lasso dominates other alternatives in terms of variable screening and that the cluster HAR serves as the top performer in forecasting the future realized volatility. The forecasting superiority of the cluster models are also demonstrated in an empirical application where the highest forecasting accuracy tends to be achieved by separating the jumps from the continuous sample path volatility process.

AB - This paper proposes a cluster HAR-type model that adopts the hierarchical clustering technique to form the cascade of heterogeneous volatility components. In contrast to the conventional HAR-type models, the proposed cluster models are based on the relevant lagged volatilities selected by the cluster group Lasso. Our simulation evidence suggests that the cluster group Lasso dominates other alternatives in terms of variable screening and that the cluster HAR serves as the top performer in forecasting the future realized volatility. The forecasting superiority of the cluster models are also demonstrated in an empirical application where the highest forecasting accuracy tends to be achieved by separating the jumps from the continuous sample path volatility process.

KW - Heterogeneous autoregressive model

KW - Clustering

KW - Lasso

KW - Realized volatility

KW - Volatility forecast

U2 - 10.1016/j.ijforecast.2019.04.017

DO - 10.1016/j.ijforecast.2019.04.017

M3 - Journal article

VL - 35

SP - 1318

EP - 1331

JO - International Journal of Forecasting

JF - International Journal of Forecasting

SN - 0169-2070

ER -