A novel cluster HAR-type model for forecasting realized volatility

Yao, Xingzhi and Izzeldin, Marwan and Li, Zhenxiong (2019) A novel cluster HAR-type model for forecasting realized volatility. International Journal of Forecasting, 35. pp. 1318-1331. ISSN 0169-2070

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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.

Item Type:
Journal Article
Journal or Publication Title:
International Journal of Forecasting
Additional Information:
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
Uncontrolled Keywords:
?? heterogeneous autoregressive modelclusteringlassorealized volatilityvolatility forecastbusiness and international management ??
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Deposited On:
15 Aug 2019 10:40
Last Modified:
17 Dec 2023 01:45