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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

    Accepted author manuscript, 650 KB, PDF-document

    Embargo ends: 14/08/21

    Available under license: CC BY-NC-ND

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A novel cluster HAR-type model for forecasting realized volatility

Research output: Contribution to journalJournal article

E-pub ahead of print
<mark>Journal publication date</mark>1/10/2019
<mark>Journal</mark>International Journal of Forecasting
Volume35
Number of pages14
Pages (from-to)1318-1331
Publication statusE-pub ahead of print
Early online date14/08/19
Original languageEnglish

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.

Bibliographic note

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