A novel cluster HAR-type model for forecasting realized volatility

Xingzhi Yao, Marwan Izzeldin and Zhenxiong Li

International Journal of Forecasting, 2019, vol. 35, issue 4, 1318-1331

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 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:35:y:2019:i:4:p:1318-1331

DOI: 10.1016/j.ijforecast.2019.04.017

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