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Forecasting realized volatility with machine learning: Panel data perspective

Haibin Zhu, Lu Bai, Lidan He and Zhi Liu

Journal of Empirical Finance, 2023, vol. 73, issue C, 251-271

Abstract: Machine learning approaches have become very popular in many fields in this big data age. This paper considers the problem of forecasting realized volatility with machine learning using high-frequency data. Instead of treating the realized volatility as a univariate time series studied by many existing works in literature, we employ panel data analysis to improve forecasting accuracy in the short term. We use six effective machine-learning methods for the realized volatility panel data. We compare our results with the traditional linear-type models under the same panel data framework and with the single time series forecasting via the same machine learning methods. The results show that the panel-data-based machine learning method (PDML) outperforms the other methods.

Keywords: Realized volatility; Panel data analysis; Machine learning; Forecasting (search for similar items in EconPapers)
JEL-codes: C13 C14 G10 G12 (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:73:y:2023:i:c:p:251-271

DOI: 10.1016/j.jempfin.2023.07.003

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Journal of Empirical Finance is currently edited by R. T. Baillie, F. C. Palm, Th. J. Vermaelen and C. C. P. Wolff

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