Forecasting the realized volatility of Energy Stock Market: A multimodel comparison

Houjian Li, Deheng Zhou, Jiayu Hu, Junwen Li, Mengying Su and Lili Guo

The North American Journal of Economics and Finance, 2023, vol. 66, issue C

Abstract: The realized volatility forecasting of energy sector stocks facilitates the establishment of corresponding risk warning mechanisms and investor decisions. In this paper, we collected two different energy sector indices and used different methods, namely principal component analysis (PCA) and sparse principal component analysis (SPCA), to extract features, and combined LSTM and GRU to construct 12 different models. The results show that the SPCA-LSTM model we constructed has the best forecasting performance in the realized volatility forecasting of energy indices, and SPCA has better forecasting results than PCA in the feature extraction stage. The results of the robustness test indicate that our results are robust.

Keywords: Deep learning; Energy stock index; Realized volatility; LSTM (search for similar items in EconPapers)
JEL-codes: C45 C52 C53 G11 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:66:y:2023:i:c:s1062940823000189

DOI: 10.1016/j.najef.2023.101895

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