Forecasting of clean energy market volatility: The role of oil and the technology sector

Štefan Lyócsa and Neda Todorova

Energy Economics, 2024, vol. 132, issue C

Abstract: This study is the first to explore whether the well-known relationship between the clean energy sector, oil prices, and technology stocks can be leveraged to enhance the accuracy of realized volatility forecasts for individual clean energy sub-sectors. Based on intraday data and various decompositions of daily realized volatility, we account for the heterogeneity across clean energy sub-sectors using the dynamic common correlated effect heterogeneous autoregressive (DCCE-HAR) model. Our findings reveal that, in the short term, price variations in technology shares are more informative for future clean energy volatility than fluctuations in oil prices. In an out-of-sample analysis, we individually forecast the volatility of each clean energy sub-index using Lasso, Ridge, and random forest approaches. We identify sub-indices that systematically benefit from technology sector price variation (e.g. Smart Grid, Operators, Energy Management), sub-indices that benefit from oil price variation (e.g. Bio Fuel, Wind and Geothermal), while also sub-indices that show limited sensitivity to price variation in the technology and oil markets.

Keywords: Clean energy; Energy transition; Technology stocks; Volatility; Forecasting (search for similar items in EconPapers)
JEL-codes: G1 G17 Q42 Q54 Q55 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:132:y:2024:i:c:s0140988324001592

DOI: 10.1016/j.eneco.2024.107451

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