The relationship between renewable energy attention and volatility: A HAR model with markov time-varying transition probability

Huayou Duan, Chenchen Zhao, Lu Wang and Guangqiang Liu

Research in International Business and Finance, 2024, vol. 71, issue C

Abstract: This study investigated whether a renewable energy attention (REA) index, based on natural language processing, Google search volume data, and dimensionality reduction methodology, can predict crude oil volatility. Considering the possible non-linear and time-varying effects, we adopted the time-varying transition probability Markov switching heterogeneous autoregressive-realized volatility (TVTP-MS-HAR-realized volatility (RV)) model. To further represent the impacts of REA, we developed an asymmetric TVTP-MS-HAR-RV model based on this model framework, i.e., the ASTVTP-MS-HAR-RV model. According to the results, the in-sample estimates indicated that West Texas Intermediate (WTI) volatility is more affected by negative REA shocks than by positive ones. Moreover, REA predicted WTI volatility better during low-volatility periods than in high ones. According to the out-of-sample findings, the ASTVTP-MS-HAR-RV-F model outperformed other competing models, indicating that time-varying transition probabilities and REA information can significantly improve volatility forecasting performance.

Keywords: Renewable energy attention; Volatility prediction; Natural language processing; Markov switching; Time-varying transition probability (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:riibaf:v:71:y:2024:i:c:s0275531924002307

DOI: 10.1016/j.ribaf.2024.102437

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