Innovative Study on Volatility Prediction Model for New Energy Stock Indices
Stock market volatility is a pivotal research area in finance, and accurately forecasting stock market volatility has long been a challenge for both academia and practice. The emergence of the new energy industry has drawn widespread attention to new energy stock indices; however, research on foreca...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10856001/ |
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| author | Yanguo Li Chao Long |
| author_facet | Yanguo Li Chao Long |
| author_sort | Yanguo Li |
| collection | DOAJ |
| description | Stock market volatility is a pivotal research area in finance, and accurately forecasting stock market volatility has long been a challenge for both academia and practice. The emergence of the new energy industry has drawn widespread attention to new energy stock indices; however, research on forecasting their volatility remains limited. To address this, this paper proposes a deep learning ensemble model based on decomposition optimization for predicting the volatility of new energy stock indices. The model comprises three components: variational mode decomposition (VMD), sparrow search algorithm (SSA), and echo state network (ESN). Initially, this paper employs VMD to decompose the original volatility series of new energy stock indices into multiple subsequences. Subsequently, SSA is utilized to optimize ESN. Finally, the constructed VMD-SSA-ESN model is employed to forecast the volatility of new energy stock indices. Through comparative analysis with other forecasting models, this paper finds that the VMD-SSA-ESN model exhibits significantly better forecasting performance across all selected new energy stock index volatility predictions. The research results indicate that the model constructed in this paper can adequately capture the characteristics of the volatility series, and both VMD and SSA effectively enhance the model’s forecasting accuracy and stability. This study can provide robust support for investment decision-making and risk management in the new energy stock market. |
| format | Article |
| id | doaj-art-1486a896e5434fba8ee8a82c013dce9d |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-1486a896e5434fba8ee8a82c013dce9d2025-08-20T02:15:28ZengIEEEIEEE Access2169-35362025-01-0113297542977710.1109/ACCESS.2025.353558410856001Innovative Study on Volatility Prediction Model for New Energy Stock IndicesYanguo Li0https://orcid.org/0009-0000-7501-3066Chao Long1School of Economics, Yunnan University of Finance and Economics, Kunming, ChinaFinance Research Institute, Yunnan University of Finance and Economics, Kunming, ChinaStock market volatility is a pivotal research area in finance, and accurately forecasting stock market volatility has long been a challenge for both academia and practice. The emergence of the new energy industry has drawn widespread attention to new energy stock indices; however, research on forecasting their volatility remains limited. To address this, this paper proposes a deep learning ensemble model based on decomposition optimization for predicting the volatility of new energy stock indices. The model comprises three components: variational mode decomposition (VMD), sparrow search algorithm (SSA), and echo state network (ESN). Initially, this paper employs VMD to decompose the original volatility series of new energy stock indices into multiple subsequences. Subsequently, SSA is utilized to optimize ESN. Finally, the constructed VMD-SSA-ESN model is employed to forecast the volatility of new energy stock indices. Through comparative analysis with other forecasting models, this paper finds that the VMD-SSA-ESN model exhibits significantly better forecasting performance across all selected new energy stock index volatility predictions. The research results indicate that the model constructed in this paper can adequately capture the characteristics of the volatility series, and both VMD and SSA effectively enhance the model’s forecasting accuracy and stability. This study can provide robust support for investment decision-making and risk management in the new energy stock market.https://ieeexplore.ieee.org/document/10856001/Volatility predictiondeep learningvariational mode decompositionoptimization algorithmnew energy |
| spellingShingle | Yanguo Li Chao Long Innovative Study on Volatility Prediction Model for New Energy Stock Indices IEEE Access Volatility prediction deep learning variational mode decomposition optimization algorithm new energy |
| title | Innovative Study on Volatility Prediction Model for New Energy Stock Indices |
| title_full | Innovative Study on Volatility Prediction Model for New Energy Stock Indices |
| title_fullStr | Innovative Study on Volatility Prediction Model for New Energy Stock Indices |
| title_full_unstemmed | Innovative Study on Volatility Prediction Model for New Energy Stock Indices |
| title_short | Innovative Study on Volatility Prediction Model for New Energy Stock Indices |
| title_sort | innovative study on volatility prediction model for new energy stock indices |
| topic | Volatility prediction deep learning variational mode decomposition optimization algorithm new energy |
| url | https://ieeexplore.ieee.org/document/10856001/ |
| work_keys_str_mv | AT yanguoli innovativestudyonvolatilitypredictionmodelfornewenergystockindices AT chaolong innovativestudyonvolatilitypredictionmodelfornewenergystockindices |