A novel hybrid neural network-based volatility forecasting of agricultural commodity prices: empirical evidence from India
Abstract This study presents a comprehensive analysis of agricultural price volatility forecasting using a hybrid long short-term memory (LSTM)-Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. Agricultural price volatility poses critical challenges for food security, economic...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-04-01
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| Series: | Journal of Big Data |
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| Online Access: | https://doi.org/10.1186/s40537-025-01131-8 |
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| author | R. L. Manogna Vijay Dharmaji S. Sarang |
| author_facet | R. L. Manogna Vijay Dharmaji S. Sarang |
| author_sort | R. L. Manogna |
| collection | DOAJ |
| description | Abstract This study presents a comprehensive analysis of agricultural price volatility forecasting using a hybrid long short-term memory (LSTM)-Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. Agricultural price volatility poses critical challenges for food security, economic stability, and the livelihoods of millions, particularly in developing countries like India. Accurately forecasting these price fluctuations is vital for effective policymaking and strategic decision-making in agricultural markets. This study investigates the potential of deep learning models, specifically LSTM, and their integration with GARCH for forecasting agricultural commodity price volatility. Using extensive historical price data for 23 commodities across 165 markets in India from February 2010 to June 2024, the proposed hybrid model demonstrates significantly enhanced accuracy and robustness compared to standalone econometric or deep learning models. The results suggest that this hybrid approach effectively addresses price instability, offering improved predictive capabilities. These findings provide valuable implications for policymakers and stakeholders, emphasizing the adoption of advanced machine learning techniques for better market risk management and policy interventions tailored to agricultural price dynamics. |
| format | Article |
| id | doaj-art-04fd1f01f62b4931a5e8fc33b8f01d59 |
| institution | OA Journals |
| issn | 2196-1115 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj-art-04fd1f01f62b4931a5e8fc33b8f01d592025-08-20T02:16:06ZengSpringerOpenJournal of Big Data2196-11152025-04-0112111910.1186/s40537-025-01131-8A novel hybrid neural network-based volatility forecasting of agricultural commodity prices: empirical evidence from IndiaR. L. Manogna0Vijay Dharmaji1S. Sarang2Department of Economics and Finance, Birla Institute of Technology and Science, Pilani, K K Birla Goa CampusDepartment of Economics and Finance, Birla Institute of Technology and Science, Pilani, K K Birla Goa CampusDepartment of Economics and Finance, Birla Institute of Technology and Science, Pilani, K K Birla Goa CampusAbstract This study presents a comprehensive analysis of agricultural price volatility forecasting using a hybrid long short-term memory (LSTM)-Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. Agricultural price volatility poses critical challenges for food security, economic stability, and the livelihoods of millions, particularly in developing countries like India. Accurately forecasting these price fluctuations is vital for effective policymaking and strategic decision-making in agricultural markets. This study investigates the potential of deep learning models, specifically LSTM, and their integration with GARCH for forecasting agricultural commodity price volatility. Using extensive historical price data for 23 commodities across 165 markets in India from February 2010 to June 2024, the proposed hybrid model demonstrates significantly enhanced accuracy and robustness compared to standalone econometric or deep learning models. The results suggest that this hybrid approach effectively addresses price instability, offering improved predictive capabilities. These findings provide valuable implications for policymakers and stakeholders, emphasizing the adoption of advanced machine learning techniques for better market risk management and policy interventions tailored to agricultural price dynamics.https://doi.org/10.1186/s40537-025-01131-8Price volatilityNeural networksForecastingAgricultural commoditiesHybrid modelDeep learning |
| spellingShingle | R. L. Manogna Vijay Dharmaji S. Sarang A novel hybrid neural network-based volatility forecasting of agricultural commodity prices: empirical evidence from India Journal of Big Data Price volatility Neural networks Forecasting Agricultural commodities Hybrid model Deep learning |
| title | A novel hybrid neural network-based volatility forecasting of agricultural commodity prices: empirical evidence from India |
| title_full | A novel hybrid neural network-based volatility forecasting of agricultural commodity prices: empirical evidence from India |
| title_fullStr | A novel hybrid neural network-based volatility forecasting of agricultural commodity prices: empirical evidence from India |
| title_full_unstemmed | A novel hybrid neural network-based volatility forecasting of agricultural commodity prices: empirical evidence from India |
| title_short | A novel hybrid neural network-based volatility forecasting of agricultural commodity prices: empirical evidence from India |
| title_sort | novel hybrid neural network based volatility forecasting of agricultural commodity prices empirical evidence from india |
| topic | Price volatility Neural networks Forecasting Agricultural commodities Hybrid model Deep learning |
| url | https://doi.org/10.1186/s40537-025-01131-8 |
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