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 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.