An approach for forecasting the Onion Price volatility in Indian Wholesale Markets using hybrid GARCH-LSTM Deep learning models

Abstract The fluctuations in the Onion prices have led to political and economic ramifications in countries such as India. In this study, we intend to estimate and then forecast the price volatility of Onion sales prices in major Indian wholesale markets. Initially, we take daily price data from major vegetable wholesale markets across India and simulate them to compute corresponding daily conditional volatilities using the traditional GARCH method. We then forecast the volatilities for the upcoming 10,15 and 21 days using the same traditional GARCH method and compare its forecasting accuracy with recent AI-led models. According to our comparisons, the deep learning-based LSTM model with various configurations provides superior results when compared to other traditional models with the highest accuracy in more than 70% of the cases. We expect that the given study could help the policymakers in managing sufficient buffer stock levels and the food supply chain stakeholders in hedging against the overall market risks due to the fluctuations in prices.