FROM BLACK GOLD TO GREEN ENERGY: GARCH-BASED FORECASTING OF INDIAN CRUDE OIL PRICE VOLATILITY IN THE ERA OF ENERGY TRANSITION

Abstract

This study investigates the volatility of crude oil prices in India from 2000 to 2025, a period marked by significant market uncertainty and the nation’s transition toward renewable energy sources. Given India’s substantial dependence on imported crude oil, accurate forecasting of price volatility is essential for effective risk management and policy formulation. Monthly crude oil price data are analyzed using a range of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) family models, including both symmetric and asymmetric specifications, and integrated with Autoregressive Integrated Moving Average (ARIMA) frameworks. Statistically significant lag structures are identified, and model selection is guided by information criteria to ensure the best fit. The ARIMA (2,1,6) - GARCH (1,1,1) model is found to be optimal, demonstrating strong statistical adequacy and effectively capturing volatility clustering. In-sample forecasts confirm the model’s accuracy in tracking actual price movements during periods of heightened uncertainty. The findings highlight the persistence of crude oil price volatility in India and provide valuable insights for policymakers and market participants as the country navigates the ongoing shift from fossil fuels to renewable energy.