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Item Crude oil Price forecasting: Leveraging machine learning for global economic stability, Technological Forecasting and Social Change,(Technological Forecasting and Social Change, 2025-07) Rao, Amar; Sharma, Gagan Deep; Tiwari, Aviral Kumar; Hossain, Mohammad Razib; Dev, DhairyaThe volatility of the energy market, particularly crude oil, significantly impacts macroeconomic indices, such as inflation, economic growth, currency exchange rates, and trade balances. Accurate crude oil price forecasting is crucial to risk management and global economic stability. This study examines various models, including GARCH (1,1), Vanilla LSTM, GARCH (1,1) LSTM, and GARCH (1,1) GRU, to predict Brent crude oil prices using different time frequencies and sample periods. The LSTM and GARCH (1,1)-GRU hybrid models showed superior performance, with LSTM slightly better in predictive accuracy and GARCH (1,1)-GRU in minimizing squared errors. These findings emphasize the importance of precise crude oil price forecasting for the global energy market and manufacturing sectors that rely on crude oil prices. Accurate forecasting helps ensure economic sustainability and stability and prevents disruptions to production and distribution chains in both developed and emerging economies. Policymakers may choose to implement energy security measures in response to the significant impact of crude oil price volatility on the macroeconomic indicators. These measures could include maintaining strategic reserves, diversifying energy sources, and decreasing the dependence on volatile oil markets. By doing so, a country's ability to handle oil price fluctuations and ensure a stable energy supply can be enhanced.Item Nexus of crude oil and clean energy stock indices: Evidence from time-vector-auto-regression in conjunction with conditional-autoregressive-value-at-risk(Heliyon, 2025-01-15) Trabelsi, Nader; Tiwari, Aviral Kumar; Ghallabi, Fahmi; Khemakhem, ImenThe current study aims to elicit information regarding the tail risk transmission mechanism between crude oil (CO) and selected clean energy (CE) stock indices across time and during certain economic events. A Time-Varying Parameter Vector Auto-Regressive model (TVP-VAR) paired with the conditional autoregressive value-at-risk (CAViaR) approach was used to investigate data from January 1, 2015 to December 29, 2022. Overall, we show that an increased vulnerability to tail risk and deficits might be linked to dynamic spillover over examined markets. We also provide evidence that connectedness rises during significant crisis situations, and the last epidemic has the potential to make a lasting impact on the various marketplaces of concern. According to the return and conditional variance time-series, CE stock indices are the most important source of return shocks to CO. However, the CO is the primary cause of volatility in CE stock indices. During the recent virus pandemic, the most significant volatility shock transmissions from CO to CE stock indices occurred. During the Russia-Ukraine war, volatility shocks to CO were mostly caused by CE stock indices. The results of our study offer concrete consequences and new perspectives to various market players in order to improve the management and understanding of risks.