Market Risk Prediction in the Oil Market Using Artificial Intelligence Approaches

The oil market, due to its extensive economic impact and high price volatility, has always posed significant challenges for risk prediction and management. In this study, advanced artificial intelligence models, particularly machine learning, were employed to achieve more accurate predictions of oil market risk and their performance was compared with traditional models such as GARCH. The research findings indicated that machine learning models—especially the Random Forest algorithm—demonstrate greater accuracy and stability in predicting oil price fluctuations and assessing associated risks. These models can simulate nonlinear complexities and capture the effects of various economic and financial factors, such as stock market turbulence, unemployment indices, and interest rates, on oil market risk. Moreover, the results revealed that negative shocks exert a stronger influence on oil market volatility, and artificial intelligence models can effectively predict these impacts. This study particularly confirms the importance of using artificial intelligence models to forecast both short-term and long-term oil market risks and provides economic decision-makers with innovative tools to manage market risk effectively. The oil market, due to its extensive economic impact and high price volatility, has always posed significant challenges for risk prediction and management. In this study, advanced artificial intelligence models, particularly machine learning, were employed to achieve more accurate predictions of oil market risk and their performance was compared with traditional models such as GARCH. The research findings indicated that machine learning models—especially the Random Forest algorithm—demonstrate greater accuracy and stability in predicting oil price fluctuations and assessing associated risks. These models can simulate nonlinear complexities and capture the effects of various economic and financial factors, such as stock market turbulence, unemployment indices, and interest rates, on oil market risk. Moreover, the results revealed that negative shocks exert a stronger influence on oil market volatility, and artificial intelligence models can effectively predict these impacts. This study particularly confirms the importance of using artificial intelligence models to forecast both short-term and long-term oil market risks and provides economic decision-makers with innovative tools to manage market risk effectively.

کلیدواژه ها:

risk prediction ، oil market ، artificial intelligence ، machine learning ، Random Forest ، GARCH ، volatility ، Value at Risk (VaR) ، oil market fluctuations