مطالب

West Texas Intermediate (WTI) crude oil is a pivotal benchmark in the global energy market, exerting a decisive influence on economic expectations and national macroeconomic policies. As the primary pricing basis on the New York Mercantile Exchange and numerous energy futures contracts, WTI is subject to persistent and severe price volatility. Such fluctuations, often appearing as abrupt upward or downward shocks, profoundly affect key macroeconomic indicators. These include inflation, economic growth, trade balances, corporate profitability, production costs, and government budgets. Consequently, variations in WTI prices influence oil and gas markets, financial stability, energy security, and even international geopolitical relations. To address these challenges, this study develops a hybrid VMD+SVR framework to model and forecast WTI crude oil futures prices across short-, medium-, and long-run horizons. Empirical findings reveal that across all three horizons, the proposed hybrid model consistently achieves the lowest forecasting errors compared with alternative approaches. Moreover, the Diebold–Mariano and Wilcoxon tests statistically confirm the superior predictive performance of the hybrid VMD+SVR model. These results highlight the importance of integrating advanced adaptive signal decomposition (VMD) with powerful nonlinear learning algorithms (SVR) for accurate oil price forecasting. The proposed approach not only enhances forecasting accuracy but also provides practical insights for policymakers in managing economic risks, stabilizing budgets dependent on oil revenues, and formulating sustainable energy strategies. It opens a new avenue for developing financial forecasting models inspired by advanced signal processing.

The exchange rate in Iran’s economy, is one of the most critical indicators influencing fiscal, trade, and monetary policymaking. Given that a substantial portion of the country’s foreign exchange earnings is dependent on crude oil exports, any fluctuations in oil revenues can directly or indirectly affect the exchange rate. This relationship becomes especially significant under circumstances such as international sanctions, global oil price volatility, and the structural dependency of the government budget on oil revenues. Consequently, a precise understanding of the mechanisms by which oil income influences the exchange rate is essential for designing sustainable currency policies and ensuring the country’s macroeconomic stability. The present study investigates the long-term relationship between oil revenues and the exchange rate in Iran during the years 2002 to 2024, utilizing the Fully Modified Ordinary Least Squares (FMOLS) econometric method. Through the analysis of quarterly time series data and unit root tests, the stationarity and then cointegration of the variables were examined. The test results indicate that the variables are integrated of order one, and a cointegration relationship exists among them, justifying the estimation of the model using FMOLS.The findings reveal that, according to the model estimates, oil revenues and non-oil exports exert a significant negative effect on the exchange rate. Specifically, a one-percent increase in oil revenues leads to a 0.41 percent decrease in the exchange rate (signifying an appreciation of the Rial), while an increase in non-oil exports results in a 0.72 percent decrease in the exchange rate.