A Supply Chain-Oriented Model to Predict Crude Oil Import Prices in South Korea Based on the Hybrid Approach

Jisung Jo, Umji Kim, Eonkyung Lee, Juhyang Lee and Sewon Kim ()
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Jisung Jo: Logistics and Maritime Industry Research Department, Korea Maritime Institute, Busan 49111, Republic of Korea
Umji Kim: Northern and Polar Regions Research Division, Korea Maritime Institute, Busan 49111, Republic of Korea
Eonkyung Lee: Logistics and Maritime Industry Research Department, Korea Maritime Institute, Busan 49111, Republic of Korea
Juhyang Lee: Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea
Sewon Kim: Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea

Sustainability, 2023, vol. 15, issue 24, 1-18

Abstract: Although numerous studies have explored key variables for forecasting crude oil prices, the role of supply chain factors has often been overlooked. In the face of global risks such as COVID-19, the Russia–Ukraine war, and the U.S.–China trade dispute, supply chain management (SCM) has evolved beyond an individual company’s concern. This research investigates the impact of a supply chain-oriented variable on the forecasting of crude oil import prices in South Korea. Our findings reveal that models incorporating the Global Supply Chain Pressure Index (GSCPI) outperform those without it, emphasizing the importance of monitoring supply chain-related variables for stabilizing domestic prices for policy makers. Additionally, we propose a novel hybrid factor-based approach that integrates time series and machine learning models to enhance the prediction performance of oil prices. This endeavor is poised to serve as a foundational step toward developing methodologically sound forecasting models for oil prices, offering valuable insights for policymakers.

Keywords: supply chain oriented; GSCPI; machine learning; forecasting; crude oil import price (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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