Crude Oil Price Modeling using Long Memory Processes: Inference and Applications

Wobo Omezuruike Gideon; Deebom Zorle Dum

doi:10.56201/ijasmt.vol.11.no4.2025.pg31.51

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Crude Oil Price Modeling using Long Memory Processes: Inference and Applications

Wobo Omezuruike Gideon, Deebom Zorle Dum

Abstract

This study uses advanced statistical tests and models to investigate the presence of long-term memory in oil price benchmarks such as Average Oil, Brent Crude, Dubai Crude, and West Texas Intermediate. The data for this study are historical prices of major benchmark crude oils. For example, average benchmark crude oil prices obtained by taking a weighted average of these various crude oils (COA), Brent Crude (COB), Dubai Crude (COD) and West Texas Intermediate (WTI) are calculated. The data is taken from the Organization of the Petroleum Exporting Countries website (https://www.opec.org) and a total of 1984 data points are extracted for months from January 1982 to April 2023. ARFIMA, FIGARCH, HYGARCH, and FIAPARCH models are used to capture the complex dynamics and long-term dependencies of oil price fluctuations. Using the Akaike Information Criteria (AIC), the ARFIMA (1, -0.021, 1) model was considered the most appropriate among the competing models for Long Memory Processes in Crude Oil Prices. The results indicate significant long-term dependencies, persistence, and volatility accumulation in the oil price benchmarks. The results show that the crude oil price benchmarks exhibit asymmetry, non-stationarity, and long-run dependence, with significant fractional integral roots (d) and other parameters estimated by the model. The study concludes that shocks have transitory effects and that all series, except brent crude, exhibit mean-reverting behavior. The results have implications for oil price forecasting and modeling and highlight the importance of considering long memory processes in capturing the complex dynamics of oil price fluctuations.

Keywords

CrudeOilPrice LongMemory ProcessesInference & Application

References

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Crude Oil Price Modeling using Long Memory Processes: Inference and Applications

Abstract

Keywords

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