Time-Varying Long Memory Hurst Parameter Evaluations of the WTI Crude Oil Market

Main Article Content

Yuan Yeping
Chin Zi Yi
Chin Wen Cheong
Lim Min

Abstract

This study investigates the time-varying long memory in returns and volatility within the West Texas Intermediate (WTI) crude oil market. The period under study includes the COVID-19 pandemic and the Ukraine-Russia conflict. The long memory observed in crude oil markets can be attributed to trading activities across various investment durations. During global crises, extreme price declines and heightened selling pressure often prompt long-term investors to shift their focus toward short-term investments. These sudden shifts significantly increase the prevalence of short-term investment horizons. As a result, market participants become more homogeneous, and this behavior induces strong dependencies in both the return and volatility series of the crude oil market.  This study employs rescaled range analysis, aggregated variance methods, and the periodogram method to capture the long memory and informational efficiency dynamics of the WTI market. The empirical results indicate that WTI returns tend to follow a random walk, while volatility exhibits long memory.

Article Details

How to Cite
Yeping, Yuan, Chin Zi Yi, Chin Wen Cheong, and Lim Min. 2025. “Time-Varying Long Memory Hurst Parameter Evaluations of the WTI Crude Oil Market”. Journal of Energy and Development 50 (1):41–59. https://doi.org/10.56476/jed.v50i1.72.
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Articles
Author Biographies

Yuan Yeping, Xiamen University Malaysia

Yuan Yeping earned a B.Sc. degree in Mathematics and Applied Mathematics from Xiamen University Malaysia. The author's research interests include financial mathematics and financial time series analysis.

Chin Zi Yi, Xiamen University Malaysia

Chin Zi Yi earned a B.Sc. degrees in Mathematics and Applied Mathematics from Xiamen University Malaysia. The author's research interests include financial mathematics and financial time series analysis.

Chin Wen Cheong , Department of Mathematics at Xiamen University Malaysia

Chin Wen Cheong is an Associate Professor in the Department of Mathematics at Xiamen University Malaysia. He holds a Ph.D. in Statistics from the National University of Malaysia. His research focuses on time series analysis and risk management.

Lim Min, Department of Mathematics at Xiamen University Malaysia

Lim Min earned a B.Sc. degree in Mathematical Sciences and Applied Statistics, as well as both a master’s and a Ph.D. degree in Statistics from the University of Toronto. Dr. Lim is currently an Assistant Professor in the Department of Mathematics at Xiamen University Malaysia. Her research areas include statistical modeling and structural equation modeling

References

Akdi, Y., S. Varlik, and H. Berument. 2023. “The Long-Run Relationship Between the Prices of WTI and Brent Crude Oils: Periodogram-Based Cointegration Analyses.” Energy Economics Letters 10 (1): 35–43. https://doi.org/10.55493/5049.v10i1.4715.

Alvarez-Ramirez, J., J. Alvarez, and E. Rodriguez. 2008. “Short-Term Predictability of Crude Oil Markets: A Detrended Fluctuation Analysis Approach.” Energy Economics 30 (5): 2645–56. https://doi.org/10.1016/j.eneco.2008.05.006.

Alvarez-Ramirez, J., M. Cisneros, C. Ibarra-Valdez, and A. Soriano. 2002. “Multifractal Hurst Analysis of Crude Oil Prices.” Physica A: Statistical Mechanics and Its Applications 313 (3–4): 651–70. https://doi.org/10.1016/S0378-4371(02)00985-8.

Chatziantoniou, I., M. Filippidis, G. Filis, and D. Gabauer. 2021. “A Closer Look into the Global Determinants of Oil Price Volatility.” Energy Economics 95: 105092. https://doi.org/10.1016/j.eneco.2020.105092.

Fernandez, V. 2010. “Commodity Futures and Market Efficiency: A Fractional Integrated Approach.” Resources Policy 35 (4): 276–82.

Grech, D., and Z. Mazur. 2004. “Can One Make Any Crash Prediction in Finance Using the Local Hurst Exponent Idea?” Physica A: Statistical Mechanics and Its Applications 336 (1): 133–45. https://doi.org/10.48550/arXiv.cond-mat/0311627.

Guo, J., Z. Zhao, J. Sun, and S. Sun. 2022. “Multi-Perspective Crude Oil Price Forecasting with a New Decomposition-Ensemble Framework.” Resources Policy 77: 102737. https://doi.org/10.1016/j.resourpol.2022.102737.

He, L.-Y., and W.-B. Qian. 2012. “A Monte Carlo Simulation to the Performance of the R/S and V/S Methods—Statistical Revisit and Real-World Application.” Physica A: Statistical Mechanics and Its Applications 391 (14): 3770–82. https://doi.org/10.1016/j.physa.2012.02.028.

Hurst, H. E. 1951. “Long-Term Storage Capacity of Reservoirs.” Transactions of the American Society of Civil Engineers 116 (1): 770–99. https://doi.org/10.1061/TACEAT.0006518.

Kristoufek, L. 2019. “Are the Crude Oil Markets Really Becoming More Efficient Over Time? Some New Evidence.” Energy Economics 82: 253–63. DOI: 10.1016/j.eneco.2018.03.019.

Mandelbrot, B. 1972. “Statistical Methodology for Nonperiodic Cycles: From the Covariance to R/S Analysis.” In Annals of Economic and Social Measurement 1: 259–90. Stanford, CA: National Bureau of Economic Research.

Mensi, W., C. Aloui, M. Hamdi, and D. K. Nguyen. 2012. “Crude Oil Market Efficiency: An Empirical Investigation via the Shannon Entropy.” International Economics 129: 119–37. https://doi.org/10.3917/ecoi.129.0119.

Narayan, P. K., S. Narayan, and X. Zheng. 2010. “Gold and Oil Futures Markets: Are Markets Efficient?” Applied Energy 87 (10): 3299–3303. https://doi.org/10.1016/j.apenergy.2010.03.020.

Oberndorfer, U. 2009. “Energy Prices, Volatility, and the Stock Market: Evidence from the Eurozone.” Energy Policy 37 (12): 5787–95. https://doi.org/10.1016/j.enpol.2009.08.043.

Tabak, B. M., and D. O. Cajueiro. 2007. “Are the Crude Oil Markets Becoming Weakly Efficient Over Time? A Test for Time-Varying Long-Range Dependence in Prices and Volatility.” Energy Economics 29 (1): 28–36. https://doi.org/10.1016/j.eneco.2006.06.007.

Wang, Y., and L. Liu. 2010. “Is WTI Crude Oil Market Becoming Weakly Efficient Over Time? New Evidence from Multiscale Analysis Based on Detrended Fluctuation Analysis.” Energy Economics 32 (5): 987–92. https://doi.org/10.1016/j.eneco.2009.12.001.

Wang, Y., and C. Wu. 2012. “Long Memory in Energy Futures Markets: Further Evidence.” Resources Policy 37 (3): 261–72. https://doi.org/10.1016/j.resourpol.2012.05.002.

Wang, Y., and C. Wu. 2013. “Efficiency of Crude Oil Futures Markets: New Evidence from Multifractal Detrending Moving Average Analysis.” Computational Economics 42 (4): 393–414. https://doi.org/10.1007/s10614-012-9347-6.

Yu, H., G. V. Nartea, C. Gan, and L. J. Yao. 2013. “Predictive Ability and Profitability of Simple Technical Trading Rules: Recent Evidence from Southeast Asian Stock Markets.” International Review of Economics & Finance 25: 356–71. https://doi.org/10.1016/j.iref.2012.07.016.