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Crude oil market autocorrelation: Evidence from multiscale quantile regression analysis

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  • Sun, Jie
  • Zhao, Xiaojun
  • Xu, Chao

Abstract

The memory and heterogeneous effects existing on the financial time series have been widely revealed, especially in the crude oil market. While both effects, related to the temporal scales where two effects are measured, have been few studied. In this paper, we provide a comprehensive description of the dependence pattern of crude oil market by investigating the heterogeneity of autocorrelation of crude oil future in the framework of multiscale analysis and quantile regression analysis. Our empirical results are based on the price analysis of the West Texas Intermediate (WTI) crude oil future. We use quantile autoregression model to analyze the return and fluctuation series on multiple time scales. Firstly, we find that the autoregressive coefficients are likely to change with quantiles. Secondly, the autoregressive coefficients in the high-frequency components are small while in the low-frequency components are large. It indicates the fact that the crude oil price is a combination of random short-term fluctuation and deterministic long-term tendency. Interestingly, the quantile regressive coefficients for the return series are S-shaped while for the fluctuation series they are inverted S-shaped. In addition, the extreme lagged return and fluctuation are found to affect the distribution of the autoregressive coefficients.

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  • Sun, Jie & Zhao, Xiaojun & Xu, Chao, 2021. "Crude oil market autocorrelation: Evidence from multiscale quantile regression analysis," Energy Economics, Elsevier, vol. 98(C).
    • Handle: RePEc:eee:eneeco:v:98:y:2021:i:c:s0140988321001444
    DOI: 10.1016/j.eneco.2021.105239
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    Cited by:

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    3. Tan, Jinghua & Li, Zhixi & Zhang, Chuanhui & Shi, Long & Jiang, Yuansheng, 2024. "A multiscale time-series decomposition learning for crude oil price forecasting," Energy Economics, Elsevier, vol. 136(C).
    4. Zhao, Xiaojun & Zhang, Na & Zhang, Yali & Xu, Chao & Shang, Pengjian, 2024. "Equity markets volatility clustering: A multiscale analysis of intraday and overnight returns," Journal of Empirical Finance, Elsevier, vol. 77(C).
    5. Mensi, Walid & Al Rababa'a, Abdel Razzaq & Alomari, Mohammad & Vo, Xuan Vinh & Kang, Sang Hoon, 2022. "Dynamic frequency volatility spillovers and connectedness between strategic commodity and stock markets: US-based sectoral analysis," Resources Policy, Elsevier, vol. 79(C).
    6. Xianling Ren & Xinping Yu, 2024. "Hedging performance analysis of energy markets: Evidence from copula quantile regression," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(3), pages 432-450, March.
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    More about this item

    Keywords

    Crude oil market; Ensemble empirical mode decomposition; Fine-to-coarse reconstruction; Quantile autoregression; Multiscale analysis;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E22 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Investment; Capital; Intangible Capital; Capacity
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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