Crude oil market autocorrelation: Evidence from multiscale quantile regression analysis

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.& nbsp; (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper provides a comprehensive description of the dependence pattern of the crude oil market by investigating the heterogeneity of autocorrelation of crude oil futures through multiscale analysis and quantile regression analysis. The study finds that autoregressive coefficients vary with quantiles and are smaller in high-frequency components, while larger in low-frequency components.