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Does the financial crisis change the economic risk perception of crude oil traders? A MIDAS quantile regression approach

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  • Lei, Likun
  • Shang, Yue
  • Chen, Yongfei
  • Wei, Yu

Abstract

In this paper, we argue that the 2008 financial crisis changes the economic risk perception of crude oil traders. Using the economic policy uncertainty (EPU) index as a proxy of economic risk, we examine the change of EPU's impacts on WTI oil spot and futures returns, i.e., the return-risk relationship in crude oil market, before and after the crisis by a MIDAS quantile regression approach. The empirical results shows that, before the crisis, the EPU has a significant negative effect on oil returns; but after the crisis, higher (lower) EPU is always accompanied with significantly higher (lower) oil returns at various quantiles. This finding indicates that the recent global financial crisis changes the risk perception of oil traders and the return-risk relationship in crude oil market. Before the crisis, traders tend to be bearish in oil market when facing large economic risk; while after the crisis, they are inclined to be bullish and take oil assets as risk hedging tools in front of increasing economic risks.

Suggested Citation

  • Lei, Likun & Shang, Yue & Chen, Yongfei & Wei, Yu, 2019. "Does the financial crisis change the economic risk perception of crude oil traders? A MIDAS quantile regression approach," Finance Research Letters, Elsevier, vol. 30(C), pages 341-351.
    • Handle: RePEc:eee:finlet:v:30:y:2019:i:c:p:341-351
    DOI: 10.1016/j.frl.2018.10.016
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    References listed on IDEAS

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    Cited by:

    1. Hao-Lin Shao & Ying-Hui Shao & Yan-Hong Yang, 2021. "New insights into price drivers of crude oil futures markets: Evidence from quantile ARDL approach," Papers 2110.02693, arXiv.org.
    2. Ha, Le Thanh & Nham, Nguyen Thi Hong, 2022. "An application of a TVP-VAR extended joint connected approach to explore connectedness between WTI crude oil, gold, stock and cryptocurrencies during the COVID-19 health crisis," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    3. Bai, Lan & Wei, Yu & Wei, Guiwu & Li, Xiafei & Zhang, Songyun, 2021. "Infectious disease pandemic and permanent volatility of international stock markets: A long-term perspective," Finance Research Letters, Elsevier, vol. 40(C).
    4. Sarit Maitra, 2023. "Impact of Economic Uncertainty, Geopolitical Risk, Pandemic, Financial & Macroeconomic Factors on Crude Oil Returns -- An Empirical Investigation," Papers 2310.01123, arXiv.org, revised Oct 2023.
    5. Ye, Wuyi & Jiang, Kunliang & Liu, Xiaoquan, 2021. "Financial contagion and the TIR-MIDAS model," Finance Research Letters, Elsevier, vol. 39(C).
    6. Li, Xiafei & Li, Bo & Wei, Guiwu & Bai, Lan & Wei, Yu & Liang, Chao, 2021. "Return connectedness among commodity and financial assets during the COVID-19 pandemic: Evidence from China and the US," Resources Policy, Elsevier, vol. 73(C).
    7. Cao, Yan & Cheng, Sheng & Li, Xinran, 2023. "How economic policy uncertainty affects asymmetric spillovers in food and oil prices: Evidence from wavelet analysis," Resources Policy, Elsevier, vol. 86(PB).
    8. Jiang, Cuixia & Xiong, Wei & Xu, Qifa & Liu, Yezheng, 2021. "Predicting default of listed companies in mainland China via U-MIDAS Logit model with group lasso penalty," Finance Research Letters, Elsevier, vol. 38(C).
    9. Chang, Chong-Chuo & Chen, Hsien-Yi & Mon, Khin Thiri, 2024. "Impact of economic policy uncertainty on the firm's working capital requirements," Pacific-Basin Finance Journal, Elsevier, vol. 86(C).
    10. Yuntong Liu & Yu Wei & Yi Liu & Wenjuan Li, 2020. "Forecasting Oil Price by Hierarchical Shrinkage in Dynamic Parameter Models," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-12, December.
    11. Avik Sinha & Arshian Sharif & Arnab Adhikari & Ankit Sharma, 2022. "Dependence structure between Indian financial market and energy commodities: a cross-quantilogram based evidence," Annals of Operations Research, Springer, vol. 313(1), pages 257-287, June.
    12. Zhang, Hongwei & Hong, Huojun & Guo, Yaoqi & Yang, Cai, 2022. "Information spillover effects from media coverage to the crude oil, gold, and Bitcoin markets during the COVID-19 pandemic: Evidence from the time and frequency domains," International Review of Economics & Finance, Elsevier, vol. 78(C), pages 267-285.
    13. Majid Mirzaee Ghazani & Mohammad Ali Jafari, 2021. "Cryptocurrencies, gold, and WTI crude oil market efficiency: a dynamic analysis based on the adaptive market hypothesis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-26, December.
    14. Bouazizi, Tarek & Galariotis, Emilios & Guesmi, Khaled & Makrychoriti, Panagiota, 2023. "Investigating the nature of interaction between crypto-currency and commodity markets," International Review of Financial Analysis, Elsevier, vol. 88(C).
    15. Yang, Kun & Wei, Yu & Li, Shouwei & Liu, Liang & Wang, Lei, 2021. "Global financial uncertainties and China’s crude oil futures market: Evidence from interday and intraday price dynamics," Energy Economics, Elsevier, vol. 96(C).
    16. Lan Bai & Xiafei Li & Yu Wei & Guiwu Wei, 2022. "Does crude oil futures price really help to predict spot oil price? New evidence from density forecasting," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3694-3712, July.
    17. Sheng Cheng & Wei Liu & Qisheng Jiang & Yan Cao, 2023. "Multi–Scale Risk Connectedness Between Economic Policy Uncertainty of China and Global Oil Prices in Time–Frequency Domains," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1593-1616, April.

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    More about this item

    Keywords

    Crude oil market; Oil financialization; MIDAS quantile regression; Economic policy uncertainty;
    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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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