Hostname: page-component-669899f699-tzmfd Total loading time: 0 Render date: 2025-04-28T14:58:29.078Z Has data issue: false hasContentIssue false
  • English
  • Français

Asymmetries in the oil market: accounting for the growing role of China through quantile regressions

Published online by Cambridge University Press:  26 September 2024

Valérie Mignon  and
Jamel Saadaoui
Valérie Mignon*
Affiliation:
EconomiX-CNRS, University of Paris Nanterre, and CEPII, Paris, France
Jamel Saadaoui
Affiliation:
University of Strasbourg, University of Lorraine, BETA, CNRS, Strasbourg, France
*
Corresponding author: Valérie Mignon; Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper assesses the role of political tensions between the USA and China and global market forces in explaining oil price fluctuations. To this end, we take part of the previous literature, which highlights (i) the importance of political events in explaining oil price dynamics, (ii) time-varying patterns in the oil market, and (iii) asymmetries in the impact of political tensions and uncertainty on oil prices. While this literature generally focuses on one of these features, we account for all of them simultaneously, allowing for a complete and meaningful investigation of political tensions on oil prices. To this end, we rely on quantile autoregressive distributed lag error-correction models, which are specifically designed to address both the long-run and short-run dynamics across a range of quantiles in a fully parametric setting. Our results show evidence of a quantile-dependent long-term relationship between oil prices and their determinants over the 1958–2022 period, which is also time varying across quantiles: the adjustment speed toward the long-term equilibrium is faster for the highest quantiles, fluctuating between 4% and 6% in the recent period. Overall, our findings highlight the increased role played by China in the oil market since the mid-2000s.

Keywords

Oil pricespolitical tensionsquantile regressionsQ41F51C22
Type
Articles
Information
Macroeconomic Dynamics , Volume 29 , 2025 , e38
Copyright
© The Author(s), 2024. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Abdel-Latif, H. and El-Gamal, M.. (2020) Financial liquidity, geopolitics, and oil prices. Energy Economics 87, 104482.CrossRefGoogle Scholar
Antonakakis, N., Cunado, J., Gupta, R. and Segnon, M.. (2019) Revisiting the twin deficits hypothesis: a quantile cointegration analysis over the period 1791-2013. Journal of Applied Economics 22(1), 117–131.CrossRefGoogle Scholar
Apergis, N., Hayat, T. and Saeed, T.. (2021) US partisan conflict uncertainty and oil prices. Energy Policy 150, 112118.CrossRefGoogle Scholar
Arouri, M. E. H., Jawadi, F. and Nguyen, D. K.. (2011) Nonlinear cointegration and nonlinear error-correction models: Theory and empirical applications for oil and stock markets. In: Arouri, M. E. H., Jawadi, F. and Nguyen, D. K.. (eds.), Nonlinear Financial Econometrics: Markov Switching Models, Persistence and Nonlinear Cointegration, pp. 171–193. London: Palgrave Macmillan UK.CrossRefGoogle Scholar
Atil, A., Lahiani, A. and Nguyen, D. K.. (2014) Asymmetric and nonlinear pass-through of crude oil prices to gasoline and natural gas prices. Energy Policy 65, 567–573.CrossRefGoogle Scholar
Azzimonti, M. (2018) Partisan conflict and private investment. Journal of Monetary Economics 93, 114–131.CrossRefGoogle Scholar
Baumeister, C. and Peersman, G.. (2013) The role of time-varying price elasticities in accounting for volatility changes in the crude oil market. Journal of Applied Econometrics 28(7), 1087–1109.CrossRefGoogle Scholar
Baumeister, C. and Hamilton, J. D.. (2019) Structural interpretation of vector autoregressions with incomplete identification: revisiting the role of oil supply and demand shocks. American Economic Review 109(5), 1873–1910.CrossRefGoogle Scholar
Baumeister, C. and Kilian, L.. (2016) Forty years of oil price fluctuations: why the price of oil may still surprise us. Journal of Economic Perspectives 30(1), 139–160.CrossRefGoogle Scholar
Ben Salem, L., Nouira, R., Jeguirim, K. and Rault, C.. (2022) The determinants of crude oil prices: evidence from ardl and nonlinear ardl approaches. Resources Policy 79, 103085. https://www.sciencedirect.com/science/article/pii/S0301420722005281 CrossRefGoogle Scholar
Bernanke, B. S., Gertler, M. and Watson, M.. (1997) Systematic monetary policy and the effects of oil price shocks. Brookings Papers on Economic Activity 28(1), 91–157.CrossRefGoogle Scholar
Bloom, N., Bond, S. and Van Reenen, J.. (2007) Uncertainty and investment dynamics. The Review of Economic Studies 74(2), 391–415.CrossRefGoogle Scholar
Bodenstein, M., Guerrieri, L. and Kilian, L.. (2012) Monetary policy responses to oil price fluctuations. IMF Economic Review 60(4), 470–504.CrossRefGoogle Scholar
Brown, S. P. and Yücel, M. K.. (2002) Energy prices and aggregate economic activity: an interpretative survey. The Quarterly Review of Economics and Finance 42(2), 193–208.CrossRefGoogle Scholar
Cai, Y., Mignon, V. and Saadaoui, J.. (2022) Not all political relation shocks are alike: assessing the impacts of US–China tensions on the oil market. Energy Economics 114, 106199.CrossRefGoogle Scholar
Cai, Z. and Xu, X.. (2008) Nonparametric quantile estimations for dynamic smooth coefficient models. Journal of the American Statistical Association 103(484), 1595–1608.CrossRefGoogle Scholar
Caldara, D. and Iacoviello, M.. (2018) Measuring Geopolitical Risk, Technical Report. Board of Governors of the Federal Reserve System (U.S.).Google Scholar
Caldara, D. and Iacoviello, M.. (2022) Measuring geopolitical risk. American Economic Review 112(4), 1194–1225.CrossRefGoogle Scholar
Chen, H., Liao, H., Tang, B.-J. and Wei, Y.-M.. (2016) Impacts of OPECs political risk on the international crude oil prices: an empirical analysis based on the SVAR models. Energy Economics 57, 42–49.CrossRefGoogle Scholar
Chernozhukov, V. and Hansen, C.. (2005) An IV model of quantile treatment effects. Econometrica 73(1), 245–261.CrossRefGoogle Scholar
Cho, J. S., Kim, T.-H. and Shin, Y.. (2015) Quantile cointegration in the autoregressive distributed-lag modeling framework. Journal of Econometrics 188(1), 281–300.CrossRefGoogle Scholar
Coleman, L. (2012) Explaining crude oil prices using fundamental measures. Energy Policy 40, 318–324.CrossRefGoogle Scholar
Cross, J., Nguyen, B. H. and Zhang, B.. (2022). The influence from a demand perspective with real economic activity: China versus the United States in world oil markets. Carbon Neutralization n/a, 1–10.CrossRefGoogle Scholar
Cross, J. L., Nguyen, B. H. and Tran, T. D.. (2022) The role of precautionary and speculative demand in the global market for crude oil. Journal of Applied Econometrics 37(5), 882–895.CrossRefGoogle Scholar
Dargay, J. M. and Gately, D.. (2010) World oil demands shift toward faster growing and less price-responsive products and regions. Energy Policy 38(10), 6261–6277.CrossRefGoogle Scholar
Edelstein, P. and Kilian, L.. (2009) How sensitive are consumer expenditures to retail energy prices? Journal of Monetary Economics 56(6), 766–779.CrossRefGoogle Scholar
Feng, X., He, X. and Hu, J.. (2011) Wild bootstrap for quantile regression. Biometrika 98(4), 995–999.CrossRefGoogle ScholarPubMed
Goldstein, J. S. (1992) A conflict-cooperation scale for WEIS events data. Journal of Conflict Resolution 36(2), 369–385.CrossRefGoogle Scholar
Hamilton, J. D. (2009a). Causes and consequences of the oil shock of 2007-08. Brookings Papers on Economic Activity 1(Spring), 215–284.CrossRefGoogle Scholar
Hamilton, J. D. (2009b). Understanding crude oil prices. The Energy Journal 30(2), 179–206.CrossRefGoogle Scholar
Hamilton, J. D. (1988) A neoclassical model of unemployment and the business cycle. Journal of Political Economy 96(3), 593–617.CrossRefGoogle Scholar
Hamilton, J. D. (2003) What is an oil shock? Journal of Econometrics 113(2), 363–398.CrossRefGoogle Scholar
Herrera, A. M., Karaki, M. B. and Rangaraju, S. K.. (2019) Oil price shocks and US economic activity. Energy Policy 129, 89–99.CrossRefGoogle Scholar
Hubbard, R. G. (1986) Supply shocks and price adjustment in the world oil market. The Quarterly Journal of Economics 101(1), 85–102.CrossRefGoogle Scholar
Kaplan, D. M. (2022) Smoothed instrumental variables quantile regression. The Stata Journal 22(2), 379–403.CrossRefGoogle Scholar
Kaplan, D. M. and Sun, Y.. (2017) Smoothed estimating equations for instrumental variables quantile regression. Econometric Theory 33(1), 105–157.CrossRefGoogle Scholar
Kilian, L. (2008) Exogenous oil supply shocks: how big are they and how much do they matter for the U.S. economy? The Review of Economics and Statistics 90(2), 216–240.CrossRefGoogle Scholar
Kilian, L. and Hicks, B.. (2013) Did unexpectedly strong economic growth cause the oil price shock of 2003-2008? Journal of Forecasting 32(5), 385–394.CrossRefGoogle Scholar
Kilian, L. and Murphy, D. P.. (2014) The role of inventories and speculative trading in the global market for crude oil. Journal of Applied Econometrics 29(3), 454–478.CrossRefGoogle Scholar
Kilian, L. and Lee, T. K.. (2014) Quantifying the speculative component in the real price of oil: the role of global oil inventories. Journal of International Money and Finance 42, 71–87.CrossRefGoogle Scholar
Kim, M.-O. (2007) Quantile regression with varying coefficients. The Annals of Statistics 35(1), 92–108.CrossRefGoogle Scholar
Koenker, R. (2017) Quantile regression: 40 years on. Annual Review of Economics 9(1), 155–176.CrossRefGoogle Scholar
Koenker, R. and Xiao, Z.. (2004) Unit root quantile autoregression inference. Journal of the American Statistical Association 99(467), 775–787.CrossRefGoogle Scholar
Korobilis, D., Landau, B., Musso, A. and Phella, A.. (2021) The Time-varying Evolution of Inflation Risks.Working Paper Series 2600. European Central Bank, Frankfurt am Main, Germany.Google Scholar
Kumar, S., Choudhary, S., Singh, G. and Singhal, S.. (2021). Crude oil, gold, natural gas, exchange rate and Indian stock market: Eevidence from the asymmetric nonlinear ARDL model. Resources Policy 73, 102194.CrossRefGoogle Scholar
Lardic, S. and Mignon, V.. (2006) The impact of oil prices on GDP in European countries: an empirical investigation based on asymmetric cointegration. Energy Policy 34(18), 3910–3915.CrossRefGoogle Scholar
Lardic, S. and Mignon, V.. (2008) Oil prices and economic activity: an asymmetric cointegration approach. Energy Economics 30(3), 847–855.CrossRefGoogle Scholar
Lee, C.-C., Lee, C.-C., Ning, S.-L.. (2017) Dynamic relationship of oil price shocks and country risks. Energy Economics 66, 571–581.CrossRefGoogle Scholar
Lippi, F. and Nobili, A.. (2012) Oil and the macroeconomy: a quantitative structural analysis. Journal of the European Economic Association 10(5), 1059–1083.CrossRefGoogle Scholar
Miao, H., Ramchander, S., Wang, T. and Yang, D.. (2017) Influential factors in crude oil price forecasting. Energy Economics 68, 77–88.CrossRefGoogle Scholar
Monge, M., Gil-Alana, L. A. and de Gracia, F. P.. (2017) Crude oil price behaviour before and after military conflicts and geopolitical events. Energy 120, 79–91.CrossRefGoogle Scholar
Mork, K. A. (1989) Oil and macroeconomy when prices go up and down: an extension of Hamiltons results. Journal of Political Economy 97(3), 740–744.CrossRefGoogle Scholar
Mork, K. A., Olsen, O. and Mysen, H. T.. (1994) Macroeconomic responses to oil price increases and decreases in seven oecd countries. The Energy Journal 15(4), 19–36.CrossRefGoogle Scholar
Mory, J. F. (1993) Oil prices and economic activity: is the relationship symmetric? The Energy Journal 14(4), 151–162.CrossRefGoogle Scholar
Noguera-Santaella, J. (2016) Geopolitics and the oil price. Economic Modelling 52, 301–309.CrossRefGoogle Scholar
Perifanis, T. and Dagoumas, A.. (2019) Living in an era when market fundamentals determine crude oil price. The Energy Journal 40(SI), 317–335.CrossRefGoogle Scholar
Peter Ferderer, J. (1996) Oil price volatility and the macroeconomy. Journal of Macroeconomics 18(1), 1–26.CrossRefGoogle Scholar
Qin, Y., Hong, K., Chen, J. and Zhang, Z.. (2020) Asymmetric effects of geopolitical risks on energy returns and volatility under different market conditions. Energy Economics 90, 104851.CrossRefGoogle Scholar
Shin, Y. and Pesaran, M.. (1999) An autoregressive distributed lag modelling approach to cointegration analysis. In: Shin, Y. and Pesaran, M.. (ed.), Econometrics and Economic Theory in the 20th Century, pp. 371–413. United States, Cambridge University Press.Google Scholar
Smith, J. L. (2009) World oil: market or mayhem? Journal of Economic Perspectives 23(3), 145–164.CrossRefGoogle Scholar
Song, Y., Chen, B., Hou, N. and Yang, Y.. (2022) Terrorist attacks and oil prices: a time-varying causal relationship analysis. Energy 246, 123340.CrossRefGoogle Scholar
Wu, W. and Zhou, Z.. (2017) Nonparametric inference for time-varying coefficient quantile regression. Journal of Business and Economic Statistics 35(1), 98–109.CrossRefGoogle Scholar
Xiao, Z. (2009) Quantile cointegrating regression. Journal of Econometrics 150(2), 248–260.CrossRefGoogle Scholar
Yan, X. (2010) The instability of China–US relations. The Chinese Journal of International Politics 3(3), 263–292.CrossRefGoogle Scholar
Yan, X. and Qi, H.. (2009) Zhongwaiguanxidingliangyuce, [ Quantitative Forecasts of China’s Foreign Relations]. Higher Education Press, Beijing, China.Google Scholar
Yan, X., Fangyin, Z., Haixia, Q., Xu, J., Zhaijiu, M. and Liangzhu, Y.. (2010) Zhongwaiguanxijianlan 1950-2005–Zhongguoyudaguoguanxidinglianghengliang [ 1950-2005– China’s Foreign Relations with Major Powers by the Numbers 1950-2005]. Higher Education Press.Google Scholar