oil, oil industry, agriculture, OLS, quantile regression


This paper determines the impact of Brent oil shocks on the price of shares of companies from the oil, agricultural and food industries that includes the period of the COVID-19 pandemic. For this purpose, they use a quantile regression approach and compare its findings with a standard Ordinary Least Squares (OLS) regression model. Moreover, in this research they use quantile regression, which enables them to analyze different quantiles of share prices of companies from the oil industry, the agricultural industry, and the food industry. They observe three different periods - a period of recession, a normal period and a period of expansion. Finally, empirical evaluations using quantile regression and OLS models show us that shocks from the oil market are more pronounced in companies from the oil industry compared to companies from the agricultural and food industries. The findings of this research provide important information for investors, economic policy makers, and other parties.


Download data is not yet available.


Adams, Z., Collot, S., & Kartsakli, M. (2020). Have commodities become a financial asset? Evidence from ten years of Financialization. Energy Economics, 89, 104769.

Aye, G. C., & Odhiambo, N. M. (2021). Oil prices and agricultural growth in South Africa: A threshold analysis. Resources Policy, 73, 102196.

Cabrera, B. L., & Schulz, F. (2016). Volatility linkages between energy and agricultural commodity prices. Energy Economics, 54, 190-203.

Cao, G., & Xie, F. (2023). The asymmetric impact of crude oil futures on the clean energy stock market: Based on the asymmetric variable coefficient quantile regression model. Renewable Energy, 218, 119303.

Charfeddine, L., Klein, T., & Walther, T. (2018). Oil price changes and US real GDP growth: is this time different?. University of St. Gallen, School of Finance Research Paper, (2018/18).

Chen, S. T., Kuo, H. I., & Chen, C. C. (2010). Modeling the relationship between the oil price and global food prices. Applied Energy, 87(8), 2517-2525.

Dai, Z., & Kang, J. (2021). Bond yield and crude oil prices predictability. Energy Economics, 97, 105205.

Dong, M., Chang, C. P., Gong, Q., & Chu, Y. (2019). Revisiting global economic activity and crude oil prices: A wavelet analysis. Economic Modelling, 78, 134-149.

Eissa, M. A., & Al Refai, H. (2019). Modelling the symmetric and asymmetric relationships between oil prices and those of corn, barley, and rapeseed oil. Resources Policy, 64, 101511.

Eroğlu, A. Y., Çakır, Ö., Sağdıç, M., & Dertli, E. (2020). Bioactive characteristics of wild Berberis vulgaris and Berberis crataegina Fruits. Journal of Chemistry, 2020, 1-9.

Gokmenoglu, K. K., Güngör, H., & Bekun, F. V. (2021). Revisiting the linkage between oil and agricultural commodity prices: Panel evidence from an Agrarian state. International Journal of Finance & Economics, 26(4), 5610-5620.

Han, L., Zhou, Y., & Yin, L. (2015). Exogenous impacts on the links between energy and agricultural commodity markets. Energy Economics, 49, 350-358.

Insaidoo, M., Ullah, A., Dziwornu, R. K., Amoako, S., & Abdul-Mumuni, A. (2023). COVID-19 pandemic and stock market performance: A comparative study of emerging economies. Heliyon, 9(5).

Jahanshahi, H., Uzun, S., Kaçar, S., Yao, Q., & Alassafi, M. O. (2022). Artificial intelligence-based prediction of crude oil prices using multiple features under the effect of Russia–Ukraine war and COVID-19 pandemic. Mathematics, 10(22), 4361.

Ji, Q., & Fan, Y. (2012). How does oil price volatility affect non-energy commodity markets?. Applied Energy, 89(1), 273-280.

Jingjian, S., Xiangyun, G., Jinsheng, Z., Anjian, W., Xiaotian, S., Yiran, Z., & Hongyu, W. (2023). The impact of oil price shocks on energy stocks from the perspective of investor attention. Energy, 127987.

Koenker, R., & Bassett Jr, G. (1978). Regression quantiles. Econometrica: journal of the Econometric Society, 33-50.

Kumar, S., Tiwari, A. K., Raheem, I. D., & Hille, E. (2021). Time-varying dependence structure between oil and agricultural commodity markets: A dependence-switching CoVaR copula approach. Resources Policy, 72, 102049. resourpol.2021.102049

Li, S. (2022). COVID-19 and A-share banks’ stock price volatility: From the perspective of the epidemic evolution in China and the US. Global Finance Journal, 54, 100751.

Maiti, M. (2021). Quantile regression, asset pricing and investment decision. IIMB Management Review, 33(1), 28-37.

Mati, S., Radulescu, M., Saqib, N., Samour, A., Ismael, G. Y., & Aliyu, N. (2023). Incorporating Russo-Ukrainian war in Brent crude oil price forecasting: A comparative analysis of ARIMA, TARMA and ENNReg models. Heliyon, 9(11).

McMillan, D. G., Ziadat, S. A., & Herbst, P. (2021). The role of oil as a determinant of stock market interdependence: The case of the USA and GCC. Energy Economics, 95, 105102.

Naeem, M. A., Karim, S., Hasan, M., Lucey, B. M., & Kang, S. H. (2022). Nexus between oil shocks and agriculture commodities: Evidence from time and frequency domain. Energy Economics, 112, 106148.

Oseni, I., & Oladele, K. S. (2018). Oil price shock and agricultural commodity prices in Nigeria: A Non-Linear Autoregressive Distributed Lag (NARDL) Approach. African Journal of Economic Review, 6(2).

Pal, D., & Mitra, S. K. (2018). Interdependence between crude oil and world food prices: A detrended cross correlation analysis. Physica A: Statistical Mechanics and its Applications, 492, 1032-1044.

Ready, R. C. (2018). Oil prices and the stock market. Review of Finance, 22(1), 155-176.

Shahzad, S. J. H., Hernandez, J. A., Al-Yahyaee, K. H., & Jammazi, R. (2018). Asymmetric risk spillovers between oil and agricultural commodities. Energy Policy, 118, 182-198.

Sharif, A., Aloui, C., & Yarovaya, L. (2020). COVID-19 pandemic, oil prices, stock market, geopolitical risk and policy uncertainty nexus in the US economy: Fresh evidence from the wavelet-based approach. International review of financial analysis, 70, 101496.

Sun, Y., Mirza, N., Qadeer, A., & Hsueh, H. P. (2021). Connectedness between oil and agricultural commodity prices during tranquil and volatile period. Is crude oil a victim indeed?. Resources Policy, 72, 102131., 1-8.

Tian, G., Peng, Y., & Meng, Y. (2023). Forecasting crude oil prices in the COVID-19 era: Can machine learn better?. Energy Economics, 106788.

Umar, Z., Gubareva, M., & Teplova, T. (2021). The impact of Covid-19 on commodity markets volatility: Analyzing time-frequency relations between commodity prices and coronavirus panic levels. Resources Policy, 73, 102164.

Vo, D. H., Vu, T. N., Vo, A. T., & McAleer, M. (2019). Modeling the relationship between crude oil and agricultural commodity prices. Energies, 12(7), 1344.

Vu, T. N., Ho, C. M., Nguyen, T. C., & Vo, D. H. (2020). The determinants of risk transmission between oil and agricultural prices: an IPVAR approach. Agriculture, 10(4), 120.

Wang, X., Li, X., & Li, S. (2022). Point and interval forecasting system for crude oil price based on complete ensemble extreme-point symmetric mode decomposition with adaptive noise and intelligent optimization algorithm. Applied Energy, 328, 120194.

Yip, P. S., Brooks, R., Do, H. X., & Nguyen, D. K. (2020). Dynamic volatility spillover effects between oil and agricultural products. International Review of Financial Analysis, 69, 101465.

Zafeiriou, E., Arabatzis, G., Karanikola, P., Tampakis, S., & Tsiantikoudis, S. (2018). Agricultural commodities and crude oil prices: An empirical investigation of their relationship. Sustainability, 10(4), 1199.

Zahraee, S. M., Shiwakoti, N., & Stasinopoulos, P. (2022). Agricultural biomass supply chain resilience: COVID-19 outbreak vs. sustainability compliance, technological change, uncertainties, and policies. Cleaner Logistics and Supply Chain, 4, 100049.

Yang, J., Li, Y., & Sui, A. (2023). From black gold to green: Analyzing the consequences of oil price volatility on oil industry finances and carbon footprint. Resources Policy, 83, 103615.




How to Cite


Similar Articles

You may also start an advanced similarity search for this article.