Comparative Analysis of Market Efficiency and Volatility of Energy Prices Before and During COVID-19 Pandemic Periods

Alaba, Oluwayemisi O. and Ojo, Oluwadare O. and Yaya, OlaOluwa S and Abu, Nurudeen and Ajobo, Saheed A. (2021): Comparative Analysis of Market Efficiency and Volatility of Energy Prices Before and During COVID-19 Pandemic Periods.

Abstract

The Covid-19 pandemic has affected energy demand and pricing globally due to different lockdown measures embarked on by governments in different economies. As a result, prices of oil and petroleum products dropped drastically at the peak of the pandemic period. The present paper, therefore, investigates the effect of the pandemic on energy markets and compared the levels of market efficiency, volatility, and volatility persistence. Two 5-monthly daily data windows are considered, each for the period before and during the pandemic, and an updated nonlinear fractional integration approach in time series analysis is employed. Having considered prices of Crude oil, Gasoline, Diesel, Heating oil, Kerosene, and Propane from US markets, we find that energy markets are less efficient during the Covid-19 pandemic period, even though with higher volatility but with lesser volatility persistence compared to the period before the pandemic. Thus, volatility shocks last for a shorter period during the 5-month pandemic period than in the 5-month period that precedes the pandemic. It is hoped that the findings of this work will be of interest to oil marketers and administrators in the international oil markets.

• Item Type: MPRA Paper
• Language: English
• Keywords: Energy price; Covid-19 pandemic; Efficient market; Volatility persistence; Fractional integration
• Subjects: C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q41 - Demand and Supply ; Prices; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q48 - Government Policy

References

Andriosopoulos, K., Galariots, E. and Spyrou, S. (2017). Contagion, volatility persistence, and volatility spill-overs: The case of energy markets during the European financial crisis. Energy Economics 66, 217-227.

Apergis, N. and Gangopadhyay, P. (2020). The asymmetric relationships between pollution, energy use, and oil prices in Vietnam: Some behavioral implications for energy policy-making. Energy Policy 140, 1-12.

Arshad, S., Rizvi, S. A., Haroon, O., Mehmood, F. and Gong, Q. (2020). Are oil prices efficient? Economic Modelling, 96, issue C, 362-370.

Bastianin, A., Conti, F. and Manera, M.(2016). The impacts of oil prices shocks on stock market volatility: evidence from the G7 countries. Energy Policy 98, 160-169.

Box, G. E. P., Jenkins, G. M., Reinsel, G. C. and Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control, 5th Edition. John Wiley & Sons, UK.

Charles, A. and Darne, O. (2014). Volatility persistence in crude oil markets. Energy Policy 65, 729-742.

Chun, D., Cho, H. and Kim, J. (2019) Crude oil Price shocks and hedging performance: A comparison of volatility models. Energy Economics 81, 1132-1147.

David, S. A., Inacio, C. M. C., Quintino, D. D. and Machado, J. A. T. (2020). Measuring the Brazilian ethanol and gasoline market efficiency using DFA-Hurst and fractal dimension. Energy Economics 85: 104-614.

Dickey, D. A. and Fuller, W. A. (1979). Distributions of the estimators for autoregressive time series with a unit root. Journal of American Statistical Association, 74(366), 427–481.

Dolado, J. J., Gonzalo, J. and Mayoral, L. (2002). A fractional Dickey-Fuller test for Unit Roots. Econometrica, 70: 1963-2006.

Ewig, B. T. and Malik, F. (2017). Modelling asymmetric volatility in oil prices under structural breaks. Energy Economics 66, 227-233.

Fama, E. F. (1965). Random walks in stock market prices. Financial Analysis Journal, 55-59.

Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. Journal of Finance, 25(2), 383-417.

Fama, E. F. and French, K. R. (1992). The cross-section of expected stock returns. Journal of Finance, 47 (2), 427-465.

Ghazani, M. M. and Ebrahimi, S. B. (2019). Testing the adaptive market hypothesis as an evolutionary perspective on market efficiency: Evidence from the crude oil prices. FinancialResearch Letters, 30: 60-68.

Granger, C. W. J. (1980). Long memory relationships and aggregation of dynamic models. Journal of Econometrics, 14: 227–238.

Granger, C. W. J., and Joyeux, R. (1980). An Introduction to Long Memory Time Series and Fractional Differencing. Journal of Econometrics 16: 121–130.

Gil-Alana, L. A. and Yaya, O. S. (2020). Testing Fractional Unit Roots with Non-linear Smooth Break Approximations using Fourier functions. Journal of Applied Statistics. https://doi.org/10.1080/02664763.2020.1757047

Gil-Alana, L. A., Gupta, R., Olubusoye, O. E. and Yaya, O. S. (2016). Time Series Analysis of Persistence in Crude Oil Price Volatility across Bull and Bear Regimes. Energy, 109: 29-37.

Gil-Alana, L. A., Yaya, O. S. and Awe, O. O. (2017). Time Series Analysis of Co-movements in the Prices of Gold and Oil: Fractional Cointegration Approach. Resources Policy, 53: 117-224.

Golpe, A., Carmona, M. and Congregado, E. (2012). Persistence in natural gas consumption in the US: An unobserved component model. Energy Policy 46, 594-600.

Kapetanios, G., Y. Shin, and A. Snell. (2003). Testing for a Unit Root in the Nonlinear STAR Framework. Journal of Econometrics, 112: 359–379.

Lean, H. H., McAleer, M. and Wong W. K. (2010). Market efficiency of oil spot and futures: A mean-variance and stochastic dominance approach. Energy Economics, 32(5), 979-986.

Lim, K. P. and Brooks, R. (2011). The evolution of stock market efficiency over time: A survey of the empirical literature. Journal of Economic Surveys, 25(1), 69-108.

Lim, C. and Lee, J. (2020). An analysis of the efficiency of the oil refining industry in the OECD countries. Energy Policy 142, 111491.

Maitra, D., Chandra, S. and Dash, S. R. (2019). Linear shipping industry and oil price volatility: Dynamic connectedness and portfolio diversification. Transportation Research Part E: Logistics and Transportation Review, 138, June 2020, 101962.

Marinucci, D. and Robinson, P. M. (1999). Alternative forms of fractional Brownian motion. Journal of Statistical Planning and Inference, 80, 111-122.

Mensi, W., Beljid, M. and Managi, S. (2014). Structural breaks and the time-varying levels of weak-from efficiency in crude oil markets: Evidence from the Hurst exponent and Shannon entropy methods. International Economics, 140, 89-106.

Mokni, K. and Youssef, M. (2019). Measuring persistence of dependence between crude oil prices and GCC stock markets: A copula approach. The Quarterly Review of Economics and Finance, 72, 14-33.

Narayan, P. K. and Narayan, S. (2007) Modelling oil price volatility. Energy Policy, 35(12) 6549-6553.

Norouzi, N. and Fani, M. (2020). Black gold falls, black plague arise-An Opec crude oil price forecast using a gray prediction model. Upstream Oil and Gas Technology, 5, 100015.

Okorie, D. I. and Lin, B. (2020). Crude oil price and cryptocurrencies: evidence of volatility connectedness and hedging strategy. Energy Economics, 87, 1-10.

Olubusoye, O. E. and Yaya, O. S. (2016). Time Series Analysis of Volatility in the Petroleum Markets: The Persistence, Asymmetry and Jumps in the Returns Series. OPEC Energy Review, 42(3), 235-262.

Qiao, K., Sun, Y. and Wang, S. (2019). Market inefficiencies associated with pricing oil stocks during shocks. Energy Economics 81, 661-671.

Ozdemir, Z. A., Gokmenoglu, K. And Ekinci, C. (2013). Persistence in crude oil spot and future prices. Energy, 59, 29-37.

Kuruppuarachchi, D., Premacheandra, I. M., Roberts, H. 2019. A novel market efficiency index for energy futures and their term structure risk premiums. Energy Economics, 77, 23-33.

Robinson, P.M. (1994). Efficient tests of nonstationary hypotheses. Journal of the American Statistical Association, 89, 1420-1437.

Sarwar, S., Tiwari, A. and Tingqiu, C. (2020). Analyzing volatility spillovers between oil market and asian stock markets. Resources Policy, 66, 1-32.

Sharma, S. (2017). Market efficiency between Indian and US crude oil future market. Information Technology and Quantitative Management, 122, 1039-1046.

Shittu, O. I. and Yaya, O. S. (2011). On the Autoregressive Fractional Unit Integrated Moving Average (ARFUIMA) Process. Journal of Sustainable Development in Africa, 13 (5), 225-232.

Tiwari, A. K., Kumar, S., Pathak, R. And Roubaud, D. (2019). Testing the oil price efficiency using various measures of longrange dependence. Energy Economics 84, 1-10

WHO (2020). Archived: WHO Timeline - COVID-19. https://www.who.int/news-room/detail/27-04-2020-who-timeline---covid-19WHO

Yang, C., Fei, L., Libing, F. and Xingxing, S. (2020). The pricing efficiency of crude oil futures in the Shanghai International Exchange. Finance Research Letters, 36. https://doi.org/10.1016/j.frl.2019.101329

Yaya, O. S. and Gil-Alana, L. A. (2020). Modelling Long range dependence and Non-linearity in the Infant Mortality Rates of Africa countries. International Advances of Economic Research, 26 (3): 303–315.

Yaya, O.S., Gil-Alana, L.A. and Carcel, H. (2015). Testing Fractional Persistence and Non-linearities in the Natural Gas Market: An Application of Non-linear Deterministic Terms based on Chebyshev Polynomials in Time. Energy Economics, 52 (A): 240-245.

Yaya, O. S., Tumala, M. M. and Udomboso, C. G. (2016). Volatility persistence and Returns spillovers between Oil and Gold Prices: Analysis before and after the global financial crisis. Resources Policy, 49: 273-281.

Yaya, O. S., Saka, L., Tumala, M. M., Akinlana, O. A. and Ogbonna, A. E. (2017). Oil Price-US Dollar Exchange Returns and Volatility Spillovers in OPEC Member Countries: Post Global Crisis Period’s Analysis. African Journal of Applied Statistics, 4(1): 191-208.

Yaya, O. S., Ogbonna, A. E., Furuoka, R. and Gil-Alana, L. A. (2021). A new unit root test for unemployment hysteresis based on the autoregressive neural network. Oxford Bulletin of Economics and Statistics. To appear.

Zhang, B. (2013). Are the crude oil becoming more efficient overtime? New evidence from generalized spectral analysis test. Energy Economics 40: 875-881.

Zavadska, M., Morales, L. and Coughlan, J. (2020). Brent crude oil prices volatility during major crises. Finance Research Letters, 32, 1-15.