Abstract
This study aims to measure the risk dependence between the two most important energy markets, oil and gas, to analyze their risk spillovers. To this end, we apply different extreme risk measures (the value at risk, conditional value at risk, delta conditional value at risk, and copula) to high-frequency energy data to capture the intraday dynamic dependence between oil and gas prices (using, in particular, a 5-min intraday sample data from November 2014 to October 2017). Our analysis shows two interesting findings. First, while we highlight an extreme risk dependence between oil and gas markets, the risk spillover from the oil to the gas market is higher than that from the gas to the oil market. Second, the upward and downward risk spillovers exhibit asymmetry, as extreme negative shocks produce a stronger spillover effect than do extreme positive shocks. The exploration of these systemic risk forms provides significant insights for policymakers and investors in terms of risk management and portfolio diversification.
Similar content being viewed by others
Notes
See Ftiti et al. (2019a) for a concise survey on the relationship between energy prices and trading volumes.
Several studies consider the CoVaR approach to model systemic risk, including the works of Trabelsi and Naifar (2017), who study systemic risk of the Islamic index market, Reboredo (2015), who analyzes the dependence between oil and renewable energy markets, and Mensi et al. (2017), who consider systemic risk between the oil and stock markets. Finally, we employ delta CoVaR as further analysis for risk spillover.
Other thresholds were also experimented and conclusions remain unchanged. They are not reported to save space but available upon request.
The reported results for the downside and upside VaR correspond to estimation with probability levels q = 5% and q = 95%, respectively.
References
Acharya, V. (2009). A theory of systemic risk and design of prudential bank regulation. Journal of Financial Stability, 5, 224–255.
Adrian, T., & Brunnermeier, M. K. (2016). CoVaR. American Economic Review, 106, 1705–1741.
Bachmeier, L. J., & Griffin, J. M. (2006). Testing for market integration crude oil, coal, and natural gas. The Energy Journal, 27, 55–71.
Bacon, R. W. (1991). Rockets and feathers: The asymmetric speed of adjustment of UK retail gasoline prices to cost changes. Energy Economics, 13(July), 211–218.
Batten, J. A., Ciner, C., & Lucey, B. M. (2017). The dynamic linkages between crude oil and natural gas markets. Energy Economics, 62, 155–170.
Boyer, M., & Filion, D. (2007). Common and fundamental factors in stock returns of Canadian oil and gas companies. Energy Economics, 29, 428–453.
Brigida, M. (2014). The switching relationship between natural gas and crude oil prices. Energy Economics, 43, 48–55.
Douglas, C., & Herrera, A. M. (2010). Why are gasoline prices sticky? A test of alternative models of price adjustment, Journal of Applied Econometrics, 25, 903–928.
Faff, R., & Brailsford, T. (1999). Oil price risk and the Australian stock market. Journal of Energy Finance and Development, 4, 69–87.
Ftiti, Z., Jawadi, F., Louhichi, W., & Midani, A. (2019). On the relationship between energy returns and trading volume: A multifractal analysis. Applied Economics, 51(29), 3122–3136.
Ftiti, Z., Tissaoui, K., & Boubaker, S. (2020). On the relationship between oil and gas markets: A new forecasting framework based on the machine learning approach. Annals of Operational Research. https://doi.org/10.1007/s10479-020-03652-2.
Gatfaoui, H. (2016). Capturing long-term coupling and short-term decoupling crude oil and natural gas prices. In: ECOMFIN2016: Energy and commodity finance conference 2016. ESSEC Business School, Paris, June 23–24.
Hamilton, J. D. (1983). Oil and the macroeconomy since World War II. Journal of Political Economy, 91, 228–248.
Hamilton, J. D. (2003). What is an oil shock? Journal of Econometrics, 113(2), 363–398.
Jawadi, F., Ftiti, Z., & Louhichi, W. (2019). Forecasting energy futures volatility with threshold augmented heterogenous autoregressive jump models. Econometric Reviews, 39, 54–70.
Karrenbrock, J. D. (1991). The behavior of retail gasoline prices: Symmetric or not? Federal Reserve Bank of St. Louis Review, July/August 19–29.
Koenker, R., & Bassett, G. (1978). Regression quantiles. Econometrica, 46, 33–48.
Lin, B., & Li, J. (2015). The spillover effects across natural gas and oil markets: Based on the VEC–MGARCH framework. Applied Energy, 155, 229–241.
Mensi, W., Hammoudeh, S., Al-Jarrah, I. M. W., Sensoy, A., & Kang, S. H. (2017). Dynamic risk spillovers between gold, oil prices and conventional, sustainability and Islamic equity aggregates and sectors with portfolio implications. Energy Economics, 67, 454–475.
Nandha, M., & Faff, R. (2008). Does oil move equity prices? A global view. Energy Economics, 30, 986–997.
Patton, A. (2006). Estimation of multivariate models for time series of possibly different lengths. Journal of Applied Econometrics, 21, 147–173.
Patton, A. (2012). A review of copula models for economic time series. Journal of Multivariate Analysis, 110, 4–18.
Ramberg, D. J., & Parsons, J. E. (2012). The weak tie between natural gas and oil prices. The Energy Journal, 33, 13–35.
Reboredo, J. C. (2015). Is there dependence and systemic risk between oil and renewable energy stock prices? Energy Economics, 48, 32–45.
Sadorsky, P. (2001). Risk factors in stock returns of Canadian oil and gas companies. Energy Economics, 23, 17–28.
Sklar, A. (1956). Fonctions de répartition et leurs marges. Publications de l’Institut de Statistique de l’Université de Paris, 8, 229–231.
Teräsvirta, T., & Zhao, Z. (2011). Stylized facts of return series, robust estimates and three popular models of volatility. Applied Financial Economics, 21, 67–94.
Trabelsi, N., & Naifar, N. (2017). Are Islamic stock indexes exposed to systemic risk? Multivariate GARCH estimation of CoVaR. Research in International Business and Finance, 42, 727–744.
Venditti, F. (2010). Down the non-linear road from oil to consumer energy prices: No much asymmetry along the way. In: Temi di discussione, Bank of Italy Economic working papers 751.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Ben Ameur, H., Ftiti, Z., Jawadi, F. et al. Measuring extreme risk dependence between the oil and gas markets. Ann Oper Res 313, 755–772 (2022). https://doi.org/10.1007/s10479-020-03796-1
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10479-020-03796-1