Modelling Oil Price Risk
American Journal of Theoretical and Applied Statistics
Volume 4, Issue 6, November 2015, Pages: 539-546
Received: Oct. 5, 2015; Accepted: Oct. 23, 2015; Published: Nov. 13, 2015
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Mwelu Susan, School of Mathematical Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Anthony Gichuhi Waititu, School of Mathematical Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
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The energy sector is regarded as a key driving force for all other sectors in the economy. This can be attributed to oil being the global main source of energy as well as oil prices having a significant impact on financial markets and world economies. With the emergence of relatively free oil markets, prices are vulnerable to high shifts resulting in increased exposure to price risk. This research project focuses on the oil markets with two main oil price benchmarks being used: Brent blend of Europe and WTI of United States of America. As opposed to estimating a single distribution for the entire return series generating process this research project focuses on the tails of the distributions using limit laws from the Extreme Value Theory. A two stage GARCH-EVT approach is preferred in the study. The focus is on the peak over threshold method for analysing the generalized Pareto distributed exceedances over some significantly high threshold. The results of this study reveal that oil prices are highly volatile, heteroscedastic and fat-tailed. In addition the GPD fits the tails adequately well and is used to estimate associated tail risks at sufficiently high probabilities.
Value-at-Risk, Oil Price Risk, GPD, Extreme Value Theory
To cite this article
Mwelu Susan, Anthony Gichuhi Waititu, Modelling Oil Price Risk, American Journal of Theoretical and Applied Statistics. Vol. 4, No. 6, 2015, pp. 539-546. doi: 10.11648/j.ajtas.20150406.25
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This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Aragonés, J., Dowd, K., and Blanco, C. (2000). Extreme value var. Derivatives Week, pages 7–8.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroscedasticity. Journal of econometrics, 31(3):307–327.
Cabedo, J. D. and Moya, I. (2003). Estimating oil price value at risk using the historical simulation approach. Energy Economics, 25(3):239–253.
Embrechts, P., Klüppelberg, C., and Mikosch, T. (1997). Modelling extremal events, volume 33. Springer Science & Business Media.
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation. Econometrica: Journal of the Econometric Society, pages 987– 1007.
Gencay, R. and Selcuk, F. (2004). Extreme value theory and value-at-risk: Relative performance in emerging markets. International Journal of Forecasting, 20(2):287–303.
Giot, P. and Laurent, S. (2003). Market risk in commodity markets: a var approach. Energy Economics, 25(5):435–457.
Ji, Q., & Guo, J. F. (2015). Oil price volatility and oil-related events: An Internet concern study perspective. Applied Energy, 137, 256-264.
Këllezi, E. and Gilli, M. (2000). Extreme value theory for tail-related risk measures. FAME.
Krehbiel, T. and Adkins, L. C. (2005). Price risk in the nymex energy complex: an extreme value approach. Journal of Futures Markets, 25(4):309–337.
Kuper, G. H. (2002). Measuring oil price volatility. Available at SSRN 316480.
Marimoutou, V., Raggad, B., and Trabelsi, A. (2009). Extreme value theory and value at risk: application to oil market. Energy Economics, 31(4):519–530.
McNeil, A. J. and Frey, R. (2000). Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach. Journal of empirical finance, 7(3): 271–300.
Sadorsky, P. (1999). Oil price shocks and stock market activity. Energy Economics, 21(5): 449– 469.
Salisu, A. A. and Fasanya, I. O. (2012). Comparative performance of volatility models for oil price. International Journal of Energy Economics and Policy, 2(3): 167–183.
Sauter, R. and Awerbuch, S. (2003). Oil price volatility and economic activity: a survey and literature review. IEA Research Paper.
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