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
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.
Anthony Gichuhi Waititu,
Modelling Oil Price Risk, American Journal of Theoretical and Applied Statistics.
Vol. 4, No. 6,
2015, pp. 539-546.
Copyright © 2015 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
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