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.
| Published in | American Journal of Theoretical and Applied Statistics (Volume 4, Issue 6) |
| DOI | 10.11648/j.ajtas.20150406.25 |
| Page(s) | 539-546 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Value-at-Risk, Oil Price Risk, GPD, Extreme Value Theory
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APA Style
Mwelu Susan, Anthony Gichuhi Waititu. (2015). Modelling Oil Price Risk. American Journal of Theoretical and Applied Statistics, 4(6), 539-546. https://doi.org/10.11648/j.ajtas.20150406.25
ACS Style
Mwelu Susan; Anthony Gichuhi Waititu. Modelling Oil Price Risk. Am. J. Theor. Appl. Stat. 2015, 4(6), 539-546. doi: 10.11648/j.ajtas.20150406.25
AMA Style
Mwelu Susan, Anthony Gichuhi Waititu. Modelling Oil Price Risk. Am J Theor Appl Stat. 2015;4(6):539-546. doi: 10.11648/j.ajtas.20150406.25
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Download@article{10.11648/j.ajtas.20150406.25,
author = {Mwelu Susan and Anthony Gichuhi Waititu},
title = {Modelling Oil Price Risk},
journal = {American Journal of Theoretical and Applied Statistics},
volume = {4},
number = {6},
pages = {539-546},
doi = {10.11648/j.ajtas.20150406.25},
url = {https://doi.org/10.11648/j.ajtas.20150406.25},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20150406.25},
abstract = {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.},
year = {2015}
}
TY - JOUR T1 - Modelling Oil Price Risk AU - Mwelu Susan AU - Anthony Gichuhi Waititu Y1 - 2015/11/13 PY - 2015 N1 - https://doi.org/10.11648/j.ajtas.20150406.25 DO - 10.11648/j.ajtas.20150406.25 T2 - American Journal of Theoretical and Applied Statistics JF - American Journal of Theoretical and Applied Statistics JO - American Journal of Theoretical and Applied Statistics SP - 539 EP - 546 PB - Science Publishing Group SN - 2326-9006 UR - https://doi.org/10.11648/j.ajtas.20150406.25 AB - 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. VL - 4 IS - 6 ER -
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