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Risk assessment of oil price from static and dynamic modelling approaches

Mi, Zhi Fu; Wei, Yi Ming; Tang, Bao Jun; Cong, Ronggang LU ; Yu, Hao; Cao, Hong and Guan, Dabo (2017) In Applied Economics 49(9). p.929-939
Abstract

The price gap between West Texas Intermediate (WTI) and Brent crude oil markets has been completely changed in the past several years. The price of WTI was always a little larger than that of Brent for a long time. However, the price of WTI has been surpassed by that of Brent since 2011. The new market circumstances and volatility of oil price require a comprehensive re-estimation of risk. Therefore, this study aims to explore an integrated approach to assess the price risk in the two crude oil markets through the value at risk (VaR) model. The VaR is estimated by the extreme value theory (EVT) and GARCH model on the basis of generalized error distribution (GED). The results show that EVT is a powerful approach to capture the risk in... (More)

The price gap between West Texas Intermediate (WTI) and Brent crude oil markets has been completely changed in the past several years. The price of WTI was always a little larger than that of Brent for a long time. However, the price of WTI has been surpassed by that of Brent since 2011. The new market circumstances and volatility of oil price require a comprehensive re-estimation of risk. Therefore, this study aims to explore an integrated approach to assess the price risk in the two crude oil markets through the value at risk (VaR) model. The VaR is estimated by the extreme value theory (EVT) and GARCH model on the basis of generalized error distribution (GED). The results show that EVT is a powerful approach to capture the risk in the oil markets. On the contrary, the traditional variance–covariance (VC) and Monte Carlo (MC) approaches tend to overestimate risk when the confidence level is 95%, but underestimate risk at the confidence level of 99%. The VaR of WTI returns is larger than that of Brent returns at identical confidence levels. Moreover, the GED-GARCH model can estimate the downside dynamic VaR accurately for WTI and Brent oil returns.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
extreme value theory, GED-GARCH, oil markets, risk quantification, Value at risk
in
Applied Economics
volume
49
issue
9
pages
11 pages
publisher
Routledge
external identifiers
  • scopus:84978525389
  • wos:000390690300007
ISSN
0003-6846
DOI
10.1080/00036846.2016.1208359
language
English
LU publication?
yes
id
7fe8dce8-952c-4afa-aa7d-92156ff960ac
date added to LUP
2017-01-12 13:51:29
date last changed
2018-05-20 04:30:26
@article{7fe8dce8-952c-4afa-aa7d-92156ff960ac,
  abstract     = {<p>The price gap between West Texas Intermediate (WTI) and Brent crude oil markets has been completely changed in the past several years. The price of WTI was always a little larger than that of Brent for a long time. However, the price of WTI has been surpassed by that of Brent since 2011. The new market circumstances and volatility of oil price require a comprehensive re-estimation of risk. Therefore, this study aims to explore an integrated approach to assess the price risk in the two crude oil markets through the value at risk (VaR) model. The VaR is estimated by the extreme value theory (EVT) and GARCH model on the basis of generalized error distribution (GED). The results show that EVT is a powerful approach to capture the risk in the oil markets. On the contrary, the traditional variance–covariance (VC) and Monte Carlo (MC) approaches tend to overestimate risk when the confidence level is 95%, but underestimate risk at the confidence level of 99%. The VaR of WTI returns is larger than that of Brent returns at identical confidence levels. Moreover, the GED-GARCH model can estimate the downside dynamic VaR accurately for WTI and Brent oil returns.</p>},
  author       = {Mi, Zhi Fu and Wei, Yi Ming and Tang, Bao Jun and Cong, Ronggang and Yu, Hao and Cao, Hong and Guan, Dabo},
  issn         = {0003-6846},
  keyword      = {extreme value theory,GED-GARCH,oil markets,risk quantification,Value at risk},
  language     = {eng},
  month        = {02},
  number       = {9},
  pages        = {929--939},
  publisher    = {Routledge},
  series       = {Applied Economics},
  title        = {Risk assessment of oil price from static and dynamic modelling approaches},
  url          = {http://dx.doi.org/10.1080/00036846.2016.1208359},
  volume       = {49},
  year         = {2017},
}