Estimating VaR and ES of the spot price of oil using futures-varying centiles Online publication date: Wed, 10-Sep-2014
by Giacomo Scandroglio; Andrea Gori; Emiliano Vaccaro; Vlasios Voudouris
International Journal of Financial Engineering and Risk Management (IJFERM), Vol. 1, No. 1, 2013
Abstract: This paper illustrates the power of modern statistical modelling in estimating measures of market risk, here applied to the Brent and WTI spot price of oil. Both Value-at-Risk (VaR) and Expected Shortfall (ES) are cast in terms of conditional centiles based upon semi-parametric regression models. Using the GAMLSS statistical framework, we stress the important aspects of selecting a highly flexible parametric distribution (skewed Student's t-distribution) and of modelling both skewness and kurtosis as non-parametric functions of the price of oil futures. Furthermore, an empirical application characterises the relationship between spot oil prices and oil futures - exploiting the futures market to explain the dynamics of the physical market. Our results suggest that NYMEX WTI has heavier tails compared with the ICE Brent. Contrary to the common platitude of the industry, we argue that 'somebody knows something' in the oil business.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Financial Engineering and Risk Management (IJFERM):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook