Estimating oil price value at risk using belief functions

© Springer International Publishing Switzerland 2015. We consider extreme value theory to study extreme price movements in crude oil market. Autoregressive-Moving-Average models are developed to describe daily log return of crude oil price. Peak-over-thresholdmodels are then used to model the log re...

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Main Authors:	Phochanachan,P., Sirisrisakulchai,J., Sriboonchitta,S.
Format:	Article
Published:	Springer Verlag 2015
Subjects:	Artificial Intelligence
Online Access:	http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84919372650&origin=inward http://cmuir.cmu.ac.th/handle/6653943832/39147
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Institution:	Chiang Mai University

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spelling	th-cmuir.6653943832-391472015-06-16T08:07:46Z Estimating oil price value at risk using belief functions Phochanachan,P. Sirisrisakulchai,J. Sriboonchitta,S. Artificial Intelligence © Springer International Publishing Switzerland 2015. We consider extreme value theory to study extreme price movements in crude oil market. Autoregressive-Moving-Average models are developed to describe daily log return of crude oil price. Peak-over-thresholdmodels are then used to model the log return forecasting errors (residuals). The maximum residuals are expressed in terms of value-at-risk or return level corresponding to accepted levels of risk so that appropriate risk measures can be taken. A likelihood-based belief function is constructed to quantify estimation uncertainty. As a result, we can assess the plausibility of various assertions about the value-at-risk of the idiosyncratic shocks in the world crude oil market. 2015-06-16T08:07:46Z 2015-06-16T08:07:46Z 2015-01-01 Article 1860949X 2-s2.0-84919372650 10.1007/978-3-319-13449-9_26 http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84919372650&origin=inward http://cmuir.cmu.ac.th/handle/6653943832/39147 Springer Verlag
institution	Chiang Mai University
building	Chiang Mai University Library
country	Thailand
collection	CMU Intellectual Repository
topic	Artificial Intelligence
spellingShingle	Artificial Intelligence Phochanachan,P. Sirisrisakulchai,J. Sriboonchitta,S. Estimating oil price value at risk using belief functions
description	© Springer International Publishing Switzerland 2015. We consider extreme value theory to study extreme price movements in crude oil market. Autoregressive-Moving-Average models are developed to describe daily log return of crude oil price. Peak-over-thresholdmodels are then used to model the log return forecasting errors (residuals). The maximum residuals are expressed in terms of value-at-risk or return level corresponding to accepted levels of risk so that appropriate risk measures can be taken. A likelihood-based belief function is constructed to quantify estimation uncertainty. As a result, we can assess the plausibility of various assertions about the value-at-risk of the idiosyncratic shocks in the world crude oil market.
format	Article
author	Phochanachan,P. Sirisrisakulchai,J. Sriboonchitta,S.
author_facet	Phochanachan,P. Sirisrisakulchai,J. Sriboonchitta,S.
author_sort	Phochanachan,P.
title	Estimating oil price value at risk using belief functions
title_short	Estimating oil price value at risk using belief functions
title_full	Estimating oil price value at risk using belief functions
title_fullStr	Estimating oil price value at risk using belief functions
title_full_unstemmed	Estimating oil price value at risk using belief functions
title_sort	estimating oil price value at risk using belief functions
publisher	Springer Verlag
publishDate	2015
url	http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84919372650&origin=inward http://cmuir.cmu.ac.th/handle/6653943832/39147
_version_	1681421601852096512

Estimating oil price value at risk using belief functions

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