VaR and CVaR estimates in BRIC’s Oil Sector: A Normal Inverse Gaussian Distribution Approach

The Value at Risk (VaR) and the Conditional Value at Risk (CVaR) as measures that estimate risk, have been used in oil sector to measure extreme and unexpected scenarios of oil prices. Additionally, the Normal Inverse Gaussian (NIG) distribution, a special case of the Generalized Hyperbolic (GH) fam...

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Autores: Sánchez Ruenes, Eduardo, Núñez Mora, José Antonio, Mota Aragón, Martha Beatriz
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:México
Institución:UNIVERSIDAD AUTÓNOMA METROPOLITANA
Repositorio:Economía: Teoría y práctica
Idioma:español
inglés
OAI Identifier:oai:ojs.pkp.sfu.ca:article/448
Acceso en línea:https://economiatyp.uam.mx/index.php/ETYP/article/view/448
Access Level:acceso abierto
Palabra clave:Value at Risk (VaR)
Conditional Value at Risk (CVaR)
Normal Inverse Gaussian (NIG) distribution
Oil Equity Returns
BRIC economies
G15
G11
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spelling VaR and CVaR estimates in BRIC’s Oil Sector: A Normal Inverse Gaussian Distribution ApproachVaR and CVaR estimates in BRIC’s Oil Sector: A Normal Inverse Gaussian distribution approachSánchez Ruenes, EduardoNúñez Mora, José AntonioMota Aragón, Martha BeatrizValue at Risk (VaR)Conditional Value at Risk (CVaR)Normal Inverse Gaussian (NIG) distributionOil Equity ReturnsBRIC economiesG15G11The Value at Risk (VaR) and the Conditional Value at Risk (CVaR) as measures that estimate risk, have been used in oil sector to measure extreme and unexpected scenarios of oil prices. Additionally, the Normal Inverse Gaussian (NIG) distribution, a special case of the Generalized Hyperbolic (GH) family, has been demonstrated to provide a better fit than Normal distribution to financial data. In this paper, we used NIG distribution to model a distribution of equity price returns in oil companies in Brazil, Russia, India and China (BRIC) economies in periods of unstable oil prices from 2004 to 2017, with the objective of demonstrating an underestimation of the risk measures when a Normal distribution is assumed and a more conservative estimate of those measures when considering a NIG distribution.The Value at Risk (VaR) and the Conditional Value at Risk (CVaR) as measures that estimate risk, have been used in oil sector to measure extreme and unexpected scenarios of oil prices. Additionally, the Normal Inverse Gaussian (NIG) distribution, a special case of the Generalized Hyperbolic (GH) family, has been demonstrated to provide a better fit than Normal distribution to financial data. In this paper, we used NIG distribution to model a distribution of equity price returns in oil companies in Brazil, Russia, India and China (BRIC) economies in periods of unstable oil prices from 2004 to 2017, with the objective of demonstrating an underestimation of the risk measures when a Normal distribution is assumed and a more conservative estimate of those measures when considering a NIG distribution.Universidad Autónoma Metropolitana2020-01-31info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdftext/htmlapplication/xmlhttps://economiatyp.uam.mx/index.php/ETYP/article/view/448Economía teoría y práctica; No. 52 (2020)Economía teoría y práctica; Núm. 52 (2020)2448-748110.24275/ETYPUAM/NE/522020reponame:Economía: Teoría y prácticainstname:UNIVERSIDAD AUTÓNOMA METROPOLITANAinstacron:UAMspaenghttps://economiatyp.uam.mx/index.php/ETYP/article/view/448/520https://economiatyp.uam.mx/index.php/ETYP/article/view/448/550https://economiatyp.uam.mx/index.php/ETYP/article/view/448/560Derechos de autor 2020 Economía teoría y prácticahttp://creativecommons.org/licenses/by-sa/4.0info:eu-repo/semantics/openAccessoai:ojs.pkp.sfu.ca:article/4482024-08-22T16:42:11Z
dc.title.none.fl_str_mv VaR and CVaR estimates in BRIC’s Oil Sector: A Normal Inverse Gaussian Distribution Approach
VaR and CVaR estimates in BRIC’s Oil Sector: A Normal Inverse Gaussian distribution approach
title VaR and CVaR estimates in BRIC’s Oil Sector: A Normal Inverse Gaussian Distribution Approach
spellingShingle VaR and CVaR estimates in BRIC’s Oil Sector: A Normal Inverse Gaussian Distribution Approach
Sánchez Ruenes, Eduardo
Value at Risk (VaR)
Conditional Value at Risk (CVaR)
Normal Inverse Gaussian (NIG) distribution
Oil Equity Returns
BRIC economies
G15
G11
title_short VaR and CVaR estimates in BRIC’s Oil Sector: A Normal Inverse Gaussian Distribution Approach
title_full VaR and CVaR estimates in BRIC’s Oil Sector: A Normal Inverse Gaussian Distribution Approach
title_fullStr VaR and CVaR estimates in BRIC’s Oil Sector: A Normal Inverse Gaussian Distribution Approach
title_full_unstemmed VaR and CVaR estimates in BRIC’s Oil Sector: A Normal Inverse Gaussian Distribution Approach
title_sort VaR and CVaR estimates in BRIC’s Oil Sector: A Normal Inverse Gaussian Distribution Approach
dc.creator.none.fl_str_mv Sánchez Ruenes, Eduardo
Núñez Mora, José Antonio
Mota Aragón, Martha Beatriz
author Sánchez Ruenes, Eduardo
author_facet Sánchez Ruenes, Eduardo
Núñez Mora, José Antonio
Mota Aragón, Martha Beatriz
author_role author
author2 Núñez Mora, José Antonio
Mota Aragón, Martha Beatriz
author2_role author
author
dc.subject.none.fl_str_mv Value at Risk (VaR)
Conditional Value at Risk (CVaR)
Normal Inverse Gaussian (NIG) distribution
Oil Equity Returns
BRIC economies
G15
G11
topic Value at Risk (VaR)
Conditional Value at Risk (CVaR)
Normal Inverse Gaussian (NIG) distribution
Oil Equity Returns
BRIC economies
G15
G11
description The Value at Risk (VaR) and the Conditional Value at Risk (CVaR) as measures that estimate risk, have been used in oil sector to measure extreme and unexpected scenarios of oil prices. Additionally, the Normal Inverse Gaussian (NIG) distribution, a special case of the Generalized Hyperbolic (GH) family, has been demonstrated to provide a better fit than Normal distribution to financial data. In this paper, we used NIG distribution to model a distribution of equity price returns in oil companies in Brazil, Russia, India and China (BRIC) economies in periods of unstable oil prices from 2004 to 2017, with the objective of demonstrating an underestimation of the risk measures when a Normal distribution is assumed and a more conservative estimate of those measures when considering a NIG distribution.
publishDate 2020
dc.date.none.fl_str_mv 2020-01-31
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://economiatyp.uam.mx/index.php/ETYP/article/view/448
url https://economiatyp.uam.mx/index.php/ETYP/article/view/448
dc.language.none.fl_str_mv spa
eng
language spa
eng
dc.relation.none.fl_str_mv https://economiatyp.uam.mx/index.php/ETYP/article/view/448/520
https://economiatyp.uam.mx/index.php/ETYP/article/view/448/550
https://economiatyp.uam.mx/index.php/ETYP/article/view/448/560
dc.rights.none.fl_str_mv Derechos de autor 2020 Economía teoría y práctica
http://creativecommons.org/licenses/by-sa/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Derechos de autor 2020 Economía teoría y práctica
http://creativecommons.org/licenses/by-sa/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
text/html
application/xml
dc.publisher.none.fl_str_mv Universidad Autónoma Metropolitana
publisher.none.fl_str_mv Universidad Autónoma Metropolitana
dc.source.none.fl_str_mv Economía teoría y práctica; No. 52 (2020)
Economía teoría y práctica; Núm. 52 (2020)
2448-7481
10.24275/ETYPUAM/NE/522020
reponame:Economía: Teoría y práctica
instname:UNIVERSIDAD AUTÓNOMA METROPOLITANA
instacron:UAM
instname_str UNIVERSIDAD AUTÓNOMA METROPOLITANA
instacron_str UAM
institution UAM
reponame_str Economía: Teoría y práctica
collection Economía: Teoría y práctica
repository.name.fl_str_mv
repository.mail.fl_str_mv
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