Modelling the relationship between crude oil and agricultural commodity prices

The food-energy nexus has attracted great attention from policymakers, practitioners and academia since the food price crisis during the 2007-2008 Global Financial Crisis (GFC), and new policies that aim to increase ethanol production. This paper incorporates aggregate demand and alternative oil sho...

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Detalles Bibliográficos
Autores: Vo, Duc Hong, Vu, Tan Ngoc, Vo, Anh The, McAleer, Michael
Tipo de recurso: informe técnico
Fecha de publicación:2019
País:España
Institución:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/17466
Acceso en línea:https://hdl.handle.net/20.500.14352/17466
Access Level:acceso abierto
Palabra clave:C32
C58
Q14
Q42
Agricultural commodity prices
Volatility
Crude oil prices
Structural Vector Autoregressive model
Impulse response functions
Decomposition.
Econometría (Economía)
5302 Econometría
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oai_identifier_str oai:docta.ucm.es:20.500.14352/17466
network_acronym_str ES
network_name_str España
repository_id_str
spelling Modelling the relationship between crude oil and agricultural commodity pricesVo, Duc HongVu, Tan NgocVo, Anh TheMcAleer, MichaelC32C58Q14Q42Agricultural commodity pricesVolatilityCrude oil pricesStructural Vector Autoregressive modelImpulse response functionsDecomposition.Econometría (Economía)5302 EconometríaThe food-energy nexus has attracted great attention from policymakers, practitioners and academia since the food price crisis during the 2007-2008 Global Financial Crisis (GFC), and new policies that aim to increase ethanol production. This paper incorporates aggregate demand and alternative oil shocks to investigate the causal relationship between agricultural products and oil markets, which is a novel contribution. For the period January 2000 - July 2018, monthly spot prices of 15 commodities are examined, including Brent crude oil, biofuel-related agricultural commodities, and other agricultural commodities. The sample is divided into three sub-periods, namely: (i) January 2000 - July 2006; (ii) August 2006 - April 2013; and (iii) May 2013 - July 2018. The Structural Vector Autoregressive (SVAR) model, impulse response functions, and variance decomposition technique are used to examine how the shocks to agricultural markets contribute to the variance of crude oil prices. The empirical findings from the paper indicate that not every oil shock contributes the same to agricultural price fluctuations, and similarly for the effects of aggregate demand shocks on the agricultural market. These results show that the crude oil market plays a major role in explaining fluctuations in the prices and associated volatility of agricultural commodities.Facultad de Ciencias Económicas y Empresariales. Instituto Complutense de Análisis Económico (ICAE)Universidad Complutense de Madrid20192019-01-0120192019-01-01technical reporthttp://purl.org/coar/resource_type/c_18ghinfo:eu-repo/semantics/reportapplication/pdfhttps://hdl.handle.net/20.500.14352/17466reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/174662026-06-02T12:44:21Z
dc.title.none.fl_str_mv Modelling the relationship between crude oil and agricultural commodity prices
title Modelling the relationship between crude oil and agricultural commodity prices
spellingShingle Modelling the relationship between crude oil and agricultural commodity prices
Vo, Duc Hong
C32
C58
Q14
Q42
Agricultural commodity prices
Volatility
Crude oil prices
Structural Vector Autoregressive model
Impulse response functions
Decomposition.
Econometría (Economía)
5302 Econometría
title_short Modelling the relationship between crude oil and agricultural commodity prices
title_full Modelling the relationship between crude oil and agricultural commodity prices
title_fullStr Modelling the relationship between crude oil and agricultural commodity prices
title_full_unstemmed Modelling the relationship between crude oil and agricultural commodity prices
title_sort Modelling the relationship between crude oil and agricultural commodity prices
dc.creator.none.fl_str_mv Vo, Duc Hong
Vu, Tan Ngoc
Vo, Anh The
McAleer, Michael
author Vo, Duc Hong
author_facet Vo, Duc Hong
Vu, Tan Ngoc
Vo, Anh The
McAleer, Michael
author_role author
author2 Vu, Tan Ngoc
Vo, Anh The
McAleer, Michael
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv C32
C58
Q14
Q42
Agricultural commodity prices
Volatility
Crude oil prices
Structural Vector Autoregressive model
Impulse response functions
Decomposition.
Econometría (Economía)
5302 Econometría
topic C32
C58
Q14
Q42
Agricultural commodity prices
Volatility
Crude oil prices
Structural Vector Autoregressive model
Impulse response functions
Decomposition.
Econometría (Economía)
5302 Econometría
description The food-energy nexus has attracted great attention from policymakers, practitioners and academia since the food price crisis during the 2007-2008 Global Financial Crisis (GFC), and new policies that aim to increase ethanol production. This paper incorporates aggregate demand and alternative oil shocks to investigate the causal relationship between agricultural products and oil markets, which is a novel contribution. For the period January 2000 - July 2018, monthly spot prices of 15 commodities are examined, including Brent crude oil, biofuel-related agricultural commodities, and other agricultural commodities. The sample is divided into three sub-periods, namely: (i) January 2000 - July 2006; (ii) August 2006 - April 2013; and (iii) May 2013 - July 2018. The Structural Vector Autoregressive (SVAR) model, impulse response functions, and variance decomposition technique are used to examine how the shocks to agricultural markets contribute to the variance of crude oil prices. The empirical findings from the paper indicate that not every oil shock contributes the same to agricultural price fluctuations, and similarly for the effects of aggregate demand shocks on the agricultural market. These results show that the crude oil market plays a major role in explaining fluctuations in the prices and associated volatility of agricultural commodities.
publishDate 2019
dc.date.none.fl_str_mv 2019
2019-01-01
2019
2019-01-01
dc.type.none.fl_str_mv technical report
http://purl.org/coar/resource_type/c_18gh
dc.type.openaire.fl_str_mv info:eu-repo/semantics/report
format report
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/17466
url https://hdl.handle.net/20.500.14352/17466
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Facultad de Ciencias Económicas y Empresariales. Instituto Complutense de Análisis Económico (ICAE)
publisher.none.fl_str_mv Facultad de Ciencias Económicas y Empresariales. Instituto Complutense de Análisis Económico (ICAE)
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812429

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