Modelling Long Memory Volatility in Agricultural Commodity Futures Returns

This paper estimates a long memory volatility model for 16 agricultural commodity futures returns from different futures markets, namely corn, oats, soybeans, soybean meal, soybean oil, wheat, live cattle, cattle feeder, pork, cocoa, coffee, cotton, orange juice, Kansas City wheat, rubber, and palm...

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Detalles Bibliográficos
Autores: Chang, Chia-Lin, McAleer, Michael, Tansuchat, Roengchai
Tipo de recurso: informe técnico
Fecha de publicación:2012
País:España
Institución:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/49087
Acceso en línea:https://hdl.handle.net/20.500.14352/49087
Access Level:acceso abierto
Palabra clave:Q14
Q11
Q22
Q51
Long memory
Agricultural commodity futures
Fractional integration
Asymmetric
Conditional volatility.
Econometría (Economía)
5302 Econometría
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oai_identifier_str oai:docta.ucm.es:20.500.14352/49087
network_acronym_str ES
network_name_str España
repository_id_str
spelling Modelling Long Memory Volatility in Agricultural Commodity Futures ReturnsChang, Chia-LinMcAleer, MichaelTansuchat, RoengchaiQ14Q11Q22Q51Long memoryAgricultural commodity futuresFractional integrationAsymmetricConditional volatility.Econometría (Economía)5302 EconometríaThis paper estimates a long memory volatility model for 16 agricultural commodity futures returns from different futures markets, namely corn, oats, soybeans, soybean meal, soybean oil, wheat, live cattle, cattle feeder, pork, cocoa, coffee, cotton, orange juice, Kansas City wheat, rubber, and palm oil. The class of fractional GARCH models, namely the FIGARCH model of Baillie et al. (1996), FIEGARCH model of Bollerslev and Mikkelsen (1996), and FIAPARCH model of Tse (1998), are modelled and compared with the GARCH model of Bollerslev (1986), EGARCH model of Nelson (1991), and APARCH model of Ding et al. (1993). The estimated d parameters, indicating long-term dependence, suggest that fractional integration is found in most of agricultural commodity futures returns series. In addition, the FIGARCH (1,d,1) and FIEGARCH(1,d,1) models are found to outperform their GARCH(1,1) and EGARCH(1,1) counterparts.Universidad Complutense de Madrid20122012-05-0120122012-05-01technical reporthttp://purl.org/coar/resource_type/c_18ghinfo:eu-repo/semantics/reportapplication/pdfhttps://hdl.handle.net/20.500.14352/49087reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Atribución-NoComercial 3.0 Españahttps://creativecommons.org/licenses/by-nc/3.0/es/info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/490872026-06-02T12:44:21Z
dc.title.none.fl_str_mv Modelling Long Memory Volatility in Agricultural Commodity Futures Returns
title Modelling Long Memory Volatility in Agricultural Commodity Futures Returns
spellingShingle Modelling Long Memory Volatility in Agricultural Commodity Futures Returns
Chang, Chia-Lin
Q14
Q11
Q22
Q51
Long memory
Agricultural commodity futures
Fractional integration
Asymmetric
Conditional volatility.
Econometría (Economía)
5302 Econometría
title_short Modelling Long Memory Volatility in Agricultural Commodity Futures Returns
title_full Modelling Long Memory Volatility in Agricultural Commodity Futures Returns
title_fullStr Modelling Long Memory Volatility in Agricultural Commodity Futures Returns
title_full_unstemmed Modelling Long Memory Volatility in Agricultural Commodity Futures Returns
title_sort Modelling Long Memory Volatility in Agricultural Commodity Futures Returns
dc.creator.none.fl_str_mv Chang, Chia-Lin
McAleer, Michael
Tansuchat, Roengchai
author Chang, Chia-Lin
author_facet Chang, Chia-Lin
McAleer, Michael
Tansuchat, Roengchai
author_role author
author2 McAleer, Michael
Tansuchat, Roengchai
author2_role author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv Q14
Q11
Q22
Q51
Long memory
Agricultural commodity futures
Fractional integration
Asymmetric
Conditional volatility.
Econometría (Economía)
5302 Econometría
topic Q14
Q11
Q22
Q51
Long memory
Agricultural commodity futures
Fractional integration
Asymmetric
Conditional volatility.
Econometría (Economía)
5302 Econometría
description This paper estimates a long memory volatility model for 16 agricultural commodity futures returns from different futures markets, namely corn, oats, soybeans, soybean meal, soybean oil, wheat, live cattle, cattle feeder, pork, cocoa, coffee, cotton, orange juice, Kansas City wheat, rubber, and palm oil. The class of fractional GARCH models, namely the FIGARCH model of Baillie et al. (1996), FIEGARCH model of Bollerslev and Mikkelsen (1996), and FIAPARCH model of Tse (1998), are modelled and compared with the GARCH model of Bollerslev (1986), EGARCH model of Nelson (1991), and APARCH model of Ding et al. (1993). The estimated d parameters, indicating long-term dependence, suggest that fractional integration is found in most of agricultural commodity futures returns series. In addition, the FIGARCH (1,d,1) and FIEGARCH(1,d,1) models are found to outperform their GARCH(1,1) and EGARCH(1,1) counterparts.
publishDate 2012
dc.date.none.fl_str_mv 2012
2012-05-01
2012
2012-05-01
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/20.500.14352/49087
url https://hdl.handle.net/20.500.14352/49087
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
Atribución-NoComercial 3.0 España
https://creativecommons.org/licenses/by-nc/3.0/es/
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
Atribución-NoComercial 3.0 España
https://creativecommons.org/licenses/by-nc/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
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 15,811543

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