Measuring volatility based on ordered weighted average operators: Agricultural products prices case of use

León-Castro, E; Espinoza-Audelo, LF; Merigó, JM; Herrera-Viedma, E; Herrera, F

Measuring volatility based on ordered weighted average operators: Agricultural products prices case of use

León-Castro, E Espinoza-Audelo, LF Merigó, JM Herrera-Viedma, E Herrera, F

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Fuzzy Sets and Systems, 2020
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	León-Castro, E
dc.contributor.author	Espinoza-Audelo, LF
dc.contributor.author	Merigó, JM
dc.contributor.author	Herrera-Viedma, E
dc.contributor.author	Herrera, F
dc.date.accessioned	2021-02-23T10:55:26Z
dc.date.available	2021-02-23T10:55:26Z
dc.date.issued	2020-01-01
dc.identifier.citation	Fuzzy Sets and Systems, 2020
dc.identifier.issn	0165-0114
dc.identifier.uri	http://hdl.handle.net/10453/146331
dc.description.abstract	© 2020 Elsevier B.V. Agricultural products have experienced sudden changes in prices in recent years as a result of volumes of production and demand at the international level. Volatility is a key element in understanding the difficulties that the market may have. However, the traditional formula for volatility only considers historical information and does not consider decision makers' knowledge and skills. To improve this approach and obtain more accurate results consistent with the reality of the market, the ordered weighted averaging (OWA) operator is used. These new approaches are the OWA-Volatility, Induced OWA-Volatility, Heavy OWA-Volatility, Probabilistic OWA-Volatility, Induced Probabilistic OWA-Volatility and Induced Heavy OWA-Volatility. In addition, some particular cases are presented in which the aggregation process is only applied to one part of the formula or quasi-arithmetic means are used. An example of volatility calculations for corn prices in 2017 is presented.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Fuzzy Sets and Systems
dc.relation.isbasedon	10.1016/j.fss.2020.08.006
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0101 Pure Mathematics, 0801 Artificial Intelligence and Image Processing
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Measuring volatility based on ordered weighted average operators: Agricultural products prices case of use
dc.type	Journal Article
utslib.for	0101 Pure Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2021-02-23T10:55:24Z
pubs.publication-status	Published

Abstract:

© 2020 Elsevier B.V. Agricultural products have experienced sudden changes in prices in recent years as a result of volumes of production and demand at the international level. Volatility is a key element in understanding the difficulties that the market may have. However, the traditional formula for volatility only considers historical information and does not consider decision makers' knowledge and skills. To improve this approach and obtain more accurate results consistent with the reality of the market, the ordered weighted averaging (OWA) operator is used. These new approaches are the OWA-Volatility, Induced OWA-Volatility, Heavy OWA-Volatility, Probabilistic OWA-Volatility, Induced Probabilistic OWA-Volatility and Induced Heavy OWA-Volatility. In addition, some particular cases are presented in which the aggregation process is only applied to one part of the formula or quasi-arithmetic means are used. An example of volatility calculations for corn prices in 2017 is presented.

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http://hdl.handle.net/10453/146331