Volatility GARCH models with the ordered weighted average (OWA) operators

Flores-Sosa, M; Avilés-Ochoa, E; Merigó, JM; Yager, RR

Volatility GARCH models with the ordered weighted average (OWA) operators

Flores-Sosa, M Avilés-Ochoa, E Merigó, JM Yager, RR

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Information Sciences, 2021, 565, pp. 46-61
Issue Date:: 2021-07-01

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Field	Value	Language
dc.contributor.author	Flores-Sosa, M
dc.contributor.author	Avilés-Ochoa, E
dc.contributor.author	Merigó, JM
dc.contributor.author	Yager, RR
dc.date.accessioned	2022-04-29T03:13:09Z
dc.date.available	2022-04-29T03:13:09Z
dc.date.issued	2021-07-01
dc.identifier.citation	Information Sciences, 2021, 565, pp. 46-61
dc.identifier.issn	0020-0255
dc.identifier.issn	1872-6291
dc.identifier.uri	http://hdl.handle.net/10453/156805
dc.description.abstract	Volatility is an important issue for companies, policy-makers, and researches. Autoregressive conditional heteroscedasticity (ARCH) and generalized ARCH (GARCH) models are frequently used to study volatility. However, forecasting efficiency tends to fail when complex data is used. This paper proposes the use of ordered weighted average (OWA) operators in combination with ordinary least squares (OLS) to create an estimator that can treat high degrees of uncertainty. In the application of the ARCH-GARCH models, we develop approaches with the OWA and the induced OWA operator. Some further generalizations are also developed by using generalized means. The main advantage of this new methodology is to add additional information to the process of estimating the models according to the attitudinal character of the decision-maker. Finally, the work presents an application in the volatility of the MX/US exchange rate, where the efficiency of the OWA operators in forecasting is proved.
dc.language	English
dc.publisher	Elsevier
dc.relation.ispartof	Information Sciences
dc.relation.isbasedon	10.1016/j.ins.2021.02.051
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Volatility GARCH models with the ordered weighted average (OWA) operators
dc.type	Journal Article
utslib.citation.volume	565
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney
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 Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-04-29T03:13:08Z
pubs.publication-status	Published
pubs.volume	565

Abstract:

Volatility is an important issue for companies, policy-makers, and researches. Autoregressive conditional heteroscedasticity (ARCH) and generalized ARCH (GARCH) models are frequently used to study volatility. However, forecasting efficiency tends to fail when complex data is used. This paper proposes the use of ordered weighted average (OWA) operators in combination with ordinary least squares (OLS) to create an estimator that can treat high degrees of uncertainty. In the application of the ARCH-GARCH models, we develop approaches with the OWA and the induced OWA operator. Some further generalizations are also developed by using generalized means. The main advantage of this new methodology is to add additional information to the process of estimating the models according to the attitudinal character of the decision-maker. Finally, the work presents an application in the volatility of the MX/US exchange rate, where the efficiency of the OWA operators in forecasting is proved.

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http://hdl.handle.net/10453/156805