Forgotten effects and heavy moving averages in exchange rate forecasting

Ochoa, EA; Castro, EL; Lindahl, JMM; Lafuente, AMG

Forgotten effects and heavy moving averages in exchange rate forecasting

Ochoa, EA Castro, EL Lindahl, JMM

Lafuente, AMG

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Publication Type:: Conference Proceeding
Citation:: 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016, 2017
Issue Date:: 2017-02-09

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Field	Value	Language
dc.contributor.author	Ochoa, EA	en_US
dc.contributor.author	Castro, EL	en_US
dc.contributor.author	Lindahl, JMM https://orcid.org/0000-0002-4672-6961	en_US
dc.contributor.author	Lafuente, AMG	en_US
dc.date.issued	2017-02-09	en_US
dc.identifier.citation	2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016, 2017	en_US
dc.identifier.isbn	9781509042401	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126393
dc.description.abstract	© 2016 IEEE. This paper presents the results of using experton, forgotten effects and heavy moving averages operators in three traditional models based purchasing power parity (PPP) model to forecast exchange rate. Therefore, the use of these methods is to improve the forecast error under scenarios of volatility and uncertainty, such as the financial markets and more precise in exchange rate. The heavy ordered weighted moving average weighted average (HOWMAWA) operator is introduced. This new operator includes the weighted average in the usual heavy ordered weighted moving average (HOWMA) operator, considering a degree of importance for each concept that includes the operator. The use of experton and forgotten effects methodology represents the information of the experts in the field and with that information were obtained hidden variables or second degree relations. The results show that the inclusion of the forgotten effects and heavy moving average operators improve our results and reduce the forecast error.	en_US
dc.relation.ispartof	2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016	en_US
dc.relation.isbasedon	10.1109/SSCI.2016.7850015	en_US
dc.title	Forgotten effects and heavy moving averages in exchange rate forecasting	en_US
dc.type	Conference Proceeding
utslib.for	080205 Numerical Computation	en_US
pubs.embargo.period	Not known	en_US
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 Information, Systems and Modelling
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. This paper presents the results of using experton, forgotten effects and heavy moving averages operators in three traditional models based purchasing power parity (PPP) model to forecast exchange rate. Therefore, the use of these methods is to improve the forecast error under scenarios of volatility and uncertainty, such as the financial markets and more precise in exchange rate. The heavy ordered weighted moving average weighted average (HOWMAWA) operator is introduced. This new operator includes the weighted average in the usual heavy ordered weighted moving average (HOWMA) operator, considering a degree of importance for each concept that includes the operator. The use of experton and forgotten effects methodology represents the information of the experts in the field and with that information were obtained hidden variables or second degree relations. The results show that the inclusion of the forgotten effects and heavy moving average operators improve our results and reduce the forecast error.

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http://hdl.handle.net/10453/126393