Signal Model for Prediction of Exchange Rates

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dc.contributor.authorAgwuegbo, S.O.N.
dc.contributor.authorOnugha, E.E.
dc.contributor.authorAkintunde, A.A.
dc.contributor.authorAdewole, A.P.
dc.date.accessioned2019-09-09T14:24:17Z
dc.date.available2019-09-09T14:24:17Z
dc.date.issued2017-05
dc.descriptionStaff publicationsen_US
dc.description.abstractThe appropriate prediction of exchange rates is an area of financiaL forecasting which attracts a great deal of attention. For many years, the volatile nature of exchange rates has been the focus of Many researchers. Many researchers attribute interest in exchange rate volatility to the fact that it is empirically difficult to predict future exchange rate values. Foreign exchange market generally produces observable outputs which can be characterised as signals. III this study we investigated the monthly average exchange rates of usDollars to the Nigerian Naira using signal modelling approach, Front the study, there is a convincing statistical evidence to believe that exchange rates can be better modelled by a Markov process as the output of a first order discrete autoregressive process. The result demonstrated that a Markov process is sometimes called a first order autoregressive process.en_US
dc.identifier.citationAgwuegbo, S.O.N, Onugha, E.E, Akintunde A.A. and Adewole, A.P. (2017). Signal Model for Prediction of Exchange Rates. Journal of the Nigerian Association of Mathematical Physics, Vol.41: 261-268pp.en_US
dc.identifier.urihttps://ir.unilag.edu.ng/handle/123456789/5404
dc.language.isoenen_US
dc.publisherJournal of the Nigerian Association of Mathematical Physicsen_US
dc.relation.ispartofseriesJournal of the Nigerian Association of Mathematical Physics;Vol.41
dc.subjectDynamicsen_US
dc.subjectStationary processen_US
dc.subjectGaussianen_US
dc.subjectMarkov processen_US
dc.subjectMartingaleen_US
dc.subjectResearch Subject Categories::TECHNOLOGY::Information technology::Computer scienceen_US
dc.titleSignal Model for Prediction of Exchange Ratesen_US
dc.typeArticleen_US
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