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dc.contributor.advisorOaxaca, Ronald L.en_US
dc.contributor.authorSugiyama, Alexandre Borgesen_US
dc.creatorSugiyama, Alexandre Borgesen_US
dc.date.accessioned2013-05-09T09:07:51Z
dc.date.available2013-05-09T09:07:51Z
dc.date.issued1998en_US
dc.identifier.urihttp://hdl.handle.net/10150/288791
dc.description.abstractThis dissertation studies intraday and daily foreign exchange market volatility. First, we address how best to model the intraday seasonality and the serial correlation in return volatility. We find there is no gain from smoothing the intraday seasonal volatility pattern. A model that jointly estimates the intraday seasonal pattern and conditional heteroskedasticity underperforms models that remove seasonal variance through deseasonalization and then model conditional heteroskedasticity with a GARCH model. Secondly, we show how intraday data can be used to create daily volatility estimates. Results show intraday data allow for daily volatility estimates which are independent of a volatility dynamics specification. Lastly, we show that intraday data improve the performance of one-step ahead forecasts based on a one year sample and show that the results are consistent with Monte Carlo simulations.
dc.language.isoen_USen_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.subjectEconomics, Finance.en_US
dc.titleModeling foreign exchange volatility with intraday dataen_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.leveldoctoralen_US
dc.identifier.proquest9829336en_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineEconomicsen_US
thesis.degree.namePh.D.en_US
dc.identifier.bibrecord.b38552322en_US
refterms.dateFOA2018-06-29T19:02:34Z
html.description.abstractThis dissertation studies intraday and daily foreign exchange market volatility. First, we address how best to model the intraday seasonality and the serial correlation in return volatility. We find there is no gain from smoothing the intraday seasonal volatility pattern. A model that jointly estimates the intraday seasonal pattern and conditional heteroskedasticity underperforms models that remove seasonal variance through deseasonalization and then model conditional heteroskedasticity with a GARCH model. Secondly, we show how intraday data can be used to create daily volatility estimates. Results show intraday data allow for daily volatility estimates which are independent of a volatility dynamics specification. Lastly, we show that intraday data improve the performance of one-step ahead forecasts based on a one year sample and show that the results are consistent with Monte Carlo simulations.


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