Modeling foreign exchange volatility with intraday data
dc.contributor.advisor | Oaxaca, Ronald L. | en_US |
dc.contributor.author | Sugiyama, Alexandre Borges | |
dc.creator | Sugiyama, Alexandre Borges | en_US |
dc.date.accessioned | 2013-05-09T09:07:51Z | |
dc.date.available | 2013-05-09T09:07:51Z | |
dc.date.issued | 1998 | en_US |
dc.identifier.uri | http://hdl.handle.net/10150/288791 | |
dc.description.abstract | This 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.iso | en_US | en_US |
dc.publisher | The University of Arizona. | en_US |
dc.rights | Copyright © 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.subject | Economics, Finance. | en_US |
dc.title | Modeling foreign exchange volatility with intraday data | en_US |
dc.type | text | en_US |
dc.type | Dissertation-Reproduction (electronic) | en_US |
thesis.degree.grantor | University of Arizona | en_US |
thesis.degree.level | doctoral | en_US |
dc.identifier.proquest | 9829336 | en_US |
thesis.degree.discipline | Graduate College | en_US |
thesis.degree.discipline | Economics | en_US |
thesis.degree.name | Ph.D. | en_US |
dc.description.note | This item was digitized from a paper original and/or a microfilm copy. If you need higher-resolution images for any content in this item, please contact us at repository@u.library.arizona.edu. | |
dc.identifier.bibrecord | .b38552322 | en_US |
dc.description.admin-note | Original file replaced with corrected file October 2023. | |
refterms.dateFOA | 2018-06-29T19:02:34Z | |
html.description.abstract | This 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. |