On forecasting exchange rates using neural networks
The paper considers the modelling, description and forecasting of four daily exchange rate returns relative to the Dutch guilder using artificial neural network models (ANNs). Based on simulations it is argued (i) that neglected GARCH does not lead to spuriously successful ANNs and (ii) that if there is some form of nonlinearity other than GARCH, ANNs will exploit this for improved forecasting. For the sample data it is found that ANNs do not yield favourable in-sample fits or forecasting performance. These results are interpreted as indicating that the nonlinearity often found in exchange rates is most likely due to GARCH and therefore ANNs are recommended as a diagnostic for mean nonlinearity.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 8 (1998)
Issue (Month): 6 ()
|Contact details of provider:|| Web page: http://www.tandfonline.com/RAFE20|
|Order Information:||Web: http://www.tandfonline.com/pricing/journal/RAFE20|
When requesting a correction, please mention this item's handle: RePEc:taf:apfiec:v:8:y:1998:i:6:p:589-596. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Michael McNulty)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.