Login (DCU Staff Only)
Login (DCU Staff Only)

DORAS | DCU Research Repository

Explore open access research and scholarly works from DCU

Advanced Search

Using extracted forward rate term structure information to forecast foreign exchange rates

Kearney, Fearghal orcid logoORCID: 0000-0002-3251-8707, Cummins, Mark orcid logoORCID: 0000-0002-3539-8843 and Murphy, Finbarr orcid logoORCID: 0000-0002-7463-7923 (2019) Using extracted forward rate term structure information to forecast foreign exchange rates. Journal of Empirical Finance, 53 . pp. 1-14. ISSN 0927-5398

Abstract
The difficulty of beating the random walk in forecasting spot foreign exchange rates is well documented. In this paper, we propose a functional principal component-based scalar response model which we benchmark versus leading VECM frameworks. Our approach leads to near systematic outperformance in terms of a comparison of performance measures, and to multiple instances of statistically significant improvements in forecast accuracy. Overall, our results provide evidence that the forward rate term structure contains substantial information about the evolution of the spot exchange rate. Finally, a stylised trading strategy is employed to demonstrate the potential economic benefits of our approach.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:foreign exchange; forward rate term structure modelling; functional data analysis; multiple hypothesis testing
Subjects:Business > Finance
DCU Faculties and Centres:DCU Faculties and Schools > DCU Business School
Publisher:Elsevier
Official URL:http://dx.doi.org/10.1016/j.jempfin.2019.05.002
Copyright Information:© 2020 Elsevier
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:24045
Deposited On:10 Jan 2020 13:58 by Thomas Murtagh . Last Modified 02 May 2022 03:30
Documents

Full text available as:

[thumbnail of MARK CUMMINS JEF FOR DORAS.pdf]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
376kB
Downloads

Downloads

Downloads per month over past year

Archive Staff Only: edit this record