High-Frequency Exchange Rate Forecasting

Cai, C.X.; Zhang, Q.

doi:10.1111/eufm.12052

High-Frequency Exchange Rate Forecasting

Cai, C.X.; Zhang, Q.

Authors

C.X. Cai

Q. Zhang

Abstract

Predictability of exchange rate movement is of great interest to both practitioners and regulators. We examine the predictability of exchange rate movement in the high-frequency domain. To this end, we apply a model designed for modelling high-frequency and irregularly spaced data, the autoregressive conditional multinomial–autoregressive conditional duration (ACM–ACD) model. Studying three pairs of currencies, we find strong predictability in the high-frequency quote change data, with the rate of correct predictions varying from 54 to 70%. We demonstrate that filtering the data, by increasing the threshold of mid-quote price change, in combination with dynamic learning, can improve forecasting performance.

Citation

Cai, C., & Zhang, Q. (2016). High-Frequency Exchange Rate Forecasting. European Financial Management, 22(1), 120-141. https://doi.org/10.1111/eufm.12052

Journal Article Type	Article
Acceptance Date	Jul 15, 2014
Online Publication Date	Aug 12, 2014
Publication Date	Jan 19, 2016
Deposit Date	Dec 3, 2015
Publicly Available Date	Aug 14, 2016
Journal	European Financial Management
Print ISSN	1354-7798
Electronic ISSN	1468-036X
Publisher	Wiley
Peer Reviewed	Peer Reviewed
Volume	22
Issue	1
Pages	120-141
DOI	https://doi.org/10.1111/eufm.12052
Keywords	Foreign exchange, High-frequency data, Forecasting, Duration model.
Public URL	https://durham-repository.worktribe.com/output/1394658

Files

Accepted Journal Article (528 Kb)
PDF

Copyright Statement
This is the accepted version of the following article: Cai, C. X. and Zhang, Q. (2016), High-Frequency Exchange Rate Forecasting. European Financial Management, 22(1): 120-141, which has been published in final form at http://dx.doi.org/10.1111/eufm.12052. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.