High-Frequency Exchange Rate Forecasting

Cai, C.X.; Zhang, Q.

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