High‐Frequency Exchange Rate Forecasting

22 Pages Posted: 21 Jan 2016

See all articles by Xiaowu Cai

Xiaowu Cai

University of Leeds - Division of Accounting and Finance

Qi Zhang

Independent

Date Written: January 2016

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.

Keywords: foreign exchange, high‐frequency data, forecasting, duration model

Suggested Citation

Cai, Xiaowu and Zhang, Qi, High‐Frequency Exchange Rate Forecasting (January 2016). European Financial Management, Vol. 22, Issue 1, pp. 120-141, 2016, Available at SSRN: https://ssrn.com/abstract=2719291 or http://dx.doi.org/10.1111/eufm.12052

Xiaowu Cai (Contact Author)

University of Leeds - Division of Accounting and Finance ( email )

Leeds LS2 9JT
United Kingdom

Qi Zhang

Independent

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