High‐Frequency Exchange Rate Forecasting

• Published date: 01 January 2016
• DOI: http://doi.org/10.1111/eufm.12052
• Date: 01 January 2016

High‐Frequency Exchange Rate

Forecasting

Charlie X. Cai and Qi Zhang

Leeds University Business School, LS2 9JT, Leeds, UK

E-mails: X.Cai@lubs.leeds.ac.uk; q.zhang@leeds.ac.uk

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 multinomi-

al–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

JEL classification: F31, F37, G17

1. Introduction

Foreign exchange rate forecasting is an ongoing challenge to academics, practitioners and

policy makers. A large number of studies have been dedicated to the examination of

exchange rate dynamics. These studies have produced at least two main conclusions

among other insights. First, although macroeconomic fundamentals should in theory

dictate the purchasing power of a country’s currency, they are less useful in predicting

exchange rates, given the nature of low‐frequency announcements.

1

Second, better

information sets or modelling techniques may help improve short‐term forecasting of

We thank John Doukas (the editor) and an anonymous referee for their constructive and

very helpful comments. We also thank Matthias Böhm the discussion and the participants at

the 2nd International conference of the Financial Engineering and Banking Society for their

helpful comments. Correspondence: Qi Zhang

1

See for example, Sarno and Taylor (2002) or Taylor and Taylor (2004) for surveys on the

purchasing power parity debate. It is also widely documented that traditional exchange rate

determination models perform poorly in explaining and forecasting exchange rates (Meese

and Rogoff, 1983; Cheung et al., 2005; Engel et al., 2008).

European Financial Management, Vol. 22, No. 1, 2016, 120–141

doi: 10.1111/eufm.12052

© 2014 John Wiley & Sons Ltd

exchange rate movements; for example, the use of customer order‐flow information.

2

While the random walk model is difficult to surpass in forecasting the mean, Hong et al.

(2007) show that sophisticated time‐series models are preferable for out‐of‐sample

density forecasting; however, there are some difficulties in the direct application of such

models.

3

In this regard, a successful model for forecasting the mean is much more useful

practically, since it can be directly applied to constructing quotation strategy for dealers

and trading strategy for traders.

To meet the challenge of finding a suitable model for foreign exchange (FX) rate

forecasting, we explore the use of the autoregressive conditional multinomial–

autoregressive conditional duration (ACM–ACD) model in high‐frequency FX rate

forecasting, as introduced by Russell and Engle (2005). We consider this model to be a

potentially useful tool for high‐frequency exchange rate forecasting for three reasons.

First, following the technological advances of the last two decades, high‐frequency

trading at the intra‐day level has become popular and technical trading rules embedded in

algorithmic trading play an important role in financial markets. Indeed, high‐frequency

algorithmic trading has been judged one of the major drivers behind the growth of FX

market turnover in the latest Bank for International Settlements (BIS, 2013) report on

global FX market activity. However, most of the previous literature on intra‐day exchange

rate forecasting has focused on regular time intervals such as 30 minutes or one hour.

None of the existing studies consider tick‐by‐tick frequency with irregular time spacing.

4

Our study fills this void by modelling the exchange rate movement tick by tick and

evaluating the model’s out‐of‐sample forecasting performance.

Second, there is empirical evidence to suggest that the ACM–ACD model performs

well in high‐frequency forecasting. Zhang et al. (2009) demonstrate, in the context of the

US equity market, that it provides better forecasting performance than either the

asymmetric ACD model (AACD) –an alternative asymmetric extension of the ACD

model proposed by Bauwens and Giot (2003) –or the random walk model. They also

show that the forecast performance of the model generally decreases as the liquidity of the

stock increases, with the exception of the most liquid stocks. This suggests that there is a

2

Motivated by the market microstructure literature, Evans and Lyons (2002, 2005) show

empirically that order flow is an important determinant of daily exchange rates. Several studies

in this area try to bring macroeconomic and microstructure approaches together to study

exchange rates (Bacchetta and van Wincoop, 2004; Evans and Lyons, 2007; Rime

et al., 2010). These studies show that the short‐term determinants of exchange rate movements

are more likely driven by the short‐term supply and demand fluctuations of the exchange rate

market.

3

Sarno and Valente (2005) were the first to document that complex (non‐linear) models of

exchange rates produce superior density forecasts of exchange rates. Hong et al. (2007)

suggest that a better density forecast can be used in many ways (e.g. risk management, option

pricing). However, they also point out that direct application of these models and evaluation of

their performance requires further exploration.

4

One exception is Engle and Russell’s (1997) study, which examines high‐frequency quote

changes in the foreign exchange rate market using an ACD model. However, their study does

not produce quote change forecasts, since it models only the duration and not the direction of

quote updates.

© 2014 John Wiley & Sons Ltd

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