High‐Frequency Exchange Rate Forecasting

Published date01 January 2016
Date01 January 2016
HighFrequency Exchange Rate
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
Predictability of exchange rate movement is of great interest to both practitioners
and regulators. We examine the predictability of exchange rate movement in the
highfrequency domain. To this end, we apply a model designed for modelling high
frequency and irregularly spaced data, the autoregressive conditional multinomi-
alautoregressive conditional duration (ACMACD) model. Studying three pairs
of currencies, we nd strong predictability in the highfrequency quote change data,
with the rate of correct predictions varying from 54 to 70%. We demonstrate that
ltering the data, by increasing the threshold of midquote price change, in
combination with dynamic learning, can improve forecasting performance.
Keywords: foreign exchange, highfrequency 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 countrys currency, they are less useful in predicting
exchange rates, given the nature of lowfrequency announcements.
Second, better
information sets or modelling techniques may help improve shortterm 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
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, 120141
doi: 10.1111/eufm.12052
© 2014 John Wiley & Sons Ltd
exchange rate movements; for example, the use of customer orderow information.
While the random walk model is difcult to surpass in forecasting the mean, Hong et al.
(2007) show that sophisticated timeseries models are preferable for outofsample
density forecasting; however, there are some difculties in the direct application of such
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 nding a suitable model for foreign exchange (FX) rate
forecasting, we explore the use of the autoregressive conditional multinomial
autoregressive conditional duration (ACMACD) model in highfrequency FX rate
forecasting, as introduced by Russell and Engle (2005). We consider this model to be a
potentially useful tool for highfrequency exchange rate forecasting for three reasons.
First, following the technological advances of the last two decades, highfrequency
trading at the intraday level has become popular and technical trading rules embedded in
algorithmic trading play an important role in nancial markets. Indeed, highfrequency
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 intraday exchange
rate forecasting has focused on regular time intervals such as 30 minutes or one hour.
None of the existing studies consider tickbytick frequency with irregular time spacing.
Our study lls this void by modelling the exchange rate movement tick by tick and
evaluating the models outofsample forecasting performance.
Second, there is empirical evidence to suggest that the ACMACD model performs
well in highfrequency 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
Motivated by the market microstructure literature, Evans and Lyons (2002, 2005) show
empirically that order ow 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 shortterm determinants of exchange rate movements
are more likely driven by the shortterm supply and demand uctuations of the exchange rate
Sarno and Valente (2005) were the rst to document that complex (nonlinear) 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.
One exception is Engle and Russells (1997) study, which examines highfrequency 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
HighFrequency Exchange Rate Forecasting 121

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