Abstract:
Purpose - To develop all integrated approach to forecasting spot foreign exchange rates by
incorporating some principles underlying long-term dependence.
Design/methodology/approach - The paper utilises the random-walk framework to develop a
stochastic forecast model wherein the sign (positive or negative) and magnitude (strong or weak) of
dependence can be separately controlled. The integrated model demonstrates superior forecast
performance over a conventional random walk.
Findings - Using spot log prices and log price changes (returns) for the USD/AUD exchange rate, the
initial outcomes of the study suggest that a priori knowledge of the underlying sign and magnitude of
long-term dependence yields out-of-sample forecasts superior to those of a random walk model.
Research limitations/implications - Independent assessment of the contribution to forecast
accuracy of controlling for the sign of dependence between successive price changes only shows little
additional improvement in out-of-sample forecast performance over the random walk null.
Practical implications - The findings of the study have important ramifications for managerial
finance as they provide important insights on expected future currency returns with potential
advantages in currency hedging and/or timing of international capital flows.
Originality/value - The contribution of this paper is to develop an original forecast model explicitly
incorporating the conceptual and theoretical characteristics of long-term dependent time series. By
separating the key characteristics and modelling each individually, the contribution of each to forecast
accuracy can be evaluated.
