A stochastic approach to modelling the USD/AUD exchange rate
Craig Ellis and
Patrick Wilson
International Journal of Managerial Finance, 2005, vol. 1, issue 1, 36-48
Abstract:
Purpose - To develop an 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 thata prioriknowledge 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.
Keywords: Time series analysis; Stochastic processes; Foreign exchange (search for similar items in EconPapers)
Date: 2005
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Persistent link: https://EconPapers.repec.org/RePEc:eme:ijmfpp:17439130510584874
DOI: 10.1108/17439130510584874
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