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Assessing the forecasting accuracy of alternative nominal exchange rate models: the case of long memory

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  • Benjamin J. C. Kim

    (Department of Economics, College of Business Administration, University of Nebraska-Lincoln, Lincoln, Nebraska, USA)

  • David Karemera

    (Department of Economics, School of Business, South Carolina State University, Orangeburg, South Carolina, USA)

Abstract

This paper presents an autoregressive fractionally integrated moving-average (ARFIMA) model of nominal exchange rates and compares its forecasting capability with the monetary structural models and the random walk model. Monthly observations are used for Canada, France, Germany, Italy, Japan and the United Kingdom for the period of April 1973 through December 1998. The estimation method is Sowell's (1992) exact maximum likelihood estimation. The forecasting accuracy of the long-memory model is formally compared to the random walk and the monetary models, using the recently developed Harvey, Leybourne and Newbold (1997) test statistics. The results show that the long-memory model is more efficient than the random walk model in steps-ahead forecasts beyond 1 month for most currencies and more efficient than the monetary models in multi-step-ahead forecasts. This new finding strongly suggests that the long-memory model of nominal exchange rates be studied as a viable alternative to the conventional models. Copyright © 2006 John Wiley & Sons, Ltd.

Suggested Citation

  • Benjamin J. C. Kim & David Karemera, 2006. "Assessing the forecasting accuracy of alternative nominal exchange rate models: the case of long memory," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(5), pages 369-380.
    • Handle: RePEc:jof:jforec:v:25:y:2006:i:5:p:369-380
    DOI: 10.1002/for.994
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    Cited by:

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    4. Zhang, Yongjie & Chu, Gang & Shen, Dehua, 2021. "The role of investor attention in predicting stock prices: The long short-term memory networks perspective," Finance Research Letters, Elsevier, vol. 38(C).

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