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Why Is It So Difficult to Beat the Random Walk Forecast of Exchange Rates?

Author

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  • Lutz Kilian

    (University of Michigan and the Centre for Economic Policy Research)

  • Mark P. Taylor

    (University of Warwick and the Centre for Economic Policy Research)

Abstract

We propose a stylized exchange rate model based on diversity and weight of opinion. Our model departs from standard assumptions in that we allow for heterogeneous agents. We show that such a model can explain both the observed volatility and the persistence of real and nominal exchange rate movements and thus in some measure resolves Rogoff’s (1996) purchasing power parity puzzle. Our empirical analysis reconciles the well-known difficulties in beating the random walk forecast model with the statistical evidence of nonlinear mean reversion in deviations from fundamentals. We find strong evidence of long-horizon predictability both in theory and in practice. We also explain why it is difficult to exploit this predictability in out-ofsample forecasts. Our results not only lend support to economists’ beliefs that the exchange rate is inherently predictable, but they also help us to understand the reluctance of applied forecasters to abandon chartists methods in favor of models based on economic fundamentals.

Suggested Citation

  • Lutz Kilian & Mark P. Taylor, 2001. "Why Is It So Difficult to Beat the Random Walk Forecast of Exchange Rates?," Working Papers 464, Research Seminar in International Economics, University of Michigan.
  • Handle: RePEc:mie:wpaper:464
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    References listed on IDEAS

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    More about this item

    Keywords

    Purchasing power parity; Real exchange rate; Random walk; Economic models of exchange rate determination; Long-horizon regression tests.;

    JEL classification:

    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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