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Inference on Predictability of Foreign Exchange Rates via Generalized Spectrum and Nonlinear Time Series Models

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Author Info
Yongmiao Hong (Cornell University and Tsinghua University)
Tae-Hwy Lee (University of California, Riverside)
Abstract

It is often documented, based on autocorrelation, variance ratio, and power spectrum, that exchange rates approximately follow a martingale process. Because these data check serial uncorrelatedness rather than martingale difference, they may deliver misleading conclusions in favor of the martingale hypothesis when the test statistics are insignificant. In this paper, we explore whether there exists a gap between serial uncorrelatedness and martingale difference for exchange rate changes, and if so, whether nonlinear time series models admissible in the gap can outperform the martingale model in out-of-sample forecasts. Applying the generalized spectral tests of Hong to five major currencies, we find that the changes of exchange rates are often serially uncorrelated, but there exists strong nonlinearity in conditional mean, in addition to the well-known volatility clustering. To forecast the conditional mean, we consider the linear autoregressive, autoregressive polynomial, artificial neural network, and functional-coefficient models, as well as their combination. The functional coefficient model allows the autoregressive coefficients to depend on investment positions via a moving-average technical trading rule. We evaluate out-of-sample forecasts of these models relative to the martingale model, using four criteria-the mean squared forecast error, the mean absolute forecast error, the mean forecast trading return, and the mean correct forecast direction. White's reality check method is used to avoid data-snooping bias. It is found that suitable nonlinear models, particularly in combination, do have superior predictive ability over the martingale model for some currencies in terms of certain forecast evaluation criteria. Copyright (c) 2003 President and Fellows of Harvard College and the Massachusetts Institute of Technology.

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Article provided by MIT Press in its journal Review of Economics and Statistics.

Volume (Year): 85 (2003)
Issue (Month): 4 (09)
Pages: 1048-1062
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Handle: RePEc:tpr:restat:v:85:y:2003:i:4:p:1048-1062

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  1. Carlo Altavilla & Matteo Ciccarelli, 2006. "Inflation Forecasts, Monetary Policy and Unemployment Dynamics: Evidence from the US and the Euro Area," Discussion Papers 7_2006, D.E.S. (Department of Economic Studies), University of Naples "Parthenope", Italy. [Downloadable!]
    Other versions:
  2. Carlo Altavilla & Paul De Grauwe, 2006. "Forecasting and Combining Competing Models of Exchange Rate Determination," CESifo Working Paper Series CESifo Working Paper No. , CESifo Group Munich. [Downloadable!]
    Other versions:
  3. Jaehun Chung & Yongmiao Hong, 2007. "Model-free evaluation of directional predictability in foreign exchange markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(5), pages 855-889. [Downloadable!]
  4. Mototsugu Shintani, 2003. "Nonlinear Forecasting Analysis Using Diffusion Indexes: An Application to Japan," Working Papers 0322, Department of Economics, Vanderbilt University, revised Apr 2004. [Downloadable!]
    Other versions:
  5. Yang Yang & Tae-Hwy Lee, 2004. "Bagging Binary Predictors for Time Series," Econometric Society 2004 Far Eastern Meetings 512, Econometric Society. [Downloadable!]
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