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Is It Possible to Beat the Random Walk Model in Exchange Rate Forecasting? More Evidence for Brazilian Case

Author

Listed:
  • Emerson Fernandes Marçal

    (Sao Paulo School of Economics and Mackenzie)

  • Eli Hadad Junior

    (Mackenzie)

Abstract

The seminal study of Meese et al. (1983) on exchange rate forecastability had a great impact on the international finance literature. The authors showed that exchange rate forecasts based on structural models are worse than a naive random walk. This result is known as the Meese--Rogoff (MR) puzzle. Although the validity of this result has been checked for many currencies, studies for the Brazilian currency are not common. In 1999, Brazil adopted the dirty floating exchange rate regime. Rossi (2013) ran an extensive study on the MR puzzle but did not analyse Brazilian data. Our goal is to run a “pseudo real-time experiment” to investigate whether forecasts based on econometric models that use the fundamentals suggested by the exchange rate monetary theory of the 80s can beat the random model for the case of the Brazilian currency. Our work has three main differences with respect to Rossi (2013). We use a bias correction technique and forecast combination in an attempt to improve the forecast accuracy of our projections. We also combine the random walk projections with the projections of the structural models to investigate if it is possible to further improve the accuracy of the random walk forecasts. However, our results are quite in line with Rossi (2013). We show that it is not difficult to beat the forecasts generated by the random walk with drift using Brazilian data, but that it is quite difficult to beat the random walk without drift. Our results suggest that it is advisable to use the random walk without drift, not only the random walk with drift, as a benchmark in exercises that claim the MR result is not valid.

Suggested Citation

  • Emerson Fernandes Marçal & Eli Hadad Junior, 2016. "Is It Possible to Beat the Random Walk Model in Exchange Rate Forecasting? More Evidence for Brazilian Case," Brazilian Review of Finance, Brazilian Society of Finance, vol. 14(1), pages 65-88.
  • Handle: RePEc:brf:journl:v:14:y:2016:i:1:p:65-88
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    References listed on IDEAS

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    1. Castle, Jennifer L. & Clements, Michael P. & Hendry, David F., 2015. "Robust approaches to forecasting," International Journal of Forecasting, Elsevier, vol. 31(1), pages 99-112.
    2. repec:bla:scandj:v:78:y:1976:i:2:p:200-224 is not listed on IDEAS
    3. Charles Engel & Kenneth D. West, 2005. "Exchange Rates and Fundamentals," Journal of Political Economy, University of Chicago Press, vol. 113(3), pages 485-517, June.
    4. Issler, João Victor & Lima, Luiz Renato, 2009. "A panel data approach to economic forecasting: The bias-corrected average forecast," Journal of Econometrics, Elsevier, vol. 152(2), pages 153-164, October.
    5. Lane, Philip R. & Milesi-Ferretti, Gian Maria, 2001. "The external wealth of nations: measures of foreign assets and liabilities for industrial and developing countries," Journal of International Economics, Elsevier, vol. 55(2), pages 263-294, December.
    6. Barbara Rossi, 2013. "Exchange Rate Predictability," Journal of Economic Literature, American Economic Association, vol. 51(4), pages 1063-1119, December.
    7. Charles Engel & Nelson C. Mark & Kenneth D. West, 2008. "Exchange Rate Models Are Not as Bad as You Think," NBER Chapters, in: NBER Macroeconomics Annual 2007, Volume 22, pages 381-441, National Bureau of Economic Research, Inc.
    8. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    9. Frankel, Jeffrey A, 1979. "On the Mark: A Theory of Floating Exchange Rates Based on Real Interest Differentials," American Economic Review, American Economic Association, vol. 69(4), pages 610-622, September.
    10. Galimberti, Jaqueson K. & Moura, Marcelo L., 2013. "Taylor rules and exchange rate predictability in emerging economies," Journal of International Money and Finance, Elsevier, vol. 32(C), pages 1008-1031.
    11. Hooper, Peter & Morton, John, 1982. "Fluctuations in the dollar: A model of nominal and real exchange rate determination," Journal of International Money and Finance, Elsevier, vol. 1(1), pages 39-56, January.
    12. Meese, Richard A. & Rogoff, Kenneth, 1983. "Empirical exchange rate models of the seventies : Do they fit out of sample?," Journal of International Economics, Elsevier, vol. 14(1-2), pages 3-24, February.
    13. Dornbusch, Rudiger, 1976. "Expectations and Exchange Rate Dynamics," Journal of Political Economy, University of Chicago Press, vol. 84(6), pages 1161-1176, December.
    14. Moura, Marcelo L. , & Lima, Adauto R. S. & Mendonça, Rodrigo M., 2008. "Exchange Rate and Fundamentals: The Case of Brazil," Insper Working Papers wpe_114, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    15. Lane, Philip & Milesi-Ferretti, Gian Maria, "undated". "External Wealth of Nations," Instructional Stata datasets for econometrics extwealth, Boston College Department of Economics.
    16. Taylor, John B., 1993. "Discretion versus policy rules in practice," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 39(1), pages 195-214, December.
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    More about this item

    Keywords

    Meese-Rogoff puzzle; forecasting; exchange rate;
    All these keywords.

    JEL classification:

    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F32 - International Economics - - International Finance - - - Current Account Adjustment; Short-term Capital Movements
    • F41 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Open Economy Macroeconomics
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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