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

Emerson Marçal and Eli Hadad Junior ()
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Eli Hadad Junior: Mackenzie

Brazilian Review of Finance, 2016, vol. 14, issue 1, 65-88

Abstract: 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.

Keywords: Meese-Rogoff puzzle; forecasting; exchange rate (search for similar items in EconPapers)
JEL-codes: C51 F31 F32 F41 (search for similar items in EconPapers)
Date: 2016
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