Since Meese and Rogoff (1983) results showed that no model could
outperform a random walk in predicting exchange rates. Many papers
have tried to find a forecasting methodology that could beat the
random walk, at least for certain forecasting periods. This Element
compares the Purchasing Power Parity, the Uncovered Interest Rate,
the Sticky Price, the Bayesian Model Averaging, and the Bayesian
Vector Autoregression models to the random walk benchmark in
forecasting exchange rates between most South American currencies
and the US Dollar, and between the Paraguayan Guarani and the
Brazilian Real and the Argentinian Peso. Forecasts are evaluated
under the criteria of Root Mean Square Error, Direction of Change,
and the Diebold-Mariano statistic. The results indicate that the
two Bayesian models have greater forecasting power and that there
is little evidence in favor of using the other three fundamentals
models, except Purchasing Power Parity at longer forecasting
horizons.
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