Agnieszka Markiewicz

Model Misspecification, Learning and the Exchange Rate Disconnect Puzzle

Rational expectations models fail to explain the disconnect between the exchange rate and macroeconomic fundamentals. In line with survey evidence on the behaviour of foreign exchange traders, we introduce model misspecification and learning into a standard monetary model. Agents use simple forecasting rules based on a restricted information set. They learn about the parameters and performance of different models and can switch between forecasting rules. We compute the implied US-UK post-Bretton Woods exchange rate and show that the excess volatility of the exchange rate return can be reproduced with low values of the learning gain. Both assumptions, misspecification and learning, are necessary to generate this result. However, the implied correlations with the fundamentals are higher than in the data. Including more lags in the model tilts the balance of our findings slightly towards rational expectations and away from the learning hypothesis.

Model Uncertainty and Exchange Rate Forecasting

We propose a theoretical framework of exchange rate behavior where investors focus on a subset of economic fundamentals. We find that any adjustment in the set of predictors used by investors leads to changes in the relation between the exchange rate and fundamentals. We test the validity of this framework via a backward elimination rule which captures the current set of fundamentals that best predicts the exchange rate. Out-of-sample forecasting tests show that the backward elimination rule significantly beats the random walk for four out of five currencies in our sample. Further, the currency forecasts generate economically meaningful investment profits.