A mixed-frequency approach for exchange rates predictions
This work addresses the issue of exchange rate unpredictability for policymakers and central bankers, but appears incremental as it builds on existing mixed-frequency approaches.
The paper tackles the problem of forecasting exchange rates, which is challenging due to the Meese and Rogoff puzzle, by proposing a mixed-frequency model to address information loss from temporal aggregation, and demonstrates its effectiveness in CAD/USD predictions.
Selecting an appropriate statistical model to forecast exchange rates is still today a relevant issue for policymakers and central bankers. The so-called Meese and Rogoff puzzle assesses that exchange rate fluctuations are unpredictable. In the literature, a lot of studies tried to solve the puzzle finding alternative predictors and statistical models based on temporal aggregation. In this paper, we propose an approach based on mixed frequency models to overcome the lack of information caused by temporal aggregation. We show the effectiveness of our approach in comparison with other proposed methods by performing CAD/USD exchange rate predictions.