A mixed-frequency approach for exchange rates predictions

TL;DR

This paper introduces a mixed-frequency modeling approach to improve exchange rate predictions, addressing the limitations of traditional models caused by temporal aggregation, and demonstrates its effectiveness through CAD/USD forecasts.

Contribution

The paper proposes a novel mixed-frequency model for exchange rate prediction that outperforms existing methods by incorporating information across different time scales.

Findings

Mixed-frequency models improve forecast accuracy.

The approach outperforms traditional models in CAD/USD predictions.

Temporal aggregation limits can be mitigated with mixed-frequency data.

Abstract

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.