We establish the out-of-sample predictability of monthly exchange rate changes via machine learning techniques based on 70 predictors capturing country characteristics, global variables, and their interactions. To guard against overfitting, we use the elastic net to estimate a high-dimensional...

For the price of crude oil, this paper aims to investigate the predictive content of a variety of variables including oil futures prices, exchange rates of particular countries and stock-market indexes. Out-of-sample forecasting results suggest that oil futures prices have marginal predictive...

An extensive literature that studied the performance of empirical exchange rate models following Meese and Rogoff's (1983a) seminal paper has not convincingly found evidence of out-of-sample exchange rate predictability. This paper extends the conventional set of models of exchange rate...

We explore whether modelling parameter time variation improves the point, interval and density forecasts of nine major exchange rates vis-a-vis the US dollar over the period 1976-2015. We find that modelling parameter time variation is needed for an accurate calibration of forecast confidence...

Facing several economic and financial uncertainties, assessing accurately global economic conditions is a great challenge for economists. The International Monetary Fund proposes within its periodic World Economic Outlook report a measure of the global GDP annual growth, that is often considered...

In a large sample of countries across different geographic regions and over a long period of time, we find limited country- and variable-specific effects of central bank transparency on forecast accuracy and their dispersion among a large set of professional forecasts of financial and...