Evaluation of exchange rate point and density forecasts: An application to Brazil
Wagner Gaglianone () and
Jaqueline Terra Moura Marins
International Journal of Forecasting, 2017, vol. 33, issue 3, 707-728
This paper constructs multi-step-ahead point and density forecasts of the exchange rate. The approaches considered vary from statistical to economics-driven models, using financial and macroeconomic data and adopting either parametric or nonparametric distributions. We employ a range of statistical tools from different strands of the literature to identify which models work in practice, in terms of forecasting accuracies across different data frequencies and forecasting horizons. We propose a novel full-density/local analysis approach for collecting the many test results, and deploy a simple risk-based decision rule for ranking models. An empirical exercise with Brazilian daily and monthly data reveals that macro fundamentals are important when modeling the risk of exchange rate appreciation, whereas models that use survey information or financial data are the best way to account for the depreciation risk. These findings are relevant for econometricians, risk managers or policymakers who are interested in evaluating the accuracy of competing exchange rate models.
Keywords: Density forecasts; Exchange rate; Risk; Model selection (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
Working Paper: Evaluation of Exchange Rate Point and Density Forecasts: an application to Brazil (2016)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:33:y:2017:i:3:p:707-728
Access Statistics for this article
International Journal of Forecasting is currently edited by R. J. Hyndman
More articles in International Journal of Forecasting from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().