ارزیابی نرخ ارز و پیش بینی تراکم: یک برنامه کاربردی به برزیل

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.