Working Paper: CEPR ID: DP6598
Authors: Pasquale Della Corte; Lucio Sarno; Ilias Tsiakas
Abstract: This paper provides a comprehensive evaluation of the short-horizon predictive ability of economic fundamentals and forward premia on monthly exchange rate returns in a framework that allows for volatility timing. We implement Bayesian methods for estimation and ranking of a set of empirical exchange rate models, and construct combined forecasts based on Bayesian Model Averaging. More importantly, we assess the economic value of the in-sample and out-of-sample forecasting power of the empirical models, and find two key results: (i) a risk averse investor will pay a high performance fee to switch from a dynamic portfolio strategy based on the random walk model to one which conditions on the forward premium with stochastic volatility innovations; and (ii) strategies based on combined forecasts yield large economic gains over the random walk benchmark. These two results are robust to reasonably high transaction costs.
Keywords: Bayesian; MCMC; Estimation; Bayesian Model Averaging; Economic Value; Exchange Rates; Forward Premium; Monetary Fundamentals; Volatility
JEL Codes: F31; F37; G11
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Forward premium + Stochastic volatility innovations (C58) | Enhanced predictive performance of exchange rate models (F37) |
| Combined forecasts (C53) | Significant economic gains over random walk benchmark (G11) |
| Monetary fundamentals (E50) | No economic value (D46) |