Abstract
SummaryWe consider how an investor in the foreign exchange market can exploit predictive information by means of flexible Bayesian inference. Using a variety of vector autoregressive models, the investor is able, each period, to learn about important data features. The developed methodology synthesizes a wide array of established approaches for modeling exchange rate dynamics. In a thorough investigation of monthly exchange rate predictability for 10 countries, we find that using the proposed methodology for dynamic asset allocation achieves substantial economic gains out of sample. In particular, we find evidence for sparsity, fast model switching, and exploitation of the exchange rate cross‐section.
Highlights
Understanding and predicting the evolution of exchange rates has long been a key component of the research agenda in international economics and finance
We find that an investor using our algorithm would experience substantial economic gains out of sample relative to the random walk model with time-varying volatility
We use the term “dynamic model learning (DML) with ALL REGRESSORS” to denote the case where DML is being done over all specification choices including all of the exogenous predictors
Summary
Understanding and predicting the evolution of exchange rates has long been a key component of the research agenda in international economics and finance. The voluminous existing literature on exchange rate forecasting, surveyed in Rossi (2013), adopts many different econometric methods Speaking, these differences fall into the following categories. These differences fall into the following categories They differ in whether they are multivariate (e.g., building a vector autoregressive (VAR) model involving a cross-section of exchange rates for many countries) or univariate. They differ in which predictors they use. They differ in how they treat the fact that there may be many potential predictors, most of which are unimportant Fourth, they differ in whether they allow for dynamic model change (i.e., whether the best forecasting model can involve different predictors at different points in time) or not. They differ in whether they allow for parameter change (both in VAR or regression coefficients and in volatilities) or not
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