Exchange Rate Predictability and Dynamic Bayesian Learning

Joscha Beckmann,Rainer Schüssler,Dimitris Korobilis,Gary Koop

doi:10.2139/ssrn.3271970

Abstract

This paper considers how an investor in the foreign exchange market can exploit predictive information by means of flexible Bayesian inference. Using a variety of different vector autoregressive models, the investor is able, each period, to revise past predictive mistakes and learn about important data features. The proposed methodology is developed in order to synthesize a wide array of established approaches for modelling exchange rate dynamics. In a thorough investigation of monthly exchange rate predictability for ten countries, we find that an investor using the proposed flexible methodology for dynamic asset allocation achieves significant economic gains out of sample relative to benchmark strategies. In particular, we find strong evidence for sparsity, fast model switching and exploiting 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

Introduction

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

Methods

Results

Conclusion