A Bayesian MS-SUR Model for Forecasting Exchange Rates

The thesis proposes a new Bayesian factor model in the forecasting exchange rates using an application of Markov chain Monte Carlo to Bayesian inference. First we describe the Zellner's Seemingly Unrelated Regression (SUR) multivariate model with ten macroeconomic fundamentals in order to forecast the six exchange rates over the years 2002-2014. Secondly, we assume a latent Markov switching process is driving the parameters of the SUR model in order to detect structural instabilities. We develop MATLAB code for analysing and forecasting monthly exchange rate series.