Forecasting exchage rates using machine learning models with time-varying volatility

This thesis is focused on investigating the predictability of exchange rate returns on monthly and daily frequency using models that have been mostly developed in the machine learning field. The forecasting performance of these models will be compared to the Random Walk, which is the benchmark model for financial returns, and the popular autoregressive process. The machine learning models that will be used are Regression trees, Random Forests, Support Vector Regression (SVR), Least Absolute Shrinkage and Selection Operator (LASSO) and Bayesian Additive Regression trees (BART). A characterizing feature of financial returns data is the presence of volatility clustering, i.e. the tendency of persistent periods of low or high variance in the time series. This is in disagreement with the machine learning models which implicitly assume a constant variance. We therefore extend these models with the most widely used model for volatility clustering, the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) process. This allows us to jointly estimate the time varying variance and the parameters of the machine learning using an iterative procedure. These GARCH-extended machine learning models are then applied to make one-step-ahead prediction by recursive estimation that the parameters estimated by this model are also updated with the new information. In order to predict returns, information related to the economic variables and the lagged variable will be used. This study is repeated on three different exchange rate returns: EUR/SEK, EUR/USD and USD/SEK in order to obtain robust results. Our result shows that machine learning models are capable of forecasting exchange returns both on daily and monthly frequency. The results were mixed, however. Overall, it was GARCH-extended SVR that shows great potential for improving the predictive performance of the forecasting of exchange rate returns