An Application of Methods of Multivariate Data Analysis to Compare Different Approaches to Exchange Rate Forecasting

Abstract

The aim of this paper is to compare different methods of forecasting exchange rates using methods of multivariate data analysis. Traditional forecasting models like the random walk, prominent structural models and approaches using the forward rate to forecast the spot rate are evaluated. Time series analysis is conducted employing univariate time series models as well as multivariate time series models and error correction models. Model identification, estimation and forecasting are examplified using the DM/US-Dollar exchange rate. Forecasting performance is measured by different criteria, within the scope of the investigation methods of multivariate data analysis are efficiently employed.KeywordsExchange RateRandom WalkTime Series ModelForecast PerformanceForward RateThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.