Signal model for prediction of exchange rates

Abstract

The appropriate prediction of exchange rates is an area of financial forecasting which attracts a great deal of attention. For many years, the volatile nature of exchange rates has been the focus of many researchers. Many researchers attribute interest in exchange rate volatility to the fact that it is empirically difficult to predict future exchange rate values. Foreign exchange market generally produces observable outputs which can be characterised as signals. In this study we investigated the monthly average exchange rates of US Dollars to the Nigerian Naira using signal modelling approach. From the study, there is a convincing statistical evidence to believe that exchange rates can be better modelled by a Markov process as the output of a first order discrete autoregressive process. The result demonstrated that a Markov process is sometimes called a first order autoregressive process.