An application of multivariate Markov chain model on the changes in exchange rates: Turkey case

Abstract

A Markov chain is a random process with the Markovian property. Markov chains are applied in a wide range of areas. In Markov chain, the examined data that belongs to the system comes from a single source. Multivariate Markov chain model is used to show the behavior of the multi categorical data sequences that were produced by the same source or by similar sources. In this study, multivariate Markov chain model, which is based on Markov chains, is explained theoretically in detail. As the application, the monthly changes of the US Dollar selling rates and the monthly changes of the Euro selling rates are taken into consideration as two categorical data sequences and it is revealed with multivariate Markov chain model to what degree these sequences affect each other in Turkey. In addition to this, simple linear regression and correlation analyses were done for categorical data sequences. Simple linear regression and correlation analyses aimed at determining the degree and the direction of affection between the US Dollar selling rates and Euro selling rates. In this study, it was shown that the results of multivariate Markov chain model analysis were very close to the results of simple linear regression and correlation analyses.