Abstract
This paper investigates models for the euro exchange rate against the currencies of Denmark, Poland, theUnited States, and the United Kingdom. The objective of this paper is to compare different methods of modeling andout-of-sample forecasting. One of the techniques is cointegration relation, which is implemented through a vector errorcorrection model. The existence of cointegration supports the long-run relationship between the nominal exchange rateand a number of fundamental variables. The evidence presented in this paper shows that a simple multivariate randomwalk model tends to have superior predictive performance, compared to other exchange rate models, for a period of lessthan one year.
Highlights
Globalization has been one of the most important trends in the world economy in the recent decades, and the exchange rate is one of the factors that has a tremendous ascendancy
We want to determine whether the forecasts based on the exchange rate models outperform the multivariate “random walk”
The cointegration, vector error correction model (VECM) and GARCH methods are utilized in this paper to examine the relationships between the exchange rate and various economic fundamental variables
Summary
Globalization has been one of the most important trends in the world economy in the recent decades, and the exchange rate is one of the factors that has a tremendous ascendancy. The exchange rate affects any economy through many channels. It has a direct impact on the nation’s international trade, economic growth, capital flows, interest rates and even inflation. In this paper, the link between exchange rates and fundamentals is analyzed. The aim of this paper is not to attempt to develop or estimate a particular exchange rate model. An analysis is made using existing and conventional class of economic models, in which the exchange rate is determined by the current and expected future values of observable fundamentals and unobservable shocks. The paper is organized as follows: the theoretical background and rationale for all the variables is discussed in Section 2, Section 3 presents the data and the estimation methodology, Section 4 highlights the results, in Section 5 conclusions are made
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