Forecasting exchange rate movement


Why is it apparently so difficult to forecast exchange rate movement? Discuss with reference to the monetary model the MUNDELLl-fleming model and/or the Dornbusch model and it is extensions.


Why is it Difficult to Predict Exchange Rates?

The money markets paradigms of late have been quite challenging to predict because of the many factors that affect a nation’s currency. Economists have developed models to explain the ideologies behind the drastic variations in the exchange rates. Realistically, the economic disparities between nations are one of the reasons why exchange rates keep fluctuating (Pacelli et al., 2011). Economic variables such as inflation, balance of trade and national income have been pinpointed as the critical elements in determining currency values. Accurate prediction of exchange rates is of paramount to help investors, multinationals, governments and financial institution within the international framework to make better, informed decision. Exchange rates are referred to as

Recent empirical research supports the notion that exchange rates are determined by future expectations about economic fundamentals, rather than by changes in the current economic situations (Engel, 2011). These studies propose that the fundamental essence of exchange rate models lie not within its ability to forecast currency values, but in its chances to forecast the predictability expectations in the opposite direction.

However, this paper critically analyzes the underlying fundamental reasons why exchange rates are difficult to predict, using the ideologies from the existing models such as MUNDELLI-Fleming model, Dornbusch model and some extensions from these two models.


The volatile changes witnessed in the currency market have proved to be difficult to predict in the context of standard monetary models. The value of a dollar, for example, has been depreciating compared to other major currencies such as Euro, Canadian dollar and British pound. On the other hand, the economic variables defined in the economic models have substantially improved in the United States of America. This implies that the movement in the exchange rates is different from the peoples’ expectation’s based on monetary models forecast.

This trend suggests that economic fundamentals such as money supply, the balance of trade, and national income have little impact in predicting movements in the exchange rate (Cerra et aks., 2010). Economists have therefore tried to find and provide the reason there is an unclear link between economic fundamentals and the exchange rates.

Reasons why exchange rates are difficult to predict

Exchange rates are more volatile compared to economic fundamentals used in the monetary models to determine the movements in the currency market. According to the Dornbusch model, changes in financial markets are instantaneous while the adjustment in the goods markets is slow and gradual. This implies that prices are rigid and tend to adjust gradually as a result of surplus demand. That is -p= -v(pp bar)

Dornbusch  model


The Dornbusch monetary model is expressed as

β + β m + β y + β i + β π + u

Where m, i and u are random variables

Changes in the commodity market cause overshooting of equilibrium (at C) S rises more-than-proportionately to M to balance expected returns. Excess demand at C causes P to rise over time until it attains LR equilibrium at B (Bouakez et als., 2010). However, it is important to note that, overshooting result from a prompt adjustment in financial markets combined with gradual adjustment in the goods market. For instance, changes in the British pound have been much more than the differences in inflation and output over the last 30 years.

The high volatility in exchange rates as compared to changes in economic variables is difficult to include in the existing monetary models without presenting random disorders within the model’s framework. Putting in mind the fact that, standard, variable-based models cannot outperform a “random walk” casts thoughtful doubts on monetary models ability to explain exchange rate fluctuations.

Monetary models use historical data to predict exchange rates: Investors use historical data in the monetary models to help them predict changes in the exchange rates. These variables may vary with time making it complicated to predict changes in the exchange rates. On the other hand, monetary policies and other policies that affect exchange rates have been in flux after Breton Woods fixed exchange rate system collapsed. These disparities in the historical data used to predict exchange rates have proved it to be hard for investors to accurately predict exchange rates movement.

Model Misspecifications: Coefficients values used in monetary models differ from their true values. Forecasting exchange rates based on these values can be out of the base as compared with those generated from the “random walk”. For instance, in the Dornbusch model, the exogenous variables are values picked from the micro-economic calculation. Such values differ from the ideal value and hence, making it difficult to predict the movement in the exchange rates.

Short time frames: Models that rely on real income, money supply, inflation and other variables have proved to be difficult to use when predicting short-term changes in the exchange rate markets because of volatility. A small change in economic variables has a greater impact on the exchange rates. For instance, political unrest which can affect the exchange rate is not accounted for well in the monetary model. However, in the long run, such models can do a better job in predicting movements within the exchange rates.

Floating exchange rates: Most economies in the world use the floating exchange rate system. That is; the exchange rate in the market is set by market forces resulting from changes in the economic conditions. In the Mundell-Fleming model, prices are exogenous. The Mundell-Fleming model is built from the IS-LM model and assumes that the economy is open.

In such an economy, there exist enormous variables which can affect the equilibrium rate hence making it difficult to predict the exchange rate movements. The exchange rates keep fluctuating depending on the fundamental changes within the commodity market and income market. However, in the long run, monetary models using fundamentals can easily predict long-term changes in exchange rates.

Different currency market characteristics: Each and every currency have different characteristics that are unique from the currency of another nation. And as such, a model, currency combination, and specification can perform exceptionally well in one market does not necessarily imply that it will perform well in another market (Rossi, 2013).


The challenge of predicting changes in the exchange rates exists mostly in the short-run because of the differences in the adjustment rate of the commodity market and money market. However, in the long-run, the monetary models can improve the predictability of exchange rates. In the long run, using the models’ fundamentals monetary models can outperform using random walk technique in anticipating changes in exchange rates.

Investors in different nations use different monetary models combining with other financial ideas such as econometrics, in order to understand the money market situation well. Therefore, predict the movement of the exchange rate accurately.

To help improve the predictability of exchange rates, the policymakers should introduce monetary policy feedback. The Central bank in some countries considers exchange rates when setting up short-term interest rates. A monetary model which takes into account this feedback from the value of the currency to the rate of interest can predict exchange rates quite well. Such models can outperform a random walk when it comes to forecasting interest rates.


Pacelli, V., Bevilacqua, V., & Azzollini, M. (2011). An artificial neural network model to forecast exchange rates. Journal of Intelligent Learning Systems and Applications, 3(02), 57.

Engel, C. (2011). The real exchange rate, real interest rates, and the risk premium (No. w17116). National Bureau of Economic Research.

Chen, Y. C., & Tsang*, K. P. (2013). What Does the Yield Curve Tell Us About Exchange Rate Predictability?. Review of Economics and Statistics, 95(1), 185-205.

Cerra, V., & Saxena, S. C. (2010). The monetary model strikes back: Evidence from the world. Journal of International Economics, 81(2), 184-196.

Bouakez, H., & Normandin, M. (2010). Fluctuations in the foreign exchange market: How important are monetary policy shocks?. Journal of International Economics, 81(1), 139-153.

Rossi, B. (2013). Exchange rate predictability.


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