Exchange rates are a function of the supply and demand for currency, and move as a result of changes in supply and demand for different currencies. In other words, exchange rates can be thought of as the "prices" in the forex market: a higher exchange rate means that it is more costly to "buy" units of that foreign currency (in terms of another currency) than a lower exchange rate. Exchange rates fluctuate for a variety of reasons related to the supply and demand model, such as relative changes in levels of income, inflation, employment and output between countries.

Exchange Rate Forecasting

Besides the fact that a successful method for forecasting exchange rate movements could yield a highly profitable forex trading strategy, exchange rates are also studied extensively because of their information regarding inflation, output growth, and other macroeconomic variables. The two broad methods for determining future exchange rates, called "fundamental analysis" and "nonstructural analysis," have been used by business professionals and economists to try to predict changes in exchange rates. However, an overview of the research suggests that while some successful predictions can be made, no one type of analysis can yield accurate forecasts for all currencies at all times.

Fundamental Analysis Methods

Large institutions, like national central banks, are interested in exchange rate forecasting for macroeconomic reasons.

A variety of "fundamental-analysis" models has been used to try to predict future exchange rates, including behavioral equilibrium models, the interest rate parity equation model, portfolio balance models, and others; but the most common and useful of these models for economics professionals has been the monetary model. However, despite a history of research beginning in the mid-1980s and some controversial recent research to the contrary, it appears that the monetary model's forecasts are no more accurate than a random forecast (based on current exchange rates) in the long run.

Nonstructural Analysis

Given the inability of structural models to yield consistent forecasting for future exchange rate movements, business professionals and academics have begun to move towards nonstructural, time-series models. In contrast to fundamental-analysis models, which attempt to make predictions based on economic models formulated from observing the economy, nonstructural analysis often uses econometrics and statistics to yield forecasts. Some types of these models include vector autoregression models, Markov switching models, non-parametric models and fractional integration models.

Results of Forecasting Guidelines

Despite the best efforts of investment professionals, accurate exchange rate forecasting remains elusive.

Despite a move toward nonstructural analysis after the failure of fundamental-analysis models, there is still scant evidence that any exchange-rate model can consistently out-perform a "random walk" (that is, an investment strategy characterized by the random predictions of a computer model) in the long run. Various reasons have been offered to explain the difficulty in doing accurate exchange rate forecasting, such as bad data quality, incorrect model specifications, and time-variance between the relationship of exchange rates to their fundamentals. Because of the value of an accurate prediction system, though, research into better forecasting

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