Abstract
This paper scrutinizes several exchange rate models, considers the effectiveness of their predictive performance after applying both parametric and nonparametric techniques to them, and chooses the forecasting predictor with the smallest root mean square forecast error (RMSE). Equation (34) displays empirical evidence consistent with a better example of an exchange rate model, although none of the evidence gives us a completely satisfactory forecast. In the end, the models’ error correction versions will be fit so that plausible long-run elasticities can be imposed on each model’s fundamental variables.
Highlights
Most economic time series do not have an invariant mean because they alternate phases of relative stability with periods of greater volatility
An analysis and summary of the empirical evidence for different models of foreign currency forecasting is included.The data given below are monthly from March 1973 through and including December 1994, are coming from Main Economic Indicators of the OECD and International Financial Statistics of the IMF, and they have been applied for the United Kingdom (U.K.)
One may infer that time-series models cannot be used in the forecasting of foreign currency exchange rates with a great degree of confidence for models with such relatively high volatility
Summary
Most economic time series do not have an invariant mean because they alternate phases of relative stability with periods of greater volatility. The dollar had gone through long periods of appreciation followed by bouts of depreciation without reversion to the long-run average; this sort of "random walk" is quite representative of non-stationary time series Any shock to such a series shows great persistence, e.g., the dollar/pound exchange rate experienced a very sharp surge upward in 1980, remained at that level for about four years, and did not return to somewhat near its previous level for another five years. These series’ volatility is not constant and some of them correlate with other series.
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