This paper examines the efficiency of the forward yen/dollar market using micro survey data. We first argue that the conventional tests of efficiency (unbiasedness) of the forward rate or of the survey forecasts do not correspond directly to the zero-profit condition. Instead, we use the survey data to calculate directly potential profits of individual forecasters based on a natural trading rule. We find that although the survey data are not the best predictor of future spot rate in terms of typical mean square forecast error criteria, the survey data can be used to obtain on average positive profits. However, these profits are small and highly variable. We also examine profits generated by a trading rule using regression forecasts, where forward premium is an explanatory variable. These profits are also small and highly variable.

This paper provides an empirical examination of the hypothesis that the forward exchange rate provides an "optimal" forecast of the future spot ex-change rate, for five currencies relative to the dollar. This hypothesis provides a convenient norm for examining the erratic behavior of exchange rates; this erratic behavior represents an efficient market that is quickly incorporating new information into the current exchange rate. This hypothesis is analyzed using two distinct, but related, approaches. The first approach is based on a regression of spot rates on lagged forward rates. When using weekly data and a one month forward exchange rate, ordinary least squares regression analysis of market efficiency is incorrect. Econometric methods are proposed which allow for consistent (though not fully efficient) estimation of the parameters and their standard errors. This paper also presents a new approach for testing exchange market efficiency. This approach is based on a general time series process generating the spot and forward exchange rate. The hypothesis of efficiency implies a set of cross-equation restrictions imposed on the parameters of the time series model. This paper derives these restrictions, proposes a maximum likelihood method of estimating the constrained likelihood function, estimates the model and tests the validity of the restrictions with a likelihood ration statistic