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Exchange Rate Economics: Theories and Evidence

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390 Spot <strong>and</strong> forward exchange rates<br />

They propose transforming the basic equation using the autocorrelation function<br />

<strong>and</strong> generalised least squares. They demonstrate that as a result of this transformation<br />

the coefficient is insignificantly different from unity. However,they also<br />

demonstrate that the error term in the estimated version of (15.4) is strongly serially<br />

correlated (they do not use overlapping contracts) which represents a violation of<br />

the basic efficiency hypothesis.<br />

15.6 The advantages of surveydata in unravelling<br />

forward rate bias<br />

As we have seen,a key problem with st<strong>and</strong>ard tests of forward rate unbiasedness<br />

is that they are tests of a joint hypothesis that agents are risk neutral <strong>and</strong> form<br />

their expectations rationally. The existence of survey data bases offers an independent<br />

measure of exchange rate expectations <strong>and</strong>,in principle,a way round the<br />

joint hypothesis problem <strong>and</strong> it has now been widely used to unravel the separate<br />

constituents of the joint hypothesis <strong>and</strong> also to test other hypothesis about the<br />

behaviour of exchange rates <strong>and</strong> expectations formation.<br />

With access to an independent source of expectations,the rational expectations<br />

assumption can be replaced with:<br />

s t+k = s e t+k + ε t+k,(15.57)<br />

where st+k e is now the (subjective) market expectation. Frankel <strong>and</strong> Froot (1987,<br />

1989) were the first to take the mean or median value from a survey data<br />

base as their measure of st+k e . The existence of an independent measure of<br />

expectations should allow a different separation of the risk <strong>and</strong> expectational<br />

parameters/coefficients to that discussed in Section 15.2. For example,as we<br />

have seen the p lim of α 1 is:<br />

α 1 = Cov(fp t, s t+k )<br />

.<br />

Var(fp t )<br />

And on using expression (15.57) in this formulae we can obtain:<br />

α 1 = Cov(se t+k − s t, fp t ) + Cov(ε t+k , fp t )<br />

Var(fp t )<br />

(15.58)<br />

where the terms in the numerator indicate the two potential reasons why α 1 may<br />

differ from unity. Using the definition of the risk premium given in Section 15.1<br />

we have:<br />

α 1 = 1 − α ω − α e ,

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