While rejecting the null hypothesis 0 of no directional predictability is evidence against market efficiency, it could be viewed as an alternative way to assess the efficacy of successful exchange market intervention.[7] Note that, under the null hypothesis 0 of (2), market intervention through a sale/purchase of the foreign (or domestic) currency, has no impact on the direction of exchange rates movements. In other words, for intervention to be effective, it should be able to systematically affect the direction of foreign exchange rates. Since the sequence of direction indicators fZtcg is a Bernoulli process, we have only two possible outcomes: ‘up’ or ‘down’. Thus, the outcomes following the intervention identified as either ‘success’ or ‘failure’ might be drawn randomly rather than resulting from the intended effects of the intervention (see, for example, Fatum and Hutchison 2002, 2003, for related discussion based on an event study approach).
A rejection of no directional predictability does not warrant a successful intervention. An intervention might move foreign exchange rates in an unintended direction, since expectations of future foreign exchange rates can be directly or indirectly affected by many other factors. For instance, when the announcement of intervention negatively affects investor sentiment, leading to uncertainty in the market, the effects of the intervention may be mediated or even aggravated (e.g., Dominguez, 1993). Kaminsky and Lewis (1996) also show that when the goal of intervention policies is inconsistent with what subsequent monetary policies aim at, they are sometimes counterproductive (Mussa, 1979). Hence our arguments on efficacy via the direction-of-change approach need be grounded only in the events of successful intervention.
The last, but not least important point of 0 in (2) is that the existence of directional predictability and/or conditional mean predictability does not necessarily lead to the rejection of the efficient market hypothesis. The market can still be efficient unless a trading strategy based on such predictable patterns yields consistent and sufficient excess-risk adjusted returns (Malkiel, 1992, 2003). Moreover, it is often perceived that the validity of predictability needs to be tested further by out-of-sample evaluation. Indeed, exchange rate predictability has been largely assessed on the basis of out-of-sample evaluation in the literature (e.g., Cheung et al., 2005; Engel, 1994; Mark, 1995; Meese and Rogoff, 1983a, 1983b). Inoue and Kilian (2004) point out, however, that once proper critical values are considered, both in-sample and out-of-sample tests are asymptotically equally reliable under the null of no predictability. They also show that any sample-splitting out-of-sample evaluation can be subject to a loss of information and thus lower power for small samples. In the present context, our evaluation of directional predictability is model free (i.e., we do not use any model), so our results are not subject to potential problems of in-sample overfitting.[8] In fact, Hong and Lee (2003) find that the degree of significance of the generalized spectral tests is positively correlated with the out-of-sample predictive ability of a best-forecast model for foreign exchange rates.
Economic theory suggests that equilibrium exchange rates are determined by factors both inside and outside the currency market. For example, interest rates are one of the most important instrumental variables in financial markets. The link between foreign exchange rates and interest rates is a well-known feature of the foreign exchange market. One commonly cited relationship in the literature is a condition known as uncovered interest rate parity (UIP):
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