Explaining the early years of the euro exchange rate: An episode of learning about a new central bank
Many observers were surprised by the depreciation of the euro after its launch in 1999. Handicapped by a short sample, explanations tended to appeal to anecdotes and lessons learned from the experiences of other currencies. Now sample sizes are just becoming large enough to permit reasonable empirical analyses. This paper begins with a theoretical model of pre- and post-euro foreign exchange trading that generates three possible causes of euro depreciation: a reduction in hedging opportunities due to the elimination of the legacy currencies, asymmetric information due to some traders having superior information regarding shocks to the euro exchange rate, and policy uncertainty on the part of the ECB. One empirical implication of the model is that higher transaction costs associated with the euro than the German mark may have contributed to euro depreciation. However, empirical evidence on percentage spreads tends to reject the hypothesis that percentage spreads were larger on the euro than the mark for all but the first few months. This seems like an unlikely candidate to explain euro depreciation over the prolonged period observed. Reviewing evidence on market dynamics around ECB, Bundesbank, and Federal Reserve meetings, there is no evidence suggesting that the market is "front running" in a different manner than the other central banks. However, we do find empirical support for the euro exchange rate to be affected by learning. By focusing on euro-area inflation as the key fundamental, the model is structured toward the dynamics of learning about ECB policy with regard to inflation. While a stated target inflation rate of 2 percent existed, it may be that market participants had to be convinced that the ECB would, indeed, generate low and stable inflation. The theory motivates an empirical model of Bayesian updating related to market participants learning about the underlying inflation process under the ECB regime. With a prior distribution drawn from t
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