Macroeconomic models of nominal exchange rates perform poorly. In sample, R 2 statistics as high as 10 percent are rare. Out of sample, these models are typically out-forecast by a naïve random walk. This paper presents a model of a new kind. Instead of relying exclusively on macroeconomic determinants, the model includes a determinant from the field of microstructure-order flow. Order flow is the proximate determinant of price in all microstructure models. This is a radically different approach to exchange rate determination. It is also strikingly successful in accounting for realized rates. Our model of daily exchange-rate changes produces R 2 statistics above 50 percent. Out of sample, our model produces significantly better short-horizon forecasts than a random walk. For the DM/ $ spot market as a whole, we find that $1 billion of net dollar purchases increases the DM price of a dollar by about 1 pfennig. eScholarship provides open access, scholarly publishing services to the University of California and delivers a dynamic

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Using a new dataset consisting of six years of real-time exchange rate quotations, macroeconomic expectations, and macroeconomic realizations (announcements), we characterize the conditional means of U.S. dollar spot exchange rates versus German Mark, British Pound, Japanese Yen, Swiss Franc, and the Euro. In particular, we find that announcement surprises (that is, divergences between expectations and realizations, or "news") produce conditional mean jumps; hence high-frequency exchange rate dynamics are linked to fundamentals. The details of the linkage are intriguing and include announcement timing and sign effects. The sign effect refers to the fact that the market reacts to news in an asymmetric fashion: bad news has greater impact than good news, which we relate to recent theoretical work on information processing and price discovery. Key Words: Exchange Rates; Macroeconomic News Announcements; Jumps; Market Microstructure; High-Frequency Data; Expectations Data; Anticipations Data; Order Flow; Asset Return Volatility; Forecasting.

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This paper uses graphical techniques and multinomial logit analysis to evaluate the causes and consequences of episodes of turbulence in foreign exchange markets. Using a quarterly panel of data from 1959 through 1993 for twenty OECD countries, we consider the antecedents and aftermath of devaluations and revaluations, flotations, fixings, and speculative attacks (which may not be successful). We find that realignments of fixed exchange rates are alike: devaluations are preceded by political instability, budget and current account deficits and fast growth of money, and prices. Revaluations are mirror images of devaluations. These movements are largely consistent with the standard speculative attack model. In contrast, few consistent correlations link regime transitions like flotations or fixings to macroeconomic or political variables. Transitions between exchange rate regimes are largely idiosyncratic, and are neither consistently provoked ex ante by systematic imbalances, nor typically justified ex post by subsequent changes in policy. We conclude that there are no clear early warning signals of many speculative attacks, and no easy solutions for

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A lot, including a few things you may not expect. Previous studies find that the term spread forecasts GDP but these regressions are unconstrained and do not model regressor endogeneity. We build a dynamic model for GDP growth and yields that completely characterizes expectations of GDP. The model does not permit arbitrage. Contrary to previous findings, we predict that the short rate has more predictive power than any term spread. We confirm this finding by forecasting GDP out-of-sample. The model also recommends the use of lagged GDP and the longest maturity yield to measure slope. Greater efficiency enables the yield-curve model to produce superior out-of-sample GDP forecasts than unconstrained OLS at all horizons.

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It is widely known that signiÞcant in-sample evidence of predictability does not guarantee signiÞcant out-of-sample predictability. This is often interpreted as an indication that in-sample evidence is likely to be spurious and should be discounted. In this paper we question this conventional wisdom. Our analysis shows that neither data mining nor parameter instability is a plausible explanation of the observed tendency of in-sample tests to reject the no predictability null more often than out-of-sample tests. We provide an alternative explanation based on the higher power of in-sample tests of predictability. We conclude that results of in-sample tests of predictability will typically be more credible than results of out-of-sample tests.