Using high-frequency data on deutschemark and yen returns against the dollar, we construct model-free estimates of daily exchange rate volatility and correlation that cover an entire decade. Our estimates, termed realized volatilities and correlations, are not only model-free, but also approximately free of measurement error under general conditions, which we discuss in detail. Hence, for practical purposes, we may treat the exchange rate volatilities and correlations as observed rather than latent. We do so, and we characterize their joint distribution, both unconditionally and conditionally. Noteworthy results include a simple normality-inducing volatility transformation, high contemporaneous correlation across volatilities, high correlation between correlation and volatilities, pronounced and persistent dynamics in volatilities and correlations, evidence of long-memory dynamics in volatilities and correlations, and remarkably precise scaling laws under temporal aggregation.

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.

A rapidly growing literature has documented important improvements in financial return volatility measurement and forecasting via use of realized variation measures constructed from high-frequency returns coupled with simple modeling procedures. Building on recent theoretical results in Barndorff-Nielsen and Shephard (2004a, 2005) for related bi-power variation measures, the present paper provides a practical and robust framework for non-parametrically measuring the jump component in asset return volatility. In an application to the DM/ $ exchange rate, the S&P500 market index, and the 30-year U.S. Treasury bond yield, we find that jumps are both highly prevalent and distinctly less persistent than the continuous sample path variation process. Moreover, many jumps appear directly associated with specific macroeconomic news announcements. Separating jump from non-jump movements in a simple but sophisticated volatility forecasting model, we find that almost all of the predictability in daily, weekly, and monthly return volatilities comes from the non-jump component. Our results thus set the stage for a number of interesting future econometric developments and important financial applications by separately modeling, forecasting, and pricing the continuous and jump components of the total return variation process.

A rapidly growing literature has documented important improvements in volatility measurement and forecasting performance through the use of realized volatilities constructed from high-frequency returns coupled with relatively simple reduced form time series modeling procedures. Building on recent theoretical results from Barndorff-Nielsen and Shephard (2003c) for related bi-power variation measures involving the sum of high-frequency absolute returns, the present paper provides a practical framework for non-parametrically measuring the jump component in the realized volatility measurements. Exploiting these ideas for a decade of high-frequency five-minute returns for the DM/ $ exchange rate, the S&P500 aggregate market index, and the 30-year U.S. Treasury Bond, we find the jump components to be distinctly less persistent than the contribution to the overall return variability originating from the continuous sample path component of the price process. Explicitly including the jump measure as an additional explanatory variable in an easy-to-implement reduced form model for the realized volatilities results in highly significant jump coefficient estimates at the daily, weekly and quarterly forecasts horizons. As such, our results hold promise for improved financial asset allocation, risk management, and derivatives pricing, by separate modeling, forecasting and pricing of the continuous and jump components of the total return variability.

This paper develops analytical methods to forecast the distribution of future returns for a new continuous-time process, the Poisson multifractal. The process captures the thick tails, volatility persistence, and moment scaling exhibited by many nancial time series. It can be interpreted as a stochastic volatility model with multiple frequencies and a Markov latent state. We assume for simplicity that the forecaster knows the true generating process with certainty but only observes past returns. The challenge in this environment is long memory and the corresponding innite dimension of the state space. We introduce a discretized version of the model that has a nite state space and an analytical solution to the conditioning problem. As the grid step size goes to zero, the discretized model weakly converges to the continuous-time process, implying the consistency of the density forecasts. JEL Classication: C22; C53; F31 Keywords: Forecasting; Long memory; Multiple frequencies; Stoch...

Local Polynomial Estimation (LPE) is implemented on a dataset of high-frequency foreign exchange (FX) quotes. This nonparametric technique is meant to provide a flexible background against which to evaluate parametric time series models. Assuming a conditionally heteroscedastic nonlinear autoregressive (CHARN) model, estimates of the mean and volatility functions are reported. The mean function displays pronounced reversion. Surprisingly, the volatility function exhibits asymmetry. The CHARN model, however, captures only the short-run behavior of conditional volatility. Nevertheless, part of the evidence of persistent conditional volatility appears in reality to be the effect of conditional kurtosis. Stochastic volatility models are ideal to capture this time series feature. Keywords: Local Polynomial Estimation, Conditional Volatility, Conditional Kurtosis, Nonlinear Autoregressive Models, Foreign Exchange Markets. 1 Introduction Conditional volatility of asset prices in general an...

It is a common view that private information in the foreign exchange market does not exist. This paper provides evidence against this view. The evidence comes from the opening of lunch-hour trading in the Tokyo market: in December 1994 the restriction on interdealer trading between 12:00 and 1:30 was abolished. We find that lunch-hour volatility is higher when the market is trading, implying the presence of private information (since the flow of public information did not change with the trading rules). In addition, the shift in regime is instructive because it induces interpretable changes in the U-shape of volatility over the trading day. We find that opening lunch-hour trade changes the volatility U-shape in three ways: the curve is (1) flatter, (2) tilted upward, and (3) higher on average. These effects on the U-shape have implications for the nature of the private information involved.

assistance was provided by Xifeng Diao. We are very appreciative of financial support provided

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It is a common view that private information in the foreign exchange market does not exist. We provide evidence against this view. The evidence comes from the introduction of trading in Tokyo over the lunch-hour. Lunch return variance doubles with the introduction of trading, which cannot be due to public information since the flow of public information did not change with the trading rules. Having eliminated public information as the cause, we exploit recent results in microstructure to discriminate between the two alternatives: private information and pricing errors. Three key results support the predictions of private-information models. First, the volatility U-shape flattens: greater revelation over lunch leaves a smaller share for the morning and afternoon. Second, the U-shape tilts upward, an implication of information whose private value is transitory. Finally, the morning exhibits a clear U-shape when Tokyo closes over lunch, and it disappears when