1,178 research outputs found
The Properties of Market-Based and Survey Forecasts for Different Data Releases
We compare the accuracy of the survey forecasts and forecasts implied by economic binary options on the U.S. nonfarm payroll change. These options are available for a number of ranges of the announced figure, and each pays $1 if the released nonfarm payroll change falls in the given range. For the first-release data both the market-based and survey forecasts are biased, while they are rational and approximately equally accurate for later releases. Both forecasts are more accurate for later releases. Because of predictability in the revision process, this indicates that the investors in the economic derivatives market are incapable of taking the measurement error in the preliminary estimates efficiently into account. This suggests that economic stability could be enhanced by more accurate first-release figures.Expectations; economic derivatives; data vintage; real-time data
Testing the Expectations Hypothesis of the Term Structure of Interest Rates in the Presence of a Potential Regime Shift
The expectations hypothesis of the term structure of interest rates is tested using monthly Eurodollar deposit rates for maturities 1, 3 and 6 months covering the period 1983:1–1996:6. Whereas classical regression-based tests indicate rejection, tests based on a new model allowing for potential – but unrealized – regime shifts provide support for the expectations hypothesis. The peso problem is modelled by means of a threshold autoregression. The estimation results suggest that potential regime shift had an effect on expectations concerning the longer-term interest rate only for a short while in the early phase of the sample period, when interest rates were at their highest.peso problem; TAR models; term structure of interest rates
Forecasting Realized Volatility by Decomposition
Forecasts of the realized volatility of the exchange rate returns of the Euro against the U.S. Dollar obtained directly and through decomposition are compared. Decomposing the realized volatility into its continuous sample path and jump components and modeling and forecasting them separately instead of directly forecasting the realized volatility is shown to lead to improved out-of-sample forecasts. Moreover, gains in forecast accuracy are robust with respect to the details of the decomposition.Mixture model, Jump, Realized volatility, Gamma distribution
A Mixture Multiplicative Error Model for Realized Volatility
A multiplicative error model with time-varying parameters and an error term following a mixture of gamma distributions is introduced. The model is fitted to the daily realized volatility series of Deutschemark/Dollar and Yen/Dollar returns and is shown to capture the conditional distribution of these variables better than the commonly used ARFIMA model. The forecasting performance of the new model is found to be, in general, superior to that of the set of volatility models recently considered by Andersen et al. (2003) for the same data.Mixture model, Realized volatility, Gamma distribution
Asymmetric News Effects on Volatility : Good vs. Bad News in Good vs. Bad Times
We study the impact of positive and negative macroeconomic US and European news announcements in different phases of the business cycle on the highfrequency volatility of the EUR/USD exchange rate. The results suggest that in general bad news increases volatility more than good news. The news effects also depend on the state of the economy: bad news increases volatility more in good times than in bad times, while there is no difference between the volatility effects of good news in bad and good times
Properties of Market-Based and Survey Macroeconomic Forecasts for Different Data Releases
We compare the accuracy of the survey forecasts and forecasts implied by economic binary options on the U.S. non-farm payroll change. For the first-release data both the market-based and survey forecasts are biased, while they are rational and approximately equally accurate for later releases. Both forecasts are also more accurate for later releases. Because of predictability in the revision process, this indicates that the investors in the economic derivatives market are incapable of taking the measurement error in the preliminary estimates efficiently into account. This suggests that economic stability could be enhanced by more accurate first-release figures.Macroeconomic derivatives, Data revision, Forecasting
Noncausal autoregressions for economic time series
This paper is concerned with univariate noncausal autoregressive models and their potential usefulness in economic applications. In these models, future errors are predictable, indicating that they can be used to empirically approach rational expectations models with nonfundamental solutions. In the previous theoretical literature, nonfundamental solutions have typically been represented by noninvertible moving average models. However, noncausal autoregressive and noninvertible moving average models closely approximate each other, and therefore,the former provide a viable and practically convenient alternative. We show how the parameters of a noncausal autoregressive model can be estimated by the method of maximum likelihood and derive related test procedures. Because noncausal autoregressive models cannot be distinguished from conventional causal autoregressive models by second order properties or Gaussian likelihood, a model selection procedure is proposed. As an empirical application, we consider modeling the U.S. inflation which, according to our results, exhibits purely forward-looking dynamics
Trading Nokia: The roles of the Helsinki vs the New York stock exchanges
We use the Autoregressive Conditional Duration (ACD) framework of Engle and Russell (1998) to study the effect of trading volume on price duration (ie the time lapse between consecutive price changes) of a stock listed both in the domestic and the foreign market. As a case study we use the example of Nokia’s share, which is actively traded both in the Helsinki Stock Exchange and the New York Stock Exchange (NYSE). We find asymmetry in the volume-price duration relationship between the two markets. In the NYSE the negative relationship is much stronger and exists both during and outside common trading hours. Outside common trading hours no such relationship is significant in Helsinki. Based on the theory of Easley and O’Hara (1992), these results could be interpreted in that informed investors in Nokia mainly trade in the US market whereas Helsinki is the more liquidity-oriented trading place.cross-listing; Autoregressive Conditional Duration; market microstructure
GMM Estimation with Noncausal Instruments
Lagged variables are often used as instruments when the generalized method of moments (GMM) is applied to time series data. We show that if these variables follow noncausal autoregressive processes, their lags are not valid instruments and the GMM estimator is inconsistent. Moreover, in this case, endogeneity of the instruments may not be revealed by the J-test of overidentifying restrictions that may be inconsistent and, as shown by simulations, its finite-sample power is, in general, low. Although our explicit results pertain to a simple linear regression, they can be easily generalized. Our empirical results indicate that noncausality is quite common among economic variables, making these problems highly relevant.Noncausal autoregression; instrumental variables; test of overidentifying restrictions
Asymmetric News Effects on Volatility: Good vs. Bad News in Good vs. Bad Times
We study the impact of positive and negative macroeconomic US and European news announcements in different phases of the business cycle on the highfrequency volatility of the EUR/USD exchange rate. The results suggest that in general bad news increases volatility more than good news. The news effects also depend on the state of the economy: bad news increases volatility more in good times than in bad times, while there is no difference between the volatility effects of good news in bad and good times.Volatility; News; Nonlinearity; Smooth Transition Models
- …