September 2002, a new market in 'Economic Derivatives' was launched allowing traders to take positions on future values of several macroeconomic data releases. We provide an initial analysis of the prices of these options. We find that market-based measures of expectations are similar to...

One of the most extended empirical stylized facts about output dynamics in the United States is the positive autocorrelation of output growth. This paper shows that the positive autocorrelation can be better captured by shifts between business cycle states rather than by the standard view of...

We set out a model to compute short-term forecasts of the euro area GDP growth in real-time. To allow for forecast evaluation, we construct a real-time data set that changes for each vintage date and includes the exact information that was available at the time of each forecast. With this data...

We develop a twofold analysis of how the information provided by several economic indicators can be used in Markov-switching dynamic factor models to identify the business cycle turning points. First, we compare the performance of a fully non- linear multivariate specification (one-step...

We extend the Markov-switching dynamic factor model to account for some of the specificities of the day-to-day monitoring of economic developments from macroeconomic indicators, such as ragged edges and mixed frequencies. We examine the theoretical benefits of this extension and corroborate the...

We examine the finite-sample performance of small versus large scale dynamic factor models. Our Monte Carlo analysis reveals that small scale factor models out-perform large scale models in factor estimation and forecasting for high levels of cross-correlation across the idiosyncratic errors of...

To perform real-time business cycle inferences and forecasts of GDP growth rates in the Euro area, we use an extension of the Markov-switching dynamic factor models that accounts for the specificities of the day to day monitoring of economic developments such as ragged edges, mixed frequencies...

This Paper proposes a new forecasting method that exploits information from a large panel of time series. The method is based on the generalized dynamic factor model proposed in Forni, Hallin, Lippi, and Reichlin (2000), and takes advantage of the information on the dynamic covariance structure...

In this paper I explore whether knowledge of the time-series properties of premia in the pricing of forward foreign exchange can be usefully exploited in forecasting future spot exchange rates. I use signal-extraction techniques, based on recursive application of the Kalman filter, to identify...

In this paper, we implement a methodology to identify and measure premia in the pricing of forward foreign exchange. The methodology involves application of signal-extraction techniques from the engineering literature. Diagnostic tests indicate that these methods are quite successful in...