We revisit the relation between stock market volatility and macroeconomic activity using a new class of component models that distinguish short-run from long-run movements. We formulate models with the long-term component driven by inflation and industrial production growth that are in terms of...

Recently, there has been increased interest in real-time forecasts of the real price of crude oil. Standard oil price forecasts based on reduced-form regressions or based on oil futures prices do not allow consumers of forecasts to explore how much the forecast would change relative to the...

We construct a monthly real-time data set consisting of vintages for 1991.1-2010.12 that is suitable for generating forecasts of the real price of oil from a variety of models. We document that revisions of the data typically represent news, and we introduce backcasting and nowcasting techniques...

We propose a new approach to forecasting the term structure of interest rates, which allows to efficiently extract the information contained in a large panel of yields. In particular, we use a large Bayesian Vector Autoregression (BVAR) with an optimal amount of shrinkage towards univariate AR...

The paper addresses the issue of forecasting a large set of variables using multivariate models. In particular, we propose three alternative reduced rank forecasting models and compare their predictive performance for US time series with the most promising existing alternatives, namely, factor...

There is a growing literature on the realized volatility (RV) forecasting of asset returns using high-frequency data. We explore the possibility of forecasting RV with factor analysis; once considering the significant jumps. A real high-frequency financial data application suggests that the...

This paper aims to provide a brief and relatively non-technical overview of state-of-the-art forecasting with large data sets. We classify existing methods into four groups depending on whether data sets are used wholly or partly, whether a single model or multiple models are used and whether a...

We propose a new approach to forecasting the term structure of interest rates, which allows to efficiently extract the information contained in a large panel of yields. In particular, we use a large Bayesian Vector Autoregression (BVAR) with an optimal amount of shrinkage towards univariate AR...

Although there is much interest in the future retail price of gasoline among consumers, industry analysts, and policymakers, it is widely believed that changes in the price of gasoline are essentially unforecastable given publicly available information. We explore a range of new forecasting...

The answer depends on the objective. The approach of combining five of the leading forecasting models with equal weights dominates the strategy of selecting one model and using it for all horizons up to two years. Even more accurate forecasts, however, are obtained when allowing the forecast...