Real-time macroeconomic data reflect the information available to market participants, whereas final data's containing revisions and released with a delays' overstate the information set available to them. We document that the in-sample and out-of-sample Treasury return predictability is...

Macroeconomic data are typically subject to future revisions and released with delay. Predictive return regressions using such data therefore potentially overstate the information set available to investors in real time. We document that data revisions account for a sizable share of in-sample...

Multi-period-ahead forecasts of returns' variance are used in most areas of applied finance where long horizon measures of risk are necessary. Yet, the major focus in the variance forecasting literature has been on one-period-ahead forecasts. In this paper, we compare several approaches of...

We examine the relationship between MIDAS regressions and the estimation of state space models applied to mixed frequency data. While in some cases the binding function is known, in general it is not, and therefore indirect inference is called for. The approach is appealing when we consider...

This paper proposes a new approach to extract quantile-based inflation risk measures using Quantile Autoregressive Distributed Lag Mixed-Frequency Data Sampling (QADL-MIDAS) regression models. We compare our models to a standard Quantile Auto-Regression (QAR) model and show that it delivers...