INTRODUCTION;ROLE OF FINANCIAL VARIABLES;A TWO-STEP APPROACH TO MODEL INFLATION ; MODELLING LONG-MEDIUM TERM COMPONENT OF INFLATION ;A MIXED-FREQUENCY MODEL FOR REAL-TIME FORECASTS OF INFLATION; 4 TWO FORECASTING APPLICATIONS IN REAL-TIME; REAL-TIME FORECASTS OF MONTHLY INFLATION; MODEL FORECASTS...

We present a mixed-frequency model for daily forecasts of euro area inflation. The model combines a monthly index of core inflation with daily data from financial markets; estimates are carried out with the MIDAS regression approach. The forecasting ability of the model in real-time is compared...

We introduce a dynamic network model with probabilistic link functions that depend on stochastically time-varying parameters. We adopt the widely used blockmodel framework and allow the high-dimensional vector of link probabilities to be a function of a low-dimensional set of dynamic factors....

We measure the time-varying strength of macrofinancial linkages within and across the US and euro area economies by relying on factor models with drifting parameters, where real and financial cycles are extracted and shocks are identified via sign and exclusion restrictions. The main results...

Equity basket correlation is an important risk factor. It characterizes the strength of linear dependence between assets and thus measures the degree of portfolio diversification. It can be estimated both under the physical measure from return series, and under the risk neutral measure from...

This paper documents that factors extracted from a large set of macroeconomic variables bear useful information for predicting monthly US excess stock returns and volatility over the period 1980-2005. Factor-augmented predictive regression models improve upon both benchmark models that only...

Real-time macroeconomic data refl ect the information available to market participants, whereas fi nal data-containing revisions and released with a delay-overstate the information set available to them. We document that the in-sample and out-of-sample Treasury return predictability is signifi...

Dimension reduction techniques for functional data analysis model and approximate smooth random functions by lower dimensional objects. In many applications the focus of interest lies not only in dimension reduction but also in the dynamic behaviour of the lower dimensional objects. The most...