We propose a new approach to model high and low frequency components of equity correlations. Our framework combines a factor asset pricing structure with other specifications capturing dynamic properties of volatilities and covariances between a single common factor and idiosyncratic returns....

The accession of ten countries into the European Union makes the forecasting of their key macroeconomic indicators an exercise of some importance. Because of the transition period, only short spans of reliable time series are available, suggesting the adoption of simple time series models as...

In a globalised world economy, global factors have become increasingly important to explain trade flows at the expense of country-specific determinants. This paper shows empirically the superiority of direct forecasting methods, in which world trade is directly forecasted at the aggregate...

We review, under a historical perspective, the development of the problem of nonfundamentalness of Moving Average (MA) representations of economic models. Nonfundamentalness typically arises when agents’ information space is larger than the econometrician’s one. Therefore it is impossible...

Forecasting the world economy is a difficult task given the complex interrelationships within and across countries. This paper proposes a number of approaches to forecast short-term changes in selected world economic variables and aims, first, at ranking various forecasting methods in terms of...

The paper presents forecasts of headline and core inflation in Estonia with factor models in a recursive pseudo out-of-sample framework. The factors are constructed with a principal component analysis and are then incorporated into vector autoregressive (VAR) forecasting models. The analyses...

This study extends the Diebold-Yilmaz Connectedness Index (DYCI) methodology and, based on forecast error covariance decompositions, derives a network risk model for a portfolio of assets. As a normalized measure of the sum of variance contributions, system-wide connectedness averages out the...

Factor and sparse models are two widely used methods to impose a low-dimensional structure in high dimension. They are seemingly mutually exclusive. In this paper, we propose a simple lifting method that combines the merits of these two models in a supervised learning methodology that allows to...

Many empirical studies have shown that factor models produce relatively accurate forecasts compared to alternative short-term forecasting models. These empirical findings have been established for different macroeconomic data sets and different forecast horizons. However, various specifications...

The paper presents forecasts of headline and core inflation in Estonia with factor models in a recursive pseudo out-of-sample framework. The factors are constructed with a principal component analysis and are then incorporated into vector autoregressive (VAR) forecasting models. The analyses...