We consider the problem of portfolio selection within the classical Markowitz meanvariance optimizing framework, which has served as the basis for modern portfolio theory for more than 50 years. Efforts to translate this theoretical foundation into a viable portfolio construction algorithm have...

We consider the problem of portfolio selection within the classical Markowitz mean-variance framework, reformulated as a constrained least-squares regression problem. We propose to add to the objective function a penalty proportional to the sum of the absolute values of the portfolio weights....

Forecasts from dynamic factor models potentially benefit from refining the data set by eliminating uninformative series. The paper proposes to use prediction weights as provided by the factor model itself for this purpose. Monte Carlo simulations and an empirical application to short-term...

This paper investigates the power of macroeconomic factors to explain euro area bond risk premia using (i) a large dataset that captures the nowadays data-rich environment (ii) the Elastic Net variable selection. We find that macroeconomic factors, in particular economic activity and sentiment...

Outlier detection in high-dimensional datasets poses new challenges that have not been investigated in the literature. In this paper, we present an integrated methodology for the identification of outliers which is suitable for datasets with higher number of variables than observations. Our...

This paper develops a novel indicator of global economic activity, the GEA Tracker, which is based on commodity prices selected recursively through a genetic algorithm. The GEA Tracker allows for daily real-time knowledge of international business conditions using a minimum amount of...

We compare sparse and dense representations of predictive models in macroeconomics, microeconomics and ftnance. To deal with a large number of possible predictors, we specify a prior that allows for both variable selection and shrinkage. The posterior distribution does not typically concentrate...