French Abstract: L’analyse des données massives en économie : ce qu’on a appris jusqu’à maintenant et quelles directions pour la prochaine étape? Les travaux sur les tests de précision des prévisions demeurent au premier plan dans le monde de la prévision. Une première raison est...

In this paper, we use factor-augmented HAR-type models to predict the daily integrated volatility of asset returns. Our approach is based on a proposed two-step dimension reduction procedure designed to extract latent common volatility factors from a large dimensional and high-frequency returns...

Research into predictive accuracy testing remains at the forefront of the forecasting field. Onereason for this is that rankings of predictive accuracy across alternative models, which under misspecification are loss function dependent, are universally utilized to assess the usefulness of...

In the field of economics, recent advances in the areas of machine learning, shrinkage, and variable selection have been spectacularly successful. In one key area of study, advances in both modelling and estimation have enabled empirical practitioners to show the usefulness of latent factors...

We utilize mixed frequency factor-MIDAS models for the purpose of carrying out pastcasting, nowcasting, and forecasting experiments using real-time data. We also introduce a new real-time Korean GDP dataset, which is the focus of our experiments. The methodology that we utilize involves first...

In recent years, the field of financial econometrics has seen tremendous gains in the amount of data available for use in modeling and prediction. Much of this data is very high frequency, and even 'tick-based', and hence falls into the category of what might be termed big data. The availability...