Forecasting-volatility models typically rely on either daily or high frequency (HF) data and the choice between these … forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the assumptions of jumping …

Forecasting volatility models typically rely on either daily or high frequency (HF) data and the choice between these … these two family forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the …

forecasting volatility model with the most appropriate error distribution. The results suggest the presence of leverage effect … forecasting model that could guarantee a sound policy decisions. …

There has been increased interest in the use of "big data" when it comes to forecasting macroeconomic time series such … as private consumption or unemployment. However, applications on forecasting GDP are rather rare. In this paper we … incorporate Google search data into a Bridge Equation Model, a version of which usually belongs to the suite of forecasting models …

This paper is concerned with problem of variable selection and forecasting in the presence of parameter instability …. There are a number of approaches proposed for forecasting in the presence of breaks, including the use of rolling windows or … variable selection and forecasting stages. In this study, we investigate whether or not we should use weighted observations at …

electricity price spreads between different hours of the day. This supports an optimal day ahead storage and discharge schedule … electricity. The four latent moments of the density functions are dynamic and conditional upon exogenous drivers, thereby …