We evaluate the forecasting performance of time series models for realized volatility, which accommodate long memory, level shifts, leverage effects, day-of-the-week and holiday effects, as well as macroeconomic news announcements. Applying the models to daily realized volatility for the S&P 500...

This paper proposes a methodology for now-casting and forecasting inflation using data with a sampling frequency which is higher than monthly. The data are modeled as a trading day frequency factor model, with missing observations in a state space representation. For the estimation we adopt the...