Using mixed-frequency and realized measures in quantile regression
Quantile regression is an efficient tool when it comes to estimate popular measures of tail
risk such as the conditional quantile Value at Risk. In this paper we exploit the availability
of data at mixed frequency to build a volatility model for daily returns with low– (for macro–
variables) and high–frequency (which may include an “–X” term related to realized volatility
measures) components. The quality of the suggested quantile regression model, labeled MF–
Q–ARCH–X, is assessed in a number of directions: we derive weak stationarity properties, we
investigate its finite sample properties by means of a Monte Carlo exercise and we apply it on
financial real data. VaR forecast performances are evaluated by backtesting and Model Confidence Set inclusion among competitors, showing that the MF–Q–ARCH–X has a consistently
accurate forecasting capability.