This paper proposes a new approach to extract quantile-based inflation risk mea-
sures using Quantile Autoregressive Distributed Lag Mixed-Frequency Data Sampling
(QADL-MIDAS) regression models. We compare our models to a standard Quantile
Auto-Regression (QAR) model and show that it delivers better quantile forecasts at
several forecasting horizons. We use the QADL-MIDAS model to construct inflation
risk measures proxying for uncertainty, third-moment dynamics and the risk of ex-
treme inflation realizations. We find that these risk measures are linked to the future
evolution of inflation and changes in the effective federal funds rate.