Quantile-based Inﬂation Risk Models
Working Paper N° 349
This paper proposes a new approach to extract quantile-based inﬂation risk measures 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 extreme inﬂation realizations. We ﬁnd that these risk measures are linked to the future evolution of inﬂation and changes in the eﬀective federal funds rate.