Quantile-based Inflation Risk Models

Eric Ghysels (), Leonardo Iania and Jonas Striaukas
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Eric Ghysels: Department of Economics and Kenan-Flagler Business School, University of North Carolina Chapel Hill and CEPR.
Jonas Striaukas: Universite catholique de Louvain. Research Fellow at F.R.S. - FNRS

No 349, Working Paper Research from National Bank of Belgium

Abstract: 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.

Keywords: regression quantiles; in ation risk; quantile forecasting (search for similar items in EconPapers)
JEL-codes: C53 C54 E37 (search for similar items in EconPapers)
Pages: 36 pages
Date: 2018-10
New Economics Papers: this item is included in nep-ecm, nep-for, nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:nbb:reswpp:201810-349

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