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Quantile-based Inflation Risk Models

Author

Listed:
  • Eric Ghysels

    (Department of Economics and Kenan-Flagler Business School, University of North Carolina Chapel Hill and CEPR.)

  • Leonardo Iania

    (Louvain School of Management and IMMAQ (CORE and LFIN), Universite catholique de Louvain.)

  • Jonas Striaukas

    (Universite catholique de Louvain. Research Fellow at F.R.S. - FNRS)

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.

Suggested Citation

  • Eric Ghysels & Leonardo Iania & Jonas Striaukas, 2018. "Quantile-based Inflation Risk Models," Working Paper Research 349, National Bank of Belgium.
    • Handle: RePEc:nbb:reswpp:201810-349
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    File URL: https://www.nbb.be/doc/ts/publications/wp/wp349en.pdf
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    References listed on IDEAS

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    Cited by:

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    2. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2023. "Tail Forecasting With Multivariate Bayesian Additive Regression Trees," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 979-1022, August.
    3. Nina Boyarchenko & Domenico Giannone & Anna Kovner, 2020. "Bank Capital and Real GDP Growth," Staff Reports 950, Federal Reserve Bank of New York.

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    More about this item

    Keywords

    regression quantiles; in ation risk; quantile forecasting;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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