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How to measure inFLAtion volatility. A note

Author

Listed:
  • Alfredo García-Hiernaux

    (DANAE and ICAE)

  • María T. González-Pérez

    (Banco de España)

  • David E. Guerrero

    (CUNEF)

Abstract

This paper proposes a statistical model and a conceptual framework to estimate inflation volatility assuming rational inattention, where the decay in the level of attention reflects the arrival of news in the market. We estimate trend inflation and the conditional inflation volatility for Germany, Spain, the euro area and the United States using monthly data from January 2002 to March 2022 and test whether inflation was equal to or below 2% in this period in these regions. We decompose inflation volatility into positive and negative surprise components and characterise different inflation volatility scenarios during the Great Financial Crisis, the Sovereign Debt Crisis, and the post-COVID period. Our volatility measure outperforms the GARCH(1,1) model and the rolling standard deviation in one-step ahead volatility forecasts both in-sample and out-of-sample. The methodology proposed in this article is appropriate for estimating the conditional volatility of macro-financial variables. We recommend the inclusion of this measure in inflation dynamics monitoring and forecasting exercises.

Suggested Citation

  • Alfredo García-Hiernaux & María T. González-Pérez & David E. Guerrero, 2023. "How to measure inFLAtion volatility. A note," Working Papers 2314, Banco de España.
  • Handle: RePEc:bde:wpaper:2314
    DOI: https://doi.org/10.53479/30092
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    References listed on IDEAS

    as
    1. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    2. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-155, January.
    3. Timothy Cogley & Argia M. Sbordone, 2008. "Trend Inflation, Indexation, and Inflation Persistence in the New Keynesian Phillips Curve," American Economic Review, American Economic Association, vol. 98(5), pages 2101-2126, December.
    4. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    Full references (including those not matched with items on IDEAS)

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    Keywords

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • E4 - Macroeconomics and Monetary Economics - - Money and Interest Rates
    • E5 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit

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