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Estimating Inflation-at-Risk (IaR) using Extreme Value Theory (EVT)

Author

Listed:
  • Santos, Edward P.
  • Mapa, Dennis S.
  • Glindro, Eloisa T.

Abstract

The Bangko Sentral ng Pilipinas (BSP) has the primary responsibility of maintaining stable prices conducive to a balanced and sustainable economic growth. The year 2008 posed a challenge to the BSP’s monetary policy making as inflation hit an official 17-year high of 12.5 percent in August after 10 months of continuous acceleration. The alarming double-digit inflation rate was attributed to rising fuel and food prices, particularly the price of rice. A high inflation rate has impact on poverty since inflation affects the poor more than the rich. From a macroeconomic perspective, high level of inflation is not conducive to economic growth. This paper proposes a method of estimating Inflation-at-Risk (IaR) similar to the Value-at-Risk (VaR) used to estimate risk in the financial market. The IaR represents the maximum inflation over a target horizon for a given low pre-specified probability. It can serve as an early warning system that can be used by the BSP to identify whether the level of inflation is extreme enough to be considered an imminent threat to its inflation objective. The extreme value theory (EVT), which deals with the frequency and magnitude of very low probability events, is used as the basis for building a model in estimating the IaR. The estimates of the IaR using the peaks-over-threshold (POT) model suggest that the while the inflation rate experienced in 2008 can not be considered as an extreme value, it was very near the estimated 90 percent IaR.

Suggested Citation

  • Santos, Edward P. & Mapa, Dennis S. & Glindro, Eloisa T., 2011. "Estimating Inflation-at-Risk (IaR) using Extreme Value Theory (EVT)," MPRA Paper 28266, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:28266
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    References listed on IDEAS

    as
    1. Stephen G. Cecchetti, 1997. "Measuring short-run inflation for central bankers," Review, Federal Reserve Bank of St. Louis, issue May, pages 143-155.
    2. Nikkin L. Beronilla & Dennis S. Mapa, 2008. "Range-based models in estimating value-at-risk (VaR)," Philippine Review of Economics, University of the Philippines School of Economics and Philippine Economic Society, vol. 45(2), pages 87-99, December.
    3. Jose Oliver Q. Suaiso & Dennis S. Mapa, 2009. "Measuring market risk using extreme value theory," Philippine Review of Economics, University of the Philippines School of Economics and Philippine Economic Society, vol. 46(2), pages 91-121, December.
    4. Jonathan Kearns, 1998. "The Distribution and Measurement of Inflation," RBA Research Discussion Papers rdp9810, Reserve Bank of Australia.
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    Cited by:

    1. Mendy, David & Widodo, Tri, 2018. "On the Inflation-Uncertainty Hypothesis in The Gambia: A Multi-Sample View on Causality Linkages," MPRA Paper 86743, University Library of Munich, Germany.
    2. Leonard Arvi & Herman Manakyan & Kashi Khazeh, 2023. "Estimated Impact of Covid-19 on Exchange Rate Risk of Multinational Enterprises Operating in Emerging Markets," International Journal of Economics and Financial Issues, Econjournals, vol. 13(4), pages 23-29, July.
    3. Bruno Ferreira Frascaroli & Wellington Charles Lacerda Nobrega, 2019. "Inflation Targeting and Inflation Risk in Latin America," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 55(11), pages 2389-2408, September.

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    More about this item

    Keywords

    Inflation-at-Risk (IaR); Extreme Value Theory (EVT); Peaks-over-Threshold (POT);
    All these keywords.

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics

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