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Estimation and decomposition of food price inflation risk

Author

Listed:
  • Kris Boudt

    (Ghent University
    Vrije Universiteit Brussel
    Vrije Universiteit Amsterdam)

  • Hong Anh Luu

    (Vrije Universiteit Brussel)

Abstract

Ensuring aggregate food price stability requires a forward-looking assessment of the risk that unexpected deviations in individual food items’ inflation lead to large shocks in the aggregate food price inflation. To do so, we propose using a multivariate GARCH framework in combination with the Euler method to (1) estimate the conditional standard deviation and quantiles of the food price inflation shocks and (2) attribute the total risk to the underlying food items. For the FAO food price index, we find that even though meat inflation systematically has the highest weight in the aggregate index, cereal inflation is the main contributor to the total food price inflation risk over the period 1990–2018. The use of time series models and the Cornish-Fisher expansion make the risk characterization forward-looking and a potentially helpful tool for risk management.

Suggested Citation

  • Kris Boudt & Hong Anh Luu, 2022. "Estimation and decomposition of food price inflation risk," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(2), pages 295-319, June.
    • Handle: RePEc:spr:stmapp:v:31:y:2022:i:2:d:10.1007_s10260-021-00574-6
    DOI: 10.1007/s10260-021-00574-6
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    1. Klein, Benjamin, 1977. "The Demand for Quality-adjusted Cash Balances: Price Uncertainty in the U.S. Demand for Money Function," Journal of Political Economy, University of Chicago Press, vol. 85(4), pages 691-715, August.
    2. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(1), pages 122-150, February.
    3. Omotosho, Babatunde S. & Doguwa, Sani I., 2012. "Understanding the dynamics of inflation volatility in Nigeria: A GARCH perspective," MPRA Paper 96125, University Library of Munich, Germany.
    4. Minot, Nicholas, 2014. "Food price volatility in sub-Saharan Africa: Has it really increased?," Food Policy, Elsevier, vol. 45(C), pages 45-56.
    5. Ruth Judson & Athanasios Orphanides, 1999. "Inflation, Volatility and Growth," International Finance, Wiley Blackwell, vol. 2(1), pages 117-138, April.
    6. Derek Headey & Shenggen Fan, 2008. "Anatomy of a crisis: the causes and consequences of surging food prices," Agricultural Economics, International Association of Agricultural Economists, vol. 39(s1), pages 375-391, November.
    7. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-836, July.
    8. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-862, November.
    9. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    10. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    11. Moser, Gabriel & Rumler, Fabio & Scharler, Johann, 2007. "Forecasting Austrian inflation," Economic Modelling, Elsevier, vol. 24(3), pages 470-480, May.
    12. Khan, Mohsin S, 1977. "The Variability of Expectations in Hyperinflations," Journal of Political Economy, University of Chicago Press, vol. 85(4), pages 817-827, August.
    13. Giordani, Paolo & Soderlind, Paul, 2003. "Inflation forecast uncertainty," European Economic Review, Elsevier, vol. 47(6), pages 1037-1059, December.
    14. Robert Engle, 2004. "Risk and Volatility: Econometric Models and Financial Practice," American Economic Review, American Economic Association, vol. 94(3), pages 405-420, June.
    15. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    16. Apergis, Nicholas & Rezitis, Anthony N., 2011. "Food Price Volatility and Macroeconomic Factors: Evidence from GARCH and GARCH-X Estimates," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 43(1), pages 1-16, February.
    17. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    18. Hirotugu Akaike, 1969. "Fitting autoregressive models for prediction," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 21(1), pages 243-247, December.
    19. Kevin B. Grier & Mark J. Perry, 2000. "The effects of real and nominal uncertainty on inflation and output growth: some garch-m evidence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(1), pages 45-58.
    20. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    21. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    22. Jones, Paul M. & Olson, Eric, 2013. "The time-varying correlation between uncertainty, output, and inflation: Evidence from a DCC-GARCH model," Economics Letters, Elsevier, vol. 118(1), pages 33-37.
    23. Christopher Gilbert & Wyn Morgan, 2010. "Has food price volatility risen?," Department of Economics Working Papers 1002, Department of Economics, University of Trento, Italia.
    24. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    25. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    26. Luc Bauwens & Sébastien Laurent & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109, January.
    27. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    28. Michal Andrle & Mr. Andrew Berg & Rogelio Morales & Mr. Rafael A Portillo & Mr. Jan Vlcek, 2013. "Forecasting and Monetary Policy Analysis in Low-Income Countries: Food and non-Food Inflation in Kenya," IMF Working Papers 2013/061, International Monetary Fund.
    29. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    30. Fountas, Stilianos & Karanasos, Menelaos & Kim, Jinki, 2002. "Inflation and output growth uncertainty and their relationship with inflation and output growth," Economics Letters, Elsevier, vol. 75(3), pages 293-301, May.
    31. Mr. Shaun K. Roache, 2010. "What Explains the Rise in Food Price Volatility?," IMF Working Papers 2010/129, International Monetary Fund.
    32. Christopher Herrington & Yash P. Mehra, 2008. "On the sources of movements in inflation expectations : a few insights from a VAR model," Economic Quarterly, Federal Reserve Bank of Richmond, vol. 94(Spr), pages 121-146.
    33. repec:bla:intfin:v:2:y:1999:i:1:p:117-38 is not listed on IDEAS
    34. Sirr, Gordon & Garvey, John & Gallagher, Liam, 2011. "Emerging markets and portfolio foreign exchange risk: An empirical investigation using a value-at-risk decomposition technique," Journal of International Money and Finance, Elsevier, vol. 30(8), pages 1749-1772.
    35. T. Jeffrey Wilks & Mark F. Zimbelman, 2004. "Decomposition of Fraud†Risk Assessments and Auditors' Sensitivity to Fraud Cues," Contemporary Accounting Research, John Wiley & Sons, vol. 21(3), pages 719-745, September.
    36. M. Saisana & A. Saltelli & S. Tarantola, 2005. "Uncertainty and sensitivity analysis techniques as tools for the quality assessment of composite indicators," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(2), pages 307-323, March.
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