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Are Bayesian Fan Charts Useful? The Effect of Zero Lower Bound and Evaluation of Financial Stability Stress Tests

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  • Michal Franta

    (International Monetary Fund)

  • Jozef Baruník

    (Institute of Economic Studies, Charles University, Prague, and UTIA, Czech Academy of Sciences)

  • Roman Horváth

    (Institute of Economic Studies, Charles University, Prague)

  • Katerina Smídková

    (Czech National Bank, and Institute of Economic Studies, Charles University, Prague)

Abstract

We show how fan charts generated from Bayesian vector autoregression models can be useful for assessing (i) the effect of the zero-lower-bound constraint on forecasting uncertainty and (ii) the credibility of stress tests conducted to evaluate financial stability. To illustrate these issues, we use a data set for the Czech Republic and macroeconomic scenarios that are used by the Czech National Bank (CNB) in stress tests of the banking sector. Our results demonstrate how different modeling approaches to the zero lower bound affect the resulting fan charts. The pros and cons of the considered methods are discussed; ignoring the zero-lower-bound constraint represents the worst approach. Next, using our fan charts, we propose a method for evaluating whether the assumptions that are employed in the bank’s stress tests regarding the macroeconomic outlook are sufficiently adverse and consistent with past cross-correlations observed in the data. We find that CNB stress tests are sufficiently conservative in this respect.

Suggested Citation

  • Michal Franta & Jozef Baruník & Roman Horváth & Katerina Smídková, 2014. "Are Bayesian Fan Charts Useful? The Effect of Zero Lower Bound and Evaluation of Financial Stability Stress Tests," International Journal of Central Banking, International Journal of Central Banking, vol. 10(1), pages 159-188, March.
    • Handle: RePEc:ijc:ijcjou:y:2014:q:1:a:5
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    References listed on IDEAS

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    Cited by:

    1. Andrew Binning & Junior Maih, 2016. "Forecast uncertainty in the neighborhood of the effective lower bound: How much asymmetry should we expect?," Working Paper 2016/13, Norges Bank.
    2. Chris McDonald & Craig Thamotheram & Shaun P. Vahey & Elizabeth C. Wakerly, 2016. "Assessing the economic value of probabilistic forecasts in the presence of an inflation target," Reserve Bank of New Zealand Discussion Paper Series DP2016/10, Reserve Bank of New Zealand.
    3. Michal Franta, 2016. "The Effect of Nonlinearity between Credit Conditions and Economic Activity on Density Forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(2), pages 147-166, March.
    4. repec:wrk:wrkemf:09 is not listed on IDEAS
    5. Farooq Akram & Andrew Binning & Junior Maih, 2016. "Joint Prediction Bands for Macroeconomic Risk Management," Working Papers No 5/2016, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    6. Knüppel, Malte & Krüger, Fabian, 2017. "Forecast Uncertainty, Disagreement, and Linear Pools of Density Forecasts," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168294, Verein für Socialpolitik / German Economic Association.
    7. repec:cnb:ocpubv:rb13/1 is not listed on IDEAS
    8. repec:cnb:ocpubv:rb13/2 is not listed on IDEAS
    9. Milan Szabo, 2020. "Growth-at-Risk: Bayesian Approach," Working Papers 2020/3, Czech National Bank.
    10. Ahmad Razi & Po Ling Loke, 2017. "Fan Chart: The art and science of communicating uncertainty," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Statistical implications of the new financial landscape, volume 43, Bank for International Settlements.
    11. repec:cnb:ocpubv:rb14/1 is not listed on IDEAS
    12. repec:cnb:ocpubv:rb12/2 is not listed on IDEAS
    13. Ohnsorge,Franziska Lieselotte & Stocker,Marc & Some,Modeste Y., 2016. "Quantifying uncertainties in global growth forecasts," Policy Research Working Paper Series 7770, The World Bank.
    14. repec:cnb:ocpubv:rb14/2 is not listed on IDEAS
    15. Malte Knüppel & Fabian Krüger, 2022. "Forecast uncertainty, disagreement, and the linear pool," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 23-41, January.
    16. Franta, Michal, 2017. "Rare shocks vs. non-linearities: What drives extreme events in the economy? Some empirical evidence," Journal of Economic Dynamics and Control, Elsevier, vol. 75(C), pages 136-157.
    17. Hamid Baghestani & Liliana Danila, 2014. "Interest Rate and Exchange Rate Forecasting in the Czech Republic: Do Analysts Know Better than a Random Walk?," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 64(4), pages 282-295, September.
    18. Milan Szabo & Zlatuse Komarkova & Martin Casta, 2020. "Vulnerable growth: Bayesian GDP-at-Risk," Occasional Publications - Chapters in Edited Volumes,, Czech National Bank.
    19. repec:cnb:ocpubv:rb12/1 is not listed on IDEAS
    20. Irving Fisher Committee, 2017. "Statistical implications of the new financial landscape," IFC Bulletins, Bank for International Settlements, number 43.
    21. Lubomír Lízal & Jirí Schwarz, 2013. "Foreign exchange interventions as an (un)conventional monetary policy tool," BIS Papers chapters, in: Bank for International Settlements (ed.), Sovereign risk: a world without risk-free assets?, volume 73, pages 127-143, Bank for International Settlements.
    22. Damian Stelmasiak & Grzegorz Szafrański, 2016. "Forecasting the Polish Inflation Using Bayesian VAR Models with Seasonality," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 8(1), pages 21-42, March.
    23. Michal Franta & Tomas Holub & Petr Kral & Ivana Kubicova & Katerina Smidkova & Borek Vasicek, 2014. "The Exchange Rate as an Instrument at Zero Interest Rates: The Case of the Czech Republic," Research and Policy Notes 2014/03, Czech National Bank.
    24. Jackson, Emerson Abraham & Tamuke, Edmund, 2021. "The Science and Art of Communicating Fan Chart Uncertainty: The case of Inflation Outcome in Sierra Leone," MPRA Paper 105892, University Library of Munich, Germany, revised 05 Jan 2021.

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    JEL classification:

    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies

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