IDEAS home Printed from https://ideas.repec.org/p/fip/fedgfe/2019-26.html
   My bibliography  Save this paper

Assessing Macroeconomic Tail Risk

Author

Listed:
  • Francesca Loria
  • Christian Matthes
  • Donghai Zhang

Abstract

What drives macroeconomic tail risk? To answer this question, we borrow a definition of macroeconomic risk from Adrian et al. (2019) by studying (left-tail) percentiles of the forecast distribution of GDP growth. We use local projections (Jord, 2005) to assess how this measure of risk moves in response to economic shocks to the level of technology, monetary policy, and financial conditions. Furthermore, by studying various percentiles jointly, we study how the overall economic outlook-as characterized by the entire forecast distribution of GDP growth-shifts in response to shocks. We find that contractionary shocks disproportionately increase downside risk, independently of what shock we look at.

Suggested Citation

  • Francesca Loria & Christian Matthes & Donghai Zhang, 2019. "Assessing Macroeconomic Tail Risk," Finance and Economics Discussion Series 2019-026, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgfe:2019-26
    DOI: 10.17016/FEDS.2019.026
    as

    Download full text from publisher

    File URL: https://www.federalreserve.gov/econres/feds/files/2019026pap.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Christina D. Romer & David H. Romer, 2004. "A New Measure of Monetary Shocks: Derivation and Implications," American Economic Review, American Economic Association, vol. 94(4), pages 1055-1084, September.
    2. Karel Mertens & Morten O. Ravn, 2013. "The Dynamic Effects of Personal and Corporate Income Tax Changes in the United States," American Economic Review, American Economic Association, vol. 103(4), pages 1212-1247, June.
    3. Ramey, V.A., 2016. "Macroeconomic Shocks and Their Propagation," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.),Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 71-162, Elsevier.
    4. Ludger Linnemann & Roland Winkler, 2016. "Estimating nonlinear effects of fiscal policy using quantile regression methods," Oxford Economic Papers, Oxford University Press, vol. 68(4), pages 1120-1145.
    5. Haroon Mumtaz & Paolo Surico, 2015. "The Transmission Mechanism In Good And Bad Times," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56, pages 1237-1260, November.
    6. Lutz Kilian & Simone Manganelli, 2008. "The Central Banker as a Risk Manager: Estimating the Federal Reserve's Preferences under Greenspan," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(6), pages 1103-1129, September.
    7. Giovanni Favara & Simon Gilchrist & Kurt F. Lewis & Egon Zakrajšek, 2016. "Updating the Recession Risk and the Excess Bond Premium," FEDS Notes 2016-10-06, Board of Governors of the Federal Reserve System (U.S.).
    8. Robert B. Barsky & Eric R. Sims, 2012. "Information, Animal Spirits, and the Meaning of Innovations in Consumer Confidence," American Economic Review, American Economic Association, vol. 102(4), pages 1343-1377, June.
    9. Adelchi Azzalini & Antonella Capitanio, 2003. "Distributions generated by perturbation of symmetry with emphasis on a multivariate skew t‐distribution," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 367-389, May.
    10. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    11. Regis Barnichon & Christian Brownlees, 2019. "Impulse Response Estimation by Smooth Local Projections," The Review of Economics and Statistics, MIT Press, vol. 101(3), pages 522-530, July.
    12. Simon Gilchrist & Egon Zakrajsek, 2012. "Credit Spreads and Business Cycle Fluctuations," American Economic Review, American Economic Association, vol. 102(4), pages 1692-1720, June.
    13. John G. Fernald, 2012. "A quarterly, utilization-adjusted series on total factor productivity," Working Paper Series 2012-19, Federal Reserve Bank of San Francisco.
    14. Valerie A. Ramey & Sarah Zubairy, 2018. "Government Spending Multipliers in Good Times and in Bad: Evidence from US Historical Data," Journal of Political Economy, University of Chicago Press, vol. 126(2), pages 850-901.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2020. "Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions," Working Papers 202002R, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
    2. Òscar Jordà & Martin Kornejew & Moritz Schularick & Alan M. Taylor, 2020. "Zombies at Large? Corporate Debt Overhang and the Macroeconomy," Staff Reports 951, Federal Reserve Bank of New York.
    3. De Santis, Roberto A. & Van der Veken, Wouter, 2020. "Forecasting macroeconomic risk in real time: Great and Covid-19 Recessions," Working Paper Series 2436, European Central Bank.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ramey, V.A., 2016. "Macroeconomic Shocks and Their Propagation," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.),Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 71-162, Elsevier.
    2. Albrizio, Silvia & Choi, Sangyup & Furceri, Davide & Yoon, Chansik, 2020. "International bank lending channel of monetary policy," Journal of International Money and Finance, Elsevier, vol. 102(C).
    3. Rüth, Sebastian K., 2020. "Shifts in monetary policy and exchange rate dynamics: Is Dornbusch's overshooting hypothesis intact, after all?," Journal of International Economics, Elsevier, vol. 126(C).
    4. Sanz, Carlos & Gonzalo Muñoz, Jesus & Alloza, Mario, 2019. "Dynamic Effects of Persistent Shocks," UC3M Working papers. Economics 29187, Universidad Carlos III de Madrid. Departamento de Economía.
    5. Nadav Ben Zeev, 2019. "Is There A Single Shock That Drives The Majority Of Business Cycle Fluctuations?," Working Papers 1906, Ben-Gurion University of the Negev, Department of Economics.
    6. Nadav Ben Zeev, 2019. "Identification of Sign-Dependency of Impulse Responses," Working Papers 1907, Ben-Gurion University of the Negev, Department of Economics.
    7. Husted, Lucas & Rogers, John & Sun, Bo, 2020. "Monetary policy uncertainty," Journal of Monetary Economics, Elsevier, vol. 115(C), pages 20-36.
    8. Mehdi El Herradi & Aurélien Leroy, 2019. "Monetary policy and the top one percent: Evidence from a century of modern economic history," DNB Working Papers 632, Netherlands Central Bank, Research Department.
    9. de Ridder, M. & Pfajfar, D., 2017. "Policy Shocks and Wage Rigidities: Empirical Evidence from Regional Effects of National Shocks," Cambridge Working Papers in Economics 1717, Faculty of Economics, University of Cambridge.
    10. Bachmann, Rüdiger & Zorn, Peter, 2020. "What drives aggregate investment? Evidence from German survey data," Journal of Economic Dynamics and Control, Elsevier, vol. 115(C).
    11. Rüth, Sebastian K. & Simon, Camilla, 2020. "How Do Income and the Debt Position of Households Propagate Public into Private Spending?," Working Papers 0676, University of Heidelberg, Department of Economics.
    12. Hacioglu Hoke, Sinem, 2019. "Macroeconomic effects of political risk shocks," Bank of England working papers 841, Bank of England.
    13. Robin Braun & Ralf Brüggemann, 2017. "Identification of SVAR Models by Combining Sign Restrictions With External Instruments," Working Paper Series of the Department of Economics, University of Konstanz 2017-07, Department of Economics, University of Konstanz.
    14. James McNeil, 2020. "Estimation of Impulse response functions with term structure local projections," Working Papers daleconwp2020-05, Dalhousie University, Department of Economics.
    15. Ettmeier, Stephanie & Kriwoluzky, Alexander, 2019. "Same, but different? Testing monetary policy shock measures," Economics Letters, Elsevier, vol. 184(C).
    16. Masahiro Tanaka, 2020. "Bayesian Inference of Local Projections with Roughness Penalty Priors," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 629-651, February.
    17. Laumer, Sebastian, 2020. "Government spending and heterogeneous consumption dynamics," Journal of Economic Dynamics and Control, Elsevier, vol. 114(C).
    18. Kim, Wongi, 2019. "Government spending policy uncertainty and economic activity: US time series evidence," Journal of Macroeconomics, Elsevier, vol. 61(C), pages 1-1.
    19. Tobias Adrian & Nina Boyarchenko & Domenico Giannone, 2019. "Vulnerable Growth," American Economic Review, American Economic Association, vol. 109(4), pages 1263-1289, April.
    20. Michael Ryan, 2020. "A Narrative Approach to Creating Instruments with Unstructured and Voluminous Text: An Application to Policy Uncertainty," Working Papers in Economics 20/10, University of Waikato.

    More about this item

    Keywords

    Macroeconomic risk; Shocks; Local projections;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:fip:fedgfe:2019-26. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (). General contact details of provider: http://edirc.repec.org/data/frbgvus.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.