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Assessing Macroeconomic Tail Risk

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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.

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  • Francesca Loria & Christian Matthes & Donghai Zhang, 2019. "Assessing Macroeconomic Tail Risk," Working Paper 19-10, Federal Reserve Bank of Richmond.
    • Handle: RePEc:fip:fedrwp:19-10
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    Cited by:

    1. Chen, Guojin & Liu, Yanzhen & Zhang, Yu, 2020. "Can systemic risk measures predict economic shocks? Evidence from China," China Economic Review, Elsevier, vol. 64(C).
    2. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2020. "Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions," Working Papers 20-02R, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
    3. Òscar Jordà & Martin Kornejew & Moritz Schularick & Alan M Taylor, 2022. "Zombies at Large? Corporate Debt Overhang and the Macroeconomy," Review of Financial Studies, Society for Financial Studies, vol. 35(10), pages 4561-4586.
    4. Luke Hartigan & Michelle Wright, 2021. "Financial Conditions and Downside Risk to Economic Activity in Australia," RBA Research Discussion Papers rdp2021-03, Reserve Bank of Australia.
    5. Gonzalez Rivera, Gloria & Rodríguez Caballero, Carlos Vladimir & Ruiz Ortega, Esther, 2021. "Expecting the unexpected: economic growth under stress," DES - Working Papers. Statistics and Econometrics. WS 32148, Universidad Carlos III de Madrid. Departamento de Estadística.
    6. 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.
    7. Deng, Chuang & Wu, Jian, 2023. "Macroeconomic downside risk and the effect of monetary policy," Finance Research Letters, Elsevier, vol. 54(C).
    8. Wang, Bo & Li, Haoran, 2021. "Downside risk, financial conditions and systemic risk in China," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    9. Eraslan, Sercan & Schröder, Maximilian, 2023. "Nowcasting GDP with a pool of factor models and a fast estimation algorithm," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1460-1476.
    10. Schüler, Yves S., 2020. "The impact of uncertainty and certainty shocks," Discussion Papers 14/2020, Deutsche Bundesbank.
    11. Moritz Schularick, 2021. "Corporate indebtedness and macroeconomic stabilisation from a long-term perspective," ECONtribute Policy Brief Series 024, University of Bonn and University of Cologne, Germany.
    12. Michal Franta & Jan Libich, 2021. "Holding the Economy by the Tail: Analysis of Short- and Long-run Macroeconomic Risks," Working Papers 2021/3, Czech National Bank.

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    More about this item

    Keywords

    macroeconomic risk; shocks; local projections;
    All these keywords.

    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

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