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Financial Variables as Predictors of Real Growth Vulnerability

Author

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  • Lucrezia Reichlin

    (London Business School)

  • Giovanni Ricco

    (OFCE - Observatoire français des conjonctures économiques (Sciences Po) - Sciences Po - Sciences Po)

  • Thomas Hasenzagl

Abstract

We evaluate the role of financial conditions as predictors of macroeconomic risk first in the quantile regression framework of Adrian et al. (2019b), which allows for non-linearities, and then in a novel linear semi-structural model as proposed by Hasenzagl et al. (2018). We distinguish between price variables such as credit spreads and stock variables such as leverage. We find that (i) although the spreads correlate with the left tail of the conditional distribution of GDP growth, they provide limited advanced information on growth vulnerability; (ii) nonfinancial leverage provides a leading signal for the left quantile of the GDP growth distribution in the 2008 recession; (iii) measures of excess leverage conceptually similar to the Basel gap, but cleaned from business cycle dynamics via the lenses of the semi-structural model, point to two peaks of accumulation of risks – the eighties and the first eight years of the new millennium, with an unstable relationship with business cycle chronology.

Suggested Citation

  • Lucrezia Reichlin & Giovanni Ricco & Thomas Hasenzagl, 2020. "Financial Variables as Predictors of Real Growth Vulnerability," Working Papers hal-03403077, HAL.
  • Handle: RePEc:hal:wpaper:hal-03403077
    Note: View the original document on HAL open archive server: https://sciencespo.hal.science/hal-03403077
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    References listed on IDEAS

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    1. Claessens, Stijn & Kose, M. Ayhan & Terrones, Marco E., 2012. "How do business and financial cycles interact?," Journal of International Economics, Elsevier, vol. 87(1), pages 178-190.
    2. Borio, Claudio, 2014. "The financial cycle and macroeconomics: What have we learnt?," Journal of Banking & Finance, Elsevier, vol. 45(C), pages 182-198.
    3. Tobias Adrian & Federico Grinberg & Nellie Liang & Sheheryar Malik & Jie Yu, 2022. "The Term Structure of Growth-at-Risk," American Economic Journal: Macroeconomics, American Economic Association, vol. 14(3), pages 283-323, July.
    4. Mathias Drehmann & James Yetman, 2018. "Why you should use the Hodrick-Prescott filter - at least to generate credit gaps," BIS Working Papers 744, Bank for International Settlements.
    5. 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.
    6. Scott Brave & R. Andrew Butters, 2012. "Diagnosing the Financial System: Financial Conditions and Financial Stress," International Journal of Central Banking, International Journal of Central Banking, vol. 8(2), pages 191-239, June.
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    Citations

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    Cited by:

    1. Aaron J. Amburgey & Michael W. McCracken, 2023. "On the real‐time predictive content of financial condition indices for growth," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(2), pages 137-163, March.
    2. Andrea Carriero & Todd E. Clark & Marcellino Massimiliano, 2020. "Nowcasting Tail Risks to Economic Activity with Many Indicators," Working Papers 20-13R2, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
    3. David Kohns & Tibor Szendrei, 2021. "Decoupling Shrinkage and Selection for the Bayesian Quantile Regression," Papers 2107.08498, arXiv.org.
    4. Tony Chernis & Patrick J. Coe & Shaun P. Vahey, 2023. "Reassessing the dependence between economic growth and financial conditions since 1973," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(2), pages 260-267, March.
    5. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2022. "Nowcasting tail risk to economic activity at a weekly frequency," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 843-866, August.
    6. Ricco, Giovanni & Hasenzagl, Thomas & Pellegrino, Filippo & Reichlin, Lucrezia, 2022. "Monitoring the Economy in Real Time: Trends and Gaps in Real Activity and Prices," CEPR Discussion Papers 17111, C.E.P.R. Discussion Papers.
    7. James Mitchell & Aubrey Poon & Dan Zhu, 2022. "Constructing Density Forecasts from Quantile Regressions: Multimodality in Macro-Financial Dynamics," Working Papers 22-12R, Federal Reserve Bank of Cleveland, revised 11 Apr 2023.
    8. 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.
    9. Figueres, Juan Manuel & Jarociński, Marek, 2020. "Vulnerable growth in the euro area: Measuring the financial conditions," Economics Letters, Elsevier, vol. 191(C).
    10. Arrigoni, Simone & Bobasu, Alina & Venditti, Fabrizio, 2020. "The simpler the better: measuring financial conditions for monetary policy and financial stability," Working Paper Series 2451, European Central Bank.
    11. Corradi, Valentina & Fosten, Jack & Gutknecht, Daniel, 2023. "Out-of-sample tests for conditional quantile coverage an application to Growth-at-Risk," Journal of Econometrics, Elsevier, vol. 236(2).
    12. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2021. "Investigating Growth at Risk Using a Multi-country Non-parametric Quantile Factor Model," Papers 2110.03411, arXiv.org.
    13. Iseringhausen, Martin, 2024. "A time-varying skewness model for Growth-at-Risk," International Journal of Forecasting, Elsevier, vol. 40(1), pages 229-246.
    14. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2023. "Tail Forecasting With Multivariate Bayesian Additive Regression Trees," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 979-1022, August.
    15. Lang, Jan Hannes & Rusnák, Marek & Greiwe, Moritz, 2023. "Medium-term growth-at-risk in the euro area," Working Paper Series 2808, European Central Bank.
    16. Simone Arrigoni & Alina Bobasu & Fabrizio Venditti, 2022. "Measuring Financial Conditions using Equal Weights Combination," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 70(4), pages 668-697, December.
    17. Delle-Monache, Davide & De-Polis, Andrea & Petrella, Ivan, 2020. "Modelling and Forecasting Macroeconomic Downside Risk," EMF Research Papers 34, Economic Modelling and Forecasting Group.
    18. Chavleishvili, Sulkhan & Fahr, Stephan & Kremer, Manfred & Manganelli, Simone & Schwaab, Bernd, 2021. "A risk management perspective on macroprudential policy," Working Paper Series 2556, European Central Bank.
    19. Behera, Harendra & Gunadi, Iman & Rath, Badri Narayan, 2023. "COVID-19 uncertainty, financial markets and monetary policy effects in case of two emerging Asian countries," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 173-189.
    20. Deng, Chuang & Wu, Jian, 2023. "Macroeconomic downside risk and the effect of monetary policy," Finance Research Letters, Elsevier, vol. 54(C).
    21. Diego Chicana & Rafael Nivin, 2021. "Evaluating Growth-at-Risk as a tool for monitoring macro-financial risks in the Peruvian economy," IHEID Working Papers 07-2021, Economics Section, The Graduate Institute of International Studies.

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    More about this item

    Keywords

    Financial cycle; Business cycle; Credit; Financial crises; Downside risk; Entropy; Quantile regressions;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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