IDEAS home Printed from https://ideas.repec.org/p/aoz/wpaper/61.html
   My bibliography  Save this paper

The real effects of financial uncertainty shocks: A daily identification approach

Author

Listed:
  • Piergiorgio Alessandri

    (Bank of Italy)

  • Andrea Gazzani

    (Bank of Italy)

  • Alejandro Vicondoa

    (Universidad Católica de Chile)

Abstract

Isolating financial uncertainty shocks is difficult because financial markets rapidly price changes in several economic fundamentals. To bypass this difficulty, we identify uncertainty shocks using daily data and use their monthly averages as an instrument in a VAR. We show that this novel approach is theoretically appealing and has dramatic implications for leading empirical studies on financial uncertainty. Daily interactions between equity returns, bond spreads and expected volatility cause previous identification schemes to fail at the monthly frequency. Once these interactions are explicitly modeled, the impact of uncertainty shocks on output and inflation is significant and similar across specifications.

Suggested Citation

  • Piergiorgio Alessandri & Andrea Gazzani & Alejandro Vicondoa, 2021. "The real effects of financial uncertainty shocks: A daily identification approach," Working Papers 61, Red Nacional de Investigadores en Economía (RedNIE).
  • Handle: RePEc:aoz:wpaper:61
    as

    Download full text from publisher

    File URL: https://drive.google.com/file/d/1TKSYKK6RkdaEklJy-_qTKr2XFSZO6cm4/view
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. Christiano, Lawrence J. & Eichenbaum, Martin, 1987. "Temporal aggregation and structural inference in macroeconomics," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 26(1), pages 63-130, January.
    3. Alexander Chudik & Georgios Georgiadis, 2022. "Estimation of Impulse Response Functions When Shocks Are Observed at a Higher Frequency Than Outcome Variables," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 965-979, June.
    4. Uhlig, Harald, 2005. "What are the effects of monetary policy on output? Results from an agnostic identification procedure," Journal of Monetary Economics, Elsevier, vol. 52(2), pages 381-419, March.
    5. Faust, Jon, 1998. "The robustness of identified VAR conclusions about money," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 49(1), pages 207-244, December.
    6. Beaudry, Paul & Saito, Makoto, 1998. "Estimating the effects of monetary shocks: An evaluation of different approaches," Journal of Monetary Economics, Elsevier, vol. 42(2), pages 241-260, July.
    7. Silvia Miranda Agrippino & Giovanni Ricco, 2018. "Identification with external instruments in structural VARs under partial invertibility," Working Papers hal-03475454, HAL.
    8. Laurent Ferrara & Pierre Guérin, 2018. "What are the macroeconomic effects of high‐frequency uncertainty shocks?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(5), pages 662-679, August.
    9. Susanto Basu & Brent Bundick, 2017. "Uncertainty Shocks in a Model of Effective Demand," Econometrica, Econometric Society, vol. 85, pages 937-958, May.
    10. Mark Gertler & Peter Karadi, 2015. "Monetary Policy Surprises, Credit Costs, and Economic Activity," American Economic Journal: Macroeconomics, American Economic Association, vol. 7(1), pages 44-76, January.
    11. Jesus Fernandez-Villaverde & Pablo Guerron-Quintana & Juan F. Rubio-Ramirez & Martin Uribe, 2011. "Risk Matters: The Real Effects of Volatility Shocks," American Economic Review, American Economic Association, vol. 101(6), pages 2530-2561, October.
    12. Kilian,Lutz & Lütkepohl,Helmut, 2018. "Structural Vector Autoregressive Analysis," Cambridge Books, Cambridge University Press, number 9781107196575.
    13. Barsky, Robert B. & Sims, Eric R., 2011. "News shocks and business cycles," Journal of Monetary Economics, Elsevier, vol. 58(3), pages 273-289.
    14. Faust, Jon & Swanson, Eric T. & Wright, Jonathan H., 2004. "Identifying VARS based on high frequency futures data," Journal of Monetary Economics, Elsevier, vol. 51(6), pages 1107-1131, September.
    15. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2018. "Measuring Uncertainty and Its Impact on the Economy," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 799-815, December.
    16. Caldara, Dario & Fuentes-Albero, Cristina & Gilchrist, Simon & Zakrajšek, Egon, 2016. "The macroeconomic impact of financial and uncertainty shocks," European Economic Review, Elsevier, vol. 88(C), pages 185-207.
    17. Cristina Arellano & Yan Bai & Patrick J. Kehoe, 2019. "Financial Frictions and Fluctuations in Volatility," Journal of Political Economy, University of Chicago Press, vol. 127(5), pages 2049-2103.
    18. repec:hal:spmain:info:hdl:2441/sb7ftvod18eb8hqptthmmeddt is not listed on IDEAS
    19. Scott R. Baker & Nicholas Bloom, 2013. "Does Uncertainty Reduce Growth? Using Disasters as Natural Experiments," NBER Working Papers 19475, National Bureau of Economic Research, Inc.
    20. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    21. Kyle Jurado & Sydney C. Ludvigson & Serena Ng, 2015. "Measuring Uncertainty," American Economic Review, American Economic Association, vol. 105(3), pages 1177-1216, March.
    22. Simon Gilchrist & Egon Zakrajsek, 2012. "Credit Spreads and Business Cycle Fluctuations," American Economic Review, American Economic Association, vol. 102(4), pages 1692-1720, June.
    23. 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.
    24. Mikkel Plagborg-Møller & Christian K. Wolf, 2022. "Instrumental Variable Identification of Dynamic Variance Decompositions," Journal of Political Economy, University of Chicago Press, vol. 130(8), pages 2164-2202.
    25. Nicholas Bloom, 2009. "The Impact of Uncertainty Shocks," Econometrica, Econometric Society, vol. 77(3), pages 623-685, May.
    26. Forni, Mario & Gambetti, Luca, 2014. "Sufficient information in structural VARs," Journal of Monetary Economics, Elsevier, vol. 66(C), pages 124-136.
    27. Hendry, David F., 1992. "An econometric analysis of TV advertising expenditure in the United Kingdom," Journal of Policy Modeling, Elsevier, vol. 14(3), pages 281-311, June.
    28. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, April.
    29. Leduc, Sylvain & Liu, Zheng, 2016. "Uncertainty shocks are aggregate demand shocks," Journal of Monetary Economics, Elsevier, vol. 82(C), pages 20-35.
    30. Michele Piffer & Maximilian Podstawski, 2018. "Identifying Uncertainty Shocks Using the Price of Gold," Economic Journal, Royal Economic Society, vol. 128(616), pages 3266-3284, December.
    31. Jesus Fernandez-Villaverde & Pablo Guerron-Quintana, 2020. "Uncertainty Shocks and Business Cycle Research," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 37, pages 118-166, August.
    32. Jonathan H. Wright, 2012. "What does Monetary Policy do to Long‐term Interest Rates at the Zero Lower Bound?," Economic Journal, Royal Economic Society, vol. 122(564), pages 447-466, November.
    33. Sims, Christopher A, 1971. "Discrete Approximations to Continuous Time Distributed Lags in Econometrics," Econometrica, Econometric Society, vol. 39(3), pages 545-563, May.
    34. repec:wrk:wrkemf:12 is not listed on IDEAS
    35. 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.
    36. Marcellino, Massimiliano, 1999. "Some Consequences of Temporal Aggregation in Empirical Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 129-136, January.
    37. Cieslak, Anna & Pang, Hao, 2020. "Common shocks in stocks and bonds," CEPR Discussion Papers 14708, C.E.P.R. Discussion Papers.
    38. Anna Cieslak & Hao Pang, 2020. "Common Shocks in Stocks and Bonds," NBER Working Papers 28184, National Bureau of Economic Research, Inc.
    39. Pascal Paul, 2020. "The Time-Varying Effect of Monetary Policy on Asset Prices," The Review of Economics and Statistics, MIT Press, vol. 102(4), pages 690-704, October.
    40. Piergiorgio Alessandri & Andrea Gazzani & Alejandro Vicondoa, 2020. "Uncertainty matters: evidence from a high-frequency identification strategy," Temi di discussione (Economic working papers) 1284, Bank of Italy, Economic Research and International Relations Area.
    41. James H. Stock & Mark W. Watson, 2012. "Disentangling the Channels of the 2007-2009 Recession," NBER Working Papers 18094, National Bureau of Economic Research, Inc.
    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. Fanelli, Luca & Marsi, Antonio, 2022. "Sovereign spreads and unconventional monetary policy in the Euro area: A tale of three shocks," European Economic Review, Elsevier, vol. 150(C).
    2. Luca Fanelli & Antonio Marsi, 2021. "Unconventional Monetary Policy in the Euro Area: A Tale of Three Shocks," Working Papers wp1164, Dipartimento Scienze Economiche, Universita' di Bologna.

    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. Alessandri, Piergiorgio & Gazzani, Andrea & Vicondoa, Alejandro, 2023. "Are the effects of uncertainty shocks big or small?," European Economic Review, Elsevier, vol. 158(C).
    2. Andrea Gazzani & Alejandro Vicondoa, 2020. "Bridge Proxy-SVAR: estimating the macroeconomic effects of shocks identified at high-frequency," Temi di discussione (Economic working papers) 1274, Bank of Italy, Economic Research and International Relations Area.
    3. Andrea Giovanni Gazzani & Alejandro Vicondoa, 2019. "Proxy-SVAR as a Bridge for Identification with Higher Frequency Data," 2019 Meeting Papers 855, Society for Economic Dynamics.
    4. Brianti, Marco, 2021. "Financial Shocks, Uncertainty Shocks, and Monetary Policy Trade-Offs," Working Papers 2021-5, University of Alberta, Department of Economics.
    5. Andrea Carriero & Alessio Volpicella, 2022. "Generalizing the Max Share Identification to multiple shocks identification: an Application to Uncertainty," School of Economics Discussion Papers 0322, School of Economics, University of Surrey.
    6. Giovanni Pellegrino & Federico Ravenna & Gabriel Züllig, 2021. "The Impact of Pessimistic Expectations on the Effects of COVID‐19‐Induced Uncertainty in the Euro Area," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(4), pages 841-869, August.
    7. Josué Diwambuena & Jean-Paul K. Tsasa, 2021. "The Real Effects of Uncertainty Shocks: New Evidence from Linear and Nonlinear SVAR Models," BEMPS - Bozen Economics & Management Paper Series BEMPS87, Faculty of Economics and Management at the Free University of Bozen.
    8. Caggiano, Giovanni & Castelnuovo, Efrem & Delrio, Silvia & Kima, Richard, 2021. "Financial uncertainty and real activity: The good, the bad, and the ugly," European Economic Review, Elsevier, vol. 136(C).
    9. Mirela Miescu, 2019. "Uncertainty shocks in emerging economies," Working Papers 277077821, Lancaster University Management School, Economics Department.
    10. Mikkel Plagborg‐Møller & Christian K. Wolf, 2021. "Local Projections and VARs Estimate the Same Impulse Responses," Econometrica, Econometric Society, vol. 89(2), pages 955-980, March.
    11. Miescu, Mirela S., 2023. "Uncertainty shocks in emerging economies: A global to local approach for identification," European Economic Review, Elsevier, vol. 154(C).
    12. Kim, Wongi, 2019. "Government spending policy uncertainty and economic activity: US time series evidence," Journal of Macroeconomics, Elsevier, vol. 61(C), pages 1-1.
    13. Stefano Fasani & Haroon Mumtaz & Lorenza Rossi, 2023. "Monetary Policy and Firm Dynamics," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 47, pages 278-296, January.
    14. Danilo Cascaldi-Garcia, 2022. "Forecast Revisions as Instruments for News Shocks," International Finance Discussion Papers 1341, Board of Governors of the Federal Reserve System (U.S.).
    15. Olli Palm'en, 2022. "Macroeconomic Effect of Uncertainty and Financial Shocks: a non-Gaussian VAR approach," Papers 2202.10834, arXiv.org.
    16. Danilo Cascaldi‐Garcia & Ana Beatriz Galvao, 2021. "News and Uncertainty Shocks," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(4), pages 779-811, June.
    17. Rivolta, Giulia & Trecroci, Carmine, 2020. "Measuring the effects of U.S. uncertainty and monetary conditions on EMEs' macroeconomic dynamics," MPRA Paper 99403, University Library of Munich, Germany.
    18. Redl, Chris, 2020. "Uncertainty matters: Evidence from close elections," Journal of International Economics, Elsevier, vol. 124(C).
    19. Fabio Bertolotti & Massimiliano Marcellino, 2019. "Tax shocks with high and low uncertainty," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(6), pages 972-993, September.
    20. Georgiadis, Georgios & Müller, Gernot J. & Schumann, Ben, 2024. "Global risk and the dollar," Journal of Monetary Economics, Elsevier, vol. 144(C).

    More about this item

    Keywords

    uncertainty shocks financial shocks structural vector autoregression high-frequency identification external instruments;

    JEL classification:

    • 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
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

    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:aoz:wpaper:61. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Laura Inés D Amato (email available below). General contact details of provider: https://edirc.repec.org/data/redniar.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.