IDEAS home Printed from https://ideas.repec.org/a/kap/empiri/v51y2024i3d10.1007_s10663-024-09618-y.html
   My bibliography  Save this article

The non-linear impact of monetary policy on shifts in economic policy uncertainty: evidence from the United States of America

Author

Listed:
  • Bogdan Dima

    (West University of Timişoara)

  • Ștefana Maria Dima

    (West University of Timişoara)

Abstract

A stochastic volatility estimation of VIX index’s latent volatility is used for the United States of America, as a proxy for the adjustments in the levels of investors’ uncertainty related to current and future economic policies. The impact of monetary policy stance on such measure is examined in the framework of the distributed lag non-linear models (DLNM). We place this analysis in the literature stream emphasizing the various sources of heterogeneity concerning investors’ expectations. The main finding is that the monetary policy does impact non-linearly the adjustments in investors’ predictions. While a tighter monetary policy does generally contribute to an increase in VIX’s latent volatility, the shape of such effect varies across different GLM and GAM specifications of DLNM. This outcome remains robust, even if: (1) we control for the global price of Brent crude and consumers’ confidence; (2) we use, instead of the stochastic framework, a Markov-switching GARCH-based estimator; or (3) we replace the monetary policy instrument with monetary policy uncertainty. We argue that accounting for its nonlinear effects on financial markets is of critical importance for the design of a monetary policy pursuing global financial stability.

Suggested Citation

  • Bogdan Dima & Ștefana Maria Dima, 2024. "The non-linear impact of monetary policy on shifts in economic policy uncertainty: evidence from the United States of America," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 51(3), pages 755-781, August.
  • Handle: RePEc:kap:empiri:v:51:y:2024:i:3:d:10.1007_s10663-024-09618-y
    DOI: 10.1007/s10663-024-09618-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10663-024-09618-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10663-024-09618-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ben S. Bernanke & Kenneth N. Kuttner, 2005. "What Explains the Stock Market's Reaction to Federal Reserve Policy?," Journal of Finance, American Finance Association, vol. 60(3), pages 1221-1257, June.
    2. Kastner, Gregor & Frühwirth-Schnatter, Sylvia, 2014. "Ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC estimation of stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 408-423.
    3. Eric T. Swanson & John C. Williams, 2014. "Measuring the Effect of the Zero Lower Bound on Medium- and Longer-Term Interest Rates," American Economic Review, American Economic Association, vol. 104(10), pages 3154-3185, October.
    4. Giovanni Dosi & Mauro Napoletano & Andrea Roventini & Joseph E. Stiglitz & Tania Treibich, 2020. "Rational Heuristics? Expectations And Behaviors In Evolving Economies With Heterogeneous Interacting Agents," Economic Inquiry, Western Economic Association International, vol. 58(3), pages 1487-1516, July.
    5. BUI, Duy-Tung & LLORCA, Matthieu & BUI, Thi Mai Hoai, 2018. "Dynamics between stock market movements and fiscal policy: Empirical evidence from emerging Asian economies," Pacific-Basin Finance Journal, Elsevier, vol. 51(C), pages 65-74.
    6. Zeileis, Achim & Leisch, Friedrich & Hornik, Kurt & Kleiber, Christian, 2002. "strucchange: An R Package for Testing for Structural Change in Linear Regression Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 7(i02).
    7. Alsalman, Zeina, 2016. "Oil price uncertainty and the U.S. stock market analysis based on a GARCH-in-mean VAR model," Energy Economics, Elsevier, vol. 59(C), pages 251-260.
    8. Hashmat Khan & Santosh Upadhayaya, 2020. "Does business confidence matter for investment?," Empirical Economics, Springer, vol. 59(4), pages 1633-1665, October.
    9. Dergiades, Theologos, 2012. "Do investors’ sentiment dynamics affect stock returns? Evidence from the US economy," Economics Letters, Elsevier, vol. 116(3), pages 404-407.
    10. Zeileis, Achim & Kleiber, Christian & Kramer, Walter & Hornik, Kurt, 2003. "Testing and dating of structural changes in practice," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 109-123, October.
    11. Simon N. Wood, 2011. "Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 3-36, January.
    12. Nicholas Bloom, 2009. "The Impact of Uncertainty Shocks," Econometrica, Econometric Society, vol. 77(3), pages 623-685, May.
    13. Husted, Lucas & Rogers, John & Sun, Bo, 2020. "Monetary policy uncertainty," Journal of Monetary Economics, Elsevier, vol. 115(C), pages 20-36.
    14. Zivot, Eric & Andrews, Donald W K, 2002. "Further Evidence on the Great Crash, the Oil-Price Shock, and the Unit-Root Hypothesis," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 25-44, January.
    15. Jordi Galí & Luca Gambetti, 2015. "The Effects of Monetary Policy on Stock Market Bubbles: Some Evidence," American Economic Journal: Macroeconomics, American Economic Association, vol. 7(1), pages 233-257, January.
    16. John Y. Campbell & John Cochrane, 1999. "Force of Habit: A Consumption-Based Explanation of Aggregate Stock Market Behavior," Journal of Political Economy, University of Chicago Press, vol. 107(2), pages 205-251, April.
    17. Claudio Borio & Leonardo Gambacorta & Boris Hofmann, 2017. "The influence of monetary policy on bank profitability," International Finance, Wiley Blackwell, vol. 20(1), pages 48-63, March.
    18. Bekaert, Geert & Hoerova, Marie, 2014. "The VIX, the variance premium and stock market volatility," Journal of Econometrics, Elsevier, vol. 183(2), pages 181-192.
    19. Harvey, Andrew C & Shephard, Neil, 1996. "Estimation of an Asymmetric Stochastic Volatility Model for Asset Returns," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(4), pages 429-434, October.
    20. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    21. Kastner, Gregor, 2016. "Dealing with Stochastic Volatility in Time Series Using the R Package stochvol," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 69(i05).
    22. Haydar Demirhan, 2020. "dLagM: An R package for distributed lag models and ARDL bounds testing," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-23, February.
    23. El Hedi Arouri, Mohamed & Jouini, Jamel & Nguyen, Duc Khuong, 2011. "Volatility spillovers between oil prices and stock sector returns: Implications for portfolio management," Journal of International Money and Finance, Elsevier, vol. 30(7), pages 1387-1405.
    24. M. Hashem Pesaran & Yongcheol Shin & Richard J. Smith, 2001. "Bounds testing approaches to the analysis of level relationships," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(3), pages 289-326.
    25. Haas Markus & Liu Ji-Chun, 2018. "A multivariate regime-switching GARCH model with an application to global stock market and real estate equity returns," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(3), pages 1-27, June.
    26. 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.
    27. Markus Haas, 2004. "A New Approach to Markov-Switching GARCH Models," Journal of Financial Econometrics, Oxford University Press, vol. 2(4), pages 493-530.
    28. Simon N. Wood, 2004. "Stable and Efficient Multiple Smoothing Parameter Estimation for Generalized Additive Models," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 673-686, January.
    29. Shiu‐Sheng Chen, 2007. "Does Monetary Policy Have Asymmetric Effects on Stock Returns?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(2‐3), pages 667-688, March.
    30. Cars Hommes & Joep Sonnemans & Jan Tuinstra & Henk van de Velden, 2005. "Coordination of Expectations in Asset Pricing Experiments," The Review of Financial Studies, Society for Financial Studies, vol. 18(3), pages 955-980.
    31. Gasparrini, Antonio, 2011. "Distributed Lag Linear and Non-Linear Models in R: The Package dlnm," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 43(i08).
    32. K. Peren Arin & Abdullah Mamun & Nanda Purushothman, 2009. "The effects of tax policy on financial markets: G3 evidence," Review of Financial Economics, John Wiley & Sons, vol. 18(1), pages 33-46, January.
    33. Jushan Bai, 1997. "Estimation Of A Change Point In Multiple Regression Models," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 551-563, November.
    34. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    35. Ardia, David & Bluteau, Keven & Boudt, Kris & Catania, Leopoldo, 2018. "Forecasting risk with Markov-switching GARCH models:A large-scale performance study," International Journal of Forecasting, Elsevier, vol. 34(4), pages 733-747.
    36. M. Angeles Carnero, 2004. "Persistence and Kurtosis in GARCH and Stochastic Volatility Models," Journal of Financial Econometrics, Oxford University Press, vol. 2(2), pages 319-342.
    37. Cedric L. Mbanga & Ali F. Darrat, 2016. "Fiscal policy and the US stock market," Review of Quantitative Finance and Accounting, Springer, vol. 47(4), pages 987-1002, November.
    38. Darrat, Ali F., 1990. "Stock Returns, Money, and Fiscal Deficits," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 25(3), pages 387-398, September.
    39. Antonio Gasparrini & Fabian Scheipl & Ben Armstrong & Michael G. Kenward, 2017. "A penalized framework for distributed lag non-linear models," Biometrics, The International Biometric Society, vol. 73(3), pages 938-948, September.
    40. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
    41. Viola Obermeier & Fabian Scheipl & Christian Heumann & Joachim Wassermann & Helmut Küchenhoff, 2015. "Flexible distributed lags for modelling earthquake data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 64(2), pages 395-412, February.
    Full references (including those not matched with items on IDEAS)

    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. Ashok Chanabasangouda Patil & Shailesh Rastogi, 2020. "Multifractal Analysis of Market Efficiency across Structural Breaks: Implications for the Adaptive Market Hypothesis," JRFM, MDPI, vol. 13(10), pages 1-18, October.
    2. DIMA, Bogdan & DIMA, Ştefana Maria & IOAN, Roxana, 2021. "Remarks on the behaviour of financial market efficiency during the COVID-19 pandemic. The case of VIX," Finance Research Letters, Elsevier, vol. 43(C).
    3. Robert A Connolly & David Dubofsky & Chris Stivers, 2021. "Economic-State Variation in Uncertainty-Yield Dynamics [Do macro variables, asset markets, or surveys forecast inflation better?]," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 11(1), pages 60-104.
    4. Chuliá, Helena & Guillén, Montserrat & Uribe, Jorge M., 2017. "Measuring uncertainty in the stock market," International Review of Economics & Finance, Elsevier, vol. 48(C), pages 18-33.
    5. Jeng-Bau Lin & Chin-Chia Liang & Wei Tsai, 2019. "Nonlinear Relationships between Oil Prices and Implied Volatilities: Providing More Valuable Information," Sustainability, MDPI, vol. 11(14), pages 1-15, July.
    6. Nikolay Gospodinov & Ibrahim Jamali, 2018. "Monetary policy uncertainty, positions of traders and changes in commodity futures prices," European Financial Management, European Financial Management Association, vol. 24(2), pages 239-260, March.
    7. Bai, Shuming & Mollick, Andre Varella, 2010. "Currency crisis and the forward discount bias: Evidence from emerging economies under breaks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 20(5), pages 556-574, December.
    8. Ana González-Urteaga & Belén Nieto & Gonzalo Rubio, 2022. "Spillover dynamics effects between risk-neutral equity and Treasury volatilities," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 13(4), pages 663-708, December.
    9. Dima, Bogdan & Dima, Ştefana Maria, 2017. "Mutual information and persistence in the stochastic volatility of market returns: An emergent market example," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 36-59.
    10. Lee, Seohyun, 2017. "Three essays on uncertainty: real and financial effects of uncertainty shocks," MPRA Paper 83617, University Library of Munich, Germany.
    11. Lakdawala, Aeimit, 2021. "The growing impact of US monetary policy on emerging financial markets: Evidence from India," Journal of International Money and Finance, Elsevier, vol. 119(C).
    12. Qadan, Mahmoud & Idilbi-Bayaa, Yasmeen, 2020. "Risk appetite and oil prices," Energy Economics, Elsevier, vol. 85(C).
    13. Jeng-Bau Lin & Wei Tsai, 2019. "The Relations of Oil Price Change with Fear Gauges in Global Political and Economic Environment," Energies, MDPI, vol. 12(15), pages 1-17, August.
    14. Umar, Muhammad & Su, Chi-Wei & Rizvi, Syed Kumail Abbas & Lobonţ, Oana-Ramona, 2021. "Driven by fundamentals or exploded by emotions: Detecting bubbles in oil prices," Energy, Elsevier, vol. 231(C).
    15. Kanjilal, Kakali & Ghosh, Sajal, 2013. "Environmental Kuznet’s curve for India: Evidence from tests for cointegration with unknown structuralbreaks," Energy Policy, Elsevier, vol. 56(C), pages 509-515.
    16. Felix Pretis & Michael Mann & Robert Kaufmann, 2015. "Testing competing models of the temperature hiatus: assessing the effects of conditioning variables and temporal uncertainties through sample-wide break detection," Climatic Change, Springer, vol. 131(4), pages 705-718, August.
    17. Martin T. Bohl & Alexander Pütz & Pierre L. Siklos & Christoph Sulewski, 2018. "Information Transmission under Increasing Political Tension – Evidence for the Berlin Produce Exchange 1887-1896," CQE Working Papers 7618, Center for Quantitative Economics (CQE), University of Muenster.
    18. Geert Bekaert & Eric C. Engstrom & Nancy R. Xu, 2022. "The Time Variation in Risk Appetite and Uncertainty," Management Science, INFORMS, vol. 68(6), pages 3975-4004, June.
    19. Dakpogan, Arnaud & Smit, Eon, 2018. "The effect of electricity losses on GDP in Benin," MPRA Paper 89545, University Library of Munich, Germany.
    20. Niko Hauzenberger & Maximilian Bock & Michael Pfarrhofer & Anna Stelzer & Gregor Zens, 2018. "Implications of macroeconomic volatility in the Euro area," Papers 1801.02925, arXiv.org, revised Jun 2018.

    More about this item

    Keywords

    Economic policy uncertainty; VIX index; Stochastic volatility; Distributed lag non-linear models; Fed’s monetary policy;
    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
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G20 - Financial Economics - - Financial Institutions and Services - - - General

    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:kap:empiri:v:51:y:2024:i:3:d:10.1007_s10663-024-09618-y. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.