IDEAS home Printed from https://ideas.repec.org/p/rim/rimwps/23-16.html
   My bibliography  Save this paper

Long-Term Volatility Shapes the Stock Market’s Sensitivity to News

Author

Listed:
  • Christian Conrad

    (Heidelberg University, Department of Economics, Germany; KOF Swiss Economic Institute, Switzerland; Heidelberg Karlsruhe Strategic Partnership, Heidelberg University, Karlsruhe Institute of Technology, Germany; Rimini Centre for Economic Analysis)

  • Julius Theodor Schoelkopf

    (Heidelberg University, Department of Economics, Germany)

  • Nikoleta Tushteva

    (European Central Bank)

Abstract

We show that the S&P 500’s instantaneous response to surprises in U.S. macroeconomic announcements depends on the level of long-term stock market volatility. When long-term volatility is high, stock returns are more sensitive to news, and there is a pronounced asymmetry in the response to good and bad news. We explain this by combining the Campbell-Shiller log-linear present value framework with a two-component volatility model for the conditional variance of cash flow news and allowing for volatility feedback. In our model, innovations to the long-term volatility component are the most important driver of discount rate news. Large announcement surprises lead to upward revisions in future required returns, which dampens/amplifies the effect of good/bad news.

Suggested Citation

  • Christian Conrad & Julius Theodor Schoelkopf & Nikoleta Tushteva, 2023. "Long-Term Volatility Shapes the Stock Market’s Sensitivity to News," Working Paper series 23-16, Rimini Centre for Economic Analysis.
    • Handle: RePEc:rim:rimwps:23-16
    as

    Download full text from publisher

    File URL: http://rcea.org/RePEc/pdf/wp23-16.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Campbell, Sean D. & Diebold, Francis X., 2009. "Stock Returns and Expected Business Conditions: Half a Century of Direct Evidence," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(2), pages 266-278.
    2. Refet S. Gürkaynak & Burçin Kisacikoğlu & Jonathan H. Wright, 2020. "Missing Events in Event Studies: Identifying the Effects of Partially Measured News Surprises," American Economic Review, American Economic Association, vol. 110(12), pages 3871-3912, December.
    3. Christian Conrad & Onno Kleen, 2020. "Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(1), pages 19-45, January.
    4. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Clara Vega, 2003. "Micro Effects of Macro Announcements: Real-Time Price Discovery in Foreign Exchange," American Economic Review, American Economic Association, vol. 93(1), pages 38-62, March.
    5. Refet S. Gürkaynak & Jonathan H. Wright, 2013. "Identification and Inference Using Event Studies," Manchester School, University of Manchester, vol. 81, pages 48-65, September.
    6. Christian Conrad & Karin Loch, 2015. "Anticipating Long‐Term Stock Market Volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(7), pages 1090-1114, November.
    7. Ravi Bansal & Amir Yaron, 2004. "Risks for the Long Run: A Potential Resolution of Asset Pricing Puzzles," Journal of Finance, American Finance Association, vol. 59(4), pages 1481-1509, August.
    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. repec:awi:wpaper:0739 is not listed on IDEAS
    2. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2013. "Financial Risk Measurement for Financial Risk Management," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, volume 2, chapter 0, pages 1127-1220, Elsevier.
    3. repec:awi:wpaper:0655 is not listed on IDEAS
    4. Turan G. Bali & Hao Zhou, 2011. "Risk, uncertainty, and expected returns," Finance and Economics Discussion Series 2011-45, Board of Governors of the Federal Reserve System (U.S.).
    5. Tim Bollerslev & Sophia Zhengzi Li & Viktor Todorov, 2014. "Roughing up Beta: Continuous vs. Discontinuous Betas, and the Cross-Section of Expected Stock Returns," CREATES Research Papers 2014-48, Department of Economics and Business Economics, Aarhus University.
    6. Marcello Pericoli & Giovanni Veronese, 2015. "Forecaster heterogeneity, surprises and financial markets," Temi di discussione (Economic working papers) 1020, Bank of Italy, Economic Research and International Relations Area.
    7. Bertelsen, Kristoffer Pons & Borup, Daniel & Jakobsen, Johan Stax, 2021. "Stock market volatility and public information flow: A non-linear perspective," Economics Letters, Elsevier, vol. 204(C).
    8. Alena Audzeyeva & Xu Wang, 2023. "Fundamentals, real-time uncertainty and CDS index spreads," Review of Quantitative Finance and Accounting, Springer, vol. 61(1), pages 1-33, July.
    9. Kerssenfischer, Mark & Schmeling, Maik, 2022. "What moves markets?," Discussion Papers 16/2022, Deutsche Bundesbank.
    10. Caruso, Alberto, 2019. "Macroeconomic news and market reaction: Surprise indexes meet nowcasting," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1725-1734.
    11. Fatum, Rasmus & Hattori, Takahiro & Yamamoto, Yohei, 2023. "Reserves and risk: Evidence from China," Journal of International Money and Finance, Elsevier, vol. 134(C).
    12. Pavel Solís, 2023. "Does the Exchange Rate Respond to Monetary Policy in Mexico? Solving an Exchange Rate Puzzle in Emerging Markets," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 55(8), pages 2093-2113, December.
    13. Alain Hecq & Marie Ternes & Ines Wilms, 2023. "Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions," Papers 2301.10592, arXiv.org.
    14. Eriksen, Jonas N., 2017. "Expected Business Conditions and Bond Risk Premia," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(4), pages 1667-1703, August.
    15. Liu, Yang, 2023. "Government debt and risk premia," Journal of Monetary Economics, Elsevier, vol. 136(C), pages 18-34.
    16. Linda S. Goldberg & Dr. Christian Grisse, 2013. "Time variation in asset price responses to macro announcements," Working Papers 2013-11, Swiss National Bank.
    17. Mikael C. Bergbrant & Patrick J. Kelly, 2016. "Macroeconomic Expectations and the Size, Value, and Momentum Factors," Financial Management, Financial Management Association International, vol. 45(4), pages 809-844, December.
    18. Nonejad, Nima, 2017. "Forecasting aggregate stock market volatility using financial and macroeconomic predictors: Which models forecast best, when and why?," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 131-154.
    19. Plakandaras, Vasilios & Gupta, Rangan & Balcilar, Mehmet & Ji, Qiang, 2022. "Evolving United States stock market volatility: The role of conventional and unconventional monetary policies," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    20. Virk, Nader & Javed, Farrukh & Awartani, Basel, 2021. "A reality check on the GARCH-MIDAS volatility models," Working Papers 2021:2, Örebro University, School of Business.
    21. Christoph E. Boehm & T. Niklas Kroner, 2020. "The US, Economic News, and the Global Financial Cycle," Working Papers 677, Research Seminar in International Economics, University of Michigan.
    22. Shaliastovich, Ivan, 2015. "Learning, confidence, and option prices," Journal of Econometrics, Elsevier, vol. 187(1), pages 18-42.

    More about this item

    Keywords

    event study; long- and short-term volatility; macroeconomic announcements; stock market response; time-varying risk premia; volatility feedback effect;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

    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:rim:rimwps:23-16. 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: Marco Savioli (email available below). General contact details of provider: https://edirc.repec.org/data/rcfeait.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.