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The Covid-19 Pandemic and the Degree of Persistence of US Stock Prices and Bond Yields

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  • Guglielmo Maria Caporale
  • Luis A. Gil-Alana
  • Carlos Poza

Abstract

This paper analyses the possible effects of the Covid-19 pandemic on the degree of persistence of US monthly stock prices and bond yields using fractional integration techniques. The model is estimated first over the period January 1966-December 2020 and then a recursive approach is taken to examine whether or not persistence has changed during the following pandemic period. We find that the unit root hypothesis cannot be rejected for stock prices while for bond yields the results differ depending on the maturity date and the specification of the error term. In general, bond yields appear to be more persistent, although there is evidence of mean reversion in case of 1-year yields under the assumption of autocorrelated errors. The recursive analysis shows no impact of the Covid-19 pandemic on the persistence of stock prices, whilst there is an increase in the case of both 10- and 1- year bond yields but not of their spread.

Suggested Citation

  • Guglielmo Maria Caporale & Luis A. Gil-Alana & Carlos Poza, 2021. "The Covid-19 Pandemic and the Degree of Persistence of US Stock Prices and Bond Yields," CESifo Working Paper Series 8976, CESifo.
    • Handle: RePEc:ces:ceswps:_8976
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    References listed on IDEAS

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    1. Francis X. Diebold & Monika Piazzesi & Glenn D. Rudebusch, 2005. "Modeling Bond Yields in Finance and Macroeconomics," American Economic Review, American Economic Association, vol. 95(2), pages 415-420, May.
    2. Sowell, Fallaw, 1992. "Maximum likelihood estimation of stationary univariate fractionally integrated time series models," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 165-188.
    3. Abakah, Emmanuel Joel Aikins & Gil-Alana, Luis A., 2022. "Persistence in US Treasury bonds," Finance Research Letters, Elsevier, vol. 45(C).
    4. Salisu, Afees A. & Vo, Xuan Vinh, 2020. "Predicting stock returns in the presence of COVID-19 pandemic: The role of health news," International Review of Financial Analysis, Elsevier, vol. 71(C).
    5. Dräger, Lena & Nguyen, Duc Binh Benno & Prokopczuk, Marcel & Sibbertsen, Philipp, 2020. "The Long Memory of Equity Volatility and the Macroeconomy: International Evidence," Hannover Economic Papers (HEP) dp-667, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    6. John Y. Campbell, 1986. "Bond and Stock Returns in a Simple Exchange Model," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 101(4), pages 785-803.
    7. Chun-Da Chen & Chin-Chun Chen & Wan-Wei Tang & Bor-Yi Huang, 2009. "The positive and negative impacts of the sars outbreak:a case of the Taiwan industries," Journal of Developing Areas, Tennessee State University, College of Business, vol. 43(1), pages 281-293, September.
    8. Adnan Kasman & Erdost Torun, 2007. "Long Memory in the Turkish Stock Market Return and Volatility," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 7(2), pages 13-27.
    9. Guglielmo Maria Caporale & Luis Alberiko Gil-Alana & Miguel Ángel Martin-Valmayor, 2022. "Non-linearities and persistence in US long-run interest rates," Applied Economics Letters, Taylor & Francis Journals, vol. 29(4), pages 366-370, February.
    10. Hiremath, Gourishankar S & Bandi, Kamaiah, 2010. "Long Memory in Stock Market Volatility:Evidence from India," MPRA Paper 48519, University Library of Munich, Germany.
    11. Daniel Štifanić & Jelena Musulin & Adrijana Miočević & Sandi Baressi Šegota & Roman Šubić & Zlatan Car, 2020. "Impact of COVID-19 on Forecasting Stock Prices: An Integration of Stationary Wavelet Transform and Bidirectional Long Short-Term Memory," Complexity, Hindawi, vol. 2020, pages 1-12, July.
    12. Diebold, Francis X. & Li, Canlin, 2006. "Forecasting the term structure of government bond yields," Journal of Econometrics, Elsevier, vol. 130(2), pages 337-364, February.
    13. Caporale, Guglielmo Maria & Gil-Alana, Luis & Plastun, Alex, 2018. "Is market fear persistent? A long-memory analysis," Finance Research Letters, Elsevier, vol. 27(C), pages 140-147.
    14. Hedayati , Amin & Hedayati , Moein & Esfandyari, Morteza, 2016. "Stock market index prediction using artificial neural network," Journal of Economics, Finance and Administrative Science, Universidad ESAN, vol. 21(41), pages 89-93.
    15. Oluwasegun B. Adekoya, 2021. "Persistence and efficiency of OECD stock markets: linear and nonlinear fractional integration approaches," Empirical Economics, Springer, vol. 61(3), pages 1415-1433, September.
    16. Daniel v{S}tifani'c & Jelena Musulin & Adrijana Miov{c}evi'c & Sandi Baressi v{S}egota & Roman v{S}ubi'c & Zlatan Car, 2020. "Impact of COVID-19 on Forecasting Stock Prices: An Integration of Stationary Wavelet Transform and Bidirectional Long Short-Term Memory," Papers 2007.02673, arXiv.org.
    17. Al-Awadhi, Abdullah M. & Alsaifi, Khaled & Al-Awadhi, Ahmad & Alhammadi, Salah, 2020. "Death and contagious infectious diseases: Impact of the COVID-19 virus on stock market returns," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    18. Reisen Valderio A & Cribari-Neto Francisco & Jensen Mark J, 2003. "Long Memory Inflationary Dynamics: The Case of Brazil," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 7(3), pages 1-18, October.
    19. Caporale, Guglielmo Maria & Gil-Alana, Luis A. & Poza, Carlos, 2020. "Persistence, non-linearities and structural breaks in European stock market indices," The Quarterly Review of Economics and Finance, Elsevier, vol. 77(C), pages 50-61.
    20. Peter C. Schotman & Rolf Tschernig & Jan Budek, 2008. "Long Memory and the Term Structure of Risk," Journal of Financial Econometrics, Oxford University Press, vol. 6(4), pages 459-495, Fall.
    21. Granger, Clive W. J., 1992. "Forecasting stock market prices: Lessons for forecasters," International Journal of Forecasting, Elsevier, vol. 8(1), pages 3-13, June.
    22. Alexander F. Wagner, 2020. "What the stock market tells us about the post-COVID-19 world," Nature Human Behaviour, Nature, vol. 4(5), pages 440-440, May.
    23. Martin Martens & Dick van Dijk & Michiel de Pooter, 2004. "Modeling and Forecasting S&P 500 Volatility: Long Memory, Structural Breaks and Nonlinearity," Tinbergen Institute Discussion Papers 04-067/4, Tinbergen Institute.
    24. Mehmet Balcilar & Zeynel Abidin Ozdemir & Esin Cakan, 2015. "Structural Breaks, Long Memory, or Unit Roots in Stock Prices: Evidence from Emerging Markets," International Econometric Review (IER), Econometric Research Association, vol. 7(1), pages 13-33, April.
    25. Mustafa Demirel & Gazanfer Unal, 2020. "Applying multivariate-fractionally integrated volatility analysis on emerging market bond portfolios," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-29, December.
    26. Monika Piazzesi, 2005. "Bond Yields and the Federal Reserve," Journal of Political Economy, University of Chicago Press, vol. 113(2), pages 311-344, April.
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    More about this item

    Keywords

    stock market prices; US bonds; persistence; fractional integration; Covid-19;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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