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Sektorowe zróżnicowanie efektu interwału akcji spółek z GPW w dobie pandemii COVID-19

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  • Bartłomiej Lisicki

Abstract

Głównym celem niniejszego opracowania jest weryfikacja występowania zróżnicowania efektu interwału współczynnika beta (β) akcji spółek notowanych na GPW w Warszawie (GPW) w trakcie trwania pandemii COVID-19 ze względu na ich przynależność makrosektorową. Współczynniki β obliczono przy zastosowaniu klasycznej metody najmniejszych kwadratów na próbie emitentów zgrupowanych w indeksach: WIG20, mWIG40 oraz sWIG80. Analizując wartości β szacowane na podstawie wskazanych horyzontów czasowych stóp zwrotu w latach pandemii COVID-19, można dostrzec występowanie efektu interwału β w przypadku spółek z makrosektorów finanse oraz produkcja przemysłowa i budowlano-montażowa. Co ciekawe, branże te w latach przedpandemicznych nie wykazywały istotnych statystycznie różnic między wartościami β. Efekt interwału w latach poprzedzających pandemię COVID-19 odnotowano z kolei w przypadku spółek z makrosektorów ochrona zdrowia oraz handel i usługi. Bazując na uzyskanych rezultatach badawczych, zaobserwować można wpływ pandemii COVID-19 na sektorowe zróżnicowanie efektu interwału. W latach jej trwania istotność statystyczna różnic w oszacowaniach współczynników β dotyczyła spółek z innych makrosektorów niż w latach ją poprzedzających.

Suggested Citation

  • Bartłomiej Lisicki, 2023. "Sektorowe zróżnicowanie efektu interwału akcji spółek z GPW w dobie pandemii COVID-19," Ekonomista, Polskie Towarzystwo Ekonomiczne, issue 2, pages 174-194.
    • Handle: RePEc:aoq:ekonom:y:2023:i:2:p:174-194
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    References listed on IDEAS

    as
    1. Serdar Neslihanoglu, 2021. "Linearity extensions of the market model: a case of the top 10 cryptocurrency prices during the pre-COVID-19 and COVID-19 periods," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-27, December.
    2. Rizvi, Syed Aun R. & Arshad, Shaista, 2018. "Understanding time-varying systematic risks in Islamic and conventional sectoral indices," Economic Modelling, Elsevier, vol. 70(C), pages 561-570.
    3. R.W. Faff & R.D. Brooks, 1998. "Time‐varying Beta Risk for Australian Industry Portfolios: An Exploratory Analysis," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 25(5‐6), pages 721-745, June.
    4. William F. Sharpe, 1963. "A Simplified Model for Portfolio Analysis," Management Science, INFORMS, vol. 9(2), pages 277-293, January.
    5. Handa, Puneet & Kothari, S. P. & Wasley, Charles, 1989. "The relation between the return interval and betas : Implications for the size effect," Journal of Financial Economics, Elsevier, vol. 23(1), pages 79-100, June.
    6. Corhay, Albert, 1992. "The intervalling effect bias in beta: A note," Journal of Banking & Finance, Elsevier, vol. 16(1), pages 61-73, February.
    7. Robert D. Brooks & Robert W. Faff & Michael D. McKenzie, 1998. "Time†Varying Beta Risk of Australian Industry Portfolios: A Comparison of Modelling Techniques," Australian Journal of Management, Australian School of Business, vol. 23(1), pages 1-22, June.
    8. Joakim Westerlund & Paresh Narayan, 2015. "Testing for Predictability in Conditionally Heteroskedastic Stock Returns," Journal of Financial Econometrics, Oxford University Press, vol. 13(2), pages 342-375.
    9. Stefano Ramelli & Alexander F Wagner, 2020. "Feverish Stock Price Reactions to COVID-19," The Review of Corporate Finance Studies, Society for Financial Studies, vol. 9(3), pages 622-655.
    10. Janusz Brzeszczyński & Jerzy Gajdka & Tomasz Schabek, 2011. "The Role of Stock Size and Trading Intensity in the Magnitude of the "Interval Effect" in Beta Estimation: Empirical Evidence from the Polish Capital Market," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 47(1), pages 28-49, January.
    11. R.W. Faff & R.D. Brooks, 1998. "Time‐varying Beta Risk for Australian Industry Portfolios: An Exploratory Analysis," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 25(5‐6), pages 721-745, June.
    12. Blume, Marshall E, 1975. "Betas and Their Regression Tendencies," Journal of Finance, American Finance Association, vol. 30(3), pages 785-795, June.
    13. Cakici, Nusret & Zaremba, Adam, 2021. "Who should be afraid of infections? Pandemic exposure and the cross-section of stock returns," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 72(C).
    14. Zhang, Dayong & Hu, Min & Ji, Qiang, 2020. "Financial markets under the global pandemic of COVID-19," Finance Research Letters, Elsevier, vol. 36(C).
    15. Choudhry, Taufiq & Lu, Lin & Peng, Ke, 2010. "Time-varying beta and the Asian financial crisis: Evidence from the Asian industrial sectors," Japan and the World Economy, Elsevier, vol. 22(4), pages 228-234, December.
    16. Hawawini, Gabriel, 1983. "Why beta shifts as the return interval changes," MPRA Paper 44893, University Library of Munich, Germany.
    17. Omair Haroon & Mohsin Ali & Abdullah Khan & Mudeer A. Khattak & Syed Aun R. Rizvi, 2021. "Financial Market Risks during the COVID-19 Pandemic," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 57(8), pages 2407-2414, June.
    18. A. Craig MacKinlay, 1997. "Event Studies in Economics and Finance," Journal of Economic Literature, American Economic Association, vol. 35(1), pages 13-39, March.
    19. Brailsford, Timothy J. & Josev, Thomas, 1997. "The impact of the return interval on the estimation of systematic risk," Pacific-Basin Finance Journal, Elsevier, vol. 5(3), pages 357-376, July.
    20. Dragos Stefan Oprea, 2015. "The Interval Effect in Estimating Beta: Empirical Evidence from the Romanian Stock Market," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 7(2), pages 016-025, December.
    21. Dimitrios Dadakas & Christos Karpetis & Athanasios Fassas & Erotokritos Varelas, 2016. "Sectoral Differences in the Choice of the Time Horizon during Estimation of the Unconditional Stock Beta," IJFS, MDPI, vol. 4(4), pages 1-13, December.
    22. Kang Hua Cao & Chi-Keung Woo & Ya Li & Yun Liu, 2022. "Covid-19’s effect on the alpha and beta of a US stock Exchange Traded Fund," Applied Economics Letters, Taylor & Francis Journals, vol. 29(2), pages 123-128, January.
    23. Francisco López Herrera & Jaime González Maiz Jiménez & Adán Reyes Santiago, 2022. "Forecasting Performance of Different Betas: Mexican Stocks before and during the COVID-19 Pandemic," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 58(13), pages 3868-3880, October.
    24. Pogue, Gerald A. & Solnik, Bruno H., 1974. "The Market Model Applied to European Common Stocks: Some Empirical Results," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 9(6), pages 917-944, December.
    25. repec:eme:mfppss:mf-05-2021-0226 is not listed on IDEAS
    26. Jacobsen, Ben & Dannenburg, Dennis, 2003. "Volatility clustering in monthly stock returns," Journal of Empirical Finance, Elsevier, vol. 10(4), pages 479-503, September.
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    More about this item

    Keywords

    akcje; COVID-19; współczynnik beta; GPW w Warszawie; efekt interwału;
    All these keywords.

    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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