IDEAS home Printed from https://ideas.repec.org/a/wut/journl/v2y2006p5-30.html
   My bibliography  Save this article

The impact of institutional investors on risk and stock return autocorrelations in the context of the Polish pension reform

Author

Listed:
  • Henryk Gurgul
  • Paweł Majdosz

Abstract

The main aim of this paper is to examine the relationship between the increasing share of institutional investors resulting from the pension reform in Poland and stock return autocorrelation as well as risk level on the Warsaw Stock Exchange. The problem under consideration is investigated by applying the M–GARCH model for the individual stocks included in the investment portfolios of the pension funds operating in Poland.

Suggested Citation

  • Henryk Gurgul & Paweł Majdosz, 2006. "The impact of institutional investors on risk and stock return autocorrelations in the context of the Polish pension reform," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 16(2), pages 5-30.
  • Handle: RePEc:wut:journl:v:2:y:2006:p:5-30
    as

    Download full text from publisher

    File URL: https://ord.pwr.edu.pl/assets/papers_archive/53%20-%20published.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sentana, Enrique & Wadhwani, Sushil B, 1992. "Feedback Traders and Stock Return Autocorrelations: Evidence from a Century of Daily Data," Economic Journal, Royal Economic Society, vol. 102(411), pages 415-425, March.
    2. Axel Börsch‐Supan & Florian Heiss & Alexander Ludwig & Joachim Winter, 2003. "Pension Reform, Capital Markets and the Rate of Return," German Economic Review, Verein für Socialpolitik, vol. 4(2), pages 151-181, May.
    3. Holger Bonin & Joan Gil & Concepció Patxot, 2001. "Beyond the Toledo agreement: the intergenerational impact of the Spanish Pension Reform," Spanish Economic Review, Springer;Spanish Economic Association, vol. 3(2), pages 111-130.
    4. Eduardo Walker & Fernando Lefort, 2002. "Pension Reform And Capital Markets: Are There Any (Hard) Links?," Abante, Escuela de Administracion. Pontificia Universidad Católica de Chile., vol. 5(2), pages 77-149.
    5. 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.
    6. Klaus Schmidt-Hebbel, 1998. "Does Pension Reform Really Spur Productivity, Saving, and Growth?," Working Papers Central Bank of Chile 33, Central Bank of Chile.
    7. A.V. Dobronogov & L.D. Mayhew, 2000. "Pension Reform in a Highly Informalized Post-Soviet Economy," Working Papers ir00041, International Institute for Applied Systems Analysis.
    8. LeBaron, Blake, 1992. "Some Relations between Volatility and Serial Correlations in Stock Market Returns," The Journal of Business, University of Chicago Press, vol. 65(2), pages 199-219, April.
    9. Axel Börsch‐Supan & Florian Heiss & Alexander Ludwig & Joachim Winter, 2003. "Pension Reform, Capital Markets and the Rate of Return," German Economic Review, Verein für Socialpolitik, vol. 4(2), pages 151-181, May.
    10. Engle, Robert F & Sheppard, Kevin K, 2001. "Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH," University of California at San Diego, Economics Working Paper Series qt5s2218dp, Department of Economics, UC San Diego.
    11. Queisser, Monika & Bailey, Clive & Woodall, John, 1997. "Reforming pensions in Zambia : an analysis of existing schemes and options for reform," Policy Research Working Paper Series 1716, The World Bank.
    12. Josef Lakonishok & Andrei Shleifer & Robert W. Vishny, 1991. "Do Institutional Investors Destabilize Stock Prices? Evidence on Herding and Feedback Trading," NBER Working Papers 3846, National Bureau of Economic Research, Inc.
    13. Fama, Eugene F, 1991. "Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-1617, December.
    14. Bollerslev, Tim & Engle, Robert F & Wooldridge, Jeffrey M, 1988. "A Capital Asset Pricing Model with Time-Varying Covariances," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 116-131, February.
    15. Barclay, Michael J. & Warner, Jerold B., 1993. "Stealth trading and volatility : Which trades move prices?," Journal of Financial Economics, Elsevier, vol. 34(3), pages 281-305, December.
    16. Michael D. McKenzie & Robert W. Faff, 2003. "The Determinants of Conditional Autocorrelation in Stock Returns," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 26(2), pages 259-274, June.
    17. Gebka, Bartosz & Henke, Harald & Bohl, Martin T., 2006. "Institutional trading and stock return autocorrelation: Empirical evidence on Polish pension fund investors' behavior," Global Finance Journal, Elsevier, vol. 16(3), pages 233-244, March.
    18. Toshiaki Watanabe, 2002. "Margin requirements, positive feedback trading, and stock return autocorrelations: the case of Japan," Applied Financial Economics, Taylor & Francis Journals, vol. 12(6), pages 395-403.
    19. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    20. Wang, Jiang, 1994. "A Model of Competitive Stock Trading Volume," Journal of Political Economy, University of Chicago Press, vol. 102(1), pages 127-168, February.
    21. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    22. Brooks, Robert D. & Faff, Robert W. & McKenzie, Michael D. & Ho, Yew Kee, 2000. "U.S. Banking Sector Risk in an Era of Regulatory Change: A Bivariate GARCH Approach," Review of Quantitative Finance and Accounting, Springer, vol. 14(1), pages 17-43, January.
    23. Tsui, Albert K. & Yu, Qiao, 1999. "Constant conditional correlation in a bivariate GARCH model: evidence from the stock markets of China," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 48(4), pages 503-509.
    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. Degiannakis, Stavros & Xekalaki, Evdokia, 2004. "Autoregressive Conditional Heteroskedasticity (ARCH) Models: A Review," MPRA Paper 80487, University Library of Munich, Germany.
    2. Kusen, Alex & Rudolf, Markus, 2019. "Feedback trading: Strategies during day and night with global interconnectedness," Research in International Business and Finance, Elsevier, vol. 48(C), pages 438-463.
    3. Marshall, Andrew & Maulana, Tubagus & Tang, Leilei, 2009. "The estimation and determinants of emerging market country risk and the dynamic conditional correlation GARCH model," International Review of Financial Analysis, Elsevier, vol. 18(5), pages 250-259, December.
    4. Kin-Yip Ho & Albert K Tsui, 2008. "Volatility Dynamics in Foreign Exchange Rates : Further Evidence from the Malaysian Ringgit and Singapore Dollar," Finance Working Papers 22571, East Asian Bureau of Economic Research.
    5. Giovanni Barone-Adesi & Francesco Audrino, 2006. "Average conditional correlation and tree structures for multivariate GARCH models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(8), pages 579-600.
    6. Chen, Carl R. & Su, Yuli & Huang, Ying, 2008. "Hourly index return autocorrelation and conditional volatility in an EAR-GJR-GARCH model with generalized error distribution," Journal of Empirical Finance, Elsevier, vol. 15(4), pages 789-798, September.
    7. Gebka, Bartosz & Henke, Harald & Bohl, Martin T., 2006. "Institutional trading and stock return autocorrelation: Empirical evidence on Polish pension fund investors' behavior," Global Finance Journal, Elsevier, vol. 16(3), pages 233-244, March.
    8. Hou, Yang & Li, Steven, 2014. "The impact of the CSI 300 stock index futures: Positive feedback trading and autocorrelation of stock returns," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 319-337.
    9. Gębka, Bartosz & Wohar, Mark E., 2013. "The determinants of quantile autocorrelations: Evidence from the UK," International Review of Financial Analysis, Elsevier, vol. 29(C), pages 51-61.
    10. Shah, Anand & Bahri, Anu, 2022. "Metanomics: Adaptive market and volatility behaviour in Metaverse," MPRA Paper 114442, University Library of Munich, Germany.
    11. Kirt Butler & Katsushi Okada, 2007. "Bivariate and higher-order terms in models of international equity returns," Applied Financial Economics, Taylor & Francis Journals, vol. 17(9), pages 725-737.
    12. Kinnunen, Jyri, 2017. "Dynamic cross-autocorrelation in stock returns," Journal of Empirical Finance, Elsevier, vol. 40(C), pages 162-173.
    13. Mr. Marcus Pramor & Ms. Natalia T. Tamirisa, 2006. "Common Volatility Trends in the Central and Eastern European Currencies and the Euro," IMF Working Papers 2006/206, International Monetary Fund.
    14. Jyri Kinnunen & Minna Martikainen, 2017. "Dynamic Autocorrelation and International Portfolio Allocation," Multinational Finance Journal, Multinational Finance Journal, vol. 21(1), pages 21-48, March.
    15. Andrikopoulos, Panagiotis & Cui, Yueting & Gad, Samar & Kallinterakis, Vasileios, 2020. "Feedback trading and the ramadan effect in frontier markets," Research in International Business and Finance, Elsevier, vol. 51(C).
    16. Anders Johansson, 2009. "An analysis of dynamic risk in the Greater China equity markets," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 7(3), pages 299-320.
    17. Berens, Tobias & Weiß, Gregor N.F. & Wied, Dominik, 2015. "Testing for structural breaks in correlations: Does it improve Value-at-Risk forecasting?," Journal of Empirical Finance, Elsevier, vol. 32(C), pages 135-152.
    18. Taufiq Choudhry & Ranadeva Jayasekera, 2015. "Level of efficiency in the UK equity market: empirical study of the effects of the global financial crisis," Review of Quantitative Finance and Accounting, Springer, vol. 44(2), pages 213-242, February.
    19. Sébastien Laurent & Luc Bauwens & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109.
    20. Akhtaruzzaman, Md & Shamsuddin, Abul & Easton, Steve, 2014. "Dynamic correlation analysis of spill-over effects of interest rate risk and return on Australian and US financial firms," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 31(C), pages 378-396.

    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:wut:journl:v:2:y:2006:p:5-30. 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: Adam Kasperski (email available below). General contact details of provider: https://edirc.repec.org/data/iopwrpl.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.