IDEAS home Printed from https://ideas.repec.org/a/spr/jecfin/v29y2005i3p300-312.html
   My bibliography  Save this article

What is the source of different levels of time-series return volatility? the intraday U-shaped pattern or time-series persistence

Author

Listed:
  • Michael Hughes
  • Drew Winters
  • Jerry Rawls

Abstract

We use the NYSE industrial index, the NYSE utility index, and the NASDAQ industrial index to examine the relationship between short-run and long-run volatility. We establish that the NASDAQ index has substantially more daily volatility than the NYSE indices. The initial examination shows that the individual U-shaped intraday patterns of the two NYSE indices are roughly similar in both position and shape, while we find that NASDAQ U-shaped pattern is distinctively different in both position and shape. However, after controlling for conditional volatility in a GARCH model, the U-shaped intraday volatility patterns of all three indices are similar. Copyright Springer 2005

Suggested Citation

  • Michael Hughes & Drew Winters & Jerry Rawls, 2005. "What is the source of different levels of time-series return volatility? the intraday U-shaped pattern or time-series persistence," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 29(3), pages 300-312, September.
    • Handle: RePEc:spr:jecfin:v:29:y:2005:i:3:p:300-312
    DOI: 10.1007/BF02761576
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/BF02761576
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/BF02761576?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. Bollerslev, Tim & Ole Mikkelsen, Hans, 1996. "Modeling and pricing long memory in stock market volatility," Journal of Econometrics, Elsevier, vol. 73(1), pages 151-184, July.
    2. Ito, Takatoshi & Lin, Wen-Ling, 1992. "Lunch break and intraday volatility of stock returns : An hourly data analysis of Tokyo and New York stock markets," Economics Letters, Elsevier, vol. 39(1), pages 85-90, May.
    3. Chou, Ray Yeutien, 1988. "Volatility Persistence and Stock Valuations: Some Empirical Evidence Using Garch," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 3(4), pages 279-294, October-D.
    4. Lockwood, Larry J & Linn, Scott C, 1990. "An Examination of Stock Market Return Volatility during Overnight and Intraday Periods, 1964-1989," Journal of Finance, American Finance Association, vol. 45(2), pages 591-601, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jang Hyung Cho & Robert T. Daigler, 2012. "An unbiased autoregressive conditional intraday seasonal variance filtering process," Quantitative Finance, Taylor & Francis Journals, vol. 12(2), pages 231-247, October.

    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. Christensen, Bent Jesper & Nielsen, Morten Ørregaard & Zhu, Jie, 2010. "Long memory in stock market volatility and the volatility-in-mean effect: The FIEGARCH-M Model," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 460-470, June.
    2. David McMillan & Alan Speight, 2006. "Heterogeneous information flows and intra-day volatility dynamics: evidence from the UK FTSE-100 stock index futures market," Applied Financial Economics, Taylor & Francis Journals, vol. 16(13), pages 959-972.
    3. Xiufeng Yan, 2021. "Autoregressive conditional duration modelling of high frequency data," Papers 2111.02300, arXiv.org.
    4. Nikolaos A. Kyriazis, 2021. "A Survey on Volatility Fluctuations in the Decentralized Cryptocurrency Financial Assets," JRFM, MDPI, vol. 14(7), pages 1-46, June.
    5. Andersen, Torben G. & Bollerslev, Tim & Cai, Jun, 2000. "Intraday and interday volatility in the Japanese stock market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 10(2), pages 107-130, June.
    6. En-Der Su & Feng-Jeng Lin, 2012. "Two-State Volatility Transition Pricing and Hedging of TXO Options," Computational Economics, Springer;Society for Computational Economics, vol. 39(3), pages 259-287, March.
    7. So, Mike K.P. & Yu, Philip L.H., 2006. "Empirical analysis of GARCH models in value at risk estimation," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 16(2), pages 180-197, April.
    8. Amanjot Singh, 2018. "A Note on Conditional Variance and Decaying Rate: Chinese Equity Market," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 16(2), pages 595-611, June.
    9. Jie Zhu, 2008. "FIEGARCH-M and and International Crises: A Cross-Country Analysis," CREATES Research Papers 2008-16, Department of Economics and Business Economics, Aarhus University.
    10. David G. McMillan & Alan E. H. Speight, 2006. "Volatility dynamics and heterogeneous markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 11(2), pages 115-121.
    11. Milton Abdul Thorlie & Lixin Song & Muhammad Amin & Xiaoguang Wang, 2015. "Modeling and forecasting of stock index volatility with APARCH models under ordered restriction," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 69(3), pages 329-356, August.
    12. Antonio Rubia & Trino-Manuel Ñíguez, 2006. "Forecasting the conditional covariance matrix of a portfolio under long-run temporal dependence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(6), pages 439-458.
    13. Asai Manabu & So Mike K.P., 2015. "Long Memory and Asymmetry for Matrix-Exponential Dynamic Correlation Processes," Journal of Time Series Econometrics, De Gruyter, vol. 7(1), pages 1-26, January.
    14. Daiki Maki, 2015. "Wild bootstrap tests for unit root in ESTAR models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(3), pages 475-490, September.
    15. Ngene, Geoffrey & Tah, Kenneth A. & Darrat, Ali F., 2017. "Long memory or structural breaks: Some evidence for African stock markets," Review of Financial Economics, Elsevier, vol. 34(C), pages 61-73.
    16. Gur Huberman & Dominika Halka, 2001. "Systematic Liquidity," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 24(2), pages 161-178, June.
    17. Athanasia Gavala & Nikolay Gospodinov & Deming Jiang, 2006. "Forecasting volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(6), pages 381-400.
    18. Mohamed Es-Sanoun & Jude Gohou & Mounir Benboubker, 2023. "Testing of Herd Behavior In african Stock Markets During COVID-19 Pandemic [Essai de vérification du comportement mimétique dans les marchés boursiers africains au cours de la crise de covid-19]," Post-Print hal-04144289, HAL.
    19. Sabur Mollah & Asma Mobarek, 2009. "Market volatility across countries – evidence from international markets," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 26(4), pages 257-274, October.
    20. Chiang, Thomas C., 2019. "Empirical analysis of intertemporal relations between downside risks and expected returns—Evidence from Asian markets," Research in International Business and Finance, Elsevier, vol. 47(C), pages 264-278.

    More about this item

    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:spr:jecfin:v:29:y:2005:i:3:p:300-312. 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.