IDEAS home Printed from https://ideas.repec.org/a/wly/jfutmk/v25y2005i9p873-892.html
   My bibliography  Save this article

A comparative study of alternative extreme‐value volatility estimators

Author

Listed:
  • Turan G. Bali
  • David Weinbaum

Abstract

Recent advances in econometric methodology and newly available sources of data are used to examine empirically the performance of the various extreme‐value volatility estimators that have been proposed over the past two decades. Overwhelming support is found for the use of extreme‐value estimators when computing daily volatility measures across all assets: Daily extreme‐value volatility estimators are both less biased and substantially more efficient than the traditional close‐to‐close estimator. In the case of weekly and monthly measures, the results still suggest that extreme‐value estimators are appropriate, but the evidence is more mixed. © 2005 Wiley Periodicals, Inc. Jrl Fut Mark 25:873–892, 2005

Suggested Citation

  • Turan G. Bali & David Weinbaum, 2005. "A comparative study of alternative extreme‐value volatility estimators," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 25(9), pages 873-892, September.
    • Handle: RePEc:wly:jfutmk:v:25:y:2005:i:9:p:873-892
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Ozgur (Ozzy) Akay & Mark D. Griffiths & Drew B. Winters, 2010. "On The Robustness Of Range‐Based Volatility Estimators," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 33(2), pages 179-199, June.
    2. Agarwal, Vikas & Arisoy, Y. Eser & Naik, Narayan Y., 2017. "Volatility of aggregate volatility and hedge fund returns," Journal of Financial Economics, Elsevier, vol. 125(3), pages 491-510.
    3. Arnerić, Josip & Matković, Mario & Sorić, Petar, 2019. "Comparison of range-based volatility estimators against integrated volatility in European emerging markets," Finance Research Letters, Elsevier, vol. 28(C), pages 118-124.
    4. Lyócsa, Štefan & Todorova, Neda & Výrost, Tomáš, 2021. "Predicting risk in energy markets: Low-frequency data still matter," Applied Energy, Elsevier, vol. 282(PA).
    5. Korkusuz, Burak & Kambouroudis, Dimos & McMillan, David G., 2023. "Do extreme range estimators improve realized volatility forecasts? Evidence from G7 Stock Markets," Finance Research Letters, Elsevier, vol. 55(PB).
    6. Ganneval, S., 2016. "Spatial price transmission on agricultural commodity markets under different volatility regimes," Economic Modelling, Elsevier, vol. 52(PA), pages 173-185.
    7. Molnár, Peter, 2012. "Properties of range-based volatility estimators," International Review of Financial Analysis, Elsevier, vol. 23(C), pages 20-29.
    8. Mazza, Paolo & Petitjean, Mikael, 2016. "On the usefulness of intraday price ranges to gauge liquidity in cap-based portfolios," Economic Modelling, Elsevier, vol. 54(C), pages 67-81.
    9. Dar-Hsin Chen & Chun-Da Chen & Su-Chen Wu, 2014. "VaR and the cross-section of expected stock returns: an emerging market evidence," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 15(3), pages 441-459, June.
    10. Neda Todorova, 2012. "Volatility estimators based on daily price ranges versus the realized range," Applied Financial Economics, Taylor & Francis Journals, vol. 22(3), pages 215-229, February.
    11. Hiroyuki Kawakatsu, 2021. "Information in daily data volatility measurements," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 1642-1656, April.
    12. I‐Ming Jiang & Jui‐Cheng Hung & Chuan‐San Wang, 2014. "Volatility Forecasts: Do Volatility Estimators and Evaluation Methods Matter?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 34(11), pages 1077-1094, November.
    13. Tan, Shay-Kee & Ng, Kok-Haur & Chan, Jennifer So-Kuen & Mohamed, Ibrahim, 2019. "Quantile range-based volatility measure for modelling and forecasting volatility using high frequency data," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 537-551.
    14. Saad Mouti, 2023. "Rough volatility: evidence from range volatility estimators," Papers 2312.01426, arXiv.org.
    15. Dilip Kumar, 2018. "Modeling and Forecasting Unbiased Extreme Value Volatility Estimator in Presence of Leverage Effect," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 16(2), pages 313-335, June.

    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:wly:jfutmk:v:25:y:2005:i:9:p:873-892. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/0270-7314/ .

    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.