IDEAS home Printed from https://ideas.repec.org/a/bla/jfnres/v30y2007i2p201-215.html
   My bibliography  Save this article

Forecasting Stock Index Volatility: Comparing Implied Volatility And The Intraday High–Low Price Range

Author

Listed:
  • Charles Corrado
  • Cameron Truong

Abstract

The intraday high–low price range offers volatility forecasts similarly efficient to high‐quality implied volatility indexes published by the Chicago Board Options Exchange (CBOE) for four stock market indexes: S&P 500, S&P 100, NASDAQ 100, and Dow Jones Industrials. Examination of in‐sample and out‐of‐sample volatility forecasts reveals that neither implied volatility nor intraday high–low range volatility consistently outperforms the other.

Suggested Citation

  • Charles Corrado & Cameron Truong, 2007. "Forecasting Stock Index Volatility: Comparing Implied Volatility And The Intraday High–Low Price Range," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 30(2), pages 201-215, June.
    • Handle: RePEc:bla:jfnres:v:30:y:2007:i:2:p:201-215
    DOI: 10.1111/j.1475-6803.2007.00210.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1475-6803.2007.00210.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1475-6803.2007.00210.x?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
    ---><---

    Citations

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


    Cited by:

    1. Hung, Jui-Cheng & Liu, Hung-Chun & Yang, J. Jimmy, 2020. "Improving the realized GARCH’s volatility forecast for Bitcoin with jump-robust estimators," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    2. Liu, Hung-Chun & Chiang, Shu-Mei & Cheng, Nick Ying-Pin, 2012. "Forecasting the volatility of S&P depositary receipts using GARCH-type models under intraday range-based and return-based proxy measures," International Review of Economics & Finance, Elsevier, vol. 22(1), pages 78-91.
    3. Brian M Lucey and Alexander Eastman, 2008. "Comparing Garman-Klass and DU Volatility and Symmetry Measures in Intraday Futures Returns and Volumes: A Vector Autoregression Analysis," The Institute for International Integration Studies Discussion Paper Series iiisdp260, IIIS.
    4. Stavros Degiannakis & George Filis & Renatas Kizys, 2014. "The Effects of Oil Price Shocks on Stock Market Volatility: Evidence from European Data," The Energy Journal, , vol. 35(1), pages 35-56, January.
    5. Dimitrios P. Louzis & Spyros Xanthopoulos‐Sisinis & Apostolos P. Refenes, 2013. "The Role of High‐Frequency Intra‐daily Data, Daily Range and Implied Volatility in Multi‐period Value‐at‐Risk Forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(6), pages 561-576, September.
    6. Fuertes, Ana-Maria & Olmo, Jose, 2013. "Optimally harnessing inter-day and intra-day information for daily value-at-risk prediction," International Journal of Forecasting, Elsevier, vol. 29(1), pages 28-42.
    7. Laura Gianfagna & Armando Rungi, 2017. "Does corporate control matter to financial volatility?," Working Papers 09/2017, IMT School for Advanced Studies Lucca, revised Nov 2017.
    8. Li, Yingzi & Fortenbery, T. Randall, 2013. "Do Speculators in Futures Markets Make Cash Markets More Volatile?," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 151296, Agricultural and Applied Economics Association.
    9. repec:eco:journ1:2014-03-20 is not listed on IDEAS
    10. Ji‐Eun Choi & Dong Wan Shin, 2018. "Forecasts for leverage heterogeneous autoregressive models with jumps and other covariates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(6), pages 691-704, September.
    11. Shailesh Rana & William H. Bommer & G. Michael Phillips, 2020. "Predicting Returns for Growth and Value Stocks: A Forecast Assessment Approach Using Global Asset Pricing Models," International Journal of Economics and Financial Issues, Econjournals, vol. 10(4), pages 88-106.
    12. Henning Fischer & Ángela Blanco‐FERNÁndez & Peter Winker, 2016. "Predicting Stock Return Volatility: Can We Benefit from Regression Models for Return Intervals?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(2), pages 113-146, March.
    13. Jui‐Cheng Hung & Hung‐Chun Liu & J. Jimmy Yang, 2023. "Does the tail risk index matter in forecasting downside risk?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(3), pages 3451-3466, July.
    14. Wang, Jying-Nan & Liu, Hung-Chun & Lee, Yen-Hsien & Hsu, Yuan-Teng, 2023. "FoMO in the Bitcoin market: Revisiting and factors," The Quarterly Review of Economics and Finance, Elsevier, vol. 89(C), pages 244-253.
    15. Stavros Degiannakis & George Filis & Renatas Kizys, 2013. "Oil price shocks and stock market volatility: evidence from European data," Working Papers 161, Bank of Greece.
    16. repec:eco:journ1:2014-03-19 is not listed on IDEAS
    17. Çiðdem Kurt Cihangir, 2018. "Küresel Risk Algýsýnýn Küresel Ticaret Üzerindeki Etkisi," Isletme ve Iktisat Calismalari Dergisi, Econjournals, vol. 6(1), pages 1-10.
    18. Bertrand B. Maillet & Jean-Philippe R. M�decin, 2010. "Extreme Volatilities, Financial Crises and L-moment Estimations of Tail-indexes," Working Papers 2010_10, Department of Economics, University of Venice "Ca' Foscari".
    19. Marius Cristian Miloș, 2021. "Impact of MiFID II on the Market Volatility—Analysis on Some Developed and Emerging European Stock Markets," Laws, MDPI, vol. 10(3), pages 1-11, June.
    20. Becker, Janis & Leschinski, Christian, 2018. "Directional Predictability of Daily Stock Returns," Hannover Economic Papers (HEP) dp-624, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    21. Manahov, Viktor & Hudson, Robert & Gebka, Bartosz, 2014. "Does high frequency trading affect technical analysis and market efficiency? And if so, how?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 28(C), pages 131-157.

    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:bla:jfnres:v:30:y:2007:i:2:p:201-215. 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: https://edirc.repec.org/data/sfaaaea.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.