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


  • Charles Corrado
  • Cameron Truong


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. 2007 The Southern Finance Association and the Southwestern Finance Association.

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.
  • Handle: RePEc:bla:jfnres:v:30:y:2007:i:2:p:201-215

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    References listed on IDEAS

    1. Ali Hortacsu & Samita Sareen, 2005. "Order Flow and the Formation of Dealer Bids: Information Flows and Strategic Behavior in the Government of Canada Securities Auctions," NBER Working Papers 11116, National Bureau of Economic Research, Inc.
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    Cited by:

    1. 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.
    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. 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.
    5. 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.
    6. Laura Gianfagna & Armando Rungi, 2017. "Does corporate control matter to financial volatility?," Working Papers 09/2017, IMT Institute for Advanced Studies Lucca, revised Nov 2017.
    7. Stavros Degiannakis & George Filis & Renatas Kizys, 2013. "Oil price shocks and stock market volatility: evidence from European data," Working Papers 161, Bank of Greece.
    8. repec:eco:journ1:2014-03-19 is not listed on IDEAS
    9. 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".
    10. 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.
    11. repec:eco:journ1:2014-03-20 is not listed on IDEAS
    12. 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.
    13. Stavros Degiannakis, George Filis, and Renatas Kizys, 2014. "The Effects of Oil Price Shocks on Stock Market Volatility: Evidence from European Data," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).

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