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On The Robustness Of Range-Based Volatility Estimators


  • Ozgur (Ozzy) Akay
  • Mark D. Griffiths
  • Drew B. Winters


Abstract We empirically examine Parkinson's range-based volatility estimate in the federal funds market, which is unique because institutional regulations create a predictable pattern in interday volatility. We find that range-based volatility estimates and standard deviations produce the expected volatility pattern. We also find that at trading pressure points where microstructure noise should be greatest, range-based estimates are less than the standard deviations. Thus, we support the argument that range-based volatility estimates remove the upward bias created by microstructure noise. We find that the Parkinson method is the most efficient range-based volatility measure among a set of alternates in this market. Copyright (c) 2010 The Southern Finance Association and the Southwestern Finance Association.

Suggested Citation

  • 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.
  • Handle: RePEc:bla:jfnres:v:33:y:2010:i:2:p:179-199

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    References listed on IDEAS

    1. Parkinson, Michael, 1980. "The Extreme Value Method for Estimating the Variance of the Rate of Return," The Journal of Business, University of Chicago Press, vol. 53(1), pages 61-65, January.
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    5. Garman, Mark B & Klass, Michael J, 1980. "On the Estimation of Security Price Volatilities from Historical Data," The Journal of Business, University of Chicago Press, vol. 53(1), pages 67-78, January.
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    8. Martens, Martin & van Dijk, Dick, 2007. "Measuring volatility with the realized range," Journal of Econometrics, Elsevier, vol. 138(1), pages 181-207, May.
    9. Turan G. Bali, 2003. "An Extreme Value Approach to Estimating Volatility and Value at Risk," The Journal of Business, University of Chicago Press, vol. 76(1), pages 83-108, January.
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    11. Cyree, Ken B & Winters, Drew B, 2001. "An Intraday Examination of the Federal Funds Market: Implications for the Theories of the Reverse-J Pattern," The Journal of Business, University of Chicago Press, vol. 74(4), pages 535-556, October.
    12. Sassan Alizadeh & Michael W. Brandt & Francis X. Diebold, 2002. "Range-Based Estimation of Stochastic Volatility Models," Journal of Finance, American Finance Association, vol. 57(3), pages 1047-1091, June.
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    14. Clouse, James A. & Dow, James Jr., 2002. "A computational model of banks' optimal reserve management policy," Journal of Economic Dynamics and Control, Elsevier, vol. 26(11), pages 1787-1814, September.
    15. Michael W. Brandt & Francis X. Diebold, 2006. "A No-Arbitrage Approach to Range-Based Estimation of Return Covariances and Correlations," The Journal of Business, University of Chicago Press, vol. 79(1), pages 61-74, January.
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    Cited by:

    1. Tseng, Tseng-Chan & Lee, Chien-Chiang & Chen, Mei-Ping, 2015. "Volatility forecast of country ETF: The sequential information arrival hypothesis," Economic Modelling, Elsevier, vol. 47(C), pages 228-234.
    2. Çankaya, Serkan & Ulusoy, Veysel & Eken, Hasan/M., 2011. "The Behavior of Istanbul Stock Exchange Market: An Intraday Volatility/Return Analysis Approach," MPRA Paper 43656, University Library of Munich, Germany.
    3. Irwin, Scott H. & Sanders, Dwight R., 2012. "Testing the Masters Hypothesis in commodity futures markets," Energy Economics, Elsevier, vol. 34(1), pages 256-269.
    4. Chen, Wei-Peng & Choudhry, Taufiq & Wu, Chih-Chiang, 2013. "The extreme value in crude oil and US dollar markets," Journal of International Money and Finance, Elsevier, vol. 36(C), pages 191-210.

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