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Estimating Daily Volatility in Financial Markets Utilizing Intraday Data

Author

Listed:
  • Bernard Bollen

    (School of Economics, La Trobe University)

  • Brett Inder

    (School of Economics, La Trobe University)

Abstract

This study proposes a new approach to the estimation of daily volatility in financial markets. To do this we evaluate a number of traditional estimators of daily volatility based upon intra-day data and propose a new estimator of daily volatility based upon intra-day data which is both unbiased and efficient.

Suggested Citation

  • Bernard Bollen & Brett Inder, 1999. "Estimating Daily Volatility in Financial Markets Utilizing Intraday Data," Working Papers 1999.01, School of Economics, La Trobe University.
    • Handle: RePEc:trb:wpaper:1999.01
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    References listed on IDEAS

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    1. Andersen T. G & Bollerslev T. & Diebold F. X & Labys P., 2001. "The Distribution of Realized Exchange Rate Volatility," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 42-55, March.
    2. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," The Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 41-66.
    3. 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.
    4. Harris, Lawrence, 1986. "Cross-Security Tests of the Mixture of Distributions Hypothesis," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 21(1), pages 39-46, March.
    5. Hasbrouck, Joel & Ho, Thomas S Y, 1987. "Order Arrival, Quote Behavior, and the Return-Generating Process," Journal of Finance, American Finance Association, vol. 42(4), pages 1035-1048, September.
    6. Owain Ap Gwilym & Mike Buckle, 1999. "Volatility forecasting in the framework of the option expiry cycle," The European Journal of Finance, Taylor & Francis Journals, vol. 5(1), pages 73-94.
    7. Andersen, Torben G. & Bollerslev, Tim, 1997. "Intraday periodicity and volatility persistence in financial markets," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 115-158, June.
    8. Wouter J. Den Haan & Andrew T. Levin, 1995. "Inferences from parametric and non-parametric covariance matrix estimation procedures," International Finance Discussion Papers 504, Board of Governors of the Federal Reserve System (U.S.).
    9. Imad Moosa & Bernard Bollen, 2001. "Is there a maturity effect in the price of the S&P 500 futures contract?," Applied Economics Letters, Taylor & Francis Journals, vol. 8(11), pages 693-695.
    10. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    11. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2000. "Exchange Rate Returns Standardized by Realized Volatility are (Nearly) Gaussian," Multinational Finance Journal, Multinational Finance Journal, vol. 4(3-4), pages 159-179, September.
    12. Beckers, Stan, 1983. "Variances of Security Price Returns Based on High, Low, and Closing Prices," The Journal of Business, University of Chicago Press, vol. 56(1), pages 97-112, January.
    13. Anat R. Admati, Paul Pfleiderer, 1988. "A Theory of Intraday Patterns: Volume and Price Variability," Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 3-40.
    14. 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.
    15. Hsieh, David A, 1991. "Chaos and Nonlinear Dynamics: Application to Financial Markets," Journal of Finance, American Finance Association, vol. 46(5), pages 1839-1877, December.
    16. Canina, Linda & Figlewski, Stephen, 1993. "The Informational Content of Implied Volatility," Review of Financial Studies, Society for Financial Studies, vol. 6(3), pages 659-681.
    17. Roll, Richard, 1984. "A Simple Implicit Measure of the Effective Bid-Ask Spread in an Efficient Market," Journal of Finance, American Finance Association, vol. 39(4), pages 1127-1139, September.
    18. Torben G. Andersen & Tim Bollerslev, 1998. "Deutsche Mark-Dollar Volatility: Intraday Activity Patterns, Macroeconomic Announcements, and Longer Run Dependencies," Journal of Finance, American Finance Association, vol. 53(1), pages 219-265, February.
    19. Christensen, B. J. & Prabhala, N. R., 1998. "The relation between implied and realized volatility," Journal of Financial Economics, Elsevier, vol. 50(2), pages 125-150, November.
    20. Harris, Lawrence, 1987. "Transaction Data Tests of the Mixture of Distributions Hypothesis," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 22(2), pages 127-141, June.
    21. Moosa, Imad A. & Bollen, Bernard, 2002. "A benchmark for measuring bias in estimated daily value at risk," International Review of Financial Analysis, Elsevier, vol. 11(1), pages 85-100.
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