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Improving the Parkinson Method of Estimating Security Price Volatilities

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  • Kunitomo, Naoto
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    Abstract

    The author proposes a new method for estimating the volatility parameters of security prices, which is an improvement of the estimation method by M. Parkinson (1980). The author assumes that the security prices follow the geometric Brownian motion. However, contrary to the setting of Parkinson, the geometric Brownian motion may have nonzero drift terms. The author shows that the efficiency of his estimator is about 10 in comparison with the standard sample variance estimator. Since the efficiency of the estimator by Parkinson is about 4.91, his estimation method may considerably improve the estimation methods already known in financial economics. Copyright 1992 by University of Chicago Press.

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    Bibliographic Info

    Article provided by University of Chicago Press in its journal Journal of Business.

    Volume (Year): 65 (1992)
    Issue (Month): 2 (April)
    Pages: 295-302
    Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
    Handle: RePEc:ucp:jnlbus:v:65:y:1992:i:2:p:295-302

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    Cited by:
    1. Alex Frino & Elvis Jarnecic & Hui Zheng, 2010. "Activity in futures: does underlying market size relate to futures trading volume?," Review of Quantitative Finance and Accounting, Springer, vol. 34(3), pages 313-325, April.
    2. Ray Chou & Chun-Chou Wu & Nathan Liu, 2009. "Forecasting time-varying covariance with a range-based dynamic conditional correlation model," Review of Quantitative Finance and Accounting, Springer, vol. 33(4), pages 327-345, November.
    3. Michael W. Brandt & Francis X. Diebold, 2004. "A No-Arbitrage Approach to Range-Based Estimation of Return Covariances and Correlations," CFS Working Paper Series 2004/07, Center for Financial Studies.
    4. Torben G. Andersen & Tim Bollerslev, 1997. "Answering the Critics: Yes, ARCH Models Do Provide Good Volatility Forecasts," NBER Working Papers 6023, National Bureau of Economic Research, Inc.
    5. L. C. G. Rogers & Fanyin Zhou, 2008. "Estimating correlation from high, low, opening and closing prices," Quantitative Finance Papers 0804.0162, arXiv.org.
    6. Yin-wong Cheung, 2006. "An Empirical Model of Daily Highs and Lows," Working Papers 072006, Hong Kong Institute for Monetary Research.
    7. Lucia Alessi & Matteo Barigozzi & Marco Capasso, 2006. "Generalized Dynamic Factor Model + GARCH
      Exploiting Multivariate Information for Univariate Prediction      ," LEM Papers Series 2006/13, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    8. Massimiliano Caporin & Angelo Ranaldo & Paolo Santucci de Magistris, 2011. "On the Predictability of Stock Prices: A Case for High and Low Prices," "Marco Fanno" Working Papers 0136, Dipartimento di Scienze Economiche "Marco Fanno".
    9. Yan-Leung Cheung & Yin-Wong Cheung & Alan T. K. Wan, 2009. "A High-Low Model of Daily Stock Price Ranges," Working Papers 032009, Hong Kong Institute for Monetary Research.
    10. Peter Hansen & Asger Lunde, 2003. "Consistent Preordering with an Estimated Criterion Function, with an Application to the Evaluation and Comparison of Volatility Models," Working Papers 2003-01, Brown University, Department of Economics.
    11. Torben G. Andersen & Tim Bollerslev, 1996. "DM-Dollar Volatility: Intraday Activity Patterns, Macroeconomic Announcements, and Longer Run Dependencies," NBER Working Papers 5783, National Bureau of Economic Research, Inc.

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