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On portfolio optimization: How and when do we benefit from high-frequency data?

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  • Qianqiu Liu

    (Shidler College of Business, University of Hawaii at Manoa, Honolulu, HI, USA)

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    Abstract

    We examine how the use of high-frequency data impacts the portfolio optimization decision. Prior research has documented that an estimate of realized volatility is more precise when based upon intraday returns rather than daily returns. Using the framework of a professional investment manager who wishes to track the S&P 500 with the 30 Dow Jones Industrial Average stocks, we find that the benefits of using high-frequency data depend upon the rebalancing frequency and estimation horizon. If the portfolio is rebalanced monthly and the manager has access to at least the previous 12 months of data, daily data have the potential to perform as well as high-frequency data. However, substantial improvements in the portfolio optimization decision from high-frequency data are realized if the manager rebalances daily or has less than a 6-month estimation window. These findings are robust to transaction costs. Copyright © 2009 John Wiley & Sons, Ltd.

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    File URL: http://hdl.handle.net/10.1002/jae.1062
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    File URL: http://qed.econ.queensu.ca:80/jae/2009-v24.4/
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    Bibliographic Info

    Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.

    Volume (Year): 24 (2009)
    Issue (Month): 4 ()
    Pages: 560-582

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    Handle: RePEc:jae:japmet:v:24:y:2009:i:4:p:560-582

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    Cited by:
    1. Boudt, Kris & Cornelissen, Jonathan & Croux, Christophe, 2012. "Jump robust daily covariance estimation by disentangling variance and correlation components," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 2993-3005.
    2. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2011. "Financial Risk Measurement for Financial Risk Management," PIER Working Paper Archive 11-037, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    3. Griffin, Jim E. & Oomen, Roel C.A., 2011. "Covariance measurement in the presence of non-synchronous trading and market microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 58-68, January.

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