IDEAS home Printed from
   My bibliography  Save this paper

Portfolio allocation: Getting the most out of realised volatility


  • Adam Clements

    () (QUT)

  • Annastiina Silvennoinen

    () (QUT)


Recent advances in the measurement of volatility have utilized high frequency intraday data to produce what are generally known as realised volatility estimates. It has been shown that forecasts generated from such estimates are of positive economic value in the context of portfolio allocation. This paper considers the link between the value of such forecasts and the loss function under which models of realised volatility are estimated. It is found that employing a utility based estimation criteria is preferred over likelihood estimation, however a simple mean squared error criteria performs in a similar manner. These findings have obvious implications for the manner in which volatility models based on realised volatility are estimated when one wishes to inform the portfolio allocation decision.

Suggested Citation

  • Adam Clements & Annastiina Silvennoinen, 2010. "Portfolio allocation: Getting the most out of realised volatility," NCER Working Paper Series 54, National Centre for Econometric Research, revised 06 May 2010.
  • Handle: RePEc:qut:auncer:2010_01

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Jeff Fleming & Chris Kirby & Barbara Ostdiek, 2001. "The Economic Value of Volatility Timing," Journal of Finance, American Finance Association, vol. 56(1), pages 329-352, February.
    Full references (including those not matched with items on IDEAS)

    More about this item


    Volatility; utility; portfolio allocation; realized volatility; MIDAS;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:qut:auncer:2010_01. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (School of Economics and Finance) The email address of this maintainer does not seem to be valid anymore. Please ask School of Economics and Finance to update the entry or send us the correct email address. General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.