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Portfolio allocation: Getting the most out of realised volatility

Author

Listed:
  • Adam Clements

    (QUT)

  • Annastiina Silvennoinen

    (QUT)

Abstract

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
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    File URL: http://www.ncer.edu.au/papers/documents/WPNo54b.pdf
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    References listed on IDEAS

    as
    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.
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    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

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