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Measuring and forecasting financial variability using realised variance with and without a model

Author

Listed:
  • Ole E. Barndorff-Nielsen

    (The Centre for Mathematical Physics and Stochastics (MaPhySto), University of Aarhus, Denmark)

  • Bent Nielsen

    (Nuffield College, Unviersity of Oxford, Oxford, UK)

  • Neil Shephard

    (Nuffield College, Unviersity of Oxford, Oxford, UK)

  • Carla Ysusi

    (Dept of Statistics, Unviersity of Oxford, Oxford, UK)

Abstract

We use high frequency financial data to proxy, via the realised variance, each day's financial variability. Based on a semiparametric stochastic volatility process, a limit theory shows you can represent the proxy as a true underlying variability plus some measurement noise with known characteristics. Hence filtering, smoothing and forecasting ideas can be used to improve our estimates of variability by exploiting the time series structure of the realised variances. This can be carried out based on a model or without a model. A comparison is made between these two methods.

Suggested Citation

  • Ole E. Barndorff-Nielsen & Bent Nielsen & Neil Shephard & Carla Ysusi, 2002. "Measuring and forecasting financial variability using realised variance with and without a model," Economics Papers 2002-W21, Economics Group, Nuffield College, University of Oxford.
    • Handle: RePEc:nuf:econwp:0221
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    File URL: http://www.nuff.ox.ac.uk/economics/papers/2002/w21/jim.pdf
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    References listed on IDEAS

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    Cited by:

    1. Nielsen, Morten Ørregaard & Frederiksen, Per, 2008. "Finite sample accuracy and choice of sampling frequency in integrated volatility estimation," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 265-286, March.
    2. Turgut Kısınbay, 2010. "Predictive ability of asymmetric volatility models at medium-term horizons," Applied Economics, Taylor & Francis Journals, vol. 42(30), pages 3813-3829.
    3. Nour Meddahi, 2003. "ARMA representation of integrated and realized variances," Econometrics Journal, Royal Economic Society, vol. 6(2), pages 335-356, December.
    4. Anzarut, Michelle & Mena, Ramsés H., 2019. "A Harris process to model stochastic volatility," Econometrics and Statistics, Elsevier, vol. 10(C), pages 151-169.
    5. Ysusi Carla, 2006. "Estimating Integrated Volatility Using Absolute High-Frequency Returns," Working Papers 2006-13, Banco de México.

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