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Estimation of Integrated Volatility in Stochastic Volatility Models

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  • Jeannette H.C. Woerner

Abstract

In the framework of stochastic volatility models we examine estimators for the integrated volatility based on the p-th power variation,i.e. the sum of p-th absolute powers of the log-returns. We derive consistency and distributional results for the estimators given high frequency data, especially taking into account what kind of process we may add to our model without affecting the estimate of the integrated volatility. This may on the one hand be interpreted as a possible flexibility in modelling, e.g. adding jumps or even leaving the framework of semimartingales by adding a fractional Brownian motion, or on the other hand as robustness against model misspecification. We will discuss possible choices of p under different model assumptions and irregularly spaced data.

Suggested Citation

  • Jeannette H.C. Woerner, 2003. "Estimation of Integrated Volatility in Stochastic Volatility Models," OFRC Working Papers Series 2003mf05, Oxford Financial Research Centre.
  • Handle: RePEc:sbs:wpsefe:2003mf05
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    File URL: http://www.finance.ox.ac.uk/file_links/finecon_papers/2003mf05.pdf
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    Cited by:

    1. Ole E. Barndorff-Nielsen & Neil Shephard, 2006. "Econometrics of Testing for Jumps in Financial Economics Using Bipower Variation," Journal of Financial Econometrics, Oxford University Press, vol. 4(1), pages 1-30.
    2. Anders Eriksson, 2010. "A Levy process for the GNIG probability law with 2nd order stochastic volatility and applications to option pricing," Quantitative Finance, Taylor & Francis Journals, vol. 10(1), pages 75-90.
    3. Barndorff-Nielsen, Ole E. & Shephard, Neil, 2006. "Impact of jumps on returns and realised variances: econometric analysis of time-deformed Levy processes," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 217-252.

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