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Estimation of Integrated Volatility in Stochastic Volatility Models


  • Jeannette H.C. Woerner


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.

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  • 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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    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, Society for Financial Econometrics, vol. 4(1), pages 1-30.
    2. Ole E. Barndorff-Nielsen & Svend Erik Graversen & Neil Shephard, 2003. "Power variation & stochastic volatility: a review and some new results," Economics Papers 2003-W19, Economics Group, Nuffield College, University of Oxford.

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