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Designing realised kernels to measure the ex-post variation of equity prices in the presence of noise

  • Ole E Barndorff-Nielsen
  • Peter Hansen
  • Asger Lunde
  • Neil Shephard


This paper shows how to use realised kernels to carry out efficient feasible inference on the ex-post variation of underlying equity prices in the presence of simple models of market frictions. The issue is subtle with only estimators which have symmetric weights delivering consistent estimators with mixed Gaussian limit theorems. The weights can be chosen to achieve the best possible rate of convergence and to have an asymptotic variance which is close to that of the maximum likelihood estimator in the parametric version of this problem. Realised kernels can also be selected to (i) be analysed using endogenously spaced data such as that in databases on transactions, (ii) allow for market frictions which are endogenous, (iii) allow for temporally dependent noise. The finite sample performance of our estimators is studied using simulation, while empirical work illustrates their use in practice.

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Paper provided by Oxford Financial Research Centre in its series OFRC Working Papers Series with number 2006fe05.

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Length: 42
Date of creation: 2006
Date of revision:
Handle: RePEc:sbs:wpsefe:2006fe05
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  1. repec:cep:stiecm:/2006/509 is not listed on IDEAS
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  25. Valeri Voev & Asger Lunde, 2007. "Integrated Covariance Estimation using High-frequency Data in the Presence of Noise," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 5(1), pages 68-104.
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