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

Neil Shephard (), Ole E. Barndorff-Nielsen (), Peter Hansen () and Asger Lunde ()

No 264, Economics Series Working Papers from University of Oxford, Department of Economics

Abstract: 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.

Keywords: Bipower Variation; Long Run Variance Estimator; Market Frictions; Quadratic Variation; Realised Variance (search for similar items in EconPapers)
JEL-codes: C13 C22 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-fmk
Date: 2006
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Related works:
Working Paper: Designing realised kernels to measure the ex-post variation of equity prices in the presence of noise (2006)
Working Paper: Designing realised kernels to measure the ex-post variation of equity prices in the presence of noise (2006)
Journal Article: Designing Realized Kernels to Measure the ex post Variation of Equity Prices in the Presence of Noise (2008)
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