Subsampling realised kernels

Neil Shephard (), Ole Barndorff-Nielsen and Asger Lunde ()

No 278, Economics Series Working Papers from University of Oxford, Department of Economics

Abstract: In a recent paper we have introduced the class of realised kernel estimators of the increments of quadratic variation in the presence of noise. We showed that this estimator is consistent and derived its limit distribution under various assumptions on the kernel weights. In this paper we extend our analysis, looking at the class of subsampled realised kernels and we derive the limit theory for this class of estimators. We find that subsampling is highly advantageous for estimators based on discontinuous kernels, such as the truncated kernel. For kinked kernels, such as the Bartlett kernel, we show that subsampling is impotent, in the sense that subsampling has no effect on the asymptotic distribution. Perhaps surprisingly, for the efficient smooth kernels, such as the Parzen kernel, we show that subsampling is harmful as it increases the asymptotic variance. We also study the performance of subsampled realised kernels in simulations and in empirical work.

Keywords: Bipower Variation; Long Run Variance Estimator; Market Frictions; Quadratic Variation; Realised Kernal; Realised Variance; Subsampling (search for similar items in EconPapers)
JEL-codes: C13 C22 (search for similar items in EconPapers)
Date: 2006-09-01
New Economics Papers: this item is included in nep-mst
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Citations: View citations in EconPapers (5)

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Related works:
Journal Article: Subsampling realised kernels (2011)
Working Paper: Subsampling realised kernels (2006)
Working Paper: Subsampling realised kernels (2006)
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