30 Pages Posted: 30 Aug 2006
Date Written: April 10, 2007
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 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 advantages 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 kernel, Realised variance, Subsampling
JEL Classification: C10, C22, C80
Suggested Citation: Suggested Citation
Barndorff-Nielsen, Ole E. and Hansen, Peter Reinhard and Lunde, Asger and Shephard, Neil, Subsampling Realised Kernels (April 10, 2007). Available at SSRN: https://ssrn.com/abstract=927483 or http://dx.doi.org/10.2139/ssrn.927483