Inference about realized volatility using infill subsampling
Ilze Kalnina and
Oliver Linton ()
LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library
We investigate the use of subsampling for conducting inference about the quadratic variation of a discretely observed diffusion process under an infill asymptotic scheme. We show that the usual subsampling method of Politis and Romano (1994) is inconsistent when applied to our inference question. Recently, a type of subsampling has been used to do an additive bias correction to obtain a consistent estimator of the quadratic variation of a diffusion process subject to measurement error, Zhang, Mykland, and Ait- Sahalia (2005). This subsampling scheme is also inconsistent when applied to the inference question above. This is due to a high correlation between estimators on different subsamples. We discuss an alternative approach that does not have this correlation problem; however, it has a vanishing bias only under smoothness assumptions on the volatility path. Finally, we propose a subsampling scheme that delivers consistent inference without any smoothness assumptions on the volatility path. This is a general method and can be potentially applied to conduct inference for quadratic variation in the presence of jumps and/or microstructure noise by subsampling appropriate consistent estimators.
Keywords: Realised Volatility; Semimartingale; Subsampling; Infill Asymptotic Scheme (search for similar items in EconPapers)
JEL-codes: C12 (search for similar items in EconPapers)
Pages: 47 pages
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Working Paper: Inference about Realized Volatility using Infill Subsampling (2007)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:4411
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