Rounding Errors and Volatility Estimation.
Financial prices are often discretized--with smallest tick size of one cent, for example. Thus prices involve rounding errors. Rounding errors affect the estimation of volatility, and understanding them is critical, particularly when using high frequency data. We study the asymptotic behavior of rea...
| Published in: | Journal of Financial Econometrics Vol. 13; no. 2; pp. 478 - 505 |
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| Main Authors: | , |
| Format: | Article |
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Oxford University Press / USA
Spring2015
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=103148962&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 103148962 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 14798409 T2Y jtl: Journal of Financial Econometrics issn: 14798409 maglogo: N pubinfo: dt: Spring2015 vid: 13 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 103148962 10.1093/jjfinec/nbu005 ppf: 478 ppct: 27 formats: tig: atl: Rounding Errors and Volatility Estimation. aug: au: YINGYING LI MYKLAND, PER A. affil: Department of Information Systems, Business Statistics and Operations Management, Hong Kong University of Science and Technology Department of Statistics, University of Chicago su: Market volatility Estimation theory Rounding errors Stochastic convergence Statistical bias sug: subj: Market volatility Estimation theory Rounding errors Stochastic convergence Statistical bias keyword: bias-correction diffusion process market microstructure realized volatility (RV) rounding errors bias-correction diffusion process market microstructure realized volatility (RV) rounding errors ab: Financial prices are often discretized--with smallest tick size of one cent, for example. Thus prices involve rounding errors. Rounding errors affect the estimation of volatility, and understanding them is critical, particularly when using high frequency data. We study the asymptotic behavior of realized volatility (RV), which is commonly used as an estimator of integrated volatility. We prove the convergence of the RV and scaled RV under varous conditions on the rounding level and the number of observations. A bias-corrected volatility estimator is proposed and an associated central limit theorem is shown. The simulation and empirical results demonstrate that the proposed method can yield substantial statistical improvement. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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