Bayesian Nonparametric Estimation of Ex-post Variance
Jia Liu and
MPRA Paper from University Library of Munich, Germany
Variance estimation is central to many questions in finance and economics. Until now ex-post variance estimation has been based on infill asymptotic assumptions that exploit high-frequency data. This paper offers a new exact finite sample approach to estimating ex-post variance using Bayesian nonparametric methods. In contrast to the classical counterpart, the proposed method exploits pooling over high-frequency observations with similar variances. Bayesian nonparametric variance estimators under no noise, heteroskedastic and serially correlated microstructure noise are introduced and discussed. Monte Carlo simulation results show that the proposed approach can increase the accuracy of variance estimation. Applications to equity data and comparison with realized variance and realized kernel estimators are included.
Keywords: pooling; microstructure noise; slice sampling (search for similar items in EconPapers)
JEL-codes: C11 C22 C58 G1 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-upt
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Journal Article: Bayesian Nonparametric Estimation of Ex Post Variance* (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:71220
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