UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Bayesian Nonparametric Estimation of Ex Post Variance*

Griffin, J; Liu, J; Maheu, JM; (2019) Bayesian Nonparametric Estimation of Ex Post Variance*. Journal of Financial Econometrics 10.1093/jjfinec/nbz034. (In press).

[img] Text
Griffin_Bayesian Nonparametric Estimation of Ex Post Variance_AAM.pdf - Accepted version
Access restricted to UCL open access staff until 7 November 2021.

Download (534kB)

Abstract

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 article 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.

Type: Article
Title: Bayesian Nonparametric Estimation of Ex Post Variance*
DOI: 10.1093/jjfinec/nbz034
Publisher version: https://doi.org/10.1093/jjfinec/nbz034
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: C11 - Bayesian Analysis: General, C58 - Financial Econometrics
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10086884
Downloads since deposit
0Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item