Title: Bayesian nonparametric estimation of ex-post variance
Authors: John Maheu - McMaster University (Canada) [presenting]
Jim Griffin - University of Kent (United Kingdom)
Jia Liu - Saint Mary's University (Canada)
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. A new exact finite sample approach is offered 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.