Bayesian Nonparametric Estimation of Ex-post Variance
نویسندگان
چکیده
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, independent and dependent microstructure noise cases are introduced and discussed. Monte Carlo simulation results show that the proposed approach can increase the accuracy of variance estimation. An application to equity returns shows that the new variance estimator outperforms realized variance and realized kernel in in-sample fit and out-of-sample forecasts. ∗School of Mathematics, Statistics and Actuarial Science, University of Kent, UK, [email protected] †DeGroote School of Business, McMaster University, Canada, [email protected] ‡DeGroote School of Business, McMaster University, Canada and RCEA, Italy, [email protected] 1
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