We propose a novel Bayesian optimization procedure for outlier detection in the Capital Asset Pricing Model. We use a parametric product partition model to robustly estimate the systematic risk of an asset. We assume that the returns follow independent normal distributions and we impose a partition structure on the parameters of interest. The partition structure imposed on the parameters induces a corresponding clustering of the returns. We identify via an optimization procedure the partition that best separates standard observations from the atypical ones. The methodology is illustrated with reference to a real dataset, for which we also provide a microeconomic interpretation of the detected outliers.
Bayesian outlier detection in Capital Asset Pricing Model
DE GIULI, MARIA ELENA;MAGGI, MARIO ALESSANDRO;TARANTOLA, CLAUDIA
2010-01-01
Abstract
We propose a novel Bayesian optimization procedure for outlier detection in the Capital Asset Pricing Model. We use a parametric product partition model to robustly estimate the systematic risk of an asset. We assume that the returns follow independent normal distributions and we impose a partition structure on the parameters of interest. The partition structure imposed on the parameters induces a corresponding clustering of the returns. We identify via an optimization procedure the partition that best separates standard observations from the atypical ones. The methodology is illustrated with reference to a real dataset, for which we also provide a microeconomic interpretation of the detected outliers.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
