Bayesian outlier detection in Capital Asset Pricing Model
  We propose a novel Bayesian optimisation 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 optimisation procedure the partition that best separates standard observations from the atypical ones. The methodology is illustrated with reference to a real data set, for which we also provide a microeconomic interpretation of the detected outliers. 
Maria Elena De Giuli (1), Mario Alessandro Maggi (1), Claudia   Tarantola (2) ((1)Department of Business Research, Universit...