Abstract
Bayesian estimation of the systematic risk of a share using product partition models (PPM) is considered in this study. The cluster structure of the PPM is used to derive a robust Bayes estimator of beta and also to identify outliers or clusters of observations. The procedure is implemented considering independent scale mixture of normals for the error terms. The results are illustrated with an application to a set of shares of companies in the Chilean stock market.