Bayesian outlier detection in Capital Asset Pricing Model

TL;DR

This paper introduces a Bayesian optimization method for outlier detection in the CAPM, using a partition model to identify atypical asset returns and improve risk estimation.

Contribution

It presents a novel Bayesian approach with a parametric partition model for robust outlier detection in financial asset returns.

Findings

Effective outlier detection in real financial data

Improved estimation of systematic risk

Microeconomic interpretation of outliers

Abstract

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.