Robust Regression for Capital Asset Pricing Model Using Bayesian Approach

This study investigate the performance of a portfolio based on capital asset pricing model using a Bayesian statistics approach. We use a hierarchical model robustly to estimate the systematic risk of an asset. We assume that the returns follow independent normal distributions. MCMC sampling is applied to calculate all the parameters in the model. Finally, the Bayesian method gives us the probability of every possible asset returns, given the market returns and also the posterior predictions is a clue that the model could be improved.