Risk-return relationship: An empirical study of different statistical methods for estimating the Capital Asset Pricing Models (CAPM) and the Fama-French model for large cap stocks

Abstract

The Capital Asset Pricing Model (CAPM) is one of the original models in explaining risk-return relationship in the financial market. However, when applying the CAPM into reality, it demonstrates a lot of shortcomings. While improving the performance of the model, many studies, on one hand, have attempted to apply different statistical methods to estimate the model, on the other hand, have added more predictors to the model. First, the thesis focuses on reviewing the CAPM and comparing popular statistical methods used to estimate it, and then, the thesis compares predictive power of the CAPM and the Fama-French model, which is an important extension of the CAPM. Through an empirical study on the data set of large cap stocks, we have demonstrated that there is no statistical method that would recover the expected relationship between systematic risk (represented by beta) and return from the CAPM, and that the Fama-French model does not have a better predictive performance than the CAPM on individual stocks. Therefore, the thesis provides more evidence to support the incorrectness of the CAPM and the limitation of the Fama-French model in explaining risk-return relationship.