The Risk Premia from the European Equity Market: An application of the Three-Pass Estimation Methodology

We develop an empirical application on a large dataset of European stock returns, in order to estimate the risk premia. We propose an application of the Three-Pass Estimation Method (3PEM) by Xiu and Giglio (2021) as a multipurpose tool in asset pricing. By assuming the Fama-French Five-Factor model (Fama and French (2015)) as baseline model, we show that the 3PEM yields risk premium estimates that are more economically plausible and statistically robust than those obtained using the traditional two-pass estimation method (2PEM). Moreover, we extend the results by Xiu and Giglio (2021) showing that the 3PEM is able to detect noise in tradable factors. Furthermore, the method is used to denoise the observed factors, providing purified versions that better capture the systematic components of risk. We also identify noisy factors, and yield denoised factor series that improve the estimation of stock-level exposures and expected returns.