Empirical Asset Pricing with Score-Driven Conditional Betas

We develop a novel empirical asset pricing framework to estimate time-varying risk premia, building on the recently introduced score-driven conditional betas model. First, we extend the conditional betas theory by establishing the asymptotic distribution of standard tests statistics for parameter constancy in the conditional regression. These tests allow to assess the significance of a given factor in the regression. In addition, we introduce a residual bootstrap procedure for the Wald statistic and establish its validity. Second, we propose a two-step estimation procedure to recover time-varying factor risk premia from individual stock returns. We illustrate the performance of our tests and risk premia estimation procedure through simulations. Third, we present an application in which we assess the existence of a time-varying risk premium associated with a carbon risk factor in the cross-section of U.S. industry portfolios