Empirical Asset Pricing with Score-Driven Conditional Betas
提出一种基于得分驱动条件贝塔的实证资产定价框架，用于估计时变风险溢价，并开发了检验因子显著性的渐近分布和自助法，通过模拟和碳风险因子应用验证了方法。
Abstract We develop a novel empirical asset pricing framework to estimate time-varying risk premia, building upon score-driven conditional betas models. First, we extend the theory by establishing the asymptotic distribution of standard test statistics, allowing us to assess the significance of a given factor in the regression. Additionally, we introduce a bootstrap procedure and establish its validity. Second, we propose a two-step estimation procedure to recover time-varying risk premia. We illustrate the performance of our tests and risk premia estimation through simulations. Third, we estimate a time-varying premium associated with a carbon risk factor in the cross-section of U.S. industry portfolios.