Empirical Asset Pricing with Many Test Assets
将随机贴现因子模型中的风险价格估计问题转化为工具变量回归，利用正则化稀疏第一阶段回归构建最优工具，在模拟和实证中验证了该方法对多资产模型的有效性。
Abstract We formulate the problem of estimating risk prices in a stochastic discount factor (SDF) model as an instrumental variables regression. The IV estimator allows efficient estimation for models with non-traded factors and many test assets. Optimal instruments are constructed using a regularized sparse first stage regression. In a simulation study, the IV estimator is close to the infeasible GMM estimator in a setting with many assets. In an empirical application, the tracking portfolio for consumption growth appears strongly correlated with consumption news. It implies that consumption is a priced factor for the cross-section of excess equity returns. A similar regularized regression, projecting the SDF on test assets, leads to an estimate of the Hansen–Jagannathan distance, and identifies portfolios that maximally violate the pricing implications of the model.