REGULARIZED GMM FOR TIME‐VARYING MODELS WITH APPLICATIONS TO ASSET PRICING
提出一种正则化广义矩方法（RegGMM），通过岭融合惩罚估计时变系数模型，仅需参数相邻值波动温和的条件，能处理结构突变和渐变，用于估计时变随机贴现因子模型，在美国股票截面回报定价中表现更优。
Abstract We propose a regularized generalized method of moments (RegGMM) approach to estimating time‐varying coefficient models via a ridge fusion penalty with a high‐dimensional set of moment conditions. RegGMM only requires a mild condition on the oscillations between consecutive parameter values, accommodating abrupt structural breaks and smooth changes throughout the sample period. RegGMM offers an alternative solution for estimating the time‐varying stochastic discount factor model when pricing U.S. equity cross‐sectional returns. Our time‐varying estimate paths for factor risk prices capture changing performance across multiple risk factors and depict potential regime‐switching scenarios. Finally, RegGMM demonstrates superior asset pricing and investment performance gains compared to alternative methods.