REGULARIZED GMM FOR TIME-VARYING MODELS WITH APPLICATIONS TO ASSET PRICING

摘要

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.

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