Measuring Dark Matter in Asset Pricing Models

• 成果类型: Article
• 署名作者: Chen, Hui; Dou, Winston wei; Kogan, Leonid
• 署名单位: Massachusetts Institute of Technology (MIT); National Bureau of Economic Research
• 刊物名称: JOURNAL OF FINANCE
• ISSN/ISSBN: 0022-1082
• DOI: 10.1111/jofi.13317
• 发表日期: 2024
• 页码: 843-902
• 关键词: Long-run risks; INSTRUMENTAL VARIABLES ESTIMATION; LARGE-SAMPLE PROPERTIES; generalized-method; equity premium; rare disasters; parameter instability; INFLATION CRITERION; structural-change; EFFICIENT TESTS

摘要

We formalize the concept of dark matter in asset pricing models by quantifying the additional informativeness of cross-equation restrictions about fundamental dynamics. The dark-matter measure captures the degree of fragility for models that are potentially misspecified and unstable: a large dark-matter measure indicates that the model lacks internal refutability (weak power of optimal specification tests) and external validity (high overfitting tendency and poor out-of-sample fit). The measure can be computed at low cost even for complex dynamic structural models. To illustrate its applications, we provide quantitative examples applying the measure to (time-varying) rare-disaster risk and long-run risk models.