Measuring “Dark Matter” in Asset Pricing Models
提出了资产定价模型中“暗物质”的正式概念，通过量化跨方程约束对基本动态的额外信息量，衡量模型的脆弱性，即内部可反驳性弱和外部有效性差的程度，并可用于复杂动态结构模型。
ABSTRACT 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.