Macro-Finance Decoupling: Robust Evaluations of Macro Asset Pricing Models
31 Pages Posted: 26 May 2020 Last revised: 17 Feb 2021
Date Written: February 16, 2021
This paper shows that robust inference under weak identification is important to the evaluation of many influential macro asset pricing models, including long-run risk models and (time-varying) rare-disaster risk models. Building on recent developments in the conditional inference literature, we provide a novel conditional specification test by simulating the critical value conditional on a sufficient statistic. This sufficient statistic can be intuitively interpreted as a measure capturing the macroeconomic information decoupled from the underlying content of asset pricing theories. Macro-finance decoupling is an effective way to improve the power of the specification test when asset pricing theories are difficult to refute because of a severe imbalance in the information content about the key model parameters between macroeconomic moment restrictions and asset pricing cross-equation restrictions. For empirical application, we apply the proposed conditional specification test to evaluate a time-varying rare-disaster risk model and construct data-driven robust model uncertainty sets.
The supplemental appendix can be found at: https://ssrn.com/abstract=3609598.
The additional materials can be found at: https://ssrn.com/abstract=3787125.
Keywords: Structural asset pricing, Conditional inference, Rare disasters, Long-run risk, Weak identification, Model uncertainty.
JEL Classification: C12, C32, C52, G12
Suggested Citation: Suggested Citation