Macro-Finance Decoupling: Robust Evaluations of Macro Asset Pricing Models

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Abstract/Contents

Abstract
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.

Description

Type of resource text
Date created July 29, 2021

Creators/Contributors

Author Cheng, Xu
Author Dou, Winston Wei
Author Liao, Zhipeng 
Organizer of meeting Judd, Kenneth
Organizer of meeting Pohl, Walter
Organizer of meeting Schmedders, Karl
Organizer of meeting Wilms, Ole

Subjects

Subject structural asset pricing
Subject conditional inference
Subject rare disasters
Subject long-run risk
Subject weak identification
Subject model uncertainty
Genre Text
Genre Working paper
Genre Grey literature

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This work is licensed under a Creative Commons Attribution 4.0 International license (CC BY).

Preferred citation

Preferred citation
Cheng, X., Dou, W., and Liao, Z. (2022). Macro-Finance Decoupling: Robust Evaluations of Macro Asset Pricing Models. Stanford Digital Repository. Available at https://purl.stanford.edu/sq684mr4896

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