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

Xu Cheng (xucheng@econ.upenn.edu), Winston Dou and Zhipeng Liao (zhipeng.liao@econ.ecla.edu)
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Xu Cheng: University of Pennsylvania
Zhipeng Liao: University of California, Los Angeles

PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania

Abstract: This paper shows that robust inference under weak identification is important to the eval-uation 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.

Keywords: Asset Pricing; Conditional Inference; Disaster Risk; Long-Run Risk; Factor Models; Speci?cation Test; Weak Identi?cation (search for similar items in EconPapers)
JEL-codes: C12 C32 C52 G12 (search for similar items in EconPapers)
Pages: 32 pages
Date: 2020-05-24
New Economics Papers: this item is included in nep-ecm and nep-mac
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Related works:
Journal Article: Macro‐Finance Decoupling: Robust Evaluations of Macro Asset Pricing Models (2022)
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