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

Abstract

This paper shows that robust inference under weak identification is important to the evaluation of many influential macro asset pricing models, including (time‐varying) rare‐disaster risk models and long‐run 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. We apply the proposed conditional specification test to the evaluation of a time‐varying rare‐disaster risk model and the construction of robust model uncertainty sets.

Dark Matter in (Volatility and) Equity Option Risk Premiums

“…These parametric restrictions have not, to our knowledge, been tested, and may be difficult to validate. Similar to the emphasis in Chen, Dou, and Kogan (2021) and Cheng, Dou, and Liao (2022), these restrictions highlight the dark matter property of option models.…”

“…The subject of our paper invites connections with Chen, Dou, and Kogan (2021) and Cheng, Dou, and Liao (2022). Like them, we utilize the dark matter link, consistent with the notion from cosmology: The dynamics of the local time, the jumps crossing the strike, and the properties of the pricing kernel may be hard to identify directly using equity index returns.…”

“…Our contributions complement, yet differ from, Chen, Dou, and Kogan (2021) and Cheng, Dou, and Liao (2022). First, they consider dark matter as the degree of fragility for potentially misspecified models formulated under the data-generating measure P, whereas our usage pertains to local time and jumps crossing the strike under both P and an equivalent martingale measure Q.…”

“…Paving the way for a better appreciation of dark matter uncertainties, Cheng, Dou, and Liao (2022) develop model evaluation procedures for testing asset pricing models. Their proposed econometric methodology, while not implemented in this paper, can be adapted to probe the dark matter restrictions of option pricing models with unspanned volatility and jump risks.…”

Neural networks for estimating Macro Asset Pricing model in football clubs

“…Table 2 also shows the estimates of other parameters, with their previous and subsequent levels for the model described previously in section 2 of this study, as is the case of habit. This parameter shows a persistent autocorrelation level, close to 0.90 and lower than other reference works (Beeler & Campbell, 2012;Cheng et al, 2022). The persistence of the level of residues is found with a similar value, around 0.90 in the case of DRCNN, but for the DNDT and QNN methodologies, it is around 0.82.…”