In parametric models a sufficient condition for local identification is that the vector of moment conditions is differentiable at the true parameter with full rank derivative matrix. We show that there are corresponding sufficient conditions for nonparametric models. A nonparametric rank...

We consider time series models in which the conditional mean of the response variable given the past depends on latent covariates. We assume that the covariates can be estimated consistently and use an iterative nonparametric kernel smoothing procedure for estimating the conditional mean...

This paper develops a Monte-Carlo backtesting procedure for risk premia strategies and employs it to study Time-Series Momentum (TSM). Relying on time-series models, empirical residual distributions and copulas we overcome two key drawbacks of conventional backtesting procedures. We create...

We show that in misspecified models with useless factors (for example, factors that are independent of the returns on the test assets), the standard inference procedures tend to erroneously conclude, with high probability, that these irrelevant factors are priced and the restrictions of the...

This supplemental appendix contains additional technical details of Cheng, Dou, and Liao (2020). Section SA provides the proofs of several lemmas on the asymptotic convergence of the random components in the test statistic T and the conditional critical value. Section SB verifies the bounded...

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, disaster risk models, and multi-factor linear asset pricing models. Building on recent developments in the conditional...

This paper develops a Bayesian methodology to compare asset pricing models containing non-traded factors and principal components. Existing comparison procedures are inadequate when models include such factors due to estimation uncertainties in mimicking portfolios and return covariances....

We show that in misspecified models with useless factors (for example, factors that are independent of the returns on the test assets), the standard inference procedures tend to erroneously conclude, with high probability, that these irrelevant factors are priced and the restrictions of the...

This note contains additional model derivation and numerical details of the main text Cheng,Dou, and Liao (2021). Section A derives the Euler equations that serve as the asset pricingmoment conditions in the disaster risk model and the long-run risk model. Section B considersthe long-run risk...

We propose a new pseudo-Siamese Network for Asset Pricing (SNAP) model, based on deep learning approaches, for conditional asset pricing. Our model allows for the deep alpha, deep beta and deep factor risk premia conditional on high dimensional observable information of financial characteristics...