Amedeo Andriollo
AP of Finance
Université Paris Dauphine - PSL
Research
Working Papers
Causality versus Serial Correlation: an Asymmetric Portmanteau Test
Abstract
This paper studies specification testing in dynamic linear models in the presence of omitted variables. The null hypothesis of interest is weak exogeneity: shocks have zero conditional expectation given their own past and the past of omitted variables. Existing tests based on quadratic forms of serial cross-correlations suffer from size distortions because their variance incorporates symmetric dependence in both directions, including causality from past shocks to present omitted variables (inverse causality). This paper proposes an asymmetric Portmanteau test that isolates violations of weak exogeneity from inverse causality, is asymptotically normal under the null, and does not require a parametric specification of the joint dynamics. An empirical application examines the Economic Policy Uncertainty shock series and rejects its weak exogeneity. Addressing this failure by controlling for omitted variables changes the estimated inflation response from negative to positive, suggesting a supply-side shock interpretation.
Misspecification and Weak Identification in the Nontraded Factor Zoo
Abstract
To explain the cross-section of asset returns, a "zoo" of nontraded factors has been proposed. In contrast to traded factors, nontraded factors exhibit lower correlations with asset returns. Standard inference on risk premium therefore tends to be more fragile, and the issue of weak identification might be exacerbated by the degree of model misspecification. Yet, robust inference has often been overlooked by many empirical studies, while limited efforts have been devoted to "domesticating" such factors. After re-evaluating the nontraded factor zoo, we find that the vast majority of the original model specifications published in top academic journals suffer from the aforementioned fragilities. Robust inference indicates that most of the proposed nontraded factors are unpriced in the commonly used portfolios. The findings are more drastic when considering multiple hypothesis testing adjustments, or when incorporating the market factor as an additional control. Complementing these tests, a comprehensive beta-sorted portfolio analysis shows that few nontraded factors translate into economically meaningful investment premiums. However, when summarizing the nontraded factors via PCA, we find that the zoo does carry some non-zero pricing information.
On the Statistical Properties of Tests of Parameter Restrictions in Beta-Pricing Models with a Large Number of Assets
Abstract
We study the size and power properties of t-tests of parameter restrictions for newly-designed methods that aim at reliably estimating risk premia in linear asset pricing models when the crosssectional dimension is large. By simulating a variety of empirically calibrated data generating processes for sample sizes that are typically encountered in empirical work, we evaluate the finitesample performance of the test statistics for scenarios where the factor structure is (i) strong and pervasive; (ii) spurious; (iii) weak/semi-strong and pervasive; (iv) weak/semi-strong and not pervasive; and (v) sparse. PCA-based methods such as those of Lettau and Pelger (2020), Giglio and Xiu (2021), and Giglio et al. (2025) work best when the factors are strong and pervasive, and they continue to exhibit good finite-sample properties when the factors are spurious. However, when the factor structure is semi-strong and pervasive, the split-sample estimator of Anatolyev and Mikusheva (2021) performs substantially better than the PCA-based estimators listed above. In the case of sparse loadings or when the factors are semi-strong and not pervasive, none of the candidate methods displays satisfactory finite-sample properties.