About me

I am a PhD candidate in Economics at the University of Warwick.

My research lies at the intersection of econometrics, time series and financial econometrics.

My main supervisor is Eric Renault. My second main supervisor is Cesare Robotti.

E-mail: amedeo[dot]andriollo[at]warwick[dot]ac[dot]uk

Research

(feedback is welcome!)

Working papers

• “On the statistical properties of tests of parameter restrictions in beta-pricing models with a large number of assets”. 2023. with Cesare Robotti and Giulio Rossetti.
  WP link. Online Appendix: Mispricing. Slides: SoFiE 2023.
  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 cross-sectional dimension is large. By simulating a variety of empirically calibrated data generating processes for sample sizes that are typically en- countered in empirical work, we evaluate the finite-sample 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. (2022) 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.

• “Misspecification and Weak Identification in the Nontraded Factor Zoo”. 2024. with Cesare Robotti and Xinyi Zhang.
  WP link. Data: Nontraded Factor Zoo (monthly); Nontraded PCs (monthly).
  Abstract:
  To explain the cross-section of asset returns, a zoo of economic factors that are not portfolio excess returns has been proposed. In contrast to traded factors, the non-traded factors tend to exhibit lower correlations with the 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 non-traded 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 non-traded 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. However, when summarizing the non-traded factors via PCA, we find that the zoo does carry some non-zero pricing information.

Works in Progress

• “Portmanteau Testing for the Martingale Difference Hypothesis”
  Abstract: TBD.

• “Identification of Impulse Responses Zoo Tamed by Local Projections”. with Eric Renault.
  Abstract: TBD.