Finance and Econometrics PhD Candidate
WBS | University of Warwick
I am a PhD candidate in Finance and Econometrics at the University of Warwick – Warwick Business School.
My research interests are in Empirical Asset Pricing and Financial Econometrics.
My supervisors are Cesare Robotti and Philippe Mueller.
We argue that the documented large abnormal returns to investors from corporate bond anomalies such as return reversals and momentum mainly stem from ignoring market microstructure noise in transaction-based bond prices and relying on ad hoc return winsorization. To address these issues, we construct bond data that is largely free of microstructure noise and closely mimics industry-grade quote data. We revisit prior findings in the literature and provide conclusive evidence that return-based anomalies, once properly constructed, generate negligible average returns and alphas. Finally, we show that the considered return-based factors (and their underlying signals) are not related to average bond returns.
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.
We explore predictability in US Treasuries bonds and assess the forecast accuracy of different statistical methodologies used to extract information from a large panel of macroeconomic indicators. Finally, we show that deviations from past realizations of macroeconomic indicators do not help improve the accuracy of forecasts of excess bond returns in the US Treasury market.