Abstract
This dissertation examines several empirical asset pricing questions using around-the-clock high-frequency data, financial text, and AI methods. The first chapter studies systematic jump risk in equity markets by linking around-the-clock market jumps to contemporaneous news narratives identified with a reasoning large language model, and finds substantial heterogeneity in risk premia across jump types. The second chapter addresses lookahead bias in financial text analysis by developing chronologically consistent language models and showing that they perform well in both standard language tasks and asset pricing applications. The third chapter studies cryptocurrency perpetual futures, derives no-arbitrage benchmark prices under realistic payoff assumptions, and documents persistent deviations from these benchmarks and economically significant arbitrage opportunities.
Committee Chair
Asaf Manela
Committee Members
Guofu Zhou, Andreas Neuhierl; Julie Fu; Maarten Meeuwis
Degree
Doctor of Philosophy (PhD)
Author's Department
Finance
Document Type
Dissertation
Date of Award
5-5-2026
Language
English (en)
DOI
https://doi.org/10.7936/pq3c-3r02
Recommended Citation
He, Songrun, "Essays in Empirical Asset Pricing" (2026). Olin Business School Graduate Student Theses and Dissertations. 71.
The definitive version is available at https://doi.org/10.7936/pq3c-3r02