Abstract
dc:descriptionThis dissertation explores the growing importance of network structures in financial markets by examining how firm-level interconnections shape risk transmission and aggregate outcomes. Through three essays, it highlights the role of production-based input-output networks in amplifying idiosyncratic shocks and driving both aggregate volatility and asset pricing dynamics. The first essay develops a dynamic model linking firm-level volatility spillovers to market-wide uncertainty, introducing novel network-based risk factors that are both predictive and priced. The second essay applies Graph Neural Networks to firm credit risk prediction, demonstrating how incorporating inter-firm network features enhances predictive power and interpretability. The final essay provides a theoretical foundation for how persistent, interconnected firm-level risks can generate macroeconomic tail events, even in the absence of large individual shocks. Together, these studies underscore the critical role of financial market networks in understanding modern economic and financial phenomena.
Degree
thesis:*- Name thesis:degree_name
- Ph.D.
- Level thesis:degree_level
- Dissertation
- Discipline thesis:degree_discipline
- Finance
- Grantor
- University of Illinois Urbana-Champaign
- Year dc:date
- 2025
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Chen, Belinda
- Contributors dc:contributor
-
- Kiku, Dana
- Kargar, Mahyar
- Pearson, Neil
- Plante, Sebastien
Subjects
dc:subject × 2Rights
dc:rights- Statement dc:rights
-
- Copyright 2025 Belinda Chen
- Language dc:language
- en, eng
Identifiers
dc:identifier.*- Handle dc:identifier
- https://hdl.handle.net/2142/129681