Networks & finance
BSc Thesis: Graph Finance
Applying graph theory to model systemic risk and relationships in financial networks.
Overview
This thesis explores financial systems as graphs, focusing on how interconnected institutions influence systemic risk. It combines theoretical graph metrics with empirical data to identify vulnerability patterns.
The project supports risk analysts with a more transparent view of cascading effects in financial markets.
Key features
- Modeled interbank exposure networks with graph centrality measures.
- Simulated shock propagation to highlight systemic risk pathways.
- Built explanatory visuals for non-technical stakeholders.
- Summarized findings in a thesis report and presentation deck.
Technical approach
The analysis uses network science libraries, Monte Carlo simulations, and graph visualization techniques to map risk exposure. Python notebooks ensure reproducible outputs.
Results & impact
The work clarifies how systemically important nodes amplify risk and offers a foundation for resilient financial network design.