Economics, Finance Research
Causal Inference, Econometrics, High-Dimensional Data Analysis, Statistical Inference, Statistical Modeling
Relevant Projects:

“A High-Dimensional Approach to Measure Connectivity in the Financial Sector”;

“Bank Risk Dynamics and Distance-to-Default”;

“Disaster Lending: “Fair Prices” but “Unfair Access”

Amiyatosh Purnanandam

Michael Stark Professor of Finance, Chair of Finance Area

Ross School of Business

My primary research is focused on measurement and monitoring of risks in banks, both at the individual bank level and at the level of financial system as a whole. In a recent paper, we have developed a high-dimension statistical approach to measure connectivity across different players in the financial sector. We implement our model using stock return data for US banks, insurance companies and hedge funds. Some of my early research has developed analytical tools to measure banks’ default risk using option pricing models and other tools of financial economics. These projects have often a significant empirical component that uses large financial datasets and econometric tools. Of late, I have been working on several projects related to the issue of equity and inclusion in financial markets. These papers use large datasets from financial markets to understand differences in the quantity and quality of financial services received by minority borrowers. A common theme across these projects is the issue of causal inference using state-of-the art tools from econometrics. Finally, some of ongoing research projects are related to FinTech with a focus on credit scoring and online lending.