Automatic Model Risk Management

Regulatory bodies, such as the Office of the Superintendent of Financial Institutions (OSFI) require that financial institutions properly assess and manage risk. Regulatory bodies aim to ensure the stability of financial markets, and the economy at large, and they do so by imposing guidelines on a firm’s risk management practices. Risk management models in general, however, rely on ex-ante assumptions, which can lead to estimation errors, even if the model is “correct”, hence subjecting them to model risk (also known as model uncertainty) and conclusions can vary widely depending on the underlying assumptions. Measuring model performance of rating systems is a major task for regulated firms. This project aims to develop methodology that helps institutions assess their exposure to model risk without the need of setting up specialized in-house teams.

Faculty Supervisor:

Sebastian Jaimungal

Student:

Partner:

BankingBook Analytics Inc

Discipline:

Mathematics

Sector:

Finance and Insurance

University:

University of Toronto

Program: