Enhancing the Accuracy and Interpretability of Canadian Macro-Financial Tail Risk Forecasts via Multi-Quantile Deep Learning with Feature Engineering to Monitor Systemic Risks at the Bank of Canada

Crises risks are notoriously hard to quantify. Yet, when systemic crises materialize, for instance the Global Financial Crisis (GFC), the cost for the economy and the society can be huge, with protracted recessions and financial hardships for firms and households. Thus, it is essential for public authorities to monitor and proactively address systemic risks, thereby ensuring a stable and efficient financial system that can sustain economic growth and raise standards of living.

In this context, the Bank of Canada seeks to leverage advanced tools such as artificial intelligence and machine learning to keep improving its assessment of systemic risks. The purpose of this joint project with the academic partners is to develop state-of-the-art forecasts of macro-financial tail risks, capturing extreme shocks like those seen during the GFC.

Faculty Supervisor:

Fred Liu

Student:

Partner:

Bank of Canada

Discipline:

Business

Sector:

Finance and Insurance; Manufacturing; Public administration

University:

University of Guelph

Program: