Thibaut Duprey is the Director of Systemic Risk Analytics in the Financial Stability Department. He was previously Senior Research Advisor and Director for Model Development and Research in the same department. Over time, he contributed to policy, modelling and research work for both the monetary policy and financial system cycles. His broad topics of interest include the financial stability-monetary policy framework, the interactions with macroprudential policies, the early warnings of financial crises, stress-testing the financial system, the macroeconomic implications of natural disasters… Before joining the Bank, he was an economist at the Financial Stability Directorate of the Banque de France and visited the European Central Bank. He received his Ph.D. in Economics from the Paris School of Economics and a double undergraduate degree in law and economics from the Université de Lorraine.
We use a suite of risk-assessment models to examine the possible impact of a hypothetical house price correction, centred in the Toronto and Vancouver areas. We also assume financial stress significantly amplifies the macroeconomic impact of the house price decline.
Financial system vulnerabilities increase the downside risk to future GDP growth. Macroprudential tightening significantly reduces financial stability risks associated with vulnerabilities. Monetary policy faces a trade-off between financial stability and macroeconomic risks.
When financial system vulnerabilities are elevated, they can give rise to asymmetric risks to the economic outlook. To illustrate this, I consider the economic outlook presented in the Bank of Canada’s October 2017 Monetary Policy Report in the context of two key financial system vulnerabilities: high levels of household indebtedness and housing market imbalances.
This note presents a composite indicator of Canadian financial system vulnerabilities—the Vulnerabilities Barometer. It aims to complement the Bank of Canada’s vulnerabilities assessment by adding a quantitative and synthesized perspective to the more granular (distributional) analysis presented in the Financial System Review.
Severe disruptions in the financial markets, as observed during the 2008 global financial crisis or the COVID-19 pandemic, can impair the stability of the entire financial system and worsen macroeconomic downturns.
The COVID-19 pandemic highlighted the need for policy-makers to closely monitor disruptions to the retail and food business sectors. We present a new method to measure business opening and closing rates using real-time data from Google Places, the dataset behind the Google Maps service.
Can central bank and government policies impact the risks around the outlook for GDP growth? We find that fiscal stimulus makes strong GDP growth more likely—even more so when monetary policy is constrained—rather than weak GDP growth less likely. Thus, fiscal stimulus should accelerate the recovery phase of the COVID-19 pandemic.
Models for macroeconomic forecasts do not usually take into account the risk of a crisis—that is, a sudden large decline in gross domestic product (GDP). However, policy-makers worry about such GDP tail risk because of its large social and economic costs.
This paper predicts phases of the financial cycle by using a continuous financial stress measure in a Markov switching framework. The debt service ratio and property market variables signal a transition to a high financial stress regime, while economic sentiment indicators provide signals for a transition to a tranquil state.
Production efficiency and financial stability do not necessarily go hand in hand. With heterogeneity in banks’ abilities to screen borrowers, the market for loans becomes segmented and a self-competition mechanism arises. When heterogeneity increases, the intensive and extensive margins have opposite effects.
This paper introduces a new methodology to date systemic financial stress events in a transparent, objective and reproducible way. The financial cycle is captured by a monthly country-specific financial stress index.
We present a new corporate default model, one of the building blocks of the Bank of Canada’s bank stress-testing infrastructure. The model is used to forecast corporate loan losses of the Canadian banking sector under stress.
We use models to better understand and assess how risks could affect the financial system. In our hypothetical scenario, a house price correction and elevated financial stress weigh on the economy. An increased number of households and businesses have difficulty repaying loans. Nonetheless, the large banks remain resilient.