Grant: Canada Research Chairs

Quantitative risk management has drawn increasing attention in both academia and industry over the past three decades because of its importance for individuals, financial institutions, and policy makers to properly understand, communicate, analyze, and manage various types of risks. Although quantitative theory and methods for risk management are rapidly developed by mathematicians, statisticians, actuaries and economists, and used on a daily basis in the financial and other industries, existing approaches are far away from being sufficient for challenges in the world of increasingly complex data, risks, and systems. Demands and challenges for developing advanced quantitative technologies of risk management and lack of highly qualified risk professionals are highlighted by examples such as the 2007 subprime mortgage crisis, the COVID-19 pandemic and its economic impact, and the rapidly evolving climate change. The long-term goal of the proposed research program of the CRC in Quantitative Risk Management is to improve society's understanding and mitigation of risks by advancing the theory and quantitative methods that will better address complex data, risks and systems, as well as other risk management challenges arising in finance, economic and natural sciences. To achieve this goal, the following short-term objectives will be pursued: 1) propose and analyze risk measures and risk assessment procedures in optimization, portfolio analysis and decision making under uncertainty; 2) develop high-dimensional dependence modelling and mass transportation, with applications in finance, economics, and large-scale testing; 3) design methods for reliable and robust inference with e-values in multiple testing and backtesting risk measures. This research theme contains both theoretical studies in mathematical and statistical methodology of broad scientific value and specialized techniques and tools for risk management, finance and economics, closely aligned with modern statistical and computational technology. Many undergraduate and graduate students and postdoc fellows will play a crucial role in the success of this scientific adventure by working together with the CRC. The research output will provide data-driven quantitative tools for scientists in a wide variety of fields that rely heavily on data and statistical methods. The common quantitative tools across the three objectives are risk measures, dependence models, and e-values.