Developing methods to manage and mitigate risk associated with complex data and systems
Major events like the 2007 subprime mortgage crisis and the COVID-19 pandemic point to the importance of quantitative risk management for the modern world. This field enables individuals, financial institutions and policy-makers to understand, communicate, analyze and manage various types of risk. Yet, existing risk management methods are insufficient for a world of increasingly complex data, risks and systems.
The Quantitative Risk Lab will improve society's understanding and mitigation of risks by advancing theory and quantitative methods to better address this complexity. Led by Dr. Ruodu Wang, a professor of actuarial science and quantitative finance and Canada Research Chair in Quantitative Risk Management, the lab will pursue the following objectives:
- Propose and analyze risk measures and risk assessment procedures in optimization, portfolio analysis and decision-making under uncertainty.
- Develop high-dimensional dependence modelling and mass transportation, with applications in finance, economics, and large-scale testing.
- Design methods for reliable and robust inference with e-values in multiple testing and backtesting risk measures.
One of the challenges I work on is how to build mathematical frameworks when you don’t have well-behaved data. In the financial world, many new and unforeseen things occur, like the COVID pandemic, geopolitical tensions, climate change or trade wars–and in even higher frequency in recent years. For these events, you don’t have a lot of data to help you predict what will happen and what consequences will follow from your actions. I am developing new methods to analyze risk in scenarios where there is a high degree of uncertainty.