Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023)

Extreme risks come from rare events accompanied by disastrous economic and social consequences; for example, the 2008 financial crisis, which directly led to the 2008 – 2012 global recession, and the 2016 Fort McMurray wildfire, which is the costliest disaster in Canadian history. These man-made or natural catastrophes, which produce outliers in statistical data, all substantially affect the financial markets and (re)insurance industry. Due to these disastrous consequences, a well-functioning risk management system is of crucial importance.

The rareness of these extreme risks makes them especially hard to predict. Extreme Value Theory (EVT) provides an efficient way to study them. In the proposed research, EVT will be an important toolbox to investigate various risk management questions, such as the portfolio diversification, the (re)insurance of catastrophic risks, and the statistical inference of measures for extreme risks. In the meantime, analytical and probabilistic techniques will be developed when existing techniques are not applicable.

Based on the aforementioned questions, the objectives of the proposed research are: 1) to analyze the heavy-tailed and dependent risks in insurance and finance; 2) to improve quantitative risk management techniques to protect from extreme risks; 3) to develop robust inference methods for quantile-based measures of dependent extreme risks. This research will contribute to the development of new theories in multivariate risk modeling and management. The proposed methods will give analytical answers to important risk management questions concerning extreme risks. Quantitative risk management techniques will be developed to improve the design and valuation of CAT bonds in practice, and, therefore to mitigate extreme risks, for example wildfire risks in Canada. Robust estimation methods will be developed such that statistical inferences are feasible for important risk quantities. This will improve the understanding of the nature of extreme risks, and therefore help insurers to better predict, evaluate and manage these risks in practice.

This research program will produce 2-3 sole-authored or joint papers per year to be published in top tier journals in actuarial science, applied probability or statistics. Graduate students and exceptional undergraduate students will be intensively involved in this program. This training will lead to a number of future academic researchers and professional employees applying and extending these areas of research even further.