Advances in Statistical Methods for Financial Risk Management

Financial systems are deeply intertwined with societal well-being as they influence economic stability, access to resources, and resilience of communities today and their advancement tomorrow. This session brings together a range of experiences - from academia, regulatory agencies, and industry - in assessment and mitigation of financial risks to society at large, while fostering economic growth and innovation. The session highlights how cutting-edge statistical methods in finance can empower communities by improving safety and soundness of individual financial institutions as well as stability of large financial networks. By addressing topics ranging from credit concentration risk and pricing kernel estimation to integrated risk assessment and macroeconomic index construction, the papers in this session emphasize the importance of statistical techniques in enhancing accuracy, efficiency, and robustness of financial models. Through the use of innovative approaches in Monte Carlo and nonparametric estimation, copula-based frameworks, regime switching, and statistics on graphs and networks, this session bridges methodological advancements in statistics with practical applications in finance. Together, these contributions underscore the importance of adapting statistical tools to evolving financial landscapes, regulatory requirements, and interconnected economic risks. The session includes presentations by early-career researchers as well as presenters with decades of experience in academia, financial regulation, and financial industry. Target audience: This session is designed for researchers, practitioners, and regulators interested in the intersection of statistics and finance. It will appeal to those working in risk management, banking, financial stability, and macroeconomic analysis.