Faculty of Actuarial Science and Insurance Research Seminars - Bård Støve

Abstract:
Pearson’s rho is a commonly used measure of dependence but can be misleading in heavy-tailed or nonlinear settings. This talk examines Local Gaussian Correlation (LGC) as an alternative, combining it with Hidden Markov Models (HMMs) to capture time-varying and regime-specific dependence structures in general insurance data. We propose a bootstrap test to assess differences in dependence across regimes and apply the framework to insurance claims and financial data. The results indicate structural breaks and varying tail dependencies over time. We also demonstrate how the model can be used to produce regime-aware risk measures such as Value-at-Risk (VaR) and Tail VaR, offering a more adaptive approach for risk assessment in insurance applications.

Biography:
Bård Støve is a Professor of Statistics at the Department of Mathematics, University of Bergen, Norway. He holds a PhD in Statistics from the University of Bergen and an MSc in Mathematics and Physics from the Norwegian University of Science and Technology. He is a qualified actuary and full member of the Norwegian Actuarial Association. His research focuses on the development of statistical methodology—particularly nonparametric methods, time series analysis, and dependence modeling—with applications in finance, insurance, climatology, and medical statistics. He has published in leading journals, including Statistical Science, Journal of Empirical Finance, Journal of the Royal Statistical Society, and Insurance: Mathematics and Economics. In addition to his academic role, he holds a part-time position as an actuarial consultant, contributing expertise in reserving, pricing, and regulatory compliance.