Resarch

Designing fair and sustainable mechanisms for sharing risk across individuals and institutions. Research topics include conditional mean risk sharing, peer-to-peer insurance, tontines, decentralized insurance, and large-pool asymptotics.

Mathematical and statistical methods for insurance risk management, including solvency, asset allocation, ruin theory, risk measures, reserving, and the valuation of insurance liabilities.

Machine-learning methods designed for actuarial applications, combining predictive performance with interpretability, statistical robustness, and the specific constraints of insurance modelling.

Statistical modelling of rare and extreme events, with particular emphasis on dependence, clustering, heavy tails, spatial extremes, max-stable processes, and rare-event inference.

Statistical inference for financial markets observed under noise, irregular sampling, and market microstructure effects, with applications to volatility, price discovery, credit risk, and high-frequency data.

Graph-based and machine-learning approaches for modelling interconnected financial markets, combining relational, textual, and market information for forecasting and prediction.

Probabilistic and statistical methods for complex dependence structures and stochastic dynamics, including hierarchical copulas, multivariate modelling, and state-dependent stochastic processes.

My research also feeds into longer-term projects aimed at organizing and synthesizing actuarial knowledge. These books bring together theoretical foundations, methodological developments, and applications developed across several research programmes.