3 papers in the archive (2023–2025), average empirical rigor 4.5/10, average math complexity 8.2/10. Quadrant mix: Holy Grail ×1, Lab Rats ×2.
Works mostly in Risk Management & Tail Risk, Machine Learning for Market Forecasting, Insurance & Actuarial Risk, Portfolio Optimization.
Frequent co-authors: Sören Bettels (1), Daniel Gaigall (1), Cosimo Munari (1), Justin Plückebaum (1).
Scores are our LLM-assisted heuristics (methodology), not peer review or citation counts. Author pages are generated from paper metadata; name variants may split one person across pages. arXiv author page →
Ordered by a blend of empirical rigor (60%) and math complexity (40%).
Distortion risk measures play a critical role in quantifying risks associated with uncertain outcomes. Accurately estimating these risk measures in the context of computationally expensive simulation models that lack analytical tractability is fundamental to effective risk management and decision ma
We introduce a framework for systemic risk modeling in insurance portfolios using jointly exchangeable arrays, extending classical collective risk models to account for interactions. Joint exchangeability is a more general probabilistic symmetric than de Finetti’s exchangeability, characterized by t
We study mean-risk optimal portfolio problems where risk is measured by Recovery Average Value at Risk, a prominent example in the class of recovery risk measures. We establish existence results in the situation where the joint distribution of portfolio assets is known as well as in the situation wh