3 papers in the archive (2023–2024), average empirical rigor 6/10, average math complexity 6.8/10. Quadrant mix: Holy Grail ×3.
Works mostly in Fixed Income & Interest Rates, Machine Learning for Market Forecasting, Insurance & Actuarial Risk, Risk Management & Tail Risk.
Frequent co-authors: Julian Sester (2), Jonathan Ansari (1), Hongyi Shen (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%).
We propose a new deep learning approach for the quantification of name concentration risk in loan portfolios. Our approach is tailored for small portfolios and allows for both an actuarial as well as a mark-to-market definition of loss. The training of our neural network relies on Monte Carlo simula
This paper presents comparison results and establishes risk bounds for credit portfolios within classes of Bernoulli mixture models, assuming conditionally independent defaults that are stochastically increasing with a common risk factor. We provide simple and interpretable conditions for conditiona
Sovereign loan portfolios of Multilateral Development Banks (MDBs) typically consist of only a small number of borrowers and hence are heavily exposed to single name concentration risk. Based on realistic MDB portfolios constructed from publicly available data, this paper quantifies the magnitude of