4 papers in the archive (2022–2026), average empirical rigor 6.2/10, average math complexity 5.9/10. Quadrant mix: Holy Grail ×2, Lab Rats ×1, Street Traders ×1.
Works mostly in Risk Management & Tail Risk, High-Frequency Trading & Optimal Execution, Options & Derivatives Pricing, Factor Investing & Asset Pricing.
Frequent co-authors: Anton J. Heckens (3), Benjamin Köhler (2), Roger Knecktys (1), Henrik Bette (1), Rüdiger Kiesel (1), Efstratios Manolakis (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%).
Extreme values and the tail behavior of probability distributions are essential for quantifying and mitigating risk in complex systems of all kinds. In multivariate settings, accounting for correlations is crucial. Although extreme value analysis for infinite correlated systems remains an open chall
To understand the emergence of Ultrafast Extreme Events (UEEs), the influence of algorithmic trading or high-frequency traders is of major interest as they make it extremely difficult to intervene and to stabilize financial markets. In an empirical analysis, we compare various characteristics of UEE
Risk assessment for rare events is essential for understanding systemic stability in complex systems. As rare events are typically highly correlated, it is important to study heavy-tailed multivariate distributions of the relevant variables, especially in the presence of non-stationarity. We use a g
Technology trends as digitalization and Industry 4.0 initiate a growing demand for new business models. Most of this models requires a fundamental shift of operational and financial risks between seller and buyer. A key question is therefore how to include additional risk pricing and hedging. In thi