4 papers in the archive (2026), average empirical rigor 3.2/10, average math complexity 4.9/10. Quadrant mix: Lab Rats ×2, Philosophers ×2.
Works mostly in Market Microstructure, Commodities & Energy Markets, Insurance & Actuarial Risk.
Frequent co-authors: Wojtek Stricker (2), Stefan Marx (1), Frank Reinhardt (1), Florian Kollberg (1), Reinhard Magg (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%).
Standard valuation methods, including discounted cash flow, the income approach standard IDW S 1 of the Institute of Public Auditors in Germany, and market multiples, compress milestone probabilities, continuation options, and risk shifts into opaque aggregate parameters; none provides a structured
This paper proposes a formal multi-agent architecture for implementing enterprise AI in regulated insurance firms, integrating economic theory with institutional design. The framework synthesises three core theoretical perspectives: Arrow’s risk pooling theory to formalise risk transformation under
European electricity trading in the EU operates as a constrained multi-layer system in which legal design, exchange microstructure, and network physics are executed jointly across forward, day-ahead, intraday, and balancing horizons. This paper develops a functional architecture for AI-supported tra
We present a compliance-first architecture for AI in regulated finance that treats regulation as an orientation layer rather than a deterministic ruleset. A matrix of regulatory intent and exposure provides a compact classification handle, which a governed policy compiler then maps into concrete pro