4 papers in the archive (2022–2024), average empirical rigor 5.1/10, average math complexity 7.1/10. Quadrant mix: Holy Grail ×3, Lab Rats ×1.
Works mostly in Options & Derivatives Pricing, Machine Learning for Market Forecasting, Insurance & Actuarial Risk, Volatility Modeling & Forecasting.
Frequent co-authors: Hauke Stier (2), Marcus C. Christiansen (1), Solveig Flaig (1), Ralf Werner (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%).
Machine learning (ML) methods are becoming increasingly important in the design economic scenario generators for internal models. Validation of data-driven models differs from classical theory-based models. We discuss two novel aspects of such a validation: first, checking dependencies between risk
Fourier pricing methods such as the Carr-Madan formula or the COS method are classic tools for pricing European options for advanced models such as the Heston model. These methods require tuning parameters such as a damping factor, a truncation range, a number of terms, etc. Estimating these tuning
The Fourier-cosine expansion (COS) method is used to price European options numerically in a very efficient way. To apply the COS method, one has to specify two parameters: a truncation range for the density of the log-returns and a number of terms N to approximate the truncated density by a cosine
Financial institutions and insurance companies that analyze the evolution and sources of profits and losses often look at risk factors only at discrete reporting dates, ignoring the detailed paths. Continuous-time decompositions avoid this weakness and also make decompositions consistent across diff