3 papers in the archive (2023–2025), average empirical rigor 3.7/10, average math complexity 8.3/10. Quadrant mix: Holy Grail ×1, Lab Rats ×2.
Works mostly in Options & Derivatives Pricing, Stochastic Control & Optimal Stopping, Volatility Modeling & Forecasting, Fixed Income & Interest Rates.
Frequent co-authors: Annemarie M. Grass (1), Daniel Hernandez-Hernandez (1), Sigrid Källblad (1), Chaorui Wang (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%).
The robust option pricing problem is to find upper and lower bounds on fair prices of financial claims using only the most minimal assumptions. It contrasts with the classical, model-based approach and gained prominence in the wake of the 2008 financial crisis, and can be used to understand the exte
We consider an agent who has access to a financial market, including derivative contracts, who looks to maximise her utility. Whilst the agent looks to maximise utility over one probability measure, or class of probability measures, she must also ensure that the mark-to-market value of her portfolio
We consider a Bayesian adaptive optimal stochastic control problem where a hidden static signal has a non-separable influence on the drift of a noisy observation. Being allowed to control the specific form of this dependence, we aim at optimising a cost functional depending on the posterior distribu