3 papers in the archive (2023–2025), average empirical rigor 3.2/10, average math complexity 8.2/10. Quadrant mix: Lab Rats ×3.
Works mostly in Stochastic Control & Optimal Stopping, Options & Derivatives Pricing, Machine Learning for Market Forecasting, Portfolio Optimization.
Frequent co-authors: John Armstrong (2), James Dalby (2), Rohan Hobbs (1), Juan Jose Francisco Miguelez (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 study the optimal investment problem for a homogeneous collective of $n$ individuals investing in a Black-Scholes model subject to longevity risk with Epstein–Zin preferences. %and with preferences given by power utility. We compute analytic formulae for the optimal investment strategy, consumpt
We study the problem of valuing and hedging a vulnerable derivative claim with bilateral cash flows between two counterparties in the presence of asymmetric funding costs, defaults and wrong way risk (WWR). We characterize the pre-default claim value as the solution to a non-linear Cauchy problem. W
We use a neural network to identify the optimal solution to a family of optimal investment problems, where the parameters determining an investor’s risk and consumption preferences are given as inputs to the neural network in addition to economic variables. This is used to develop a practical tool t