3 papers in the archive (2025–2026), average empirical rigor 6.7/10, average math complexity 8/10. Quadrant mix: Holy Grail ×3.
Works mostly in Commodities & Energy Markets, Stochastic Control & Optimal Stopping, Volatility Modeling & Forecasting, Risk Management & Tail Risk.
Frequent co-authors: Michael Samet (1), Raúl Tempone (1), Xianglin Wu (1), Cornelis W. Oosterlee (1), Truong Ngoc Nguyen (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 rapid growth of weather-dependent renewable generation increases price volatility and imbalance penalty risk in power markets, creating the need for advanced quantitative trading strategies. We develop a data-driven continuous-time stochastic optimal control framework for intraday electricity tr
Stochastic differential equations (SDEs) driven by fractional Brownian motion (fBm) are increasingly used to model systems with rough dynamics and long-range dependence, such as those arising in quantitative finance and reliability engineering. However, these processes are non-Markovian and lack a s
Multivariate shortfall risk measures provide a principled framework for quantifying systemic risk and determining capital allocations prior to aggregation in interconnected financial systems. Despite their well established theoretical properties, the numerical estimation of multivariate shortfall ri