4 papers in the archive (2025–2026), average empirical rigor 5.8/10, average math complexity 8.2/10. Quadrant mix: Holy Grail ×3, Lab Rats ×1.
Works mostly in Commodities & Energy Markets, Stochastic Control & Optimal Stopping, Volatility Modeling & Forecasting, Portfolio Optimization.
Frequent co-authors: Iosif Zimbidis (2), Patrick Chan (1), Emma Hubert (1), Dimitrios Lolas (1), Alexander Crosier (1), Kyle Onghai (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 problem of forecasting and optimally trading day-ahead versus real-time (DART) price spreads in U.S. wholesale electricity markets. Building on the framework of Galarneau-Vincent et al., we extend spike prediction from a single zone to a multi-zone setting and treat both positive and ne
The twenty-first century’s transformative technology, artificial intelligence, is increasingly constrained by the twentieth century’s transformative technology, the electricity grid. Rapid growth in electricity demand from data centers is leading to higher electricity prices, without a compensating
On April 20, 2020, the May front-month WTI oil futures contract, one day before its expiration date, opened near $$17/$barrel and dropped far below zero in a single trading day, reaching an intraday low of $-$40.32$ and settling at $-$37.63$. Such market behavior was unforeseen at the time. This
We consider a dynamic portfolio optimization problem that incorporates predictable returns, instantaneous transaction costs, price impact, and stochastic volatility, extending the classical results of Garleanu and Pedersen (2013), which assume constant volatility. Constructing the optimal portfolio