ConvexBeta Investments

The firm focuses on identifying and capturing convex, option-like payoff structures embedded in deregulated power and gas markets. Its thesis rests on the view that the energy transition — particularly the rapid build-out of intermittent renewables — introduces persistent structural volatility that systematically misprices embedded optionality in financial transmission rights, spark spreads, and other energy derivatives. This creates a durable edge for operators with both quantitative firepower and operational understanding of grid physics.

The edge derives from a proprietary integration of stochastic grid modeling, high-performance computing, and practitioner-level understanding of market rules across ISOs. Rather than applying generic statistical arbitrage techniques, the firm builds bottom-up simulations of generation stacks, transmission flows, and regulatory rule changes that most pure-quant funds overlook. This granularity allows ConvexBeta to spot regime shifts before they manifest in observable price data, front-running the slower reaction time of less specialized capital.

ConvexBeta operates on proprietary capital and does not manage external investor commitments. This structure eliminates the mismatch between long-dated, volatile energy-convexity trades and short-term LP redemption expectations. It also exempts the firm from the disclosure and risk-constraint frameworks that would otherwise expose the strategy's positions to front-running or crowding by competitors.

The firm is active across major North American independent system operators, including PJM, ERCOT, and CAISO, where liquidity and volatility are highest. It also evaluates European gas and carbon markets when structural dislocations provide analytical overlap with its North American models. The strategy is designed to pivot capital toward whichever region presents the most severely mispriced convexity at a given time.

The firm's staffing combines quantitative researchers, former power traders, and grid engineers — a cross-disciplinary mix more commonly found within utility proprietary desks than at standalone investment firms. This hybrid composition ensures that statistical models remain grounded in physical reality: a stochastic simulation is only as valuable as its ability to capture generator outage patterns, RTO rule amendments, and transmission derate schedules that pure data-science teams frequently miss.