4 papers in the archive (2023–2026), average empirical rigor 3.6/10, average math complexity 6.9/10. Quadrant mix: Holy Grail ×1, Lab Rats ×2, Philosophers ×1.
Works mostly in Options & Derivatives Pricing, Volatility Modeling & Forecasting, Reinforcement Learning for Trading, Machine Learning for Market Forecasting.
Frequent co-authors: Charlie Che (3), Shouvanik Chakrabarti (3), Dylan Herman (2), Marco Pistoia (2), Jin-Peng Liu (1), Rob Otter (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 develop a global framework for joint S&P 500 (SPX)-VIX smile calibration across multiple maturities without the conditional-independence restriction induced by Markovian stitching. Exact local and global feasibility are equivalent: every globally feasible law has a block-preserving SPX-Markovizat
We establish dual attainment for the multimarginal, multi-asset martingale optimal transport (MOT) problem, a fundamental question in the mathematical theory of model-independent pricing and hedging in quantitative finance. Our main result proves the existence of dual optimizers under mild regularit
This paper explores advancements in quantum algorithms for derivative pricing of exotics, a computational pipeline of fundamental importance in quantitative finance. For such cases, the classical Monte Carlo integration procedure provides the state-of-the-art provable, asymptotic performance: polyno
Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance. In our work we look at the problem of hedging where deep reinforcement learning offers a powerful framework for real markets. We develop quantum reinforcement learning methods