4 papers in the archive (2023–2026), average empirical rigor 5/10, average math complexity 7.5/10. Quadrant mix: Holy Grail ×2, Lab Rats ×2.
Works mostly in Options & Derivatives Pricing, Volatility Modeling & Forecasting, Machine Learning for Market Forecasting, Market Simulation & Agent-Based Models.
Frequent co-authors: Hyeng Keun Koo (2), Jaehyuk Choi (1), Nan Su (1), Seungwon Jeong (1), Hyun-Gyoon Kim (1), Byung Hwa Lim (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%).
This paper introduces MarketGAN, a factor-based generative framework for high-dimensional asset return generation under severe data scarcity. We embed an explicit asset-pricing factor structure as an economic inductive bias and generate returns as a single joint vector, thereby preserving cross-sect
We present a Pontryagin-Guided Direct Policy Optimization (PG-DPO) framework for Merton’s portfolio problem, unifying modern neural-network-based policy parameterization with the adjoint viewpoint from Pontryagin’s maximum principle (PMP). Instead of approximating the value function (as done in deep
We study continuous-time portfolio choice in diffusion markets with parameter $θ\in Θ$ and uncertainty law $q(dθ)$. Nature draws latent $θ\sim q$ at time 0; the investor cannot observe it and must deploy a single $θ$-blind feedback policy maximizing an ex-ante CRRA objective averaged over diffusion
Using the option delta systematically, we derive tighter lower and upper bounds of the Black-Scholes implied volatility than those in Tehranchi [SIAM J. Financ. Math. 7 (2016), 893-916]. As an application, we propose a Newton-Raphson algorithm on the log price that converges rapidly for all price ra