activity
20182026
most citedTighter 'uniform bounds for Black-Scholes implied volatility' and the applications to root-finding

1 citations · 1 across the 13 of their papers we have counts for

collaborators

15 papers

math.OC2026

Self-Consistent Adjoint Policy Iteration for Constrained Dynamic Portfolio Choice

Jeonggyu Huh, Yeoneung Kim, Seungwon Jeong

We develop simulation-based policy iteration for continuous-time portfolio choice with predictable returns and convex constraints. Each outer step re-evaluates a fixed-latent open-…

q-fin.PM2026

Scalable Pontryagin-Guided Adjoint-to-Control Recovery for Constrained Dynamic Portfolio Choice

Jaegi Jeon, Jeonggyu Huh, Hyeng Keun Koo +1

We study continuous-time multi-asset portfolio choice and consumption under smooth pointwise constraints, including state-dependent feasible sets. The method separates dynamic info…

q-fin.MF2026

From Value Bounds to Policy-Distance and Active-Face Certificates: Same-Grid Duality for Constrained Dynamic Portfolios

Jeonggyu Huh

Neural and numerical policy solvers can produce feasible controls even when the optimal rule and its binding constraints are unavailable. A primal-dual bracket certifies value loss…

cs.LG2026

Beyond the Bellman Recursion: A Pontryagin-Guided Framework for Non-Exponential Discounting

Hojin Ko, Jeonggyu Huh

Most value-based and actor--critic reinforcement learning methods rely on Bellman-style recursions, yet these recursions collapse under non-exponential discounting common in human…

q-fin.CP2026

Breaking the Dimensional Barrier: Dynamic Portfolio Choice with Parameter Uncertainty via Pontryagin Projection

Jeonggyu Huh, Hyeng Keun Koo

We study continuous-time CRRA portfolio choice in diffusion markets with estimated and hence uncertain coefficients. Nature draws a latent parameter at time and keeps…

q-fin.ST2026

MarketGANs: Multivariate financial time-series data augmentation using generative adversarial networks

Jeonggyu Huh, Seungwon Jeong, Hyun-Gyoon Kim +2

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 fac…