1 citations · 1 across the 13 of their papers we have counts for
16 papers
Amortized Feedback Planning: Turning Model-Based Rollouts into Executable Policies
Jeonggyu Huh
Closed-loop planning accounts for future observation-dependent actions but can be expensive to repeat at deployment. Bellman-gradient (BG) refinement differentiates conditional rol…
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-…
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…
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…
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…
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…
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…
Breaking the Dimensional Barrier: A Pontryagin-Guided Direct Policy Optimization for Continuous-Time Multi-Asset Portfolio Choice
Jeonggyu Huh, Jaegi Jeon, Hyeng Keun Koo +1
We introduce the Pontryagin-Guided Direct Policy Optimization (PG-DPO) framework for high-dimensional continuous-time portfolio choice. Our approach combines Pontryagin's Maximum P…
Pontryagin-Guided Policy Optimization for Merton's Portfolio Problem
Jeonggyu Huh, Jaegi Jeon
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 a…
Breaking the Dimensional Barrier for Constrained Dynamic Portfolio Choice
Jeonggyu Huh, Jaegi Jeon, Hyeng Keun Koo +1
We propose a scalable, policy-centric framework for continuous-time multi-asset portfolio-consumption optimization under inequality constraints. Our method integrates neural polici…
Tighter 'uniform bounds for Black-Scholes implied volatility' and the applications to root-finding
Jaehyuk Choi, Jeonggyu Huh, Nan Su
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…
Newton Raphson Emulation Network for Highly Efficient Computation of Numerous Implied Volatilities
Geon Lee, Tae-Kyoung Kim, Hyun-Gyoon Kim +1
In finance, implied volatility is an important indicator that reflects the market situation immediately. Many practitioners estimate volatility using iteration methods, such as the…
Extensive networks would eliminate the demand for pricing formulas
Jaegi Jeon, Kyunghyun Park, Jeonggyu Huh
In this study, we generate a large number of implied volatilities for the Stochastic Alpha Beta Rho (SABR) model using a graphics processing unit (GPU) based simulation and enable…
Consistent and Efficient Pricing of SPX and VIX Options under Multiscale Stochastic Volatility
Jaegi Jeon, Geonwoo Kim, Jeonggyu Huh
This study provides a consistent and efficient pricing method for both Standard & Poor's 500 Index (SPX) options and the Chicago Board Options Exchange's Volatility Index (VIX) opt…
Measuring Systematic Risk with Neural Network Factor Model
Jeonggyu Huh
In this paper, we measure systematic risk with a new nonparametric factor model, the neural network factor model. The suitable factors for systematic risk can be naturally found by…
Pricing Options with Exponential Levy Neural Network
Jeonggyu Huh
In this paper, we propose the exponential Levy neural network (ELNN) for option pricing, which is a new non-parametric exponential Levy model using artificial neural networks (ANN)…