Papers
Papers
Breaking the Dimensional Barrier: A Pontryagin-Guided Direct Policy Optimization for Continuous-Time Multi-Asset Portfolio, Jeonggyu Huh*, Jaegi Jeon, Hyeng-Keun Koo, Byung Hwa Lim* [Paper] [Code] [Slides]
+ Supplementary proofs: Detailed Proof of Theorem 3 — BPTT–BSDE Equivalence [PDF] · Detailed Proof of Theorem 4 — Policy-Gap Bounds [PDF]
Breaking the Dimensional Barrier for Constrained Dynamic Portfolio Choice, Jeonggyu Huh*, Jaegi Jeon*, Hyeng-Keun Koo, Byung Hwa Lim [Link] [Code]
Bounded Rationality, Reinforcement Learning, and Market Efficiency, Hyun Soo Doh*, Jeonggyu Huh, Byung Hwa Lim* [Link]
Forward-Looking Predictors and Shapley Screening in Risk Premium Forecasting, Jeonggyu Huh*, Jaegi Jeon, Seung-Won Jeong*
Dual‑Uncertainty Modeling in Financial Time‑Series via VMD‑LSTM with Concrete Dropout and VMD‑WGAN, Jeonggyu Huh*, Dajin Kim, Minseok Jung, Seung-Won Jeong*, under revision in Networks and Heterogeneous Media
LSTM-based Dynamic Correlation Forecasting with Economic Conditions, Jeonggyu Huh*, Seungwoo Ha, Seung-Won Jeong*, Finance Research Letters, 2025 [Link]
Improved Accuracy of an Analytical Approximation for Option Pricing under Stochastic Volatility Models using Deep Learning Techniques, Donghyun Kim*, Jeonggyu Huh*, Ji-Hun Yoon, Computers and Mathematics with Applications, 2025 [Link]
Learning Distributions for Continuous-Time Financial Models, Jeonggyu Huh*, Seung-Won Jeong*, Computational Economics, 2025 [Link]
Pontryagin-Guided Direct Policy Optimization for Continuous-Time Portfolio Problem, Jeonggyu Huh*, Jaegi Jeon*, Seung-Won Jeong, Journal of Industrial and Management Optimization, 2025 [Link]
Reliable option pricing through deep learning: An anomaly score-based approach, Jihong Park*, Jeonggyu Huh, Jaegi Jeon*, Networks and Heterogeneous Media [Link]
[Conference] Bounded Rationality, Reinforcement Learning, and Market Efficiency, Hyun Soo Doh*, Byung Hwa Lim, Jeonggyu Huh, China International Conference in Finance (CICF), 2025 [Link]
[Insight Report] AI Bringing Dynamic Portfolio Choice into Reality, The Korean Journal of Financial Studies [Link]
Considering Appropriate Input Features of Neural Network to Calibrate Option Pricing Models, Hyun-Gyoon Kim*, Hyungmi Kim, Jeonggyu Huh*, Computational Economics, 2024 [Link]
Deep Learning of Optimal Exercise Boundaries for American Options, Hyun-Gyoon Kim*, Jeonggyu Huh*, International Journal of Computer Mathematics, 2024 [Link]
Tighter 'Uniform Bounds for Black-Scholes Implied Volatility' and the Applications to Root-Finding, Jaehyuk Choi*, Jeonggyu Huh, Su Nan, Operations Research Letter, 2024 [Link]
Accelerating SDE Simulation through Learning of Stochastic Dynamics, Seung-Won Jeong*, Ji-Hun Kim, Jitae Jung, Jeonggyu Huh*, Journal of Korean Society for Industrial and Applied Mathematics, 2024 [Link]
[Conference] Continuous-Time Portfolio Optimization via Model-based Reinforcement Learning, Jeonggyu Huh, Hyeng-Keun Koo, Byung Hwa Lim*, Financial Management Association (FMA) Asia/Pacific, 2024 [Link]
Variable Annuity with a Surrender Option under Multi-Scale Stochastic Volatility, Jeonggyu Huh*, Junkee Jeon, Kyunghyun Park*, Japan Journal of Industrial and Applied Mathematics, 2023 [Link]
Analytical Pricing of Exchange Option with Default Risk under a Stochastic Volatility Model, Jaegi Jeon*, Jeonggyu Huh, Geonwoo Kim*, Advances in Continuous and Discrete Models, 2023 [Link]
Random Augmentation Technique for Mitigating Overfitting in Neural Networks for Financial Time Series Forecasting, Yeonglong Kwak*, Jeonggyu Huh*, Journal of The Korean Data Analysis Society, 2023 [Link]
Extensive Networks Would Eliminate the Demand for Pricing Formulas, Jaegi Jeon*, Kyunghyun Park, Jeonggyu Huh*, Knowledge-Based Systems, 2022 [Link]
Pricing Path-Dependent Exotic Options with Flow-Based Generative Networks, Hyun-Gyoon Kim*, Se-Jin Kwon, Jeong-Hoon Kim, Jeonggyu Huh*, Applied Soft Computing, 2022 [Link]
Large Scale Online Learning of Implied Volatilities, Tae-Kyoung Kim*, Hyun-Gyoon Kim, Jeonggyu Huh*, Expert Systems with Applications, 2022 [Link]
Newton–Raphson Emulation Network for Highly Efficient Computation of Numerous Implied Volatilities, Journal of Risk and Financial Management, Geon Lee*, Tae-Kyoung Kim, Hyun-Gyoon Kim, Jeonggyu Huh*, 2022 [Link]
Consistent and Efficient Pricing of SPX Options and VIX Options under Multi-Scale Stochastic Volatilities, Jaegi Jeon*, Geonwoo Kim, Jeonggyu Huh*, Journal of Futures Markets, 2021 [Link]
Asymptotic Expansion Approach to the Valuation of Vulnerable Option under a Multiscale Stochastic Volatility Model, Jaegi Jeon*, Geonwoo Kim, Jeonggyu Huh*, Chaos, Solitons & Fractals, 2021 [Link]
Pricing of Vulnerable Power Exchange Option under the Hybrid Model, Jaegi Jeon*, Jeonggyu Huh, Geonwoo Kim*, East Asian Mathematical Journal, 2021
Simplified Approach to Valuation of Vulnerable Exchange Option under a Reduced-Form Model, Jeonggyu Huh*, Jaegi Jeon, Geonwoo Kim*, East Asian Mathematical Journal, 2021
Measuring Systematic Risk with Neural Network Factor Model, Jeonggyu Huh*, Physica A : Statistical Mechanics and its Applications, 2020 [Link]
Static Hedges of Barrier Options under Fast Mean-Reverting Stochastic Volatility, Jeonggyu Huh*, Jaegi Jeon, Yong-Ki Ma*, Computational Economics, 2020 [Link]
An Analytic Approximation for the Valuation of American Option in Two Regimes, Junkee Jeon*, Jeonggyu Huh, Kyunghyun Park*, Computational Economics, 2020 [Link]
Pricing Options with Exponential Levy Neural Network, Jeonggyu Huh*, Expert Systems with Applications, 2019 [Link]
A Reduced PDE Method for European Option Pricing under Multi-Scale, Multi-Factor stochastic volatility, Jeonggyu Huh*, Jaegi Jeon, Jeong-Hoon Kim*, Hyejin Park, Quantitative Finance, 2019 [Link]
Barrier Option Pricing with Heavy-Tailed Distribution, Geonwoo Kim*, Jeonggyu Huh*, Economic Computation and Economic Cybernetics Studies and Research, 2019
A Scaled Version of the Double-Mean-Reverting Model for VIX Derivatives, Jeonggyu Huh*, Jaegi Jeon, Jeong-Hoon Kim*, Mathematics and Financial Economics, 2018 [Link]