Preprints
Preprints
>> PG-DPO
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: Dynamic Portfolio Choice with Parameter Uncertainty via Pontryagin Projection, Jeonggyu Huh*, Hyeng-Keun Koo [Paper] [Slide]
>> PG-DPO
Unverified Optimism in World Models: Pontryagin-Guided Control Verification
Finite Horizon and Optimal Portfolio Choice with Stochastic Income: A Reinforcement Learning Approach, with Seyoung Park, Hojin Ko
Pontryagin-Guided Deep Hedging , with Seungho Na
Utility Maximization under Shortfall Tail Risk: A CRRA–CVaR Formulation, with Jung Min Lee
>> DeepONet
Deep Operator Learning for Option Pricing with Functional Coefficients: A MIONet Approach, with Myeongsik Kim, Woo-Chul Choi
Real-Time Pricing of Equity-Linked Securities Using Deep Operator Networks, with Yoonyoung Byun
Macro-Based Forecasting of Implied Volatility Surface Dynamics, with Hojin Ko, Ho-Jun Lee, Wonwoo Choi
>> Asset Pricing
Decision Focused Learning of Asset Betas for Sharpe-Optimal Portfolios, with Dongwan Shin, Hojin Ko
Scalable Dynamic Portfolio Allocation via Physics-Informed Neural Networks, with Seungwon Jeong, Yeoneung Kim
Adversarial Time-Series Domain Adaptation for Early-Stage IPO Price Prediction, with Youngwoo Lee
Discounted Alpha: A Machine Learning Framework for Equity Valuation, with Dongwan Shin, Thummim Cho, Hyeng-Keun Koo
Beyond its current scope, PG-DPO admits natural extensions to partial equilibrium settings—including taxation, jump, rough volatility, optimal stopping, Epstein-Zin utility, smooth ambiguity and belief-state dynamics—as well as general equilibrium frameworks such as mean-field control and multi-agent games.