구성원
- HOME
- 구성원
- 전임교원
전임교원
김현균Hyun-Gyoon Kim
전공 : 금융공학과
학위 : 박사
이메일 : hyungyoonkim@ajou.ac.kr
연구실 : 다산관 408호
연구실번호 : 3667
연구실관심분야 : 금융AI응용, 생성모델
학력
- 2017.02 연세대학교 학사
- 2023.02 연세대학교 박사
경력
- 2023~현재: 아주대학교
연구분야
- 금융공학 AI 활용, 생성모델
연구활동(주요논문)
- S. Y. Cho, S. Lee, and H. G. Kim, "Forecasting VIX using interpretable Kolmogorov-Arnold networks." Expert Systems with Applications, 2025
- J. Y. Kim, H. Go, S. Kwon, and H. G. Kim, "Denoising task difficulty-based curriculum for training diffusion models," ICLR, 2025.
- H. G. Kim and J. H. Kim, "A stochastic-local volatility model with Lévy jumps for pricing derivatives," Applied Mathematics and Computation, 2023.
- H. G. Kim, S. J. Kwon, J. H. Kim, and J. Huh, "Pricing path-dependent exotic options with flow-based generative networks," Applied Soft Computing, 2022.
국제학술논문지
- Y. S. Kim, H. G. Kim, and F. J. Fabozzi, "Risk-neutral pricing of Quanto options with generative machine learning techniques," Journal of Derivatives, 2025.
- S. Y. Cho, S. Lee, and H. G. Kim, "Forecasting VIX using interpretable Kolmogorov-Arnold networks," Expert Systems with Applications, 2025.
- J. Y. Kim, S. Kwon, H. Go, Y. Lee, S. Choi, and H. G. Kim, "ScoreCL: Augmentation-adaptive contrastive learning via score-matching function," Machine Learning, 2025.
- H. G. Kim and J. Huh, "Deep learning of optimal exercise boundaries for American options," International Journal of Computer Mathematics, 2024.
- H. G. Kim, H. Kim, and J. Huh, "Considering appropriate input features of neural network to calibrate option pricing models," Computational Economics, 2024.
- H. G. Kim, S. W. Kim, and J. H. Kim, "Variance and volatility swaps under the exponential fractional Ornstein-Uhlenbeck model," North American Journal of Economics and Finance, 2024.
- H. G. Kim, S. Y. Cho, and J. H. Kim, "A martingale method for option pricing under a CEV based fast-varying fractional stochastic volatility model," Computational and Applied Mathematics, 2023.
- H. G. Kim and J. H. Kim, "A stochastic-local volatility model with Lévy jumps for pricing derivatives," Applied Mathematics and Computation, 2023.
- H. G. Kim, J. Cao, J. H. Kim, and W. Zhang, "A Mellin transform approach to pricing barrier options under stochastic elasticity of variance," Applied Stochastic Models in Business and Industry, 2023.
- H. G. Kim and J. H. Kim, "Forecasting the elasticity of variance with LSTM recurrent neural networks," International Journal of Computer Mathematics, 2023.
- G. Lee, T. K. Kim, H. G. Kim, and J. Huh, "Newton–Raphson emulation network for highly efficient computation of numerous implied volatilities," Journal of Risk and Financial Management, 2022.
- T. K. Kim, H. G. Kim, and J. Huh, "Large-scale online learning of implied volatilities," Expert Systems with Applications, 2022.
- H. G. Kim, S. J. Kwon, J. H. Kim, and J. Huh, "Pricing path-dependent exotic options with flow-based generative networks," Applied Soft Computing, 2022.
- H. G. Kim, S. J. Kwon, and J. H. Kim, "Fractional stochastic volatility correction to CEV implied volatility," Quantitative Finance, 2021.
- S. T. Kim, H. G. Kim, and J. H. Kim, "ELS pricing and hedging in a fractional Brownian motion environment," Chaos, Solitons & Fractals, 2021.
국제학술발표
- J.Y. Kim, H. Go, S. Kwon, and H.G. Kim, "Denoising task difficulty-based curriculum for training diffusion models," ICLR, 2025.
- 입학
우) 16499 경기도 수원시 영통구 월드컵로 206 아주대학교 인공지능학과
Copyright 2021 Department of Artificial Intelligence. All Rights Reserved.