16. Risk-neutral pricing of Quanto options with generative machine learning techniques.
Young Shin Aaron Kim, Hyun-Gyoon Kim, and Frank J. Fabozzi*.
Journal of Derivatives (2025).
15. Forecasting VIX using interpretable Kolmogorov-Arnold networks.
So-Yoon Cho, Sungchul Lee, and Hyun-Gyoon Kim*.
Expert Systems with Applications (2025).
14. Denoising task difficulty-based curriculum for training diffusion models.
Jin-Young Kim¹*, Hyojun Go¹, Soonwoo Kwon¹, and Hyun-Gyoon Kim*.
ICLR (2025).
13. ScoreCL: Augmentation-adaptive constrative learning via score-matching function.
Jin-Young Kim¹, Soonwoo Kwon¹, Hyojun Go¹, Yunsung Lee, Seungtaek Choi, and Hyun-Gyoon Kim*.
Machine Learning (2025).
12. Deep learning of optimal exercise boundaries for American options.
Hyun-Gyoon Kim and Jeonggyu Huh*.
International Journal of Computer Mathematics (2024).
11. Considering appropriate input features of neural network to calibrate option pricing models.
Hyun-Gyoon Kim, Hyeongmi Kim and Jeonggyu Huh*.
Computational Economics (2024).
10. Variance and volatility swaps under the exponential fractional Ornstein-Uhlenbeck model.
Hyun-Gyoon Kim, See-Woo Kim and Jeong-Hoon Kim*.
North American Journal of Economics and Finance (2024).
9. A martingale method for option pricing under a CEV based fast-varying fractional stochastic volatility model.
Hyun-Gyoon Kim, So-Yoon Cho and Jeong-Hoon Kim*.
Computational and Applied Mathematics (2023).
8. A stochastic-local volatility model with Lévy jumps for pricing derivatives.
Hyun-Gyoon Kim and Jeong-Hoon Kim*.
Applied Mathematics and Computation (2023).
7. A Mellin transform approach to pricing barrier options under stochastic elasticity of variance.
Hyun-Gyoon Kim, Jiling Cao, Jeong-Hoon Kim* and Wenjun Zhang,
Applied Stochastic Models in Business and Industry (2023).
6. Forecasting the elasticity of variance with LSTM recurrent neural networks.
Hyun-Gyoon Kim and Jeong-Hoon Kim*,
International Journal of Computer Mathematics (2023).
5. Newton-Raphson emulation network for highly efficient computation of numerous implied volatilities.
Geon Lee, Tae-Kyoung Kim, Hyun-Gyoon Kim and Jeonggyu Huh*,
Journal of Risk and Financial Management (2022).
4. Large-scale online learning of implied volatilities.
Tae-Kyoung Kim, Hyun-Gyoon Kim and Jeonggyu Huh*,
Expert Systems with Applications (2022).
3. Pricing path-dependent exotic options with flow-based generative networks.
Hyun-Gyoon Kim, Se-Jin Kwon, Jeong-Hoon Kim and Jeonggyu Huh*,
Applied Soft Computing (2022).
2. Fractional stochastic volatility correction to CEV implied volatility.
Hyun-Gyoon Kim, Se-Jin Kwon and Jeong-Hoon Kim*,
Quantitative Finance (2021).
ELS pricing and hedging in a fractional Brownian motion environment.
Seong-Tae Kim, Hyun-Gyoon Kim and Jeong-Hoon Kim*,
Chaos, Solitons & Fractals (2021).
A generative neural network-based approach for efficient estimation of option prices and Greeks.
So-Yoon Cho, Sungchul Lee, and Hyun-Gyoon Kim*.