3 papers in the archive (2023–2024), average empirical rigor 6/10, average math complexity 7.5/10. Quadrant mix: Holy Grail ×3.
Works mostly in Options & Derivatives Pricing, Machine Learning for Market Forecasting, Volatility Modeling & Forecasting, Risk Management & Tail Risk.
Frequent co-authors: Hyangju Kim (1), Jaehyung Choi (1), Hyun-Gyoon Kim (1).
Scores are our LLM-assisted heuristics (methodology), not peer review or citation counts. Author pages are generated from paper metadata; name variants may split one person across pages. arXiv author page →
Ordered by a blend of empirical rigor (60%) and math complexity (40%).
In this study, we discuss a machine learning technique to price exotic options with two underlying assets based on a non-Gaussian Levy process model. We introduce a new multivariate Levy process model named the generalized normal tempered stable (gNTS) process, which is defined by time-changed multi
This paper proposes analytic forms of portfolio CoVaR and CoCVaR on the normal tempered stable market model. Since CoCVaR captures the relative risk of the portfolio with respect to a benchmark return, we apply it to the relative portfolio optimization. Moreover, we derive analytic forms for the mar
This paper explores Artificial Neural Network (ANN) as a model-free solution for a calibration algorithm of option pricing models. We construct ANNs to calibrate parameters for two well-known GARCH-type option pricing models: Duan’s GARCH and the classical tempered stable GARCH that significantly im