3 papers in the archive (2024–2026), average empirical rigor 7.7/10, average math complexity 7.5/10. Quadrant mix: Holy Grail ×3.
Works mostly in Options & Derivatives Pricing, Machine Learning for Market Forecasting, Portfolio Optimization, Volatility Modeling & Forecasting.
Frequent co-authors: Hengyi Yang (1), Sida Lin (1), Yiyan Qi (1), Yankai Chen (1), Haohan Zhang (1), Jian Guo (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%).
Stock price forecasting is a long-standing challenge in computational finance, driven by the inherent randomness of markets and complex temporal patterns. While recent deep-learning models have raised forecasting accuracy by jointly modeling inter-stock and temporal price dynamics, they conflate int
We propose a new risk sensitive reinforcement learning approach for the dynamic hedging of options. The approach focuses on the minimization of the tail risk of the final P&L of the seller of an option. Different from most existing reinforcement learning approaches that require a parametric model of
The Chicago Board Options Exchange Volatility Index (VIX) is calculated from SPX options and derivatives of VIX are also traded in market, which leads to the so-called ``consistent modeling" problem. This paper proposes a time-changed Lévy model for log price with a composite change of time structur