4 papers in the archive (2025–2026), average empirical rigor 5.4/10, average math complexity 6.9/10. Quadrant mix: Holy Grail ×2, Lab Rats ×1, Philosophers ×1.
Works mostly in Volatility Modeling & Forecasting, Options & Derivatives Pricing, NLP & LLMs in Finance, Machine Learning for Market Forecasting.
Frequent co-authors: Yingli Wang (1), Liyuan Chen (1), Shuoling Liu (1), Jiangpeng Yan (1), Henglin Liu (1), Chuang Li (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%).
Shallow models are special cases of deep models, and deep models theoretically have the potential to outperform the shallow ones. However, the existing empirical asset pricing literature provides strong benchmarks for shallow models. Residual learning allows neural network models in asset pricing to
We study variance-optimal stock hedging and the convergence of approximate strategies in the rough Hawkes–Heston model. Starting from the model’s affine conditional transform and the affine Volterra jump framework, we obtain semi-explicit hedges for European calls and a representation of the minimu
We present a study of the short-maturity asymptotics for VIX and European option prices in local-stochastic volatility models with compound Poisson jumps. Both out-of-the-money (OTM) and at-the-money (ATM) asymptotics are considered. The leading-order asymptotics are obtained in closed-form. We appl
The advent of foundation models (FMs), large-scale pre-trained models with strong generalization capabilities, has opened new frontiers for financial engineering. While general-purpose FMs such as GPT-4 and Gemini have demonstrated promising performance in tasks ranging from financial report summari