4 papers in the archive (2023–2025), average empirical rigor 4.1/10, average math complexity 8.6/10. Quadrant mix: Holy Grail ×2, Lab Rats ×2.
Works mostly in Volatility Modeling & Forecasting, Stochastic Control & Optimal Stopping, Crypto Markets & DeFi.
Frequent co-authors: Jin Ma (1), Jianfeng Zhang (1), Boyi 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%).
Recent empirical evidence has highlighted the crucial role of jumps in both price and volatility within the cryptocurrency market. In this paper, we integrate price–volatility co-jumps and volatility short-term dependency into a coherent model framework, featuring fractional stochastic volatility.
We consider a Kyle-type model where insider trading takes place among a potentially large population of liquidity traders and is subject to legal penalties. Insiders exploit the liquidity provided by the trading masses to “camouflage” their actions and balance expected wealth with the necessary stea
The main objective of this paper is to develop a martingale-type solution to optimal consumption–investment choice problems ([Merton, 1969] and [Merton, 1971]) under time-varying incomplete preferences driven by externalities such as patience, socialization effects, and market volatility. The marke
In this paper we study set-valued Volterra-type stochastic integrals driven by Lévy processes. Upon extending the classical definitions of set-valued stochastic integral functionals to convoluted integrals with square-integrable kernels, set-valued convoluted stochastic integrals are defined by taki