4 papers in the archive (2024–2026), average empirical rigor 2.1/10, average math complexity 8.4/10. Quadrant mix: Lab Rats ×4.
Works mostly in Stochastic Control & Optimal Stopping, Options & Derivatives Pricing, Volatility Modeling & Forecasting, Risk Management & Tail Risk.
Frequent co-authors: Yan Dolinksy (1), Yan Dolinsky (1), Shuoqing Deng (1), Na Ren (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%).
We consider the distribution-constrained optimal stopping problem $\sup_{τ\sim μ} \mathbb E[B^τ]$, where $μ$ is a probability distribution on $\mathbb R+$, and $(B^_t)$ denotes the running maximum of a standard Brownian motion. This problem was introduced in Beiglbock et al. (PTRF, 2018), where
The frequent occurrence of cyber risks and their serious economic consequences have created a growth market for cyber insurance. The calculation of aggregate losses, an essential step in insurance pricing, has attracted considerable attention in recent years. This research develops a path-based k-ge
We study scaled trinomial models converging to the Black–Scholes model, and analyze exponential certainty-equivalent prices for path-dependent European options. As the number of trading dates $n$ tends to infinity and the risk aversion is scaled as $nl$ for a fixed constant $l>0$, we derive a nontr
In this paper, we consider scaling limits of exponential utility indifference prices for European contingent claims in the Bachelier model. We show that the scaling limit can be represented in terms of the \emph{specific relative entropy}, and in addition we construct asymptotic optimal hedging stra