3 papers in the archive (2025–2026), average empirical rigor 6.8/10, average math complexity 7.5/10. Quadrant mix: Holy Grail ×3.
Works mostly in Market Microstructure, High-Frequency Trading & Optimal Execution, Crypto Markets & DeFi, Options & Derivatives Pricing.
Frequent co-authors: Hyoeun Lee (1), Jiwon Jung (1), Alok Das (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%).
Multivariate Hawkes models are widely used to measure endogenous interactions in event data, but latent common signals can generate apparent excitation even without structural interaction. We characterize the resulting linear projection and construct stable, nonnegative Hawkes processes that share t
We study the joint dynamics of the best bid and ask prices with a spread-gated Hawkes-flocking model. The model tracks four types of best-quote movements: spread-narrowing movements are switched off when the spread is at its one-tick minimum, and a cross-side excitation term, whose activation depend
Deep hedging uses recurrent neural networks to hedge financial products that cannot be fully hedged in incomplete markets. Previous work in this area focuses on minimizing some measure of quadratic hedging error by calculating pathwise gradients, but doing so requires large batch sizes and can make