3 papers in the archive (2023–2026), average empirical rigor 4.3/10, average math complexity 7.5/10. Quadrant mix: Holy Grail ×1, Lab Rats ×2.
Works mostly in Volatility Modeling & Forecasting, Stochastic Control & Optimal Stopping, High-Frequency Trading & Optimal Execution, Risk Management & Tail Risk.
Frequent co-authors: Camilla Damian (1), Theresa Traxler (1), Luna Rigby (1), Erik Schlögl (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%).
In this paper, we focus on the estimation of historical volatility of asset prices from high-frequency data. Stochastic volatility models pose a major statistical challenge: since in reality historical volatility is not observable, its current level and, possibly, the parameters governing its dynami
Model risk arises from the misspecification of probabilistic models used for pricing and hedging derivatives. While model risk for European-style claims has been widely studied, much less attention has been given to American-style derivatives and the associated optimal stopping problems. This paper
We study the impact of regulatory capital constraints on fire sales and financial stability in a large banking system using a mean field game model. In our model banks adjust their holdings of a risky asset via trading strategies with finite trading rate in order to maximize expected profits. Moreov