3 papers in the archive (2023–2025), average empirical rigor 6.3/10, average math complexity 6.5/10. Quadrant mix: Holy Grail ×2, Lab Rats ×1.
Works mostly in Volatility Modeling & Forecasting, Options & Derivatives Pricing, Commodities & Energy Markets, High-Frequency Trading & Optimal Execution.
Frequent co-authors: Fabienne Schmid (1), Tobias Pfingsten (1), Jan Hamaekers (1), Kay F. Pilz (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%).
Battery Energy Storage Systems (BESS) are a cornerstone of the energy transition, as their ability to shift electricity across time enables both grid stability and the integration of renewable generation. This paper investigates the profitability of different market bidding strategies for BESS in th
We present a robust Deep Hedging framework for the pricing and hedging of option portfolios that significantly improves training efficiency and model robustness. In particular, we propose a neural model for training model embeddings which utilizes the paths of several advanced equity option models w
We discuss and analyze a neural network architecture, that enables learning a model class for a set of different data samples rather than just learning a single model for a specific data sample. In this sense, it may help to reduce the overfitting problem, since, after learning the model class over