3 papers in the archive (2022–2026), average empirical rigor 8.2/10, average math complexity 8/10. Quadrant mix: Holy Grail ×3.
Works mostly in Risk Management & Tail Risk, Volatility Modeling & Forecasting.
Frequent co-authors: Yannick Hoga (1), Roxana Halbleib (1), Jeannine Polivka (1), Jasper Rennspies (1), Sina Streicher (1), Axel Friedrich Wolter (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 introduce inference methods for score decompositions, which partition scoring functions for predictive assessment into three interpretable components: miscalibration, discrimination, and uncertainty. Our estimation and inference relies on a linear recalibration of the forecasts, which is applicab
This paper analyzes the benefits of sampling intraday returns in intrinsic time for the realized variance (RV) estimator. We theoretically show in finite samples that depending on the permitted sampling information, the RV estimator is most efficient under either hitting time sampling that samples w
Following several episodes of financial market turmoil in recent decades, changes in systemic risk have drawn growing attention. Therefore, we propose surveillance schemes for systemic risk, which allow to detect misspecified systemic risk forecasts in an “online” fashion. This enables daily monitor