3 papers in the archive (2024–2026), average empirical rigor 6.5/10, average math complexity 8.3/10. Quadrant mix: Holy Grail ×3.
Works mostly in Machine Learning for Market Forecasting, Volatility Modeling & Forecasting, Portfolio Optimization, Risk Management & Tail Risk.
Frequent co-authors: Christian Bongiorno (2), Rosario Nunzio Mantegna (2), Anton J. Heckens (1), Benjamin Köhler (1), Thomas Guhr (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 develop a rotation-invariant neural network that provides the global minimum-variance portfolio by jointly learning how to lag-transform historical returns and how to regularise both the eigenvalues and the marginal volatilities of large equity covariance matrices. This explicit mathematical mapp
A new wave of work on covariance cleaning and nonlinear shrinkage has delivered asymptotically optimal analytical solutions for large covariance matrices. Building on this progress, these ideas have been generalized to empirical cross-covariance matrices, whose singular-value shrinkage characterizes
Risk assessment for rare events is essential for understanding systemic stability in complex systems. As rare events are typically highly correlated, it is important to study heavy-tailed multivariate distributions of the relevant variables, especially in the presence of non-stationarity. We use a g