4 papers in the archive (2026), average empirical rigor 7.6/10, average math complexity 6.3/10, 1 with linked code. Quadrant mix: Holy Grail ×4.
Works mostly in Risk Management & Tail Risk, Machine Learning for Market Forecasting, Reinforcement Learning for Trading, Portfolio Optimization.
Frequent co-authors: Ying Chen (2), Vincent Gurgul (1), Victor Medina-Olivares (1), Wangzhen Xia (1), Nadja Klein (1), Christopher Gerling (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 propose a semi-structured discrete-time multi-state model to analyse mortgage delinquency transitions. This model combines an easy-to-understand structured additive predictor, which includes linear effects and smooth functions of time and covariates, with a flexible neural network component that
Accurate forecasting of recovery rates (RR) is central to credit risk management and regulatory capital determination. In many loan portfolios, however, RR modeling is constrained by data scarcity arising from infrequent default events. Transfer learning (TL) offers a promising avenue to mitigate th
This paper presents a Quantum Reinforcement Learning (QRL) solution to the dynamic portfolio optimization problem based on Variational Quantum Circuits. The implemented QRL approaches are quantum analogues of the classical neural-network-based Deep Deterministic Policy Gradient and Deep Q-Network al
Survival analysis has become a standard approach for modelling time to default by time-varying covariates in credit risk. Unlike most existing methods that implicitly assume a stationary data-generating process, in practise, mortgage portfolios are exposed to various forms of data drift caused by ch