3 papers in the archive (2022–2025), average empirical rigor 4.7/10, average math complexity 8.5/10. Quadrant mix: Holy Grail ×2, Lab Rats ×1.
Works mostly in Stochastic Control & Optimal Stopping, Volatility Modeling & Forecasting, Machine Learning for Market Forecasting, Portfolio Optimization.
Frequent co-authors: Kristoffer Andersson (1), Camilo Andrés García Trillos (1), Silvia Lavagnini (1), Athena Picarelli (1), Francesca Biagini (1), Katharina Oberpriller (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 present the first deep-learning solver for backward stochastic Volterra integral equations (BSVIEs) and their fully-coupled forward-backward variants. The method trains a neural network to approximate the two solution fields in a single stage, avoiding the use of nested time-stepping cycles that
We propose a novel computational procedure for quadratic hedging in high-dimensional incomplete markets, covering mean-variance hedging and local risk minimization. Starting from the observation that both quadratic approaches can be treated from the point of view of backward stochastic differential
We consider the pricing and hedging of counterparty credit risk and funding when there is no possibility to hedge the jump to default of either the bank or the counterparty. This represents the situation which is most often encountered in practice, due to the absence of quoted corporate bonds or CDS