4 papers in the archive (2023–2026), average empirical rigor 4/10, average math complexity 8.5/10, 1 with linked code. Quadrant mix: Holy Grail ×1, Lab Rats ×3.
Works mostly in Fixed Income & Interest Rates, Factor Investing & Asset Pricing.
Frequent co-authors: Nicolas Camenzind (1), Andreas Celary (1), Zehra Eksi-Altay (1), Paul Krühner (1), Andrea Ruglioni (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 PanelGP, a conditional factor model for large panels of stock returns in which firm characteristics determine the factor loadings and economic conditions determine the expected factors. Both relations are estimated nonparametrically with Gaussian-process priors. The kernel choice nests in
We propose a framework for transfer learning of discount curves across different fixed-income product classes. Motivated by challenges in estimating discount curves from sparse or noisy data, we extend kernel ridge regression (KR) to a vector-valued setting, formulating a convex optimization problem
Kernel ridge regression estimates the discount curve as the minimizer of a pricing error plus a penalty given by the norm of a reproducing kernel Hilbert space. By the representer theorem, the estimate lies in a finite-dimensional affine space that depends only on the kernel and on the cash flow dat
Discount is the difference between the face value of a bond and its present value. I propose an arbitrage-free dynamic framework for discount models, which provides an alternative to the Heath–Jarrow–Morton framework for forward rates. I derive general consistency conditions for factor models, and