Paul Schneider

Paul Schneider is a Full Professor in the Faculty of Economic Sciences at the University of Italian Switzerland (USI), where he has been a faculty member since 2012. He is affiliated with the Institute of Finance (IFin) and the Euler Institute (EUL), contributing to interdisciplinary research in quantitative finance and econometrics.

His research focuses on financial econometrics, asset pricing, and statistical methods in finance, with an emphasis on extracting latent market information under minimal assumptions. He integrates techniques from engineering, mathematics, and data science to develop robust models for financial markets. His work spans risk premia, ambiguity in investment decisions, nonlinear pricing, and model-free recovery methods.

His recent publications (2023–2024) in journals such as Review of Finance, Management Science, and SIAM Journal on Mathematics of Data Science highlight trends in adaptive learning, empirical scenario generation, constrained likelihood estimation, and optimal investment under ambiguity. These reflect a strong focus on data-driven, computationally efficient, and theoretically sound approaches to financial modeling.

• Adaptive joint distribution learning
• Fast empirical scenarios
• Optimal Investment under Ambiguity
• Constrained polynomial likelihood
• Dispersion of Beliefs and Sentimental Recovery

Scientific Awards:

No specific awards or fellowships are mentioned in the provided text.

Advising and Grants: While no formal list of advisees is provided, Paul Schneider has collaborated extensively with researchers such as Damir Filipovic, Fabio Trojani, and Christian Wagner, suggesting a strong mentorship and collaborative role. He has contributed to funded research projects, particularly in financial modeling and econometrics, though specific grant names are not detailed.

Labs and Research Teams: He is actively involved with the Institute of Finance (IFin) and the Euler Institute at USI, which support interdisciplinary research in finance, mathematics, and data science. He has also developed computational tools such as the KDM R package for kernel density machines, indicating engagement with data science and open research practices.