I am a PhD student at the Vienna Graduate School of Finance (VGSF) and the Institute of Statistics and Operations Research (ISOR) at the University of Vienna.
My research interests lie in portfolio optimization, financial econometrics, quantitative risk management, and behavioral finance. I am particularly interested in modeling real-life household portfolio optimization problems that incorporate behavioral biases. My goal is to quantify decision-making processes in practical scenarios and develop suitable digital tools to help investors reassess and improve their investment behavior.
E-Mail: yuan.chen@univie.ac.at
CV: Yuan Chen
I will be on the 2025-2026 academic job market.
While correlations play a central role in Markowitz portfolio selection, evidence shows that investors often neglect them, relying on simple heuristics rather than Pearson correlation. Standard theory suggests that incorporating correlations should improve performance, yet out-of-sample results frequently favor strategies that ignore them. This paper asks: Is correlation neglect always harmful, and which aspects of correlation truly matter? I propose a transformation that isolates the directional component of correlations and show that both fully ignoring and fully relying on correlation are suboptimal. Empirically, the directional component captures the most relevant information for diversification and improves portfolio performance. By distinguishing between the beneficial and irrelevant components of correlation coefficients, the paper provides a framework for constructing more robust portfolios.
Multivariate Inference for Dynamic Systemic Risk Measures. (with Nikolaus Hautsch, Melanie Schienle, and Jérémy Leymarie ), presented at SOFIE 2025 (Paris), QFFE 2025 (Marseille), R&R Journal of Econometrics
Cardinality-Constrained Optimization for Large-Scale Portfolio (with Immanuel Bomze, Nikolaus Hautsch, and Bo Peng), presented at EURO 2025 (Leeds), EUROPT 2025 (Southampton)
: ) I am working hard to make it happen asap.