High-dimensional Robust Portfolio Optimization Under Contamination: A Factor-Analytic Approach
Apr 4, 2025·
,·
0 min read
Stefano Blando
Alessio Farcomeni
Abstract
This thesis addresses the challenge of portfolio optimization in high-dimensional settings (p > n) affected by sparse contamination (outliers). By proposing a robust Factor-Analytic approach, the study demonstrates how to stabilize covariance matrix estimation against data anomalies. The resulting allocation strategies exhibit superior resilience and risk-adjusted returns compared to traditional Mean-Variance frameworks, especially during market shocks.
Type
Publication
Computational Economics (in review)
Authors
Stefano Blando
(he/him)
PhD Student in Artificial Intelligence
Stefano Blando is a PhD student in the National PhD Program in Artificial Intelligence at
Scuola Superiore Sant’Anna and the University of Pisa. His research lies at the intersection
of AI, agent-based modeling, and economics. He studies adaptive multi-agent systems,
statistical verification of economic simulations, and robust quantitative methods for
financial and socio-economic data.
Authors