300. Advanced Techniques for Portfolio Optimization Under Uncertainty
Invited abstract in session TC-3: Theoretical and algorithmic advances in large scale nonlinear optimization and applications Part 2, stream Large scale optimization: methods and algorithms.
Tuesday, 14:00-16:00Room: B100/4011
Authors (first author is the speaker)
| 1. | Valentina De Simone
|
| Mathematics and Physics, University of Campania "L. Vanvitelli" |
Abstract
Portfolio optimization under uncertainty remains a critical challenge in financial decision-making, requiring advanced numerical optimization
techniques to handle the complexities of real-world markets.
This talk examines two contrasting approaches, the Worst-Case Approach and Cumulative Prospect Theory (CPT), within a mean-variance framework.
The worst-case approach prioritizes robustness, minimizing potential losses under the most adverse market conditions. In contrast, CPT integrates behavioural factors such as probability weighting and loss aversion, providing a more psychologically realistic framework for decision-making.
Both approaches pose significant computational challenges due to nonsmooth or nonconvex optimization problems. We discuss how leveraging problem
structure and specialized numerical techniques enable efficient portfolio construction, even under severe uncertainty. By combining rigorous mathematical methods with practical insights, we explore how portfolio managers can navigate risk,uncertainty, and behavioural biases to develop more resilient investment strategies.
Keywords
- Distributionally robust optimization
- Non-smooth optimization
Status: accepted
Back to the list of papers