Portfolio optimization with stochastic volatility models
File(s)
Author(s)
Patsilivas, Ioannis
Type
Thesis
Abstract
This thesis explores portfolio optimization, balancing theoretical advancements with practical applications. It demonstrates that optimal portfolios, constructed from Dow Jones stocks, can outperform the benchmark index. From a methodological perspective, it pioneers the use of the SMC2 algorithm in portfolio optimization, enhancing robustness through theoretical innovations. By automating the optimization process, it mitigates biases from human intervention, offering practical insights for academics and investors. This research bridges the gap between mathematical rigor and real-world applicability, contributing to the fields of financial mathematics and investment management.
Version
Open Access
Date Issued
2024-12-09
Date Awarded
01/08/2025
License URL
Advisor
Dr Kantas, Nikolas
Sponsor
Engineering and Physical Sciences Research Council
Publisher Department
Department of Mathematics
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)