Hierarchical portfolio stress-testing with flexible probabilities
| dc.contributor.advisor | Gebbie, Timothy | |
| dc.contributor.author | Matthews, Nina | |
| dc.date.accessioned | 2026-09-22T08:21:18Z | |
| dc.date.available | 2026-09-22T08:21:18Z | |
| dc.date.issued | 2026 | |
| dc.date.updated | 2026-09-22T08:14:07Z | |
| dc.description.abstract | Traditionally, portfolio optimisation has heavily relied on historical data to predict future outcomes, with the embedded assumption that historical distributional properties persist over time. However, observations have shown that many of these properties are not necessarily preserved within financial time series. This research challenges these assumptions by adopting a non-parametric, scenario-based approach using the Historical Simulation with Flexible Probabilities (HS-FP) framework (Meucci [89]). The study extends this framework by integrating the Hierarchical Risk Parity (HRP) strategy (De Prado [29]) and the Mean-Variance (MV) approach (Markowitz [69]) to enhance portfolio resilience within a South African financial market context. The HS-FP methodology quantifies future market risk by mapping drivers into state- and time-conditioned probabilities, generating forward-looking estimates of returns and volatilities. This study reveals that while the Sharpe-Ratio maximising MV portfolio demonstrated stronger cumulative performance, HRP achieved superior risk-adjusted returns, highlighting its robust diversification and resilience to extreme market volatility. The integration of the HS-FP framework, while not consistently improving overall performance, illuminated the sensitivity of traditional methods to underlying assumptions and underscored the value of incorporating market regime considerations. The findings advocate for adaptive approaches that move away from rigid assumptions, proposing a systematic, scenario-based approach to regime capturing in order to enhance the real-world applicability of stress tests. | |
| dc.identifier.apacitation | Matthews, N. (2026). <i>Hierarchical portfolio stress-testing with flexible probabilities</i>. (). University of Cape Town ,Faculty of Science ,Department of Statistical Sciences. Retrieved from http://hdl.handle.net/11427/43795 | en_ZA |
| dc.identifier.chicagocitation | Matthews, Nina. <i>"Hierarchical portfolio stress-testing with flexible probabilities."</i> ., University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 2026. http://hdl.handle.net/11427/43795 | en_ZA |
| dc.identifier.citation | Matthews, N. 2026. Hierarchical portfolio stress-testing with flexible probabilities. . University of Cape Town ,Faculty of Science ,Department of Statistical Sciences. http://hdl.handle.net/11427/43795 | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Matthews, Nina AB - Traditionally, portfolio optimisation has heavily relied on historical data to predict future outcomes, with the embedded assumption that historical distributional properties persist over time. However, observations have shown that many of these properties are not necessarily preserved within financial time series. This research challenges these assumptions by adopting a non-parametric, scenario-based approach using the Historical Simulation with Flexible Probabilities (HS-FP) framework (Meucci [89]). The study extends this framework by integrating the Hierarchical Risk Parity (HRP) strategy (De Prado [29]) and the Mean-Variance (MV) approach (Markowitz [69]) to enhance portfolio resilience within a South African financial market context. The HS-FP methodology quantifies future market risk by mapping drivers into state- and time-conditioned probabilities, generating forward-looking estimates of returns and volatilities. This study reveals that while the Sharpe-Ratio maximising MV portfolio demonstrated stronger cumulative performance, HRP achieved superior risk-adjusted returns, highlighting its robust diversification and resilience to extreme market volatility. The integration of the HS-FP framework, while not consistently improving overall performance, illuminated the sensitivity of traditional methods to underlying assumptions and underscored the value of incorporating market regime considerations. The findings advocate for adaptive approaches that move away from rigid assumptions, proposing a systematic, scenario-based approach to regime capturing in order to enhance the real-world applicability of stress tests. DA - 2026 DB - OpenUCT DP - University of Cape Town KW - portfolio optimisation KW - Hierarchical Risk Parity LK - https://open.uct.ac.za PB - University of Cape Town PY - 2026 T1 - Hierarchical portfolio stress-testing with flexible probabilities TI - Hierarchical portfolio stress-testing with flexible probabilities UR - http://hdl.handle.net/11427/43795 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/43795 | |
| dc.identifier.vancouvercitation | Matthews N. Hierarchical portfolio stress-testing with flexible probabilities. []. University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 2026 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/43795 | en_ZA |
| dc.language.iso | en | |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Statistical Sciences | |
| dc.publisher.faculty | Faculty of Science | |
| dc.publisher.institution | University of Cape Town | |
| dc.subject | portfolio optimisation | |
| dc.subject | Hierarchical Risk Parity | |
| dc.title | Hierarchical portfolio stress-testing with flexible probabilities | |
| dc.type | Thesis / Dissertation | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MSc |