Browsing by Subject "portfolio optimisation"

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    Open Access
    Flexible risk-based portfolio optimisation
    (2020) Landman, Jayson; Mahomed, Obeid; Flint, Emlyn
    The purpose of this study is to present and test a general framework for risk-based investing. It permits various risk-based portfolios such as the global minimum variance, equal risk contribution and equal weight portfolios. The framework also allows for different estimation techniques to be used in finding the portfolios. The design of the study is to collate the existing research on risk-based investing, to analyse some modern methods to reduce estimation risk, to incorporate them in a single coherent framework, and to test the result with South African equity data. The techniques to reduce estimation risk draw from the usual mean-variance and risk-based optimisation literature. The techniques include regime switching, quantile regression, regularisation and subset resampling. In the South African experiment, risk-based portfolios materially outperformed the market weight portfolio out-of-sample using a Sharpe ratio measure. Additionally, the global minimum variance portfolio performed better than other risk-based portfolios. Given the long estimation window, no estimation techniques consistently outperformed the application of sample estimators only.
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    Open Access
    Hierarchical portfolio stress-testing with flexible probabilities
    (2026) Matthews, Nina; Gebbie, Timothy
    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.