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Portfolio optimization using risk rarity strategies based on clustering methods in python
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This thesis compares classic portfolio allocation techniques such as the Equally Weighted-(EW) and 60/40-portfolio (60/40) against more advanced approaches, namely Naïve Risk Parity (NRP) and Hierarchical Risk Parity (HRP). The different portfolios are constructed using 20 diversified equity and fixed income futures across 20 years, applying dynamic leverage (volatility target), and implementing monthly rebalancing with transaction costs. In addition, robustness tests are applied to the Hierarchical Risk Parity strategy to infer how different clustering methods influence HRP’s performance. Compared to EW and 60/40, NRP and HRP show better risk-adjusted returns, while HRP’s diversification leads to more stability and lower and shorter drawdowns in comparison to all other strategies.
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Portfolio management Asset management Portfolio optimization