Schur Complementary Allocation is now part of skfolio v0.13.0. Thanks to Peter Cotton for his contribution and review! This allocation method uses Schur-complement-inspired augmentation of sub-covariance matrices to smoothly interpolate between Hierarchical Risk Parity (HRP) and the Minimum-Variance Portfolio (MVP). HRP, which leverages hierarchical clustering, tends to yield more diversified and stable allocations than MVP, which is more sensitive to estimation error and can produce concentrated weights. Schur Complementary Allocation allows us to explore the continuum between them by tuning a regularization factor that controls how much off-diagonal information is incorporated into the augmented covariance blocks. We can then leverage the scikit-learn toolkit using randomized search cross-validation to select the regularization factor that maximizes a chosen out-of-sample portfolio metric. A key implementation focus was numerical robustness, maintaining a well-conditioned covariance matrix across the regularization path. Example in the comments below. #skfolio #opensource #machinelearning #portfoliooptimization #quantitativefinance #quant #portfoliomanagement
Phil Boddy
1mo
Congrats on the release Hugo! great to see skfolio continuing to evolve with new allocation methods.
Alexandre Moulti
1mo
Thanks Hugo!
Co-Founder at Skfolio Labs | Former QIS & Derivatives Trader
1moExample: https://skfolio.org/auto_examples/clustering/plot_6_schur.html Paper: https://arxiv.org/abs/2411.05807