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Hierarchical Risk Parity
cluster the realized correlation tree, then allocate by recursive bisection
López de Prado (2016) builds risk parity via hierarchical clustering + quasi-diagonalization + recursive bisection, avoiding covariance-matrix inversion; it produces more stable, less concentrated weights and lower out-of-sample risk than both mean-variance and classic risk parity — valuable as the sleeve widens and correlation estimates get noisier.
Universe
A broader ETF universe where clustering adds value — e.g. 10–15 assets spanning US sector ETFs (XLK/XLF/XLE/...) plus cross-asset diversifiers (IEF, TLT, GLD, DBC).
How it works
Each month compute the 126-day realized correlation/covariance, hierarchically cluster assets, quasi-diagonalize, and set weights by recursive bisection (inverse-variance within clusters); long-only, monthly rebalance, one-day lag.
Expected performance
Research-derived Sharpe estimate: 0.5–0.9.
Backtest this idea with SignalChain
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/signalchain hierarchical risk parity on 12 sector+cross-asset ETFs using 126d realized correlation clustering and recursive bisection, long-only, monthly rebalance
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Research & sources
- papers.ssrn.com — papers.ssrn.com
- papers.ssrn.com — papers.ssrn.com
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Not financial advice. This page describes a research idea, not a recommendation. Any performance figures are hypothetical, research-derived estimates and are not indicative of future results. SignalChain is a research and educational tool; you are solely responsible for any decisions you make.