Combining Risk Parity and Black-Litterman for Asset Allocation

Summary

This article outlines an asset allocation method that combines risk parity with the Black-Litterman framework. Risk parity portfolio weights serve as prior weights, which are used to infer prior expected returns. Short-term momentum views are then incorporated to form posterior return estimates, and a mean-variance optimization uses those estimates to set portfolio weights. A tracking-error constraint is included to control portfolio volatility.

The reported examples cover a stock-and-bond portfolio and a broader portfolio spanning stocks, bonds, commodities, and overseas assets. The summary gives annualized return and volatility, risk-adjusted return, and maximum drawdown figures for selected lower- and higher-volatility versions, and compares some results with risk parity. These figures are presented as strategy outcomes, but the supplied text does not include the underlying portfolio construction details, sample period, benchmark definitions, or testing methodology. The claims therefore cannot be independently assessed from this excerpt, and historical results do not establish future performance.

Key ideas

• Risk parity weights are used to derive prior returns within a Black-Litterman framework.
• Short-term momentum supplies views that update expected returns.
• The resulting estimates feed a mean-variance optimization with a tracking-error constraint.
• The excerpt reports examples across two asset universes but omits the testing methodology and sample details.

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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.