The All-Weather Portfolio: Static or Dynamic Risk Allocation?
The All-Weather Portfolio was designed by Ray Dalio (and clearly influenced by Harry Browne of the Permanent Portfolio) as a robust static allocation that can be used by investors to deliver consistent performance over time. The logic of the portfolio construction is to be neutral to risk/uncertainty with respect to inflation or economic growth–the two primary factors considered to explain all asset returns. The allocations are a function of the long-term expected sensitivity of each asset to the change in these factors- based on whether they are rising or falling substantially in relation to historical norms.
We know that the “Static” All-Weather Portfolio (using the method above) has a very good long-term track record to back up the story. The more interesting question is whether a dynamic risk allocation can outperform using the static method. Theoretically, risk inputs- especially standard deviations- should be easy to model in a dynamic context since they are fairly predictable. Furthermore, we do not necessarily need to pre-specify the relationships between assets because we can observe their changing relationships via clustering. Since the All-Weather approach has often been considered interchangeable with Risk Parity, it is interesting to see if the purely mechanical and dynamic approaches to risk parity perform in comparison using the same assets. Michael Kapler of Systematic Investor, ran the following tests in R using different risk parity variants. We also show for comparison the more sophisticated “Cluster Risk Parity” (Kapler, Varadi, 2012) which removes the universe bias from portfolio allocation and delivers a more precise risk allocation. The assets used below to represent the different asset classes are a combination of funds and ETFs to maximize data history:
The relative risk-adjusted performance of the Static All-Weather Portfolio versus the dynamic variations is presented below.
We can see that all dynamic methods perform better than the static method by a fairly substantial margin in terms of risk-adjusted returns. This suggests that the changing risk and correlations of each asset class already reflect expectations for changes in the economic factor risk to both inflation and economic growth. Furthermore, these changes can be predicted by looking at recent historical data. In addition, we also can see that more complex versions of risk parity (ERC and Clustering variants) slightly underperform the simplest version of risk parity that ignores the correlations between securities and only uses the risk information. This potentially implies either a constant correlation between assets, or that the careful choice of these different assets already reflects an embedded static clustering method (which would make the correlation information much less useful than risk in a dynamic context). Since previous tests demonstrate the superiority of clustering methods (both static and dynamic) to basic risk parity, this implies that the universe chosen is a good static clustering approach. In conclusion, the results at least suggest that dynamic risk allocation is a valid way to create an effective “All-Weather” Portfolio. In practical terms, using cluster risk parity with a diverse and large asset pool is the easiest way to capture this profile while avoiding a lot of pre-specification.