Market Narratives Podcast: Inflation and Allocating to Real Assets
Harsh Parikh joins an episode of Investment Magazine’s “Market Narratives” podcast to discuss how real assets can be used as a hedge against inflation.
Most assets under management in futures-based commodity strategies are benchmarked to first-generation, “plain-vanilla” benchmarks like the Bloomberg Commodity Index (BCOM) or S&P GSCI (GSCI). The design of this generation of benchmarks was simply to represent the investible universe, not to meet a specific investment objective. However, what CIO investment objective is aligned with this benchmark?
Ideally, CIOs could use a framework to help them design a commodity benchmark to meet their investment objectives. For example, suppose a CIO wants commodity exposure that might perform well in a high inflation environment. Today’s index technology enables a CIO to construct a commodity benchmark, with independent third-party calculation and reporting, to track the commodity exposure that better matches their investment objectives.¹ We illustrate how this can be done using RASA and our portfolio construction methodology.²
First-generation, “plain-vanilla” Benchmarks
First-generation benchmarks represent the investable universe by selecting and weighting commodity futures based on relative world commodity production, economic value and liquidity in commodity futures markets. Some benchmarks also address any unintended concentrated exposures with commodity- and sector-level weighting caps.
BCOM is a well-diversified first-generation benchmark as it includes several weighting criteria such as relative world production, economic value, trading volume and other position and sector constraints. However, BCOM does not address an investor’s investment objectives such as portfolio diversification or inflation-protection.
Second-generation, Alternative Benchmarks
Alternative benchmarks have a role to play in commodities. One type of alternative commodity benchmark dynamically rolls into commodity futures with the most backwardation, while another type uses price momentum or other trading signals. Such benchmarks are more akin to active investment strategies. Other alternative commodity benchmarks use risk-parity type weighting schemes to address the relatively high volatility and concentration of plain-vanilla benchmarks. While alternative benchmarks may fulfill one possible CIO objective of better risk-adjusted returns, they still may not meet other possible CIO investment objectives such as hedging low growth or high inflation.
Next-generation, RASA Benchmarks
So how might a CIO design a commodity benchmark to meet their specific investment objective? After first identifying the relevant commodities, how should they then be weighted in the benchmark? The RASA framework identifies commodities sensitive to high inflation or low growth and provides an approach to weight the commodities to meet desired objectives.
Using RASA we estimate the 1y CPI (headline inflation) and CFNAI (Chicago Fed National Activity Index, a measure of economic growth) betas for the 22 commodities as well as confidence intervals for the beta estimates. With these beta estimates we determine commodity weights to match the investor’s specific ex ante beta target. For example, we construct a RASA high-inflation benchmark that targets a CPI 1y beta value of 10. We also construct a RASA low-growth benchmark that targets a zero CFNAI 1y beta. Sector allocations for the low-growth benchmark are very different compared to the high-inflation benchmark (Figure 1).
Compared with the plain-vanilla and equal-weighted benchmarks, the RASA high-inflation benchmark not only had high sensitivity to inflation, but also was able to improve returns and lower risk historically compared to other benchmarks (Figure 2). The RASA low-growth benchmark had the lowest sensitivity to growth (0.028) and had the lowest annualized volatility (13.8%) and the highest return (1.0%/y).
While we targeted a CPI 1y beta of 10 and a CFNAI 1y beta of 0, an investor may target other beta values and customize their benchmarks depending on their investment views and overall portfolio exposures. Besides, some of the approaches elaborated in second generation benchmarks can be incorporated into the RASA benchmarks.
Using the Real Asset Sensitivity Analysis (RASA™) framework, investors can construct commodity benchmarks better aligned to their investment objectives. That’s important because macroeconomic sensitivities differ significantly across individual commodities, so a plain-vanilla benchmark could easily be inconsistent with a CIO’s specific investment objectives.
1. A CIO can hire an investment manager to manage the commodities investment strategy either actively or passively against such a benchmark.
2. See Institutional Gold! PGIM IAS, November 2019 and What’s in Your Real Assets Portfolio? Introducing RASATM, PGIM IAS, May 2020.