Indexology^® Blog

In a previous blog, we highlighted the objective of the S&P Multi-Asset Dynamic Inflation Strategy Index and summarized its methodology. We justified our strategy choice for each inflation regime (high, medium, low) and illustrated how the index dynamically switches between these strategies based on a monthly inflation forecast. In this blog, we will take a closer look at the performance of the index in comparison to its underlying strategies and demonstrate some possible advantages of the dynamic switching approach.

During a recent three-year period ending July 2021 (see Exhibit 1), we see that the S&P Multi-Asset Dynamic Inflation Strategy Index (SPMADIST) outperformed its component strategies as well as some of the popular “inflation hedge” asset classes, such as broad commodities (GSCI), real estate (REIT), and inflation-protected bonds (TIPS). To understand how the index achieved this, let us examine the inflation regimes in detail.

Based on our definitions, inflation stayed in the medium regime until February 2020, during which time the volatility-weighted (VolWt) strategy took effect. This led to gains from exposure to fixed income (Bonds, TIPS), which had relatively lower volatility. During the low inflation regime that followed, the index switched into the 60/40 strategy, outperforming REIT and GSCI, which were particularly hard hit during the early months of the COVID-19 pandemic. Starting March 2021, higher CPI (YoY) readings led the index to switch into the pro-inflation beta (ProIB) strategy, which yielded strong returns due to being overweight GSCI and Gold.

Cumulatively, over the course of the three-year period, the observed outperformance of the index is the result of these dynamic adjustments. This case study demonstrates how the switching mechanism could enhance the performance of the index as the inflation regime changes over time.

Performance Attribution

To assess how the dynamic index times its exposure to the underlying asset classes, we ran a performance attribution analysis over the same three-year period, treating each strategy as a separate benchmark. The results (see Exhibit 2) indicate that in comparison to 60/40, the index performance was adversely affected by its GSCI exposure, but this was more than compensated for by gains from its exposure to Bonds, Gold, and TIPS, leading to an outperformance of 1.46%. While this may not seem significant, it is important to note that this outperformance is achieved with lower volatility, leading to a higher risk-adjusted return.

The index exhibited stronger outperformance in comparison to VolWt primarily due to its higher equity exposure, as well as better timing of its exposure to Bonds and TIPS. The ProIB strategy had a higher exposure to GSCI and lower exposure to TIPS, which explains the bulk of its underperformance relative to the index over the entire three-year period.

Correlation to Unexpected Inflation

During periods of rising inflation, we are likely to obtain inflation readings higher than expected for several months in a row. For our index to effectively hedge inflation during such periods, its performance must be positively correlated to unexpected inflation. If the forecast of CPI (YoY) is considered the “expected” inflation rate for a given month, then the unexpected portion would be the excess (i.e., above the “realized” inflation) for that month.

Exhibit 3 illustrates that the dynamic index had a relatively better correlation to unexpected inflation compared to some of its component strategies. While ProIB had a higher correlation overall, its performance was affected by significantly higher volatility and more severe drawdowns.

Inflation is one of the most significant risks to investment returns over the long term. Core equities and conventional bonds tend to deliver below-average returns in rising inflation environments, which can encourage investors to seek out inflation-sensitive assets, such as commodities, inflation-linked bonds, REITs, natural resource stocks, and gold, to protect their portfolios from inflation shocks.

Currently, the risk of inflation centers on whether the post-pandemic recovery will be merely reflationary or truly inflationary. It may be difficult for market participants to assign a probability to a sustained period of inflation and to adapt portfolio construction, should the probability be sufficiently high.

For an investor seeking protection against rising or high inflation, there is no “one-size-fits-all” solution—the portfolio must dynamically adjust to changes in the market environment, and this is exactly what the S&P Multi-Asset Dynamic Inflation Strategy Index is designed to do. The S&P Multi-Asset Dynamic Inflation Strategy Index seeks to measure the performance of a dynamic rotational strategy across a series of subindices designed to reflect the most appropriate response to the prevailing inflation regime in the U.S., as measured by the U.S. CPI. The index switches between three multi-asset strategies that have historically performed well in either a low, medium, or high inflation regime. The component indices within each multi-asset strategy are weighted based on dynamic weighting allocations (see Exhibit 1).

Component indices included in the S&P Multi-Asset Dynamic Inflation Strategy Index include those representing U.S. equities, commodity futures, U.S. sovereign bonds, U.S. inflation-linked bonds, and U.S. REITS.

The inflation regime is identified based on cutoffs for specific levels of inflation, as illustrated in Exhibit 2. For each inflation regime, a suitable strategy is identified based on inflation sensitivity, historical performance, and economic justification. Each month, the inflation regime is determined using the realized U.S. CPI (YoY), and the appropriate allocation is chosen based on the strategy identified for that regime.

The index demonstrated strong inflation beta in the high inflation regime and is relatively easy to implement and manage. The back-tests (though limited in terms of history) show that its performance and turnover were both reasonable. In a low inflation environment, equities tend to perform well, and a traditional 60/40 allocation that is overweight in equities can be expected to provide reasonable returns. During periods of medium inflation, fixed income assets tend to yield better risk-adjusted returns, so the volatility-weighted strategy that overweights them is a reasonable choice. In a high inflation regime, it makes sense to switch to the pro inflation beta strategy, which overweights commodities and real assets, since those asset classes have better inflation-hedging properties.

Exhibit 3 demonstrates that the dynamic index generally outperformed the component strategies on a risk-adjusted basis, and its drawdowns were not as severe. It is also worth noting that it had consistently higher inflation sensitivity than the 60/40 or volatility weighted strategies.

For more information, please visit the index webpage and read the thought leadership research paper.

How does index design influence sector diversification? Look under the hood of the S&P BSE SENSEX 50 Index with S&P DJI’s Ved Malla.

The S&P GSCI declined 2.3% in August, as industrial commodities retreated in line with a surge in COVID-19 cases attributed to the Delta variant, as well as signs that China intended to dampen industrial production to curb carbon emissions. In contrast, performance across the soft commodities was impressive, with a variety of production-related disruptions elevating the risk of lower supply.

The S&P GSCI Petroleum fell 5.1% over the month. Oil production picked up across the world, while demand dipped slightly in the wake of a rise in COVID-19 cases. The resumption of aviation, especially long-haul passenger flights, would be critical to the full recovery in petroleum consumption.

Within the industrial metals complex, copper, lead, and nickel fell as China intensified its crackdown on some of the least environmentally friendly components of its economy. The S&P GSCI Aluminum was an exception, reaching a new 10-year high and ending the month up 4.5% and up 34.6% YTD. Supplies of the metal are becoming increasingly tighter as Beijing seeks to curb pollution from the metal’s energy-intensive production process. Environmental concerns are a double-edged sword for industrial metals, as they tend to be some of the worst GHG-emitting commodities, but they are key components in almost every new green technology.

Iron ore prices touched record highs this year, but the S&P GSCI Iron Ore suffered one of its biggest monthly losses ever in August (down 13.3%) as China’s curbs on carbon emissions included limits on the output of steel. Demand for steel has been healthy, but Chinese steel mills are constrained by Beijing’s instruction that production for 2021 should not exceed the record 1.065 billion metric tons produced last year. To meet that goal, steel producers would have to cut output by roughly 10% for the rest of 2021 from their record first-half pace.

The annual U.S. Fed meeting in Jackson Hole came and went without significant market volatility, and the S&P GSCI Gold ended flat for the month. Gold is often viewed as a hedge against inflation and currency debasement, and the Fed’s tapering would tackle both those conditions, thereby diminishing gold’s appeal.

The S&P GSCI Agriculture rose 0.3%, with the softs outperforming. The S&P GSCI Sugar rose 10.8%, marking a new three-year high as estimates for the size of the Brazilian cane crop continued to fall. The S&P GSCI Coffee rose 7.3%, continuing its impressive and volatile two-month rally. Several weather and logistical issues, including new pandemic lockdowns in Vietnam, coalesced to raise the price of a cup of joe.

The combination of stronger-than-expected pork demand and the ongoing lack of labor at U.S. packing plants pushed the S&P GSCI Lean Hogs up 0.9% for the month.

While the rationale behind risk parity is well understood, the implementation frameworks often differ. For example, some implementations are purely passive while others are more active, the way risk is defined can differ, and the underlying instruments used can vary.

In 2018, the S&P Risk Parity Indices became the first transparent, rules-based benchmarks offered in the space. Since their launch, several firms have adopted these indices as benchmarks and for replication. Moreover, we have been in regular dialogue with several of the leading asset owners, asset managers, and consultants focused on risk parity. Insights from these valuable interactions motivated us to launch a second series of indices, the S&P Risk Parity 2.0 Indices. In this blog, we will introduce this new index series and compare it to the original.

High-Level Methodology Comparison

Exhibit 1 highlights the features of the methodologies that are shared or different. The S&P Risk Parity 2.0 Indices differentiate themselves from the first series in two distinct ways: they incorporate a fourth asset class (TIPS) and assess risk differently. Details on the construction for both series will be covered more extensively in an upcoming blog

Risk parity strategies are often assessed in terms of how they are expected to perform across periods when growth and inflation are either rising or falling. TIPS is an important asset class for many investors because it can be an effective way to guard against inflation. TIPS are also expected to outperform equities and commodities during periods of falling growth.

The S&P Risk Parity 2.0 Indices also switch from the simplistic definition of risk (as volatility) to define it in terms of volatility and covariance. By including both volatility and covariance, the risk of each asset class is being assessed in terms of its contribution to the overall portfolio. Furthermore, instead of allocating risk equally across all four asset classes, it uses a budgeted approach where commodities, equities, and fixed income are each assigned a 28.3% risk budget and TIPS is assigned a 15% risk budget. To achieve this, an optimizer solves for the weights that satisfy these risk budgets.

Headline Performance Statistics

Exhibit 2 summarizes the high-level performance characteristics for each of the series using the 10% target volatility indices.

Over the long-term, both index series have posted risk-adjusted returns that are superior to the manager composite, the HFR Risk Parity Vol 10 Index. For reference, the HFR Risk Parity Indices represent the weighted average performance of the universe of active fund managers employing an equal risk-contribution approach in their portfolio construction. We intend to review the performance characteristics in more detail in upcoming blogs in this series.

Conclusion

Despite their commonalities, these two series are certainly distinct, and we anticipate demand for both. Thus far, the original series has proved popular, given it is 100% futures-based and assesses risk in a simple and straightforward manner. The S&P Risk Parity 2.0 Index Series cannot be fully implemented using futures (since it incorporates TIPS) and cedes some simplicity (by using an optimizer), but in doing so it accounts for cross-asset correlations as it strives to achieve improved risk diversification. We look forward to delving further into this new index series in future blogs.