Investor interest in commodities indexing has increased dramatically over the past decade due to the increased adoption of alternative investments and the need for exposure to asset classes that are not correlated with stocks and bonds. Still, commodities indexing is in the early stages (relative to other asset classes) and since these indexes are composed of futures contracts, unique challenges have been identified regarding investments that seek to replicate these indexes. These "commodities specific" challenges include:
• Inconsistent component inclusion among various commodities indexes
• Inconsistent weightings mechanisms among various commodities indexes
• High index-replication costs
Component Selection And Weighting Methods Of Commodities Indexes
The commodities markets are among the most liquid, highly developed and strongly regulated markets in the world, with balanced participation of buyers and sellers. The commodities markets represent one of the most appropriate environments to apply an index application within the alternative investment universe. The question is, How best to capture commodities market beta for investors?
Rather than being just one asset class, raw materials could be more accurately viewed as multiple asset classes broadly categorized in sectors as energy, metals, grains, softs, etc. The components of each category often correlate to one another but may not correlate to the other categories or components. It stands to reason that commodities indexes that have relatively high or low exposures to any particular sector/component would vary meaningfully from other commodities indexes in certain periods. However, the well-known commodities indexes do correlate highly to one another, and this is strong evidence that they provide "asset class exposure." For the most part, commodities indexers attempt to include the most "important" commodities, but they often disagree about how many to include and how much of each.
Commodities indexes generally have governing committees that implement the component selection rules and decide which components to include. The primary component selection requirement shared by most commodities indexes is liquidity as measured by open interest. Additional selection criteria beyond liquidity are often more judgmental. We subscribe to the fundamental tenet that an index approach is best applied to highly liquid, "efficient" markets. Therefore, component inclusion screens for volume, open interest and narrow bid/ask spreads are appropriate selection criteria. We agree that additional judgmental factors such as "relevancy" and overall balance objectives are appropriate considerations for commodities index committees. Longview's component and contract month selection process consists of liquidity, volume and open interest screens as well as relevancy and overall balance objectives. This process results in the selection of 16 components across the major sector categories (energy, metals and agriculture). Ultimately, there is considerable component overlap between indexes as to what commodities are most liquid and most important (highlighted in Figure 5). The primary differences come as a result of how many additional components are included and the weightings. (See Figure 5 for recently published weightings targets.)
The Rogers International Commodity Index (the "RICI") is a consumption-weighted index and has historically included the most components—currently 38. While the RICI does include the same core componentry as other commodities indexes, a number of the RICI components—e.g., lumber, rapeseed, adzuki beans and milk—may not meet the liquidity requirements of most commodities indexes. The Deutsche Bank Liquid Commodity Index (the "DBLCI") has historically incorporated the fewest components and, as the name implies, has high liquidity requirements. The S&P GSCI, DBLCI and RICI each have high energy sector allocations relative to the more evenly balanced DJ-UBSCI and Longview Extended Commodity Index and therefore may reasonably be expected to experience periods of over- or underperformance based on that exposure.
If one looks at trends of componentry changes, one will see that the DJ-UBSCI has reduced industrial metals components: Within the past two years, tin, lead and platinum were removed from metals, as was cocoa from the softs sector. While silver and platinum are strictly classified as "precious," both have surprisingly high "industrial" percentage uses of their annual production. The RICI changes inclusion of its more "obscure" components: Within the past year, greasy wool was removed, while rapeseed and milk were added. The DBLCI, when first introduced, included only six components and has significantly increased components over time to 14. The S&P GSCI, DBLCI and RICI tend to have higher energy sector allocations, while the DJ-UBSCI and the LEXTR are most similarly weighted and balanced, with energy sector allocations in the range of 33 to 35 percent. Ultimately, the most meaningful differences between the indexes lie in weightings and roll mechanics.
Equity indexes tend to be weighted by market capitalization, equal weighting or fundamental weighting, but commodities have fundamentally different characteristics that must be considered in the index design and weighting process. In addition to having expiration dates, commodities futures contracts differ from stocks in that they do not have "shares outstanding," which, along with price, is the basis of equity market-cap weighting. However, each commodities futures contract specifies the quantity of each commodity deliverable per contract. These amounts are determined and established by the exchanges and commercial market participants (hedgers) based on what is deemed the commercially suitable and relevant amount, and these have been remarkably stable over time. This then can be used as a proxy to determine a commodity's "market capitalization."
High Index Replication Costs
While roll yield (discussed earlier) is a function of index replication, it is factored into the calculation of commodities index levels (if the index is correctly calculated to industry standards). An investment cannot be made directly in an index, so the index must be replicated within an investment of some type (private fund, exchange-traded product, mutual fund, swap, note, etc.). Replicating an index costs money; the more often an index rebalances or rolls its exposure, the higher the trading costs are to track it, resulting in tracking error. Indexes that are practical to implement in the real-world markets are usually less costly to effect and, therefore, more efficient.
The potential negative effect of replication costs on investors can be highlighted with a very basic illustration. Assume an investor were to hold the same 16 commodities in two different accounts. One account holds nearby contracts rolled frequently (nine times a year), the other holds long-dated contracts rolled infrequently (once a year). Figure 6 represents a reasonable estimate of what the replication costs to the accounts might look like.
Both accounts experience the same 8 basis points of commissions. "Slippage" is based on bid/ask spread analysis of front- and back-month contracts. It represents the extent to which an investor cannot achieve the same execution as the index marks—usually negative. We assume 20 basis points of slippage (well within the "normal" range for this figure) in the front-month and 25 basis points for the back-month contracts, since longer-dated contracts usually have a slightly wider bid/ask spread. As Figure 6 illustrates, the front-month contracts must perform 2.19 percent better than the long-dated contracts of the exact same commodities to make up for replication costs.
Most commodity indexes, including LEXTR, roll their exposure over a five-day period. This is done primarily to diminish the exposure of the index transactions to the volatilities of a single trading day and secondarily to reduce exposure to poaching by opportunistic strategies that may attempt to front-run the index. Reducing replication costs is a tertiary objective of extending roll periods. Our estimates of commissions and front/back-month slippage are based on our experience managing both long/short active and index strategies in the commodity markets. Obviously, economies of scale will impact expense ratios, and replication costs will vary; however, the message is clear—turnover matters.
The challenges to indexing the commodities markets discussed above have given rise to differing approaches to commodities index design. These approaches primarily focus on curve shape in an attempt to address the "contango/roll yield problem" by optimizing the components and/or contracts selected for inclusion based on curve shape. Other commodities index methodologies consider whether component market prices are in upward momentum or reverting (channeling). These index approaches also consistently require frequent rolls. Examples include the DBLCI – Optimum Yield and the DBLCI – Mean Reversion.
Longview's approach could best be described as a bias toward efficiency and attempts to minimize the effects of contango/backwardation and price momentum/reversion. Biasing an index toward minimal contango or mean reversion/momentum requires that an investor become a student of and monitor price curves and current price action in an effort to be in the right index for the circumstances of the moment. We assert that efficiency can be consistently achieved across the widest array of market environments and, therefore, may be the best approach for asset allocating/index investors.
We propose that a well-constructed commodities index should have five key features:
Efficiency: A primary objective of index investing is to gain exposure to an asset class in the most cost-efficient manner possible. Beta investing, by its nature, must be efficient. By the same token, only active managers that consistently deliver alpha can justify higher fees. We assert that in the commodities asset class, long-dated components combined with minimal rolling best adhere to this concept.
Independence: By basing weightings mechanics on observable, defined characteristics of the commodities markets within a rules-based construct rather than primarily on the decisions of a committee, index weightings will be more independent of arbitrary influence.
Consistency: Consistency is an important element of the usefulness of indexes. This can be achieved by basing the total index unit value on one commercial contract for each component. A longer holding period for index contracts also enhances consistency.
Transparency: "Market-cap weighting" is intuitively transparent. Investors can readily identify the amount of exposure to each index component represented in their investment, and the contract tenor remains more consistent. This results in a sound basis for asset allocation decisions and management of individual component and sector exposure. Those investors that choose to be more active in overweight/underweight decisions have the ability to take complementary positions with less risk that the underlying index exposure changes and results in unintended allocations.
Fundamental Soundness: Contract deliverable quantities and price are immediately identifiable, market-driven inputs on which to base commodities index weighting mechanics. Because the quantity and quality of the commodity to be delivered are established by the exchanges and market participants (primarily hedgers) based on commercially relevant amounts, we posit that standardized contract quantities are fundamentally sound capitalization-weighting inputs appropriately adapted to the commodities asset class. Basing index weightings on such inputs as production or consumption figures from various sources exposes an index to compilation errors of external parties, subsequent data revisions and reporting lags.
With numerous commodities indexes available and an unusual degree of differences between them, it is important for investors to understand these concepts and choose the best beta suitable for them. We have addressed the unique challenges of the commodities asset class and applied the core tenets of index philosophy to create indexes designed for investors. The combination of sound index construction, fundamental design, long-dated contracts and minimal rolls results in superior commodities beta (see Figure 7).