Articles

Better Beta In Commodities Indexing
By Jonathan Guyer

Related ETFs: OIL

Page 3 of 4

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.

Longview Funds Management

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.

Necessary Features
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.