dynamic roll of commodities futures: an extended framework

February 2011

DYNAMIC ROLL OF COMMODITIES FUTURES: AN EXTENDED FRAMEWORK

Contributors: Peter Tsui Srikant Dash, CFA, FRM Director, Global Research & Design Managing Director, Channel Management & Solutions [email protected] [email protected]

Executive Summary Using the Standard Roll strategy to compare their performances versus an extended Dynamic Roll strategy, this research paper analyzes a basket of 24 major commodities included in the S&P GSCI. Also presented is an innovative, path-dependent version of the Dynamic Roll strategy. The concepts of Rank Order and the Dynamic Roll Parity Principle are introduced, allowing for an extended Dynamic Roll framework for more optimal results. Within this extended framework, a differentiating Rank Order is assigned to each commodity. The order is based on empirical backtesting results of the four Dynamic Roll algorithms for the given commodity, in recognition of the fact that each commodity has unique characteristics that affect market dynamics. Findings show that the generic Dynamic Roll strategy is not limited to rolling into the next nearby most liquid contracts. Rather, it chooses from a larger number of highly liquid contract months, using the Implied Roll Yield measure to select the new Rolled-In Months. On a backtested basis, this strategy has demonstrated substantially enhanced index performance.

Often focused on maximum liquidity and transparency, Standard Roll strategies generally roll into the next most liquid nearby contracts. However, traditional rolling strategies may not be the most attractive rolling strategies for all commodities investors. To illustrate the evolution of commodities indices over the past two decades, a number of active and dynamic roll index choices have been highlighted.

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MCGRAW-HILL FINANCIAL

Introduction Commodities investors can choose to either buy physical commodities or invest in real assets via futures contracts. Since few commodities offer practical opportunities to invest in their physical instantiations, futures contracts are often favored. Due to the expiration dates of futures contracts, this form of investing requires a process of rolling wherein the soon-to-expire futures contracts are sold and futures contracts with future expiration dates are bought. Commodities indices vary in their weighting schemes, rolling strategies and methods of choosing constituents. The first tradable commodities index, the S&P GSCI (originally the Goldman Sachs Commodity Index), was launched in April 1991 and was followed by the Dow Jones – UBS Commodity Index (originally the Dow Jones – AIG Commodity Index), launched in 1998. The rolling strategies employed by these two benchmark indices are very similar. They invest in a single futures contract that is generally the first nearby most active contract month (the one closest to expiration), and upon nearing the expiration date, roll into another single futures contract that is generally the next nearby most active contract month. S&P Indices refers to this as the Standard Roll strategy. A Standard Roll strategy’s roll schedule is known in advance and can sometimes diminish total returns due to periodic rolling expenses.

Although the first nearby contracts are generally the most liquid, based on open interest, they may also become the most expensive (resulting in lower total returns) when the commodities market is in contango (when further out future contracts trade at a premium). Conversely, when commodity markets are in backwardation (when further out future contracts trade at a discount), indices that employ a Standard Roll strategy generally boast higher total returns. The negative impact of contango on total returns is a source of frustration to commodities investors who sometimes see the spot prices going up while their commodities investments lag significantly behind.

A number of rolling strategies have been developed to exploit the fungibility of futures contracts and the notion that when maintaining a long futures position, there is little need to be in any particular futures contract month, if liquidity and more immediate performance considerations are excluded.

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The Evolution of Commodities Indices There are a number of dynamic roll index choices currently available in the marketplace, all of which represent attempts to mitigate the effects of contango and curtail the costs of replication. They can be categorized into four basic strategies: 1. Enhanced Roll Strategy When employing an Enhanced Roll strategy, a specific contract month is chosen for its liquidity characteristics used for a whole year. For example, in November 2009, the investor may roll into the December 2010 contract which has an expiration date twelve months ahead, thereby going further out onto the futures curve, and avoiding all monthly rolls until November 2010. While this strategy substantially reduces the costs of replication, it also commits to the same contract for a long period of time, during which the market may undergo significant changes. The S&P GSCI Enhanced Index employs this strategy.

2. Forward Roll Strategy This approach takes the roll schedule from the Standard Roll framework and places all the contracts farther out on the futures curve, depending on the number of months left to go forward on the curve (one month, three months, six months, etc.). Since the Forward Roll strategy is carried out each month, it may not diminish the costs of replication. The S&P GSCI 3-Month Forward Index employs this strategy.

3. Constant Maturity Strategy Instead of investing in a single futures contract, the Constant Maturity strategy spreads the long

futures position over a number of contract months along the futures curve. It can be spread equally over each of the six active contracts, or it can be spread over designated intervals (for example, the three-month, six-month, one-year, two-year and three-year constant maturities). The UBS Bloomberg Constant Maturity Commodity Index (CMCI), BNP Paribas Commodity Market Representative Index (CMRI) and JP Morgan Commodity Curve Index (JPM CCI) employ this strategy.

4. Implied Roll Yield Strategy This approach determines the Implied Roll Yields among the contracts out to a particular maturity, and chooses the contract that has the maximum Implied Roll Yield, where the Implied Roll Yield is defined as the basis of the contract. There are two variations on this approach: i) the basis can be based on the front futures contract for all the contracts, or ii) the basis can be based on the two consecutive futures contracts. In both cases, the Implied Roll Yields would have to be adjusted for the time difference between the expiration dates of the respective contracts. An example of an index that employs this approach is the Deutsche Bank Liquid Commodity Index – Optimum Yield Index, where the basis is referenced from the front futures contract. Another example is the Diapason Commodity Index – DCI BNP Paribas Enhanced, where the basis is based on consecutive futures contracts.

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Historical Performances

Exhibit 1 shows the historical annualized total returns of the commodity indices mentioned, from January 1995 to August 2010.

Exhibit 1: Annualized Total Return of Select Commodity Indices, Jan. 1995 to Aug. 2010

Commodity Index Bloomberg Ticker

Composite Annualized Total Return Jan. 1995 – Aug. 2010

(%)

S&P GSCI SPGSCITR 4.1

DJ-UBSCI DJUBSTR 5.6

DJ-UBS 3 Mo Forward DJUBSF3 6.6

JPMorgan Commodity Curve Index JMCXTR 7.8

S&P GSCI 3 Mo Forward SG3MCITR 11.5

S&P GSCI Enhanced SPGSCISI 12.0

Deutsche Bank Liquid Commodity Index - Optimum Yield DBLCOYTR 12.3

S&P GSCI Dynamic Roll (pro forma) SPDYCITR 14.0 Source: Bloomberg. Data as of August 31, 2010. Charts and graphs are provided for illustrative purposes only. Indices are unmanaged statistical composites and their returns do not include payment of any sales charges or fees an investor would pay to purchase the securities the index represents. Such costs would lower performance. It is not possible to invest directly in an index. Past performance is not an indication of future results.

Chart 1: Annualized Total Return of Select Commodities Indices (Jan. 1995 to Aug. 2010)

0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 14.0% 16.0%

SPGSCITR

DJUBSTR

DJUBSF3

JMCXTR

SG3MCITR

SPGSCISI

DBLCOYTR

SPDYCITR

Commodity Index Ann.Total Return, Jan 1995 to Aug 2010

Source: Bloomberg. Data as of August 31, 2010. Charts and graphs are provided for illustrative purposes only. Indices are unmanaged statistical composites and their returns do not include payment of any sales charges or fees an investor would pay to purchase the securities the index represents. Such costs would lower performance. It is not possible to invest directly in an index. Past performance is not an indication of future results.

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Roll Matrix: Changing Conceptions The evolution of the various commodities indices referenced above can also be examined through the lens of the Roll Matrix. Essentially, the recent evolution of investable commodities indices correlates with the use of an ever expanding Roll Matrix. In a Standard Roll strategy, there is only one futures contract in the Roll Matrix, which is always focused on the front part of the curve. As we move to the Enhanced Roll strategy, the eligible set of contracts increases, but the outcome is still predetermined. Likewise, the Forward Roll strategy considers a broader part of the curve, in a predetermined manner, and uses the Roll Matrix of the Standard Roll strategy as the starting point.

The Constant Maturity strategy is essentially a multi-prong Forward Roll strategy, staking out multiple points and going out much further into the futures curve.

The Implied Roll Yield strategy takes a dynamic approach to choosing the new rolled-in contract, The Roll Matrix contains a list of eligible contract months, typically covering a much broader range of maturity. The selection of the new rolled-in contract is a function of the prevailing market conditions expressed by the futures curve. In their historical sketch of the commodities indices (see Note 1), Adam Dunsby and Kurt Nelson described the next new generation of commodities indices based on active commodities selection. For the new generation, the Roll Matrix is no longer the active lever, replaced by the overweighting or underweighting of commodities. Chart 2 illustrates the evolution of the aforementioned commodities indices.

Chart 2: The Evolution of Commodities Index

Extended Implied Roll Yield Strategy

Implied Roll Yield Strategy

Constant Maturity Strategy

Forward Roll Strategy

Enhanced Roll Strategy

Standard Roll Strategy

Typ

es o

f R

ollin

g S

trate

gie

s

Degree of Dynamism (Low to High)

S&P GSCI

S&P GSCI Enhanced

Dow Jones-UBS Commodity 3-Month Forward

JP Morgan Commodity Curve Index (JPM CCI)

DCI BNP Paribas Enhanced

S&P GSCI Dynamic Roll Index

Source: S&P Indices. Note: Chart above is not comprehensive and does not include all commodities indices in these categories.

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New Entrant into the Field of Commodity Indices For all the roll strategies discussed here, the Markov property holds: the past choice of rolled-in contracts has no bearing on the future choice of rolled-in contracts. This is not a fundamental aspect of the design of these strategies, but is merely the consequence of a number of assumptions and choices.

Among the key assumptions that lead to this consequence is the assumption that the best predictor of the future shape of the forward curve is its shape today (the same goes for each future, the expected value of which is its price today). Exposure decisions are then directed by observations made on the roll determination date.

At this point, developing an index that employs an even more fine-tuned variation on the Implied Roll Yield strategy, without sacrificing index investability, has become the challenge. Specifically, this paper introduces a way to take into account real world trading costs, as well as achieve a compromise between enhanced return, accomplished by selecting the best rolled-in contract, and the cost of rolling from one contract to another. In this case, the Markov property no longer holds (the rolled-in contract depends on the rolled-out contract, rather than the shape of the curve), but investability is improved. Abandonment of the Markov property differentiates this approach from its predecessors.

To formulate the framework for such an index, the first step is to set up the Standard Roll framework and then present a Dynamic Roll framework that takes advantage of the most liquid range of the forward curve, using the Implied Roll Yield strategy. Lastly, the Dynamic Roll framework is extended to introduce the concepts of Rank Order and the Dynamic Roll Parity Principle.

The Standard Roll Framework Traditionally, investors hold on to the nearest-to-maturity contract until a short time before the contract expiration date, when they roll into the next nearby contract.

Rolled-Out Contract, Rolled-In Contract, Spot Price and Forward Curve The futures contract that the investors are holding will be referred to as the Rolled-Out Contract and the new futures contract they are buying, the Rolled-in Contract.

Spot Price is the cash price at which investors can buy the physical commodities.

Forward curve refers to the spectrum of futures contracts prices, distinguished by their expiration dates.

Available Contract Months Exhibit 2 on the following page shows the generic contract months that are available for each of the 24 commodities in this study. In general, a specific futures contract can be uniquely identified by its commodity code, its expiration month, and its expiration year.

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Exhibit 2: Available Contract Months

Commodity Code Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Corn C X X X X X

Cocoa CC X X X X X

Crude Oil CL X X X X X X X X X X X X

Brent Crude CO X X X X X X X X X X X X

Cotton CT X X X X X

Feeder Cattle FC X X X X X X X X

Gold GC X X X X X X

Heating Oil HO X X X X X X X X X X X X

Coffee KC X X X X X

Kansas Wheat KW X X X X X

Aluminum LA X X X X X X X X X X X X

Live Cattle LC X X X X X X

Lean Hogs LH X X X X X X X X

Lead LL X X X X X X X X X X X X

Nickel LN X X X X X X X X X X X X

Copper LP X X X X X X X X X X X X

Zinc LX X X X X X X X X X X X X

Natural Gas NG X X X X X X X X X X X X

Gasoil QS X X X X X X X X X X X X

Soybeans S X X X X X X X

Sugar SB X X X X X

Silver SI X X X X X X

Wheat W X X X X X

Gasoline XB X X X X X X X X X X X X Source: S&P Indices. Data as of August 31, 2010. Charts and graphs are provided for illustrative purposes only. The GSCI Dynamic Roll indices listed above were not in existence throughout the time period represented and all data presented reflect hypothetical historical performance. Please see the Performance Disclosure at the end of this document for more information on the Indices and the inherent limitations associated with back-tested index performance. Legend: “X” indicates a futures contract is available. Highlighted “X” indicates that due to lack of liquidity, the specific contract month is not used for rolling purposes.

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The Standard Roll Schedule Exhibit 3 shows the complete monthly roll schedule prescribed by the Standard Roll strategy. The Rolled-In Contract Months are listed for each of the 24 commodities.

Exhibit 3: Standard Roll Schedule

Commodity Code Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Corn C H0 K0 K0 N0 N0 U0 U0 Z0 Z0 Z0 H1 H1

Cocoa CC H0 K0 K0 N0 N0 U0 U0 Z0 Z0 Z0 H1 H1

Crude Oil CL H0 J0 K0 M0 N0 Q0 U0 V0 X0 Z0 F1 G1

Brent Crude CO J0 K0 M0 N0 Q0 U0 V0 X0 Z0 F1 G1 H1

Cotton CT H0 K0 K0 N0 N0 Z0 Z0 Z0 Z0 Z0 H1 H1

Feeder Cattle FC H0 J0 K0 Q0 Q0 Q0 U0 V0 X0 F1 F1 H1

Gold GC J0 J0 M0 M0 Q0 Q0 Z0 Z0 Z0 Z0 G1 G1

Heating Oil HO H0 J0 K0 M0 N0 Q0 U0 V0 X0 Z0 F1 G1

Coffee KC H0 K0 K0 N0 N0 U0 U0 Z0 Z0 Z0 H1 H1

Kansas Wheat KW H0 K0 K0 N0 N0 U0 U0 Z0 Z0 Z0 H1 H1

Aluminum LA H0 J0 K0 M0 N0 Q0 U0 V0 X0 Z0 F1 G1

Live Cattle LC J0 J0 M0 M0 Q0 Q0 V0 V0 Z0 Z0 G1 G1

Lean Hogs LH J0 J0 M0 M0 N0 Q0 V0 V0 Z0 Z0 G1 G1

Lead LL H0 J0 K0 M0 N0 Q0 U0 V0 X0 Z0 F1 G1

Nickel LN H0 J0 K0 M0 N0 Q0 U0 V0 X0 Z0 F1 G1

Copper LP H0 J0 K0 M0 N0 Q0 U0 V0 X0 Z0 F1 G1

Zinc LX H0 J0 K0 M0 N0 Q0 U0 V0 X0 Z0 F1 G1

Natural Gas NG H0 J0 K0 M0 N0 Q0 U0 V0 X0 Z0 F1 G1

Gasoil QS H0 J0 K0 M0 N0 Q0 U0 V0 X0 Z0 F1 G1

Soybeans S H0 K0 K0 N0 N0 X0 X0 X0 X0 F1 F1 H1

Sugar SB H0 K0 K0 N0 N0 V0 V0 V0 H1 H1 H1 H1

Silver SI H0 K0 K0 N0 N0 U0 U0 Z0 Z0 Z0 H1 H1

Wheat W H0 K0 K0 N0 N0 U0 U0 Z0 Z0 Z0 H1 H1

Gasoline XB H0 J0 K0 M0 N0 Q0 U0 V0 X0 Z0 F1 G1

Source: S&P Indices. Data as of August 31, 2010. Charts and graphs are provided for illustrative purposes only. The S&P GSCI Dynamic Roll indices listed above were not in existence throughout the time period represented and all data presented reflect hypothetical historical performance. Please see the Performance Disclosure at the end of this document for more information on the Indices and the inherent limitations associated with back-tested index performance. Legend:

“0” stands for the current year, “1” for the year after “0”, and “P” for theyear before “0”. “F” stands for January, “G” stands for February, “H” stands for March, “J” stands for April, “K” stands for May, “M” stands for

June, “N” stands for July, “Q” stands for August, “U” stands for September, “V” stands for October, “X” stands for November, and “Z” stands for December.

Thus, if today is October 31, 2012, “Z0” stands for the December 2012 contract month.

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When commodity indices were first introduced, a monthly roll schedule seemed reasonable because the next nearby contract has always been among the most liquid of all available contracts. It is predetermined for all times, and there is no room for choosing a different contract month.

In contrast to the Standard Roll framework, a Dynamic Roll framework takes advantage of the most liquid range of the forward curve. This framework evaluates the eligible contract months listed in the Roll Matrix and chooses the optimal contract month based on the Implied Roll Yield metric. The choice of a new Rolled-In Contract month will be reflective of the current market conditions, and possibly the current exposure of the strategy.

The Dynamic Roll Framework Suppose at the beginning of a given month, a commodity has n available contracts, besides the one nearest-to-maturity, C0.

Call them C0, C1, C2, …, Cn, arranged in order of their maturities.

Based on liquidity considerations, we select a subset of m contracts from these n contracts, and call them S0 (defined as the nearest-to-maturity contract), S1, S2, …, Sm, with the corresponding futures prices labeled as P0,P1, P2, ..., Pm.

Roll Matrix The list of contracts selected in this way varies from month to month. The Roll Matrix for a given commodity contains all such lists for each month of the year. At roll determination dates (typically two business days before the roll) for each month, the new Rolled-In Contracts are selected from the list of eligible contracts for that month, based on the Implied Roll Yields.

The eligible contract months are determined based on the following criteria:

(a) Individual contract months must pass a daily open interest volume threshold of at least US$ 100 Million,

(b) The sum total of all the open positions of the eligible contract months must be at least 80% of the total open interest of the given commodity, and,

(c) The number of eligible contract months must not exceed 11.

Furthermore, there is a restriction that the eligible contract months must be no more than four years from the initial contract month. Essentially, the eligible contract months are chosen freely along the forward curve within that maturity range, subject only to liquidity considerations.

Example: Crude Oil (CL) Roll Matrix

Using the Standard Roll strategy, the monthly roll schedule for Crude Oil can be outlined in the following way, as in Exhibit 4.

The Rolled-Out Contracts are listed under column “0”, while the new Rolled-In Contracts are listed under column “1”. The identities of the new Rolled-In Contracts are known in advance.

Exhibit 4: Crude Oil – Standard Roll Schedule

CL 0 1 2 3 4 5 6 7 8 9 10 11Jan G0 H0 Feb H0 J0 Mar J0 K0 Apr K0 M0 May M0 N0 Jun N0 Q0 Jul Q0 U0 Aug U0 V0 Sep V0 X0 Oct X0 Z0 Nov Z0 F1 Dec F1 G1 Source: S&P Indices. Data as of August 31, 2010. Charts and graphs are provided for illustrative purposes only. The S&P GSCI Dynamic Roll indices listed above were not in existence throughout the time period represented and all data presented reflect hypothetical historical performance. Please see the Performance Disclosure at the end of this document for more information on the Indices and the inherent limitations associated with back-tested index performance.

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Using the Dynamic Roll strategy, the monthly roll schedule cannot be specified in advance. Rather, it would have to be dynamically determined from among the eleven eligible contract months for the given month, at the close of business of the new roll determination date. Furthermore, the contract

months listed under column “0” are no longer the Rolled-Out Contracts, but are the generic “front futures” of the forward curve, used to calculate the Implied Roll Yield (as it will be seen later) of the contracts listed under column “1”.

Exhibit 5: The Dynamic Roll Matrix of Crude Oil (CL)

CL 0 1 2 3 4 5 6 7 8 9 10 11

Jan G0 H0 J0 K0 M0 N0 U0 Z0 M1 Z1 Z2 Z3

Feb H0 J0 K0 M0 N0 Q0 U0 Z0 M1 Z1 Z2 Z3

Mar J0 K0 M0 N0 Q0 U0 V0 Z0 M1 Z1 Z2 Z3

Apr K0 M0 N0 Q0 U0 V0 Z0 F1 M1 Z1 Z2 Z3

May M0 N0 Q0 U0 V0 X0 Z0 F1 M1 Z1 Z2 Z3

Jun N0 Q0 U0 V0 X0 Z0 F1 M1 N1 Z1 Z2 Z3

Jul Q0 U0 V0 X0 Z0 F1 G1 M1 N1 Z1 Z2 Z3

Aug U0 V0 X0 Z0 F1 G1 H1 M1 N1 Z1 Z2 Z3

Sep V0 X0 Z0 F1 G1 H1 J1 M1 N1 Z1 Z2 Z3

Oct X0 Z0 F1 G1 H1 J1 M1 N1 U1 Z1 Z2 Z3

Nov Z0 F1 G1 H1 J1 M1 N1 U1 Z1 Z2 Z3 Z4

Dec F1 G1 H1 J1 K1 M1 N1 U1 Z1 Z2 Z3 Z4 Source: S&P Indices. Data as of August 31, 2010. Charts and graphs are provided for illustrative purposes only. The S&P GSCI Dynamic Roll indices listed above were not in existence throughout the time period represented and all data presented reflect hypothetical historical performance. Please see the Performance Disclosure at the end of this document for more information on the Indices and the inherent limitations associated with back-tested index performance.

The roll matrix for Crude Oil in Exhibit 5 shows all the eligible contract months in any given month. Column “0” shows the nearest-to-maturity contract month, or “front futures.” The eligible contracts are listed in columns “1” through “11.” Contract months listed under column “1” are the same as those listed in the Standard Roll schedule for the same commodity.

To better understand the nature of roll matrices, let us contrast Crude Oil’s roll matrix with that of Sugar, shown in Exhibit 6. The roll matrices for these two commodities represent two basic types, with important differences regarding the computation of the index values. In the case of Crude Oil, because its Standard Roll counterpart requires monthly rolling, Dynamic Roll determinations are also carried out at monthly intervals; whereas in the case of Sugar, because the availability of contract months is limited even in the Standard Roll, there are fewer monthly rollings involved when Dynamic Roll is employed. This means that if we are going to determine the rolls for Sugar on a Dynamic Roll basis, we want to avoid injecting additional dynamism when unwarranted.

Exhibit 6: The Dynamic Roll Matrix of Sugar (SB) SB 0 1 2 3 4 5 6 7 8 9 10 11 Jan VP H0 K0 N0 V0 H1 Feb H0 K0 N0 V0 H1 Mar H0 K0 N0 V0 H1 Apr K0 N0 V0 H1 N1 May K0 N0 V0 H1 N1 Jun N0 V0 H1 K1 N1 V1 Jul N0 V0 H1 K1 N1 V1 Aug N0 V0 H1 K1 N1 V1 Sep V0 H1 K1 N1 V1 H2 Oct V0 H1 K1 N1 V1 H2 Nov V0 H1 K1 N1 V1 H2 Dec V0 H1 K1 N1 V1 H2 Source: S&P Indices. Data as of August 31, 2010. Charts and graphs are provided for illustrative purposes only. The S&P GSCI Dynamic Roll indices listed above were not in existence throughout the time period represented and all data presented reflect hypothetical historical performance. Please see the Performance Disclosure at the end of this document for more information on the Indices and the inherent limitations associated with back-tested index performance.

Notice that for Sugar, not all calendar months have the same number of eligible contract months. To make sure that the results obtained from the Dynamic Roll strategy are comparable to those obtained from the Standard Roll, it is important to

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make sure that the granularity of the rolls are the same. According to Sugar’s Standard Roll schedule, in February and March, the same contract month K0 is used. Thus, once the new Rolled-in Contract month has been determined for February in the Dynamic Roll framework, the same contract will be used for March as well, without carrying out another Dynamic Roll determination.

Likewise, for June, July and August, V0 is the designated Rolled-In Contract month for the Standard Roll. This means that once the Dynamic Rolled-In Contract month is determined from the set of candidates for Dynamic Roll (V0, H1, K1, N1, V1) it will continue to be used for two more months without repeating the Dynamic Roll process. If the Dynamic Roll process were carried out for July and August as well, it could result in potentially different Rolled-In Contract months for July and/or August, rendering the comparison of the Standard Roll and Dynamic Roll results inappropriate.

Exhibit 7 below shows the span of the individual roll matrices. As demonstrated later, the span of the roll matrix has a bearing on the applicability of the various Dynamic Roll algorithms. If the minimum span of the roll matrix of a given commodity is two, then it would not be possible to consider carrying out Dynamic Roll algorithms of a higher Rank Order for that commodity.

The relationship between the span of the roll matrix and the total number of available contracts for a given commodity is a good indication of how liquid the eligible contracts are. Natural Gas has 148 available contracts, but only ten are included for eligibility. In the case of Live Cattle, two out of nine are included. In general, the eligible contracts are close to the front part of the curve, though not necessarily, as some farther out contracts can be more liquid (on an open interest basis) for market-specific reasons, notably seasonality and production factors.

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Source: S&P Indices. Data as of August 31, 2010. Charts and graphs are provided for illustrative purposes only. The S&P GSCI Dynamic Roll indices listed above were not in existence throughout the time period represented and all data presented reflect hypothetical historical performance. Please see the Performance Disclosure at the end of this document for more information on the Indices and the inherent limitations associated with back-tested index performance.

The minimum span of all the roll matrices ranges from two to 11, while the total number of all tradable contracts ranges from nine to 148.

The Implied Roll Yield (IRY)

The Implied Roll Yield between two adjacent contracts, S i-1 and Si, with prices of Pi-1 and Pi, respectively, in the Roll Matrix for a given month is calculated as follows:

IRY(Si) = ( (Pi-1/Pi ) - 1 )/d,

Where d is the number of months apart between Si-1 and Si, for i=1 to m.

We rank the Si based on its IRY, from high to low.

The sequence of contracts thus ranked are called R1, R2, R3,..,Rm

Decision Rule

To maximize the Implied Roll Yield, the Ri with the best IRY will be selected as the Rolled-In Contract.

Dynamic Roll Algorithm We refer to this process as the Dynamic Roll Algorithm 1 (DRA1) strategy. Exhibit 8 compares the backtesting results (from January 16, 1995 to August 31, 2010) of the Standard Roll and the DRA1 strategies. Standard Roll (%) and Dynamic Roll (%) refer to the frequency of monthly rolls. DR/Standard (%) represents the proportion of Dynamic Roll in relation to the Standard Roll. Standard Roll IRR and DRA1 IRR refer to the annualized rates of returns.

Exhibit 7: Comparison of Dynamic Roll Matrices

Commodity Code Span of RM (Min) Span of RM (Max) Number of all Tradable Contracts (Approx.)

Corn C 4 5 15

Cocoa CC 2 2 10

Crude Oil CL 11 11 70

Brent Crude CO 9 10 71

Cotton CT 2 3 14

Feeder Cattle FC 2 3 9

Gold GC 4 4 19

Heating Oil HO 7 7 33

Coffee KC 2 4 15

Kansas Wheat KW 2 3 10

Aluminum LA 9 10 124

Live Cattle LC 2 2 9

Lean Hogs LH 2 3 12

Lead LL 5 5 64

Nickel LN 5 5 64

Copper LP 9 9 124

Zinc LX 5 5 64

Natural Gas NG 10 10 148

Gasoil QS 8 8 42

Soybeans S 3 4 18

Sugar SB 4 5 12

Silver SI 3 4 20

Wheat W 3 4 15

Gasoline XB 4 6 36

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Exhibit 8: Comparison Between the Standard Roll and the DRA1 Strategies

Commodity Code Standard Roll (%)

Dynamic Roll (%)

DR/ Standard (%)

Standard Roll IRR

DRA1 IRR DRA1 -Standard

Corn C 43% 16% 39% -9.5% 1.1% 10.5%

Cocoa CC 43% 38% 89% -2.7% -0.9% 1.8%

Crude Oil CL 100% 45% 45% 6.0% 19.3% 13.2%

Brent Crude CO 100% 54% 54% 10.2% 19.2% 9.0%

Cotton CT 34% 22% 64% -10.8% -7.8% 2.9%

Feeder Cattle FC 67% 54% 80% -0.2% 2.2% 2.4%

Gold GC 42% 31% 73% 4.3% 4.4% 0.1%

Heating Oil HO 100% 66% 66% 5.0% 10.6% 5.6%

Coffee KC 43% 30% 71% -7.1% -4.2% 2.9%

Kansas Wheat KW 43% 16% 39% -3.4% 1.8% 5.3%

Aluminum LA 100% 37% 37% -3.0% 2.4% 5.4%

Live Cattle LC 50% 38% 77% -3.4% 1.4% 4.7%

Lean Hogs LH 59% 37% 63% -15.9% -6.5% 9.4%

Lead LL 100% 52% 52% 9.8% 14.0% 4.3%

Nickel LN 100% 45% 45% 10.7% 14.2% 3.4%

Copper LP 100% 29% 29% 9.4% 13.5% 4.1%

Zinc LX 100% 47% 47% -2.9% 0.6% 3.5%

Natural Gas NG 100% 30% 30% -21.7% 4.6% 26.3%

Gasoil QS 100% 28% 28% 8.6% 11.6% 3.0%

Soybeans S 42% 11% 27% 4.3% 4.5% 0.1%

Sugar SB 34% 23% 67% 0.2% 3.7% 3.5%

Silver SI 43% 15% 35% 5.5% 6.2% 0.8%

Wheat W 43% 14% 34% -10.9% -1.7% 9.2%

Gasoline XB 100% 39% 39% -9.1% -9.4% -0.3% Source: S&P Indices. Data as of August 31, 2010. Charts and graphs are provided for illustrative purposes only. The S&P GSCI Dynamic Roll indices listed above were not in existence throughout the time period represented and all data presented reflect hypothetical historical performance. Please see the Performance Disclosure at the end of this document for more information on the Indices and the inherent limitations associated with back-tested index performance.

For certain commodities, the backtesting period has been shortened due to the timing of the introduction of specific futures contracts. All backtesting periods are stated explicitly for each commodity in a separate Appendix (see Note 2).

Using the DRA1 strategy, the annualized excess returns of all but one of the 24 commodities surpassed the annualized excess returns obtained using the Standard Roll strategy. For both Standard Roll and Dynamic Roll, the rolling was executed in the same manner, over a period of five days, from the fifth business day to the ninth business day.

Comparing the percentage of monthly rolls that took place during this backtesting period, the

DRA1 strategy involves fewer monthly rolls. This is a desirable outcome and is due to the fact that the same contract months may be the top-ranked choices for Rolled-In Contracts over multiple consecutive months, thus reducing the turnover in futures contracts.

Investability

Commodities futures contracts have provided a solution for investors seeking exposure to commodities markets. While the Dynamic Roll Algorithm has proven its capacity to reduce turnover, it is still possible to enhance the strategy, from both return and turnover perspectives.

14



Differences among Top-Tier Contracts

In calculating the Implied Roll Yield and in examining the contracts with the best Implied Roll Yields, we noticed that the yield differences among the top-tier contracts were quite small. If the yield differences are small, and if the measured yield indeed represents the expected value of the gain or loss due to roll effect only, then in some cases, it is possible that executing a

switch from one contract to another may be more expensive than the gain expected to result from this switch. If true, then it is desirable to reduce the number of rolls, thereby reducing the cost of replicating the index, at the cost of a portion of the enhanced returns. Exhibit 9 shows the distribution of the average Implied Roll Yield differences from the top-ranked contract month R1 to the fourth-ranked contract month R4, wherever appropriate.

Exhibit 9: Comparison of the Average Yield Spread Differences

Commodity Code [IRY(R1) - IRY(R2)]

(%) [IRY(R2) - IRY(R3)]

(%) [IRY(R3) - IRY(R4)]

(%)

Corn C 1.05 0.38 0.40

Cocoa CC 0.23

Crude Oil CL 0.15 0.13 0.11

Brent Crude CO 0.15 0.13 0.10

Cotton CT 0.61

Feeder Cattle FC 0.65

Gold GC 0.01 0.02 0.02

Heating Oil HO 0.59 0.65 0.61

Coffee KC 0.29

Kansas Wheat KW 1.04

Aluminum LA 0.15 0.04 0.04

Live Cattle LC 1.61

Lean Hogs LH 3.02

Lead LL 0.50 0.07 0.08

Nickel LN 0.15 0.06 0.08

Copper LP 0.11 0.05 0.03

Zinc LX 0.12 0.06 0.05

Natural Gas NG 4.72 1.25 1.41

Gasoil QS 0.32 0.28 0.28

Soybeans S 0.95 0.33

Sugar SB 0.82 0.63 0.39

Silver SI 0.04 0.04

Wheat W 1.12 0.63

Gasoline XB 2.59 0.43 0.46

Source: S&P Indices. Data as of August 31, 2010. Charts and graphs are provided for illustrative purposes only. The S&P GSCI Dynamic Roll indices listed above were not in existence throughout the time period represented and all data presented reflect hypothetical historical performance. Please see the Performance Disclosure at the end of this document for more information on the Indices and the inherent limitations associated with back-tested index performance.

15



Set of Optimum Contracts From the list of ranked contracts, R1, R2, R3, …, Rm, we designate a Set of Optimum Contracts, which is a subset of the top-tier contracts. A Set of Optimum Contracts of size two means that only R1 and R2 will be included in this set. Similarly, a Set of Optimum Contracts of size three means that only R1, R2, and R3 will be included.

The Dynamic Roll Parity Principle If the Rolled-Out Contract is part of the Set of Optimum Contracts, continue using the Rolled-Out Contract as the new Rolled-In Contract.

If not, choose the top candidate in the Set of Optimum Contracts to be the new Rolled-In Contract.

Backtesting We have run three roll strategies, referred to as DRA2, DRA3, and DRA4. In DRA2, we consider a Set of Optimum Contracts of size two. In DRA3, we expand the Set of Optimum Contracts to size three. In DRA4, we further expand of the set to size 4.

As we move from DRA1 to DRA2, and then from DRA3 to DRA4, we expect returns to degrade somewhat while replication costs decline since less rolling is required. However, these expectations are predicated on the assumption that the forward curve will not change during the coming month. When the forward curve changes, from the backtesting results, we see that the returns will not necessarily decline as we move from DRA1 to DRA4.

The results for Crude Oil are presented in Exhibit 10 on the following page. (Note: The full set of results are available in the Appendix at www.indices.standardandpoors.com)

Exhibit 10: Backtesting Results for Crude Oil Backtesting Period: January 16, 1995 to August 31, 2010 (Index Base = 100)

Crude Oil Standard Roll DRA1 DRA2 DRA3 DRA4

Return (%) 150.20 1,473.00 1,452.90 1,706.20 1,109.50

IRR (%) 6.00 19.30 19.20 20.30 17.30

Volatility (%) 36.10 27.70 27.80 27.50 27.90

Sharpe 0.17 0.70 0.69 0.74 0.62

Max Daily DD (%) -82.40 -67.10 -67.60 -60.70 -69.20

Var (95%) 5d (%) -7.70 -5.90 -5.90 -5.90 -6.00

Best Month (%) 36.20 20.80 20.80 20.80 20.80

Worst Month (%) -32.50 -30.00 -30.00 -20.60 -32.10

% Pos Month 55.90 55.90 54.80 54.30 55.30

% Roll 100.00 63.00 51.00 45.00 40.00 Source: S&P Indices. Data as of August 31, 2010. Charts and graphs are provided for illustrative purposes only. The S&P GSCI Dynamic Roll indices listed above were not in existence throughout the time period represented and all data presented reflect hypothetical historical performance. Please see the Performance Disclosure at the end of this document for more information on the Indices and the inherent limitations associated with back-tested index performance. For Crude Oil, the backtesting period spans from January 16, 1995 to August 31, 2010, a total of 187 months. Over this time period, the total excess return was 150.2%, or an annualized return of 6.0%.

The results of using the four Dynamic Roll algorithms are shown alongside the results of the Standard Roll. Using the Dynamic Roll algorithms, the performance of Crude Oil improved significantly as the algorithms mitigated the negative roll yields during this time period, while

16



the percentage of monthly rolling declined (the same contract months may have been selected as the new Rolled-In Months for a few consecutive months).

Rank Order

Based on the backtesting results, we chose the most appropriate Dynamic Roll algorithm for each commodity based on the following criteria:

a) performance,

b) percentage of monthly roll, and

c) operational ease.

The empirical data reveals how each individual commodity behaves in its own market environment over time. For some commodities, it makes sense to consider a larger Set of Optimum Contracts.

For others, due to the larger Roll Yield differences between the top-ranked contract and the rest of the contracts, a Set of Optimum Contract of size 1 would be the most appropriate choice. Thus, the 24 commodities can be categorized based on which Dynamic Roll algorithms are chosen for them individually. A given commodity is said to have a Rank Order of k if DRA(k) is its assigned strategy. The range of values for the Rank Order runs from 1 to 4.

An Extended Dynamic Roll Framework

In the extended Dynamic Roll framework, each commodity has been assigned a specific Rank Order based on the backtesting results over the last 187 months (fewer for some commodities), which cover a wide range of market conditions.

Exhibit 11 below shows the Rank Orders of the 24 commodities.

Exhibit 11: Rank Orders

Commodity Code Rank Order

Corn C 1

Cocoa CC 1

Crude Oil CL 3

Brent Crude CO 1

Cotton CT 1

Feeder Cattle FC 1

Gold GC 1

Heating Oil HO 1

Coffee KC 1

Kansas Wheat KW 2

Aluminum LA 2

Live Cattle LC 1

Lean Hogs LH 1

Lead LL 3

Nickel LN 3

Copper LP 3

Zinc LX 3

Natural Gas NG 1

Gasoil QS 4

Soybeans S 1

Sugar SB 1

Silver SI 2

Wheat W 2

Gasoline XB 2 Source: S&P Indices. Data as of August 31, 2010. Charts and graphs are provided for illustrative purposes only. The S&P GSCI Dynamic Roll indices listed above were not in existence throughout the time period represented and all data presented reflect hypothetical historical performance. Please see the Performance Disclosure at the end of this document for more information on the Indices and the inherent limitations associated with back-tested index performance.

17



At each Roll Determination Date (the third business day of each month), for each commodity, we carry out the standard Dynamic Roll determinations. Then, based on the Rank Order of the given commodity, we select the appropriate Dynamic Roll algorithm, and apply the Dynamic Roll Parity Principle.

For example, if the Rank Order of a given commodity is 3, then we would apply DRA3 to that commodity. This process provides the new Rolled-In Contract for the given commodity.

Exhibit 12 shows a comparison of the various commodities indices for Crude Oil, on an annualized excess Return basis, from January 1995 to August 2010.

Exhibit 12: Comparison of Annualized Excess Returns of Select Crude Oil Commodity Indices

Commodity Index Bloomberg Ticker

Crude Oil Annualized Excess Return

Jan. 1995 to Aug. 2010 (%)

S&P GSCI SPGSCLP 5.8 DJ-UBSCI DJUBSCL 7.1 JPMorgan Commodity Curve Index JMCXCLER 11.5 S&P GSCI 3-Month Forward SG3MCLP 13.9 DJ-UBS 3 Mo Forward DJUBSCL3 14.7 S&P GSCI Enhanced SGECCLP 15.1 Deutsche Bank Liquid Commodity Index - Optimum Yield DBLCOCLE 16.3 S&P GSCI Dynamic Roll (pro forma) SPDYCLP 20.3 Source: S&P Indices, Bloomberg. Data as of August 31, 2010. Charts and graphs are provided for illustrative purposes only. Indices are unmanaged statistical composites and their returns do not include payment of any sales charges or fees an investor would pay to purchase the securities the index represents. Such costs would lower performance. It is not possible to invest directly in an index. Past performance is not an indication of future results.

18



Chart 3: Comparison of Annualized Excess Returns of Select Crude Oil Commodity Indices (Jan. 1995 to Aug. 2010)

0.0% 5.0% 10.0% 15.0% 20.0% 25.0%

S&P GSCI (CL)

DJ-UBSCI (CL)

JPM CCI (CL)

S&P GSCI 3-Mo Forward (CL)

DJ-UBS CI F3 (CL)

S&P GSCI Enhanced (CL)

DBLCI OY (CL)

S&P GSCI Dynamic Roll (CL)

Crude Oil Ann. Excess Return, Jan 1995 to Aug 2010

Source: S&P Indices, Bloomberg. Data as of August 31, 2010. Charts and graphs are provided for illustrative purposes only. Indices are unmanaged statistical composites and their returns do not include payment of any sales charges or fees an investor would pay to purchase the securities the index represents. Such costs would lower performance. It is not possible to invest directly in an index. Past performance is not an indication of future results.

Exhibit 13 shows the aggregate outperformance over a Standard Roll strategy, using the 2010 S&P GSCI Rebalancing Weights as a proxy for a

benchmark commodity index and using the Rank Orders selected for the Extended Dynamic Roll Framework.

19



The Composite Index Results

Exhibit 13: Pro Forma Results of a Hypothetical Composite Commodity Index, with Optimal Rank Orders Chosen for All 24 Commodities

Commodity Code Rank Order (k)

Standard Roll

(%)

Dynamic Roll (%)

DR/

Std. (%) Standard Roll IRR (%)

DRA(k) Out-performance (%)

2010 S&P GSCI Weight (%)

Contrib. (%)

Corn C 1 43 16 39 -9.5 10.5 4.04 0.42

Cocoa CC 1 43 38 89 -2.7 1.8 0.38 0.01

Crude Oil CL 3 100 45 45 6.0 14.3 34.82 4.98

Brent Crude CO 1 100 54 54 10.2 9.0 14.22 1.27

Cotton CT 1 34 22 64 -10.8 2.9 0.98 0.03

Feeder Cattle FC 1 67 54 80 -0.2 2.4 0.5 0.01

Gold GC 1 42 31 73 4.3 0.1 2.8 0.00

Heating Oil HO 1 100 66 66 5.0 5.6 4.64 0.26

Coffee KC 1 43 30 71 -7.1 2.9 0.78 0.02

Kansas Wheat KW 2 43 16 39 -3.4 6.0 0.83 0.05

Aluminum LA 2 100 37 37 -3.0 5.5 2.47 0.13

Live Cattle LC 1 50 38 77 -3.4 4.7 3.08 0.15

Lean Hogs LH 1 59 37 63 -15.9 9.4 1.59 0.15

Lead LL 3 100 52 52 9.8 3.6 0.41 0.01

Nickel LN 3 100 45 45 10.7 3.0 0.65 0.02

Copper LP 3 100 29 29 9.4 3.5 2.83 0.10

Zinc LX 3 100 47 47 -2.9 3.1 0.56 0.02

Natural Gas NG 1 100 30 30 -21.7 26.3 5.21 1.37

Gasoil QS 4 100 28 28 8.6 2.7 5.82 0.16

Soybeans S 1 42 11 27 4.3 0.1 2.84 0.00

Sugar SB 1 34 23 67 0.2 3.5 1.94 0.07

Silver SI 2 43 15 35 5.5 0.6 0.31 0.00

Wheat W 2 43 14 34 -10.9 9.7 4.02 0.39

Gasoline XB 2 100 39 39 -9.1 0.5 4.3 0.02

Total 100.00 9.65

Source: S&P Indices. Data as of August 31, 2010. Charts and graphs are provided for illustrative purposes only. The S&P GSCI Dynamic Roll indices listed above were not in existence throughout the time period represented and all data presented reflect hypothetical historical performance. Please see the Performance Disclosure at the end of this document for more information on the Indices and the inherent limitations associated with back-tested index performance. By executing the appropriate Dynamic Roll algorithms individually for each of the 24 commodities, based on the assigned Rank Orders over the period January 16, 1995 to August 31, 2010, there was an improvement of 965 basis points on a pro forma basis, as well as a

significant reduction in turnovers of the futures contracts.

A comparison using the DRA1 strategy for all 24 commodities shows is presented in Exhibit 14. By definition, this means that all commodities are given a Rank Order of 1.

20 MCGRAW-HILL FINANCIAL


Exhibit 14: Pro Forma Results of a Hypothetical Composite Commodity Index, with Rank Orders of All 24 Commodities Equal to One


Standard Roll

(%)

Dynamic Roll (%)

DR/

Std. (%)

Standard Roll IRR (%)

DRA1 – Standard (%)

2010 S&P GSCI Weight (%)

Contrib. (%)

Corn C 1 43 16 39 -9.5 10.5 4.04 0.42

Cocoa CC 1 43 38 89 -2.7 1.8 0.38 0.01

Crude Oil CL 1 100 63 63 6.0 13.2 34.82 4.61

Brent Crude CO 1 100 54 54 10.2 9.0 14.22 1.27

Cotton CT 1 34 22 64 -10.8 2.9 0.98 0.03

Feeder Cattle FC 1 67 54 80 -0.2 2.4 0.5 0.01

Gold GC 1 42 31 73 4.3 0.1 2.8 0.00

Heating Oil HO 1 100 66 66 5.0 5.6 4.64 0.26

Coffee KC 1 43 30 71 -7.1 2.9 0.78 0.02

Kansas Wheat KW 1 43 23 55 -3.4 5.3 0.83 0.04

Aluminum LA 1 100 55 55 -3.0 5.4 2.47 0.13

Live Cattle LC 1 50 38 77 -3.4 4.7 3.08 0.15

Lean Hogs LH 1 59 37 63 -15.9 9.4 1.59 0.15

Lead LL 1 100 82 82 9.8 4.3 0.41 0.02

Nickel LN 1 100 90 90 10.7 3.4 0.65 0.02

Copper LP 1 100 54 54 9.4 4.1 2.83 0.12

Zinc LX 1 100 90 90 -2.9 3.5 0.56 0.02

Natural Gas NG 1 100 30 30 -21.7 26.3 5.21 1.37

Gasoil QS 1 100 53 53 8.6 3.0 5.82 0.17

Soybeans S 1 42 11 27 4.3 0.1 2.84 0.00

Sugar SB 1 34 23 67 0.2 3.5 1.94 0.07

Silver SI 1 43 24 56 5.5 0.8 0.31 0.00

Wheat W 1 43 19 44 -10.9 9.2 4.02 0.37

Gasoline XB 1 100 43 43 -9.1 -0.3 4.3 -0.01

Total 100 9.26

Source: S&P Indices. Data as of August 31, 2010. Charts and graphs are provided for illustrative purposes only. The S&P GSCI Dynamic Roll indices listed above were not in existence throughout the time period represented and all data presented reflect hypothetical historical performance. Please see the Performance Disclosure at the end of this document for more information on the Indices and the inherent limitations associated with back-tested index performance. Applying the DRA1 strategy to all commodities, and using the 2010 S&P GSCI Rebalancing Weights, we achieved an improvement of 926 basis points on a pro forma basis. This may not seem to diverge greatly from the 965 basis points improvement obtained using the various Dynamic Roll algorithms. However, this return data does not capture the fact that commodities with a Rank Order greater than 1 also enjoy a greater reduction in turnover than what is shown here.

For example, the turnover for Crude Oil as a percent of the Standard Roll strategy drops from 63% to 45% as it moves from a Rank Order of 1 to 3. This additional reduction in turnover results in a lower cost of replication, and it flows directly to the bottom line of the fund’s portfolio manager. In addition, its annualized excess return increases

from 13.2% to 14.3%, even though there should typically be a degradation in performance as we move from the DRA1 to DRA4 strategy. This boost in performance seems to stem from dynamically choosing the new Rolled-In Contract months over a 187-month period, during which the forward curve of Crude Oil may have fluctuated greatly, from contango to backwardation, producing positive results for this particular commodity.

Exhibit 15 shows the data for all 24 commodities.

DRA1 IRR (%) and DRA(k) IRR (%) refer to the annualized excess returns of the respective strategies. Improvement/Degradation IRR (%) equals the difference between DRA1 and DRA(k).



Exhibit 15: Performance Characteristics


DRA1 IRR

(%) DRA(k) IRR(%)

Improvement/Degradation IRR (%)

% Roll DRA1 (%)

% Roll DRA(k) (%)

% Standard Roll (%)

Corn C 1 1.1 1.1 0 16 16 43

Cocoa CC 1 -0.9 -0.9 0 38 38 43

Crude Oil CL 3 19.3 20.3 1 63 45 100

Brent Crude CO 1 19.2 19.2 0 54 54 100

Cotton CT 1 -7.8 -7.8 0 22 22 34

Feeder Cattle FC 1 2.2 2.2 0 54 54 67

Gold GC 1 4.4 4.4 0 31 31 42

Heating Oil HO 1 10.6 10.6 0 66 66 100

Coffee KC 1 -4.2 -4.2 0 30 30 43

Kansas Wheat KW 2 1.8 2.6 0.8 23 16 43

Aluminum LA 2 2.4 2.4 0 55 37 100

Live Cattle LC 1 1.4 1.4 0 38 38 50

Lean Hogs LH 1 -6.5 -6.5 0 37 37 59

Lead LL 3 14 13.4 -0.6 82 52 100

Nickel LN 3 14.2 13.7 -0.5 90 45 100

Copper LP 3 13.5 12.9 -0.6 54 29 100

Zinc LX 3 0.6 0.2 -0.4 90 47 100

Natural Gas NG 1 4.6 4.6 0 30 30 100

Gasoil QS 4 11.6 11.3 -0.3 53 28 100

Soybeans S 1 4.5 4.5 0 11 11 42

Sugar SB 1 3.7 3.7 0 23 23 34

Silver SI 2 6.2 6.1 -0.1 24 15 43

Wheat W 2 -1.7 -1.2 0.5 19 14 43

Gasoline XB 2 -9.4 -8.6 0.8 43 39 100

Source: S&P Indices. Data as of August 31, 2010. Charts and graphs are provided for illustrative purposes only. The S&P GSCI Dynamic Roll indices listed above were not in existence throughout the time period represented and all data presented reflect hypothetical historical performance. Please see the Performance Disclosure at the end of this document for more information on the Indices and the inherent limitations associated with back-tested index performance. Comparing the results of DRA1 and DRA(k), where k is the assigned Rank Order for the given commodity, it appears that for some commodities (Lead, Nickel, Copper, Zinc, Gasoil and Silver), the Dynamic Roll algorithm enhances investability, such that more costly rolls can be reduced at a reasonable tradeoff in return, while for others there are no adverse effects. Conclusion Backtested results seem to indicate that the extended Dynamic Roll framework has the capacity to enhance the performance of both

active and passive commodity funds. The flexible rolling management strategy employed under the Dynamic Roll framework moves away from an exclusive focus on the first nearby contracts. This approach is informed by the Dynamic Roll Parity Principle, which shows that futures contracts are to an extent fungible and the concept of Rank Order, which offers a way to leverage the idiosyncrasies of individual commodities markets. In the commodities futures market, innovative monthly rolling strategies may be employed to take into account fluctuating market conditions and thereby demonstrate various performance outcomes.



S&P Indices Global Research & Design Contact Information

About S&P Indices Research & Design S&P Indices is the world’s leading index provider, maintaining a wide variety of investable and benchmark indices to meet a wide array of investor needs. Our research team is dedicated to conducting unbiased and in-depth analysis on a broad range of topics and issues facing investors in today’s marketplace. Research by S&P Indices’ Global Research & Design provokes discussion on investment matters related to benchmarking in the asset management, derivatives and structured products communities. The series covers all asset classes and is often used to float new indexing concepts or explain substantive changes to well-known S&P indices. Notes 1. “A Brief History of Commodities Indexes,” by Adam Dunsby and Kurt Nelson,

Journal of Indexes, May/June 2010

http://www.indexuniverse.com/publications/journalofindexes/joi-articles/7451-a-brief-history-of-commodities-indexes.html

2. For the full set of backtesting results, please visit http://www.spindices.com/assets/files/commodities/pdf/Dynamic_Roll_Commodities-Research-Design-Appendix.pdf

or

www.indices.standardandpoors.com

Global Head

Frank Luo +1 212-438-5057 frank_luo@ standardandpoors.com

New York

Berlinda Liu +1 212-438-7834 berlinda_liu@ standardandpoors.com

Aye Soe +1 212-438-1677 aye_soe@ standardandpoors.com

Peter Tsui +1 212-438-1493 peter_tsui@ standardandpoors.com

London

Xiaowei Kang +020 7176-8443 [email protected]

Beijing

Liyu Zeng +86 10-6569-2947 liyu_zeng@ standardandpoors.com

Hong Kong

Simon Karaban +852 2532-8050 simon_karaban@ standardandpoors.com



Performance Disclosures Indices are not collective investment funds and are unmanaged. It is not possible to invest directly in an S&P index. Past performance of an index is not an indication of future results. The inception date for the following indices at the market close is January 27, 2011: the S&P GSCI Brent Crude Dynamic Roll Index, the S&P GSCI Cocoa Dynamic Roll Index, S&P GSCI Crude Oil Dynamic Roll Index, S&P GSCI Corn Dynamic Roll Index, S&P GSCI Cotton Dynamic Roll Index, S&P GSCI Feeder Cattle Dynamic Roll Index, S&P GSCI Gold Dynamic Roll Index, S&P GSCI GasOil Dynamic Roll Index, S&P GSCI Heating Oil Dynamic Roll Index, S&P GSCI Unleaded Gasoline Dynamic Roll Index, S&P GSCI Aluminum Dynamic Roll Index, S&P GSCI Copper Dynamic Roll Index, S&P GSCI Nickel Dynamic Roll Index, S&P GSCI Lead Dynamic Roll Index, S&P GSCI Zinc Dynamic Roll Index, S&P GSCI Coffee Dynamic Roll Index, S&P GSCI Kansas Wheat Dynamic Roll Index, S&P GSCI Live Cattle Dynamic Roll Index, S&P GSCI Lean Hogs Dynamic Roll Index, S&P GSCI Natural Gas Dynamic Roll Index, S&P GSCI Sugar Dynamic Roll Index, S&P GSCI Silver Dynamic Roll Index, S&P GSCI Soybeans Dynamic Roll Index, S&P GSCI Wheat Dynamic Roll Index. These indices have not been in existence prior to that date and all data presented prior to that date is back-tested. The back-test calculations are based on the same methodology that was in effect when the indices were officially launched. Complete index methodology details are available at www.indices.standardandpoors.com. Prospective application of the methodology used to construct the S&P GSCI Brent Crude Dynamic Roll Index, S&P GSCI Cocoa Dynamic Roll Index, S&P GSCI Crude Oil Dynamic Roll Index, S&P GSCI Corn Dynamic Roll Index, S&P GSCI Cotton Dynamic Roll Index, S&P GSCI Feeder Cattle Dynamic Roll Index, S&P GSCI Gold Dynamic Roll Index, S&P GSCI GasOil Dynamic Roll Index, S&P GSCI Heating Oil Dynamic Roll Index, S&P GSCI Unleaded Gasoline Dynamic Roll Index, S&P GSCI Aluminum Dynamic Roll Index, S&P GSCI Copper Dynamic Roll Index, S&P GSCI Nickel Dynamic Roll Index, S&P GSCI Lead Dynamic Roll Index, S&P GSCI Zinc Dynamic Roll Index, S&P GSCI Coffee Dynamic Roll Index, S&P GSCI Kansas Wheat Dynamic Roll Index, S&P GSCI Live Cattle Dynamic Roll Index, S&P GSCI Lean Hogs Dynamic Roll Index, S&P GSCI Natural Gas Dynamic Roll Index, S&P GSCI Sugar Dynamic Roll Index, S&P GSCI Silver Dynamic Roll Index, S&P GSCI Soybeans Dynamic Roll Index, and S&P GSCI Wheat Dynamic Roll Index may not result in performance commensurate with the back-test returns shown. The back-test period does not necessarily correspond to the entire available history of the indices. Please refer to the methodology paper for the indices, available at www.standardandpoors.com for more details about the indices, including the manner in which they are rebalanced, and the timing of such rebalancing, criteria for additions and deletions and index calculation. The indices are rules based, although the Index Committee reserves the right to exercise discretion, when necessary. The index performance has inherent limitations. The index returns shown do not represent the results of actual trading of investor assets. Standard & Poor’s maintains the indices and calculates the index levels and performance shown or discussed, but does not manage actual assets. Indices are statistical composites and their returns do not reflect payment of any sales charges or fees an investor would pay to purchase the securities they represent. The imposition of these fees and charges would cause actual and back-tested performance to be lower than the performance shown. For example, if an index returned 10% on a US$ 100,000 investment for a 12-month period (or US$ 10,000) and an annual asset-based fee of 1.5% were imposed at the end of the period (or US$ 1,650), the net return would be 8.35% (or US$ 8,350) for the year. Over 3 years, an annual 1.5% fee taken at year end with an assumed 10% return per year would result in a cumulative gross return of 33.1%, a total fee of US$ 5,375, and a cumulative net return of 27.2% (or US$ 27,200).

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