Trilogy for Troubleshooting Convergence: Manipulation, Structural Imbalance, and

Storage Rates

Scott H. Irwin1

November 2018

Forthcoming in the Journal of Commodity Markets

Abstract: Historically unprecedented episodes of non-convergence occurred during 2005-2010 in Chicago Board of Trade (CBOT) corn, soybean, and wheat futures contracts and Kansas City Board of Trade (KCBOT) wheat futures contracts. A trilogy of explanations has been offered to troubleshoot these episodes—manipulation, structural imbalances, and low storage rates. Theoretical and empirical analysis shows that convergence failures were gen-erated by a disequilibrium between the higher market value of storage in the physical market for grain compared to the storage rate paid to holders of the delivery instrument for grain futures contracts. How to adjust storage rates higher in recognition of this market reality is a highly contentious issue in the grain industry.

Key words: convergence, delivery, grain futures, storage, VSR

JEL categories: D84, G12, G13, G14, Q13, Q41

1 Scott H. Irwin is the Lawrence J. Norton Chair of Agricultural Marketing, Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign. Several people deserve special thanks for their assistance. Aaron Smith helped with updating the market price of storage computations and provided numerous helpful comments. Fred Seamon and Dave Lehman answered many questions about grain futures contract specifications and provided updated data on shipments and stocks. Hongxia Jiao helped with up-dating the futures and cash price data. Helpful comments from the Editor, Sjur Westgaard, and participants at “Protecting America’s Agricultural Markets: An Agricultural Commodity Futures Conference” held April 5-6 in Kansas City are also greatly appreciated. Correspondence can be directed to Scott Irwin. Postal address: 344 Mumford Hall, 1301 W. Gregory Dr. University of Illinois at Urbana-Champaign, Urbana, IL 61801. Phone: (217)-333-6087, Email: sirwin@illinois.edu.

2

1. Introduction

For grain futures contracts with physical delivery, such as Chicago Board of Trade (CBOT) corn, soybean, and wheat futures contracts and Kansas City Board of Trade (KCBOT) wheat, the contract terms include the deliverable grades, delivery territory, and period of delivery.2 This is a consequence of the fact that futures contracts are standardized forward contracts with all terms fixed except price (Peck, 1985). The delivery process ties futures and cash prices together.3 In a perfectly competitive market with costless delivery at one location and date, arbitrage should force the futures price at expiration to equal the cash price. Otherwise, the law of one price would be violated. A well-designed contract should involve few actual deliveries because the terms of the contract balance the commercial in-terests of long and short futures position holders (Hieronymus, 1977). This discussion should make clear that convergence of futures and spot (cash) markets during the delivery period is a bedrock principle of commodity futures markets. Pirrong, Haddock, and Kormendi (1993, p. 11) state, “The emphasis on convergence stems from a belief that the prime function of any forward market is to enable producers, processors, and merchants to “hedge”—to reduce the price risk that they face—and to facilitate price discovery. Achieve-ment of both objectives requires the futures price and spot prices to be closely related.” In short, the central functions of a futures market are threatened without convergence of fu-tures and spot prices during delivery.

As shown in Figures 1 through 4, Chicago Board of Trade (CBOT) corn, soybean, and wheat futures contracts and Kansas City Board of Trade (KCBOT) wheat futures contracts, respectively, experienced several episodes of non-convergence since the mid-1980s.4 The most severe episodes were concentrated in 2005-2010, with non-convergence frequently well outside any reasonable bound based on the cost of delivery. Non-convergence also exceeded 50 cents per bushel in all four markets at least once. The poster child for non-convergence during this period was CBOT wheat. In September 2008, the CBOT wheat futures price

2 Both the CBOT and KCBOT are now part of the CME Group, Inc. After the merger of the CBOT and the CME, the CBOT retained its designation as a “contract market” regulated by the Commodity Futures Trading Commission (CFTC). Consequently, the CBOT is still the legal entity with regulatory approval to list corn, soybean, and wheat futures contracts for trading. The KCBOT is no longer a “designated contract market,” so the former KCBOT wheat contract is now officially listed by the CBOT. However, we refer to the original exchanges associated with the respective grain futures contracts in order to avoid confusion.

3 See Appendix A: Grain Market Delivery for a detailed presentation of delivery systems for grain futures markets in the U.S.

4 See the Appendix B: Data for details on the construction of the basis series shown in Figures 1-4.

3

expired an astounding $2.50 above the cash price at the cheapest-to-deliver location. The duration and magnitude of these convergence failures was unlike anything seen in the mod-ern record of grain futures markets.5

Heated public and academic debate ensued as to the possible causes of the 2005-2010 con-vergence failures. Many blamed new financial index traders in grain futures markets. For example, a widely-publicized report by the United States Senate Permanent Subcommittee on Investigations (USS/PSI, 2009) claimed commodity index trading caused the non-con-vergence in wheat markets. The USS/PSI report maintained that index fund capital over-powered the normal functioning of delivery arbitrage. Others argued that the grain futures markets were “broken” and questioned whether the contracts could remain a useful hedging tool. There were equal concerns that the price discovery function of agricultural futures markets was seriously threatened, which would have ripple effects through the agricultural sector, as the forward contracts generally preferred by grain producers to hedge downside price risks are priced off of futures. Despite these fears, average daily trading volume in the CBOT corn, soybean, and wheat contracts doubled between September 2005, when non-convergence first appeared, and September 2008 when non-convergence was at its worst.

The CBOT and KCBOT made various changes to grain futures contract specifications in an attempt to address the 2005-2010 non-convergence problems. The number of warehouse receipts and shipping certificates that a trader could hold was limited, delivery locations were expanded for CBOT wheat, and the KCBOT changed to a seasonal storage rate system for its wheat contract. By far the most fundamental change was the implementation of a variable storage rate (VSR) rule for CBOT wheat beginning with the September 2010 con-tract (Seamon, 2009). VSR is keyed to the level of calendar spreads (difference in price across futures contract maturities on a given date) in the period immediately preceding the expiration of the nearby contract. This change was highly contentious in the grain industry when it was proposed, and by all indications, has remained controversial.

In sum, non-convergence has been a major issue in grain futures markets for well over a decade. Three general categories of explanations have been offered to troubleshoot these large non-convergence episodes. The first is manipulation in the form of traditional corners and squeezes. The second is a structural imbalance in contract design or market conditions that favors one side of the market. The third is futures contract storage rates that are set below the market-clearing price of storage in the physical market. The purpose of this paper is to review non-convergence episodes in grain futures markets since 2005 and determine

5 To the best of my knowledge, the 2005-2010 non-convergence episodes were the most severe and sustained in the history of U.S. grain futures markets.

4

which of the “trilogy” best explains recent problems. Without a determination of the best explanation, there is the risk that changes to grain futures contracts will not address the true underlying problem.

2. Troubleshooting: Manipulation

Grain futures markets have a long history of idiosyncratic pricing anomalies that have arisen due to market manipulation in the form of corners and squeezes. (e.g., Paul, 1976; Hieron-ymus, 1977; Pirrong, 2004), so it is a logical starting place when attempting to explain the non-convergence problems in grain futures markets over 2005-2010. In a classic manipula-tion, a trader or group of traders acquire market power by building up large long positions in futures and the cash market at delivery locations. Once having cornered the market, the trader or group of traders can use their market power to squeeze the shorts in the market and force prices during the delivery period to be much higher than otherwise would be the case. As shown in Figure 5, the classic signature of these episodes is short-run artificiality in: i) the level of expiring futures prices compared to cash prices in the delivery area, ii) the level of cash prices in the delivery area relative to more distant cash prices, and iii) the level of expiring futures prices compared to prices for later to expire futures contract. The arti-ficiality seldom lasts more than one contract cycle because it is difficult to prevent additional supplies from being moved into deliverable position. It is also why secrecy and surprise is important to a successful corner and squeeze.

The delivery basis patterns found in Figures 1 through 4 make it relatively easy to dismiss traditional manipulation as the explanation for non-convergence problems over 2005-2010. The magnitude and persistence of non-convergence in all four markets simply was too large to be explained by a series of corners and squeezes. It defies belief that the kind of artifici-ality associated with market manipulation in Figure 5 could explain the non-convergence episodes of 2005-2010. As a result, it is not too surprising that manipulation in the form of corners and squeezes was never seriously considered as an explanation for the recent con-vergence failures in grain futures markets.

2.1. Troubleshooting: Structural Imbalance

The next possible explanation for the non-convergence problems over 2005-2010 is a struc-tural imbalance in contract design or market conditions that systematically favors one side of the market. Hieronymus (1977, p. 341) provides an important perspective:

“Delivery on futures contracts is a sampling of value process. The ob-jective is to get a representative sample. There must be a sufficient amount of the commodity move to and through the delivery points that

5

no one can control and distort the price. The amount must be large enough that the price is representative of the value of the commodity generally so that the relationships with prices at other points of com-merce are rational.”

If these conditions do not hold, Hieronymus (1977, p. 340) warns, “When a contract is out of balance the disadvantaged side ceases trading and the contract disappears.” Exchanges are certainly aware of this critical dimension of contract success and invest considerable resources in structuring and modifying contracts to reflect commercial market activities and changes in hedging effectiveness.

The initial response to the non-convergence problems of 2005-2010 by exchange staff, market participants, regulators, legislators, and academic researchers was to focus on potential structural problems with grain futures markets. Much of the discussion centered on the emergence of large-scale participation by a new type of speculator in grain futures markets—financial index investors. These investors desire long-only exposure to an index of commod-ity prices for portfolio diversification, inflation hedging, and return enhancement. Aulerich, Irwin, and Garcia (2013) document the tremendous growth of the participation by index investors in grain futures markets. For example, they report that the net long position of index investors in CBOT wheat increased from an average of 25,702 contracts in 2003 to 134,408 contracts in 2005, over a fivefold increase. The rapid growth in CIT positions is also apparent in CBOT wheat as a percentage of total open interest (long), which increased from 25 to 55 percent over the same period. The growth of financial index positions in other grain futures markets was comparable to that in CBOT wheat. By any measure, the growth of financial index investment was a major structural change in the market partici-pants trading in agricultural futures markets.6

It was widely argued at the time that the wave of buying pressure from financial index investors created large and long-lasting bubbles in commodity futures prices. The U.S. Senate’s Permanent Subcommittee on Investigations (USS/PSI 2009, p. 2) concluded that this was the cause of non-convergence in the CBOT wheat futures market:

“This Report finds that there is significant and persuasive evidence to conclude that these commodity index traders, in the aggregate, were one of the major causes of “unwarranted changes”—here, increases—in the price of wheat futures contracts relative to the price of wheat in the cash market. The resulting unusual, persistent and large disparities

6 See Irwin and Sanders (2012) for a detailed discussion of this point.

6

between wheat futures and cash prices impaired the ability of partici-pants in the grain market to use the futures market to price their crops and hedge their price risks over time, and therefore constituted an un-due burden on interstate commerce. Accordingly, the Report finds that the activities of commodity index traders, in the aggregate, constituted “excessive speculation” in the wheat market under the Commodity Ex-change Act.”

In essence, the USS/PSI report concluded that the structural change in market participation associated with financial index investment was so large that it overpowered the normal functioning of delivery arbitrage. Based on these findings, the Subcommittee recommended: 1) the phase out of existing position limit waivers for index traders in wheat; 2) if necessary, the imposition of additional restrictions on index traders, such as a position limit of 5,000 contracts per trader; 3) the investigation of index trading in other agricultural markets; and 4) the strengthening of data collection on index trading in non-agricultural markets.

Spurred on by charges in the USS/PSI (2009) report and similar charges made by others (e.g., Masters, 2008), a rapidly expanding literature developed to analyze the influence of financial index positions on commodity futures prices. Consider that no less than six review papers related to this topic have been published since 2011 (Irwin and Sanders, 2011; Fattouh, Kilian, and Mahadeva, 2013; Irwin, 2013; Cheng and Xiong, 2014; Will et al. 2016; Hasse, Zimmerman, and Zimmerman, 2016). Since the heart of the matter according to the USS/PSI report is the existence of large bubbles in grain futures prices, we focus on direct tests for the existence of bubbles. A recent study by Etienne, Irwin, and Garcia (2015) is instructive. The authors applied a new statistical testing procedure to detect and date-stamp bubbles in corn, soybean, and wheat futures markets during 2004-2013. The test detects bubble periods based on departures from a random walk process in daily futures prices. Figure 6 is drawn from their study and it plots futures prices for the five grain futures markets included in the study and all statistically significant bubble periods (shaded bars). While bubbles do occur during some high price periods, such as June 2008 in corn and soybeans, there is no evidence of bubbles in other periods over 2005-2010 when prices reached historical highs and non-convergence was at its worst. This pattern is especially notable in CBOT wheat, where bubbles did not occur at any point during 2008 when prices reached historical highs and non-convergence reached $2.50 per bushel. Overall, Etienne, Irwin, and Garcia (2015) find that grain futures markets experienced price explosiveness only about two percent of the time and when bubbles did occur, they were generally short-lived and small in magnitude. This indicates grain futures markets were occasionally “frothy” but were not frequently “bubbly” as the term is conventionally used, which directly contradicts the conclusion of the USS/PSI report regarding the cause of non-convergence.

7

Other recently published studies that test for bubbles in agricultural futures prices also fail to find evidence of the type of large bubbles alleged in the USS/PSI report (e.g., Areal, Balcombe, and Rapsomanikis, 2016; Li et al., 2017).7

An alternative argument regarding the role of financial index investors is that increasing index positions did not cause large bubbles in the level of futures prices, but instead caused a structural change in the term structure (“carry”) of grain futures prices. The term struc-ture refers to calendar spreads between prices for futures contracts with different maturities. This argument relies upon well-established patterns of hedger and speculator trading in grain futures markets. Petzel (2009, pp. 8-9) provides a useful synthesis of this argument:

“Seasoned observers of commodity markets know that as non‐commer-cial participants enter a market, the opposite side is usually taken by a short‐term liquidity provider, but the ultimate counterparty is likely to be a commercial. In the case of commodity index buyers, evidence suggests that the sellers are not typically other investors or leveraged speculators. Instead, they are owners of the physical commodity who are willing to sell into the futures market and either deliver at expira-tion or roll their hedge forward if the spread allows them to profit from continued storage. This activity is effectively creating “synthetic” long positions in the commodity for the index investor, matched against real inventories held by the shorts. We have seen high spot prices along with large inventories and strong positive carry relationships as a result of the expanded index activity over the last few years.”

This implies that the initiation of large positions by index funds in a “crowded market space” was the source of non-convergence, not bubbles per se.

The “crowded market space” argument can be cast in the one-period supply of storage model of Working (1948, 1949). In Figure 7, the market value of physical storage is plotted on the y-axis and the amount of grain storage (inventory) is plotted on the x-axis. The

7 Even though there is very little evidence of large bubbles in grain futures prices during 2005-2010 this does not necessarily mean that financial index investment did not have any impact in these markets. For example, index investment could have changed the level of risk premiums in the markets. Despite the theoretical logic of this kind of impact, there is limited empirical evidence supporting such an impact in agricultural futures markets. See Sanders and Irwin (2017) for an up-to-date summary of empirical findings and a comprehensive set of empirical tests on the impact of financial index investment in agricultural futures markets.

8

market value of physical storage consists of two components—warehousing cost and conven-ience yield—and this determines the expected change in cash market prices.8 Warehousing cost is the fixed component of physical storage and includes the rental fee for warehouse space, handling and in- and out-charges, and insurance. Convenience yield is the variable component of physical storage and is typically motivated as an option value generated by transactions costs associated with sourcing the commodity (e.g., Kaldor, 1939; Telser 1958; Routledge, Seppi, and Spatt, 2000). Two types of option values are particularly relevant in grain markets. First, having grain in storage allows firms to take advantage of merchandiz-ing opportunities that require immediate access to grain. Second, filling physical storage with one grain imposes an opportunity cost because that space cannot be used to store another type of grain (Paul, 1970). The first option increases willingness to hold inventory and is likely to have high value when inventory levels are low, whereas the second reduces willingness to hold inventory and is likely to have high value when inventory levels are high. The net of these two option values determines convenience yield. It is usually assumed that the first type is larger than the second, which implies a positive convenience yield that subtracts from warehousing costs. Positive convenience yield can become large enough that the market value of physical storage actually turns negative. For example, it is costly for a soybean processor to shut down their plant and the processor may be willing to pay a large premium for having stocks on hand even though the current price of soybeans is high.

Petzel’s argument boils down to long futures positions of financial index investors being offset by short futures positions of commercial hedgers, who hedge long holdings of physical grain inventories. Hence, the buying pressure of index investors ultimately causes an in-crease in physical inventories because commercials are not willing to take outright short positions. In terms of the storage model depicted in Figure 7, this shifts out the demand for storage curve. Since the supply of storage curve is assumed to be fixed and upward sloping throughout its range, both the level of storage and the market value of storage increases. This hypothesis was thoroughly tested by Irwin et al. (2011) and Garcia, Irwin, and Smith (2015). Irwin et al. (2011) conducted a battery of Granger causality tests be-tween futures spreads, used to measure the market value of storage, and index positions in CBOT corn, soybeans, and wheat and KCBOT wheat over 2004-2010. No evidence of statistically significant and positive causality from index positions to spreads was found. Garcia, Irwin, and Smith (2015) estimated a reduced-form regression model over 1986-2013 of the difference between the market value of physical storage and the futures storage rate

8 This assumes cash prices in the future are interest-adjusted for the time value of money. Interest opportunity costs are sometimes considered a third component of storage costs.

9

and a set of conditioning variables that included financial index positions. Again, no evi-dence of a significant impact of index positions was found for CBOT corn, soybeans, and wheat and KCBOT wheat.

Some studies test for a narrower type of spread impact on the part of financial index inves-tors, i.e., spread behavior during the window when index positions are rolled from the ex-piring to the next nearest contract (“the 5-day Goldman Roll”). These studies generally find that find that spreads in agricultural futures markets are either unaffected or narrow following index rolls (Stoll and Whaley, 2010; Aulerich, Irwin, and Garcia, 2013; Hamilton and Wu, 2015; Sanders and Irwin, 2016). This is usually attributed to a “sunshine trading” effect (Admati and Pfleiderer, 1991), whereby large traders that preannounce their inten-tions attract additional potential counterparties. Two studies (Mou, 2010; Brunetti and Reiffen, 2014) report evidence of expanded spreads after index rolls.

The previous discussion indicates there is little evidence that the wave of financial index investment that washed over commodity futures markets starting in the mid-2000s unbal-anced grain futures markets and created a series of large bubbles and/or increased the market value of storage. However, this does not preclude other forms of structural imbalance in the grain futures markets from playing a role in the non-convergence episodes. As noted earlier, a key consideration is the adequacy of commercial flows through delivery locations, so that “sampling of value” in the delivery process can occur efficiently and at relatively low cost.

Figures 8 through 10 show annual commercial shipments of grain through facilities regular for delivery of CBOT corn, soybeans, and wheat, respectively, from 1975 through 2017.9 Declining commercial activity in Chicago and Toledo led to Toledo being deleted as a de-livery location and Illinois River locations being added in 2000 for corn and soybean con-tracts. The magnitude of commercial activity at corn and soybean delivery locations in-creased sharply as a result. However, shipments for corn have dropped dramatically in the last decade at Illinois River locations. In contrast, soybean shipments have increased sub-stantially since the change in delivery locations in 2000. The CBOT will extend the delivery territory for corn through St. Louis, the same as soybeans, in March 2019 in an effort to increase the magnitude of commercial flows in the corn delivery area.

Commercial activity through facilities regular for delivery of CBOT wheat was very small before the addition of Northwest Ohio, Ohio River, and Mississippi River locations in 2009

9 Shipments for Toledo wheat from 1989-1996 could not be located. Missing observations for these years were replaced by the average level of shipments over 1975-1988 and 1997-2009.

10

(Figure 10). For most of the time over 1975-2008, shipments at CBOT wheat delivery locations were less than 30 million bushels per year. Even with the addition of the new locations in 2009 it has been unusual for annual shipments at delivery locations to exceed 100 million bushels. This is not much bigger than corn and soybean flows before the 2000 change in delivery locations. Non-convergence continued unabated after the addition of the wheat delivery locations in 2009; but this was not surprising since concerns about commer-cial flows for CBOT wheat predated the non-convergence episodes of 2005-2010. Grey and Peck (1981) note that concerns about commercial flows at CBOT wheat delivery locations actually stretch back to the 1920s. The fundamental problem is that changes in wheat production patterns, transportation logistics, and trade flows have left the CBOT wheat contract with an increasingly narrow commercial flow of wheat to draw upon in the delivery process.

Another structural issue is the continuing role of Chicago as a par delivery point for CBOT corn, soybeans, and wheat contracts. Figure 11 shows that total commercial flows of corn, soybeans, and wheat through Chicago delivery locations have shrunk to less than 20 million bushels in recent years, indicating that Chicago is well outside normal commercial flows of grain in the U.S. The impact of this decline has long been a concern for the hedging effectiveness of the CBOT grain futures contracts. Writing 25 years ago, Pirrong, Haddock, and Kormendi (1993, p. 31) described the problem this way:

Prices in two markets can move idiosyncratically, however, when there are no commodity movements between them, if demand shocks in the market are less than perfectly correlated. These idiosyncrasies create basis risk when one of the locations is a futures delivery point. A change in commodity flows—such as the decline of the Great lakes/Chi-cago region as a major transshipping point and the concomitant rise of the Gulf—therefore may affect price relations dramatically, and conse-quently increase or decrease the amount of risk hedgers away from de-livery points (i.e., “out of position” hedgers) must bear.”

The status of Chicago as a par delivery point may not materially harm the performance of CBOT corn, soybean, and wheat futures contracts, since other delivery locations have been added to all three contracts in the last 25 years. If Chicago functions as a “safety valve” location and rarely serves as the cheapest-to-deliver location, then any performance prob-lems are likely minimized. However, if deliveries still regularly occur in Chicago this indi-cates that Chicago cash grain prices could have an outsized influence on futures prices and create additional basis risk for hedgers in non-delivery locations. Since the role of Chicago has been a concern for decades, it likely has little to do with the 2005-2010 non-convergence

11

episodes. Nonetheless, it is another example of possible structural imbalances in the current design of CBOT corn, soybean, and wheat futures contracts.

3. Troubleshooting: Storage Rates

The key role that storage rates for grain futures contracts play in delivery decisions has long been highlighted in the academic literature (e.g., Peck and Williams, 1991 1992; Pirrong, Haddock, and Kormendi, 1993; Hranaiova and Tomek, 2002). Because grain is costly to store, a long taking delivery on a grain futures contract incurs a storage cost for as long as the entity holds the delivery instrument. The storage fee is assessed daily and the rate is set by the futures exchange rather than by the market.10 The importance of storage rates has also been emphasized by the grain industry in response to episodes of non-convergence that predated 2005-2010. For example, the CBOT lowered storage rates for corn and soy-bean futures contracts in March 2000 and January 2000, respectively, when the delivery instrument was changed from a warehouse receipt to a shipping certificate and delivery locations were re-focused on the Illinois River waterway system. This resulted in an ex-tended period of non-convergence in both markets during 2000-2001 (see Figures 1 and 2) that ended only after the grain industry prodded the CBOT to restore storage rates back to their pre-2000 levels.

While there has always been an awareness on the part of traders, exchange staff, and regu-lators of the general relationship between futures storage rates and non-convergence, the magnitude of the convergence failures in 2005-2010 seemed too large to be explained by storage rates that were too low. In addition, most of the debate about the factors driving non-convergence focused on the role of financial index investors, who were alleged to have created a series of bubbles that kept grain futures prices well above cash prices. If this were true, fixing non-convergence simply would require limiting the positions of financial index traders. As discussed earlier, this logic was undermined by the failure to find evidence of large bubbles in grain futures prices during 2005-2010.

The first major breakthrough regarding the role of storage rates and non-convergence oc-curred when Irwin et al. (2009, 2011) discovered an empirical relationship between calendar spreads and delivery location basis.11 Following standard practice in the grain industry, Irwin et al. computed the spread in grain futures prices between the expiring contract and

10 See Appendix A: Grain Market Delivery for additional details on the role of storage rates in the delivery process for grain futures markets in the U.S.

11Aulerich, Fishe, and Harris (2011) provided another important contribution to understanding non-conver-gence at this time.

12

the first deferred contract and expressed the spread as a percentage of the “full carry” for storing grain between the delivery period for the two contracts. Full carry is computed as the sum of interest opportunity costs for storing grain and the maximum storage rate that can be charged under the contract specifications. Irwin et al. found that non-convergence systematically appeared in CBOT corn, soybean, and wheat futures markets whenever nearby spreads began to exceed about 80 percent of full carry. This result is reproduced in Figure 12 for CBOT corn, soybeans, and wheat over the 1986 through 2017 contracts and KCBOT wheat over the 1996 through 2017 contracts.12 Delivery location basis averaged between 1.9 and 3.7 times higher when the level of the nearby spread exceeded 80 percent of full carry.

Based on the initial findings in Irwin et al. (2009), the CBOT instituted a Variable Rate Storage (VSR) rule for its wheat contract starting with the September 2010 contract. This allowed the contract storage rate for CBOT wheat to adjust up and down based on the percent of full carry for the nearby spread. Specifically, if the average spread between the expiring and next nearby contract during the specified averaging period is more than 80 percent (less than 50 percent) of “full financial carry” then the daily storage rate is increased (decreased) by 10/100 of a cent per bushel per day for the next nearby contract. If the average spread between the expiring and next nearby contract during the averaging period is between 50 and 80 percent of “full financial carry” then the daily storage rate remains the same. After implementation of VSR, the storage rate increased quickly from 16.5 cents per bushel per day (4.95 cents per month) to a peak rate of 66.5/100 of a cent per bushel per day (19.95 cents per month) for the July, September, and December 2011 contracts. The massive non-convergence failures in CBOT wheat before implementation of VSR sub-sequently disappeared.

While it was clear after implementation of VSR for CBOT wheat that adjusting contract storage rates upward was the key to solving the non-convergence problems plaguing grain futures contracts during 2005-2010, the underlying market dynamic that created the prob-lem in the first place was not well understood. In particular, it was still not clear precisely how low storage rates could generate non-convergence of the magnitude experienced during this period. Without a clear economic linkage between the two, the case for raising storage rates to fix non-convergence rested on a purely empirical relationship of uncertain founda-tion. A significant breakthrough occurred when Garcia, Irwin, and Smith (2015) developed a dynamic rational expectations model of commodity storage and showed that the conver-gence failures were generated by a disequilibrium between the market value of storage in 12 We follow the procedure used by the CBOT to compute full carry and add 200 basis points to the LIBOR interest rate when calculating interest opportunity costs.

13

the physical market for grain and the storage rate paid to holders of the delivery instrument for grain futures contracts.

The essential insights from Garcia, Irwin, and Smith’s model (GIS hereafter) can be illus-trated graphically. Specifically, Figure 13 shows the disequilibrium created by low futures storage rates using the same one-period supply of storage model discussed in the previous section. The only difference from the model in the previous section is that a line has been added to represent the storage rate for the grain futures contract. Panel A shows a case with low storage demand and a market value of physical storage below the storage rate on the futures contract. In this case, the spread in the futures market equals the market value of physical storage and the delivery location basis is zero. Panel B shows a case with high storage demand and a market value of physical storage that is higher than the fixed storage rate allowed on the futures contract. In this case, the spread in the futures market expands to the maximum allowed by the futures contract storage rate (“full carry”), but this is still below the market value of physical storage. The futures spread cannot go any higher than full carry; otherwise, risk-free arbitrage would be possible between futures contracts. The only way for the disequilibrium to be resolved is for the delivery location basis to take on a positive value such that the sum of the futures spread and the delivery location basis equals the market value of physical storage. Otherwise, the futures market will offer inventory holders a lower return for storage than is offered in the physical cash market. Another way of saying the same thing is that holders of the delivery instrument can effectively store grain at below-market storage rates. In equilibrium, this storage “windfall” opportunity will re-sult in futures prices being bid up above prices in the delivery cash market by exactly the amount of the windfall.

A numerical example is helpful to further understand the process by which low futures storage rates lead to non-convergence. Panel A of Figure 14 presents a scenario where the market value of physical storage and the futures storage rate are both 5 cents per month. It is also assumed there is only one delivery location and date and that today’s date is the delivery date for the expiring futures contract. Under these assumptions, the expiring fu-tures price equals the delivery location spot price of $4.00 per bushel. Given that the physical value of storage is 5 cents per month, the expected spot price in one month is $4.05. Arbitrage forces today’s price for a futures contract that matures in one month to also equal $4.05. Notice that the futures price spread, or the difference between the current one-month ahead futures price and the expiring futures price, equals the market value of physical stor-age.

Panel B of Figure 14 considers a scenario where the market value of physical storage and the futures storage rate increase to 10 cents per month. The expiring futures price once

14

again equals the delivery location spot price of $4.00 per bushel. Both the expected one-month ahead spot price and today’s one-month ahead futures price increase to $4.10 per bushel to reflect the increase from 5 cents to 10 cents per month in the market value of physical storage and the futures storage rate. In futures market terminology, the slope of the futures term structure curve increases by 5 cents per month. As in the first scenario, the futures price spread equals the market value of physical storage.

In each of the previous two scenarios, the market value for physical storage equalled the futures storage rate. Panel C of Figure 14 presents a scenario where the market value of physical storage exceeds the futures storage rate. Specifically, the market value of physical storage is 10 cents per month and the futures storage rate is 5 cents per month. The expected one-month ahead spot price increases to $4.10, the same is in the second scenario (Panel B), in order to reflect the higher market value of physical storage. However, starting from the current spot price of $4.00, the one-month ahead futures price cannot exceed $4.05 because the futures storage rate is capped at 5 cents. Without further market adjustment, an inventory holder hedging in the futures market would earn a return of only 5 cents when the market value of physical storage is 10 cents. The market adjustment needed to restore equilibrium between storage in the futures and physical markets is a parallel upward shift in the futures term structure curve by 5 cents. Crucially, this means that today’s price of the expiring futures contract must increase from $4.00 to $4.05, which implies that the delivery location basis is not zero but opens up to 5 cents. In this new equilibrium, the sum of the futures price spread and the delivery location basis equals the market value of physical storage.

The model in Figure 13 and the numerical examples in Figure 14 are limited to one period in order to simplify the analysis. The analytics are much more complex for the multi-period case, but the essential insight from the GIS model is that the current delivery location basis widens by the expected value of positive “wedges” between the market value of storage in the physical market and the futures contract storage rate. Consider a highly simplified example where the wedge between the price of physical storage and the futures storage rate is 5 cents per month and this wedge is expected to last for 12 months. The current delivery location basis does not widen by 5 cents, but instead by 60 cents = 5 cents x 12 months in order to reflect the cumulative value of the expected disequilibrium. This is an important insight because it shows how relatively small wedges between the physical price of storage and the futures storage rate can generate a surprisingly wide delivery location basis if the wedges are expected to persist for a lengthy period of time.

GIS derive empirical estimates of the wedge between the market value of physical storage and futures contract storage rates for CBOT corn, soybeans, and wheat and KCBOT wheat

15

and their estimates are updated in Figures 15 through 18, respectively. These figures con-tain two lines for each of the four markets. The red dashed line is the storage rate for each of the grain futures markets. The blue line is the GIS estimate of the value, or price, of storage in the physical market. It is estimated based on the following relationship:

Market Value of Physical Storage = Current Futures Spread + Current Delivery Basis – Next Period Delivery Basis.

Note that if non-convergence is only expected to last one period, the delivery basis in the next period is expected to be zero, which is exactly the same relationship shown in Panel B of Figure 13 and Panel C of Figure 14. The change in the basis is relevant when the non-convergence is expected to persist for multiple periods, which in practice should be the norm. The actual computation of the estimate of the market value of physical storage proceeds in three steps. First, the estimate of the market value is computed for each day of the delivery period according to the above relationship. Second, estimates for the first five days of each delivery period are averaged. Third, a centered three-contract moving average is applied to the 5-day averages in order to smooth out the noise associated with each delivery period.

Positive “wedges” between the market value of physical storage and the futures contract storage rate can be easily observed in Figures 15 through 18.13 It is not a coincidence that the largest wedges in each of the four grain futures markets occurred in 2005-2010 when non-convergence was at its worst. GIS conducted extensive econometric tests of wedges for CBOT corn, soybeans, and wheat and KCBOT wheat, with explanatory variables that included inventories, credit spreads, financial index trader positions, and other structural variables. The regression results indicate that inventory in deliverable locations was strongly related to the wedge in all cases and yielded similar coefficient values across commodities. Hence, the market value of physical storage during 2005-2010 was high because inventories were high, consistent with a high demand for storage.

GIS also showed that the wedge between the market value of physical storage and futures storage rates could be used to explain the magnitude of non-convergence in delivery location basis for CBOT corn, soybeans, and wheat and KCBOT wheat. Their findings are illus-trated graphically with the aid of Figure 19 for CBOT corn, which plots cheapest-to-deliver basis and a predicted basis. Recall that the GIS model predicts a basis level of zero when the wedge is negative or zero (market value of physical storage less than or equal to the 13 Notice that the y-axis scale is cutoff at -5 cents in each of the figures. This is done to avoid distortions caused by large negative values of physical storage, which are caused by backwardations in the term structure of grain futures markets.

16

futures storage rate) and a positive basis when the wedge is positive (market value of phys-ical storage is greater than the futures storage rate). When the wedge is positive, the magnitude of the positive delivery location basis depends on how long the market expects positive wedges to persist. As a simple approximation to market expectations, we follow GSI and assume that positive wedges will continue until the next harvest. The predicted basis is then computed as the wedge estimate for a given contract times the number of months until the expiration month of the next new crop futures contract. Figure 19 shows that basis predicted in this manner for CBOT corn aligns reasonably well with spikes in the delivery location basis, especially during 2000-2001 and 2005-2010. Of course, we do not expect the magnitude of spikes in the basis to align perfectly with predicted basis because we do not know the market’s expectation of the duration of positive spikes in the wedge.

A particularly controversial issue in the CBOT wheat futures market is the relationship between VSR and the market value of storage in the physical wheat market. Some in the grain trade have vehemently argued that the institution of VSR was the cause of high market values of physical storage for wheat, and especially, historically wide price spreads in the CBOT wheat futures market. Figure 17 for CBOT wheat convincingly demonstrates that the upward VSR adjustments in storage rates after July 2010 followed increases in the market value of storage in the physical wheat market, not the reverse. This figure also shows why non-convergence in CBOT wheat during 2005-2010 could not be solved by adding delivery points or limiting the holding of delivery certificates, as many advocated, but in-stead, the solution was most effectively addressed by raising storage rates.

Another interesting issue is why grain market futures trading volume could increase in the midst of the severe non-convergence problems of 2005-2010. The GIS model suggests a solution to the puzzle. In short, traders “can do the math” and add the difference between market and contract storage rates to the delivery location basis. This requires a certain level of market sophistication regarding the relationship between futures prices, cash prices, and storage rates. Nonetheless, some market participants may have lacked the ability to “decode” the message from market prices, and as a result, may have been very confused about how to interpret market signals. This could have adversely affected stockholding, price discovery, and risk management strategies.

While the study by GIS answered several key questions regarding futures storage rates and non-convergence, it did not answer the question of what drove the market value of physical storage so high in 2005-2010. This is especially important in CBOT wheat, where the market value of physical storage is estimated to have reached 29 cents per month at the peak in March 2010 (Figure 17), an astonishing annual rate of $3.48 per bushel and more

17

than enough pay for new physical storage within a year. An important clue to understand-ing what may have driven such unprecedented returns to storage can be found in the recent study by Carter, Rausser, and Smith (2017). The theoretical and econometric analysis in this study suggests that the mid-2000s boom in ethanol production and the passage of the Renewable Fuels Standards (RFS) mandates in 2007 was viewed by the grain markets as an unusually large and permanent demand shock. The rightward shift in future expected de-mand also shifted the demand for storage to the right. However, since current supply and demand remained relatively constant, the current cash price, stock level, and market value of storage all increased. It was as if the market suddenly could forecast a series of major droughts in the future, which would obviously increase the current cash price, stock levels, and the market value of storage. Because soybeans and soft red winter wheat compete for the same physical storage space (“binspace”) as corn in the U.S. Corn Belt (Paul, 1970), this also increased the market value of storage for these crops. While this is unlikely to entirely explain the high market value of storage in grain markets during 2005-2010, it does suggest that the expansion of ethanol production and mandates played an important role.

4. Fixed Storage Rates vs. VSR

It should now be clear that the systemic convergence failure of grain futures contracts over 2005-2010 was due to contract storage rates that were too low relative to the market clearing price of storage in the physical market. The main question, then, is how best to adjust contract storage rates to the market reality of higher values of physical storage. This re-mains an important question because, as shown in Figures 1 through 4, non-convergence problems did not entirely disappear after 2010. Another major episode of non-convergence began for the KCBOT wheat futures contract in May 2016, with delivery location basis exceeding 80 cents per bushel at the peak. In response, the KCBOT implemented VSR starting with the March 2018 contract. Even though implementation of VSR has eliminated the worst of the convergence failures in CBOT wheat, the contract has continued to display some tendency towards non-convergence since 2010, at times exceeding 50 cents per bushel. CBOT corn and soybeans also began exhibiting notable levels of non-convergence starting in late 2016, culminating with the November 2017 soybean contract going off the board with futures prices nearly 50 cents above the cheapest-to-deliver location cash price. The CBOT considered moving to VSR for corn and soybeans (Polansek and Hirtzer, 2018), but instead settled on a proposal to increase the storage rate to 26.5 cents per bushel per day (7.95 cents per bushel) starting with the December 2019 corn and November 2019 soybean con-tracts (Kemp, 2018).

A futures exchange has several options in the face of a fixed storage rate for a contract that is too low compared to the market value of physical storage. The first option is to do

18

nothing and wait for the market value of storage to decrease. Essentially, this is what the CBOT did in corn and soybeans and the KCBOT did in wheat during most of the 2005-2010 non-convergence episodes.14 The risk, of course, is that conditions reverse and non-convergence reappears, as it did for CBOT corn and soybeans and KCBOT wheat. The second option is to adjust fixed storage rates upward until the non-convergence disappears. The KCBOT took this approach in response to non-convergence that peaked in the Sep-tember 2010 wheat contract at more than $1 per bushel. Beginning with the September 2011 KCBOT wheat contract, storage rates for the July and September contracts were increased to 29.6/100 of a cent (8.88 cents per month) and to 19.7/100 of a cent (5.91 cents per month) the rest of the year. The harvest seasonal rate was double the previous fixed rate of 14.8/100 (4.44 cents per month), an unusually large increase from a historical per-spective. The fixed seasonal storage rates appeared to work well until another major episode of non-convergence in 2016. The third option is an automatic rule like VSR, which has already been adopted for CBOT wheat and KCBOT wheat. The fourth option is to elimi-nate the need for contract storage rates all together by switching to a demand certificate system where there market determines financial full carry without reference to a fixed con-tract storage rate. This would of course be a very fundamental change to a futures contract based on either a fixed or variable storage rate.

From a practical standpoint, adjusting fixed storage rates upward or adopting VSR appear to be the most realistic alternatives for dealing with low contract storage rates. Each in-volves tradeoffs. The main advantage of fixed storage rates is that financial full carry is known with certainty as long as the fixed rate is in place. This is thought by many in the grain industry to be crucial to maintaining volume and liquidity in deferred grain futures contracts. The disadvantage is the difficulty of adjusting the contract storage rate quickly as market conditions change. This may be especially problematic when the market value of storage increases substantially, as it did for CBOT wheat in 2005-2010. In contrast, VSR has the advantage of providing market participants with a pre-specified rule for adjusting rates. This assures that the futures storage rate will eventually catch up with the market value of storage. The disadvantage is that financial full carry can become unpredictable,

14 The CBOT did increase storage rates modestly for corn and soybeans from 15/100 of a cent per bushel per day (4.5 cents per month) to 16.5/100 of a cent (4.95 cents per month) beginning with the December 2008 contract for corn and the November 2008 contract for soybeans. In a similar fashion, the KCBOT increased the storage rate on wheat from 13.3/100 of a cent (3.99 cents per month) to 14.8/100 of a cent (4.44 cents per month) starting with the July 2008 contract.

19

particularly when the market is close to the threshold for adjusting rates and this may harm volume and liquidity in deferred grain futures contracts.

Evidence on the market liquidity impact of VSR in CBOT wheat is presented in Figure 20. Panels A and B show the percentage of open interest and volume, respectively, in deferred CBOT wheat futures before and after implementation of VSR. Deferred futures for this analysis is defined as the second through the fifth contracts in terms of nearness-to-maturity. The evidence on the impact of VSR is mixed. The percentage of open interest in deferred contracts was about 30 percent before VSR, rose for a couple of years to about 35 percent, and then dropped in recent years to about 25 percent. A similar but less pronounced pattern is observed for the percentage of volume in deferred contracts. Overall, it appears there is some evidence that trading activity in deferred CBOT wheat contracts declined since the introduction of VSR.

The balance of the evidence suggests that fixed storage rates are preferred over VSR if the market value of physical storage is monitored and adjusted as market conditions dictate. After all, this is the system used by the CBOT and KCBOT for most of the last 150 years to successfully manage grain futures contracts. In this context, it is interesting to compare the adjustments made to storage rates for CBOT corn, soybeans and wheat in the mid-1970s and early 1980s to those made over 2005 through 2010. The mid-1970s was the last time that spikes in grain futures prices occurred that were comparable to the spikes during 2005-2010. Figure 21, reprinted from Peck and Williams (1991, p. 165), shows that the CBOT increased storage rates four times between 1973 and 1981 in the face of interest-adjusted spreads that, on average, were increasing relative to financial full carry. More specifically, storage rates for CBOT corn, soybeans, and wheat jumped from just under 2 cents per month in 1973 to nearly 5 cents per month by 1981, or about a two-and-a-half fold increase in rates. Figure 22 shows the Chicago delivery location basis for CBOT corn during the 1970s, and it demonstrates that the storage rate increases prevented the type of large non-convergence episodes experienced over 2005-2010. Figures 23 and 24 for CBOT soybeans and wheat, respectively, show some evidence of increased non-convergence during the 1970s, but, again, nothing like duration and magnitude of the episodes in 2005-2010.15

15 When comparing Figures 22-24 with Figures 1-3 it is important to note the dramatically smaller scale for basis in Figures 22-24. Also, note that St. Louis and Toledo were added as a delivery location for CBOT corn in December 1976, Toledo was added as a delivery location for CBOT soybeans in November 1979, and Toledo was added as a delivery location for CBOT wheat in July 1973. Hence, the Chicago delivery location basis in Figures 22-24 is not necessarily the cheapest-to-deliver basis after these dates. Only the Chicago delivery location basis is shown because we were not able to obtain spot prices for these additional locations during the 1970s.

20

It is instructive to consider why the CBOT was able to better manage the adjustment of storage rates in the 1970s compared to the most recent period. In the 1970s (and before), the level of storage rates was not written directly into CBOT grain contract specifications. In other words, a specific storage rate was not listed in the CBOT rulebook. Instead, the storage rate for individual warehouses was determined and approved by a member commit-tee of the CBOT known as the Warehouse Committee.16 This is the source for the “official” storage rate for delivery of CBOT corn, soybeans, and wheat contracts found in Figure 21 reprinted from Peck and Williams (1991). Since the storage rate set by the Warehouse Committee was not a fixed part of contract terms, it could be adjusted by the Committee as market conditions dictated, which is precisely what happened in the 1970s when storage rates were raised on four separate occasions.17 A key factor used by the Committee in setting the CBOT storage rate was the rate that warehouses could charge for grain stored under USDA Commodity Credit Corporation (CCC) programs.

Starting with the December 1993 contract for corn, the November 1993 contract for soy-beans, and the July 1993 contract for wheat, the CBOT changed the way that storage rates were set. Specifically, storage rates were converted to a single maximum rate that all regular warehouses could charge and fixed as an element in contract terms.18 Hence, storage rates became an official part of the CBOT rulebook, and crucially, adjusting the storage rate required approval by the CFTC.19

The momentous nature of the 1993 change to a maximum storage rate fixed in the terms of CBOT grain futures contracts was not evident for most of the next dozen years. An early warning sign occurred in 2000 after storage rates for corn and soybeans were reduced as part of the introduction of the new Illinois River delivery system. As noted earlier, this led

16 CBOT annual reports indicate that the name of the committee changed slightly over time.

17 Before the 1970s, storage rates at regular warehouses apparently changed extremely slowly. For, example the 1921 annual report of the CBOT states that the storage rate for 1921-22 at warehouses in Chicago was 1 and ½ cents for the first 10 days and 5/100 of a cent per day thereafter (Board of Trade, 1922). After the first 10 days, this is a monthly rate of 1.5 cents. Figure 21 from Peck and Williams (1991) indicates that the prevailing storage rate from the early 1960s to early 1970s was 6/100 per day, or 1.8 cents per month.

18 While warehouses have the option of charging less than the maximum storage rate, there is no evidence that warehouses have ever charged less than the maximum rate to takers of delivery.

19 The institution of a fixed maximum for CBOT corn, soybean, and wheat storage rates was apparently was in response to a warehouse operator that proposed a higher storage rate for their warehouses than other warehouses were willing to charge at the time.

21

to an extended period of non-convergence in corn and soybeans during 2000-2001 (see Fig-ures 1 and 2) that ended only after the grain industry prodded the CBOT to restore storage rates back to their pre-2000 levels. The full impact of the 1993 change in specifications only became evident when the market value of physical storage for corn, soybeans, and wheat began to rise rapidly starting in 2005 (see Figures 16-18). The CBOT no longer had the institutional flexibility to increase contract storage rates in a timely manner as it did in the 1970s. Any changes required a full proposal to the CFTC and the normal rulemaking process of comment and potential revisions. In addition, there may have been a loss of institutional memory at the CBOT about the storage rate changes made in the 1970s and the process used to make the changes. To the best of my knowledge, the 1970s experience with storage rates was never referenced by the CBOT, and for that matter, the CFTC or any other interested party, during discussions about responding to the historically unprece-dented non-convergence problems of 2005-2010.20

5. Managing Storage Rates

The historical record indicates that not only are fixed storage rates for grain futures con-tracts preferred but they can also be successfully managed if a system is in place for moni-toring the market value of physical storage. One tool for monitoring the market value of storage is to survey delivery location and interior country elevators. For example, Irwin et al. (2011) report that the CBOT conducted a storage cost survey of 47 firms in mid-2008 and the results indicated that storage rates (mainly at interior country elevators) averaged approximately 4.3, 4.6, and 7.1 cents per bushel per month for corn, soybeans, and wheat, respectively. Since these public rates were near the storage rates on CBOT contracts at the time the survey was taken (4.5 cents per bushel), this was taken as evidence that storage rages did not need to be raised. The problem with this kind of survey evidence is that it can be misleading as to the true marginal value of storage space to operators of elevators. It is true that elevators must honor the posted storage rate so long as they have space available, but they also have to decide how much space to allocate to their customers at this rate and how much to allocate to their own merchandising activities that may have a much higher return. This latter activity reflects the true market value of physical storage and it is what needs to be measured when monitoring storage rates for grain futures con-tracts.

It would be extremely difficult to obtain information on the true marginal value of physical storage from grain elevator operators, as this is crucial proprietary information. Fortunately, 20 To be fair, VSR can be thought of as an automated version of the CBOT Warehouse Committee process used before 1993 to adjust storage rates for grain futures contracts.

22

the GIS measure of the value of storage presented in the previous section provides an esti-mate at the market level. In the case of corn and soybeans, Figures 15 and 16, respectively, show that the market value of physical storage since 2013 in corn and 2016 in soybeans has been perilously close to the current contract rate of 5 cents per bushel. Not coincidently, this is a period when the U.S. has had large corn and soybean crops and inventories have risen substantially. This new tool suggests that the contract storage rate for these two commodities could be raised to 8-10 cents per bushel and there would be a very low risk of price spreads in the corn futures market not being able to fully accommodate the market value of physical storage. This should prevent non-convergence episodes except in all but the most extreme market conditions.

The CBOT could also consider a seasonal storage rate for corn and soybeans similar to what the it and the KCBOT had use previously in wheat. Figure 25 provides an interesting perspective on this idea for corn and soybeans, respectively. The average storage schedules shown in the figure are based on two surveys. The first is a 1982 survey by Hill, Kunda and Rehtmeyer (1983) of Illinois elevators, which indicated an average fixed charge for corn and soybeans from harvest through January of 12.9 cents per bushel and 14.2 cents per bushel, respectively. The average monthly storage charge after January was 2.1 cents per bushel for corn and 2.4 cents per bushel for soybeans. The second is an annual informal survey of storage schedules for corn and soybeans at nine central Illinois elevators conducted by the grain price outlook programs at the University of Illinois since 1995. The rates from this survey also have a fixed and variable component, which have been very stable over time. From 1995 through 2006, the storage schedule for both corn and soybeans was 13 cents from harvest through December 31 and 2 cents per month thereafter. Given the similarity of the estimates from the two surveys in this earlier period, the 1995-2006 schedule is assumed to be the average for the entire 1986-2006 period. This implies an average storage rate before December 31 of about 5 cents per bushel, which reflects the more rapid basis appreciation in Central Illinois immediately following harvest. Note that the average harvest storage rate for 1995-2006 is very close to the storage rate for CBOT corn and soybeans over this time frame. By comparison, the average storage schedule for corn over 2007-2017 increased to 16 cents from harvest through December 31 and 2 cents per month thereafter and for soybeans increased to 17 cents from harvest through December 31 and 2 cents per month thereafter. This implies an average storage rate before December 31 of about 6.5 cents per bushel for corn and 7 cents per bushel for soybeans, well above the storage rate for CBOT corn and soybeans. There have been further increases in storage schedules in the recent years. In 2017, the average storage schedule for corn was 16 cents from harvest through December 31 and 3 cents per month thereafter and for soybeans was 18 cents from harvest through December 31 and 3 cents per month thereafter.

23

One can combine information on the seasonal pattern of storage costs for elevators in central Illinois in Figure 25 with the estimated market value of physical storage in Figures 15 and 16, respectively, to develop a potential seasonal fixed storage rate for CBOT corn and soy-beans. These two data sources suggest a reasonable seasonal fixed storage rate of about 5 and 8 cents per bushel or 6 and 9 cents per bushel. The higher rate would apply to Sep-tember through December for corn and September through November for soybeans and the lower rate to other months. A storage rate schedule along these lines would allow full carry in the corn and soybean markets to accommodate the market value of physical storage in all but a few extreme cases in recent years.

This same type of analysis is also useful for KCBOT wheat. Figure 4 shows that the fixed seasonal storage rate for this contract appeared to be working well until it was simply overwhelmed by the massive size of the 2016 hard red winter wheat crop in the U.S. and the concomitant need to store historically very large stocks of wheat. Figure 18 suggests that increasing the seasonal fixed storage rates to say, 9 and 12 cents per bushel, would have fixed the recent non-convergence problems and provided adequate carry in the KCBOT wheat market except under the most extreme conditions, such as those in 2008. The KCBOT has already implemented VSR for wheat starting with the May 2018 contract. If this variable rate system is deemed unsatisfactory in the future, going back to a seasonal fixed rate at the higher levels recommended here would be a reasonable alternative.

The most difficult case remains CBOT wheat. Figure 17 reveals that the market value of physical storage for soft red winter wheat has remained stubbornly high much of the time since 2010, twice exceeding 15 cents per bushel. VSR has pushed rates high enough to eventually bring about convergence, but not before experiencing several episodes of non-convergence exceeding 50 cents per bushel (Figure 3). A fixed storage rate for CBOT wheat that would be high enough to cover the spikes in market value of storage may not be acceptable to the grain industry. This suggests that there may deeper structural problems with the CBOT wheat contract. Some insight is provided by Figure 26, which plots the estimated market value of physical storage versus stocks of wheat at deliverable locations in Chicago and Toledo over 1986-2017. If variation in the demand for storage curve is larger than the variation in the supply of storage curve, the observations roughly trace out the supply of storage curve for CBOT wheat. The plot suggests two noteworthy observations. The first is that the average market value of physical storage for wheat is relatively high regardless of the stock levels in Chicago and Toledo. Values between 5 and 10 cents per bushel occur very frequently, as highlighted by the red dots in the figure. The second is that the market value of physical storage increases rapidly once stocks of wheat in Chicago and Toledo exceed about 35 to 40 million bushels. This appears to be evidence of storage capacity constraints in Chicago and Toledo that sharply increase the marginal cost of wheat

24

storage once stocks reach this level.21 All of the observations with stocks above 35 million bushels and the market value of storage above 10 cents per bushel occurred after September 2008. Therefore, it is puzzling why the addition of new delivery locations and storage capacity in July 2009 did not relieve the apparent capacity constraint. It is certainly an issue that deserves further analysis.

6. Summary and Conclusions

Non-convergence of cash and futures prices during delivery strikes at the heart of the func-tioning of grain futures markets. Historically unprecedented episodes of non-convergence occurred during 2005-2010 in Chicago Board of Trade (CBOT) corn, soybean, and wheat futures contracts and Kansas City Board of Trade (KCBOT) wheat futures contracts. At the extreme, CBOT wheat futures prices expired an astounding $2.50 above the cash price at the cheapest-to-deliver location. Three explanations have been offered for “troubleshoot-ing these very large non-convergence episodes. The first is manipulation in the form of traditional corners and squeezes. The second is a structural imbalance in contract design or market conditions that favors one side of the market. The third is futures contract storage rates that are set below the market-clearing price of storage in the physical market.

It is relatively easy to dismiss the traditional type of manipulation as an explanation for non-convergence problems over 2005-2010. The magnitude and persistence of non-conver-gence in all four grain futures markets was simply too large and lasted too long to be explained by a series of corners and squeezes. Much of the discussion about the 2005-2010 non-convergence episodes centered on the emergence of large-scale participation by a new type of speculator in grain futures markets—financial index investors. A careful review of the evidence indicates that the wave of financial index investment that washed over com-modity futures markets starting in the mid-2000s did not unbalance grain futures markets, create a series of large bubbles, or increase the market value of physical storage. Among others, this directly contradicts the conclusion of a report by the U.S. Senate’s Permanent Subcommittee on Investigations (USS/PSI 2009) regarding the cause of the convergence failures.

It turns out that the simplest explanation for the 2005-2010 non-convergence episodes is also the correct explanation. Theoretical and empirical analysis (Irwin, et al., 2009, 2011; Garcia, Irwin, and Smith, 2015) shows that convergence failures were generated by a dise-quilibrium between the higher market value of storage in the physical market for grain

21 See Tomek and Kaiser (2014, p. 260) for further discussion of storage capacity constraints in the supply of storage model.

25

compared to the storage rate paid to holders of the delivery instrument for grain futures contracts. In simplest terms, the current delivery location basis widened by the expected value of positive “wedges” between the higher price of storage in the physical market and the futures contract storage rate. For example, assume the price of physical storage is 10 cents per month and the maximum storage rate is 5 cents per month and the wedge between the two is expected to last for 12 months. The current delivery location basis widens by 60 cents = 5 cents x 12 months in order to reflect the cumulative value of the expected dise-quilibrium. This is an important insight because it shows how relatively modest wedges between the physical price of storage and the contract storage rate can generate a surpris-ingly wide delivery location basis if the wedges are expected to persist for a lengthy period of time.

Given that low contract storage rates were the main problem in 2005 through 2010, the main question is how best to adjust the rates higher. The CBOT broke with its long practice of fixed storage rates and implemented a seasonal storage rate for wheat starting with the September 2009 contract. Beginning with the September 2010 contract, the CBOT imple-mented a variable storage rate (VSR) rule for wheat that fully automates adjustment of storage rates. The KCBOT followed suit and implemented a VSR rule for its wheat futures contract starting with the May 2018 contract. VSR is highly contentious within the grain industry because “financial full carry” is unpredictable and some argue this harms volume and liquidity in deferred futures contracts. Fixed versus variable storage rates continues to be an important question because non-convergence problems in grain futures markets did not entirely disappear after 2010.

Because of the concerns about trading activity in deferred futures contracts, fixed storage rates are likely preferred over VSR. This is not overly surprising since fixed storage rates were used by futures exchanges in the U.S. for most of the last 150 years to successfully manage grain futures contracts. An important historical benchmark is the mid-1970s, the last period with spikes in grain futures prices comparable to those during 2005-2010. The CBOT raised storage rates four times during the mid-1970s and early 1980s and this pre-vented the type of large non-convergence episodes experienced in recent years. The CBOT had a more flexible process for changing rates in the 1970s and this undoubtedly contributed to the timely adjustment of storage rates during the mid-1970s grain price boom. Since 1993, any changes to CBOT storage rates have required a full proposal to the CFTC and the normal rulemaking process of comment and potential revisions. In addition, there may have been a loss of institutional memory at the CBOT about the storage rate changes made in the 1970s and the process used to make the changes.

26

The historical record indicates that not only are fixed storage rates for grain futures con-tracts preferred but they can also be successfully managed if a system is in place for moni-toring the market value of physical storage. A new tool is available for estimating the market value of physical storage based on the work of Garcia, Irwin, and Smith (2015). This may rekindle interest in fixed storage rates because it provides a simple method of monitoring trends in the market value of storage.

Focusing on storage rates makes sense given the nature of non-convergence problems since 2005, but it does not mean that more traditional structural factors can be ignored. For example, we do not fully understand why the market value of physical storage has been so high at times in the CBOT and KCBOT wheat markets. Were the high values due to unusual market conditions? Alternatively, were the high values due to something in the structure of the delivery markets, namely, capacity constraints? We also do not have a good understanding of the role of Chicago as a par delivery point for CBOT corn, soybean, and wheat futures contracts when it is clearly out of the main commercial flow of grains in the U.S. These and other related questions will undoubtedly require additional research in the future.

7. References

Admati, Anat R., and Paul Pfleiderer. 1991. “Sunshine Trading and Financial Market Equi-librium.” Review of Financial Studies, vol. 4, no. 3 (July):443-481.

Areal, Francisco José, Kelvin Balcombe, and George Rapsomanikis. 2016. “Testing for Bubbles in Agriculture Commodity Markets.” Economía Agraria y Recursos Naturales, vol. 16, no. 1:59-79.

Aulerich, Nicole M., Raymond P.H. Fishe, and Jeffery H. Harris. 2011. “Why Do Expiring Futures and Cash Prices Diverge for Grain Markets?” Journal of Futures Markets, vol. 31, no. 6(June):503–533.

Aulerich, Nicole M., Scott H. Irwin, and Philip Garcia. 2013. “Bubbles, Food Prices, and Speculation: Evidence from the CFTC’s Daily Large Trader Data Files.” Working Paper No. 19065. National Bureau of Economics Research.

Brunetti, Celso, and David Reiffen. 2014. “Commodity Index Trading and Hedging Costs.” Journal of Financial Markets, vol. 21, (November):153-180.

Carter, Colin A., Gordon C. Rausser, and Aaron Smith. 2017. “Commodity Storage and the Market Effects of Biofuel Policies.” American Journal of Agricultural Economics, vol. 99, no. 4, (July):1027–1055

27

Cheng, Ing-Haw, and Wei Xiong. 2014. “The Financialization of Commodity Markets.” An-nual Review of Financial Economics, vol. 6, (December):419-441.

Etienne, Xiaoli L., Scott H. Irwin, and Philip Garcia. 2015. “Price Explosiveness, Specula-tion, and Grain Futures Prices.” American Journal of Agricultural Economics, vol. 97, no. 1, (January):65-87.

Fattouh, Bassom, Lutz Kilian, and Lavan Mahadeva, L. 2013. “The Role of Speculation in Oil Markets: What Have We Learned So Far?” Energy Journal, vol. 34, no. 3, 7-33.

Garcia, Philip, Scott H. Irwin, and Aaron Smith. 2015. “Futures Market Failure?” Ameri-can Journal of Agricultural Economics, vol. 97, no. 1, (January):40-64.

Gray, Roger W., and Anne E. Peck. 1981. “The Chicago Wheat Futures Market: Recent Problems in Historical Perspective.” Food Research Institute Studies, vol. 18, no. 1, 89-115.

Haase, Marco, Yvonne Seiler Zimmermann, and Heinz Zimmermann. 2016. “The Impact of Speculation on Commodity Futures Markets A Review of the Findings of 100 Empirical Studies.” Journal of Commodity Markets, vol. 3, no. 1, (September):1-15.

Hamilton, James D. and Jing Cynthia Wu. 2015. “Effects of Index-Fund Investing on Com-modity Futures Prices.” International Economic Review, vol. 56, no. 1, (February):187-205.

Heath, David. 2009. “Convergence Failure in CBOT Wheat Futures.” Working Paper. Uni-versity of Chicago.

Hieronymus, Thomas A. 1977. Economics of Futures Trading for Commercial and Per-sonal Profit, Second Edition. New York, NY: Commodity Research Bureau.

Hill, Lowell, Eugene Kunda, and Clint Rehtmeyer. 1983. “Price Related Characteristics of Illinois Grain Elevators, 1982.” AE-4561, Department of Agricultural Economics, University of Illinois at Urbana-Champaign.

Hranaiova, Jana, Robert A. Jarrow, and William G. Tomek. 2005. “Estimating the Value of Delivery Options in Futures Contracts.” Journal of Financial Research, vol. 28, no. 3, (September):363-383.

Hranaiova, Jana, and William G. Tomek. 2002. “Role of Delivery Options in Basis Conver-gence.” Journal of Futures Markets, vol. 22, no. 8, (August):783-809.

28

Irwin, Scott H. 2013. “Commodity Index investment and Food Prices: Does the ‘Masters Hypothesis’ Explain Recent Price Spikes?” Agricultural Economics, vol. 44, no. s1, (No-vember):29-41.

Irwin, Scott H., Philip Garcia, Darrel L. Good and Eugene L. Kunda. 2009. “Poor Conver-gence Performance of CBOT Corn, Soybean and Wheat Futures Contracts: Causes and Solutions.” Marketing and Outlook Research Report 2009-02, Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign.

Irwin, Scott H., Philip Garcia, Darrel L. Good and Eugene L. Kunda. 2011. “Spreads and Non-Convergence in Chicago Board of Trade Corn, Soybean, and Wheat Futures: Are Index Funds to Blame?” Applied Economics Perspectives and Policy, vol. 33, no. 1, (March):116-142.

Irwin, Scott H. and Dwight R. Sanders. 2011. “Index Funds, Financialization, and Com-modity Futures Markets.” Applied Economic Perspectives and Policy, vol. 33, no. 1, (March):1-31.

Irwin, Scott H. and Dwight R. Sanders. 2012. “Financialization and Structural Change in Commodity Futures Markets.” Journal of Agricultural and Applied Economics, vol. 44, no. 3, (August):371-396.

Johnson, Herb. 1987. “Options on the Maximum or Minimum of Several Assets.” Journal of Financial and Quantitative Analysis, vol. 22, no. 3, (September):277-283.

Kaldor, Nicholas. 1939. “Speculation and Economic Stability.” Review of Economic Studies, vol. 7, no. 1, (October):1-27.

Kemp, Todd. 2018. “CME Announces Increases to Corn and Soybean Futures Storage Rates.” Risk Management Newsletter, National Grain and Feed Association, July 13. https://www.ngfa.org/newsletter/cme-announces-increases-to-corn-and-soybean-futures-storage-rates/

Li, Jian, Jean‐Paul Chavas, Xiaoli L. Etienne, and Chongguang Li. 2017. “Commodity Price Bubbles and Macroeconomics: Evidence from the Chinese Agricultural Markets.” Agricultural Economics, vol. 48, no. 6, (November):755-768.

29

Masters, Michael. W. 2008. “Testimony before the Committee on Homeland Security and Government Affairs U.S. Senate.” May 20. https://www.hsgac.senate.gov/imo/me-dia/doc/052008Masters.pdf

Board of Trade. 1922. The Sixty-Fourth Annual Report of the Trade and Commerce of Chicago for the Year Ended December 31, 1921. Chicago: Hedstrom-Barry Co., Printers.

Mou, Yiqun. 2010. “Limits to Arbitrage and Commodity Index Investment: Front-Running the Goldman Roll.” Working Paper, Columbia University.

Paul. Allen B. 1970. “The Pricing of Binspace: A Contribution to the Theory of Storage.” American Journal of Agricultural Economics, vol. 52, :1-12.

Paul, Allen B. 1976. “Treatment of Hedging in Commodity Market Regulation.” Technical Bulletin No.1538, Economic Research Service, U. S. Department of Agriculture.

Peck, Anne E. 1985. “Economic Role of Traditional Commodity Futures Markets.” In Fu-tures Markets: Their Economic Role, Anne E. Peck, ed., pp.1-81. Washington, D.C.: Amer-ican Enterprise Institute for Public Policy Research.

Peck, Anne E., and Jeffrey C. Williams. 1991. “An Evaluation of the Performance of the Chicago Board of Trade Wheat, Corn, and Soybean Futures Contracts during Delivery Periods, 1964/65 - 1988/89.” Food Research Institute Studies, vol. 22, no. 2, 129-225.

Peck, Anne E., and Jeffrey C. Williams. 1992. “Delivery on Commodity Futures Contracts.” Economic Record, vol. 68, no. s1, (December):63-74.

Petzel, Todd E. 2009. “Testimony before the Commodity Futures Trading Commission.” July 28. http://www.cftc.gov/ucm/groups/public/@newsroom/documents/file/hear-ing072809_petzel2.pdf

Pirrong, Craig. 2004. “Detecting Manipulation in Futures Markets: The Ferruzzi Soybean Episode.” American Law and Economics Review, vol. 6, no. 1, (March):28-71.

Pirrong, S. Craig, David Haddock, and Roger Kormendi. 1993. Grain Futures Contracts: An Economic Appraisal. Boston, MA: Kluwer Academic Publishers.

Polansek, Tom, and Michael Hirtzer. 2018. “Exclusive: CME Ways Changes to Soy, Corn Contracts—Customers” Reuters, February 6. https://www.reuters.com/article/us-cme-group-grains-storage/exclusive-cme-mulls-changes-to-soy-corn-contracts-customers-idUSKBN1FQ1Q8

30

Routledge, Bryan R., Duane J. Seppi, and Chester S. Spatt. 2000. “Equilibrium Forward Curves for Commodities.” Journal of Finance, vol. 55, no. 3, (June):1297-1328.

Sanders, Dwight R. and Scott H. Irwin. 2016. “The ‘Necessity’ of New Position Limits in Agricultural Futures Markets: The Verdict from Daily Firm Level Position Data.” Applied Economic Perspectives and Policy, vol. 38, no. 2, (June):292-317.

Sanders, Dwight R. and Scott H. Irwin. 2017. “Bubbles, Froth and Facts: Another Look at the Masters Hypothesis in Commodity Futures Markets.” Journal of Agricultural Eco-nomics, vol. 68, no. 2, (June):345-365.

Seamon, Fred. 2009. “An Introduction to Variable Storage Rates in Wheat Futures.” Re-search and Product Development, CME Group.

Stoll, Hans R., and Robert E. Whaley. 2010. “Commodity Index Investing and Commodity Futures Prices.” Journal of Applied Finance, vol. 20, no. 1, 7-46.

Stulz, Rene M. 1982. “Options on the Minimum or the Maximum of Two Risky Assets: Analysis and Applications,” Journal of Financial Economics, vol. 10, no. 2, (July):161-185.

Telser, Lester G. 1958. “Futures Trading and the Storage of Cotton and Wheat.” Journal of Political Economy, vol. 66, no. 3, (June):233-255.

Tomek, William G., and Harry M. Kaiser. 2014. Agricultural Product Prices, Fifth Edi-tion. Ithaca, NY: Cornell University Press.

United States Senate, Permanent Subcommittee on Investigations (USS/PSI). 2009. Exces-sive Speculation in the Wheat Market. Washington, D.C.: U.S. Government Printing Office.

Will, Mattias George, Soren Prehn, Ingo Pies, and Thomas Glauben. 2016. “Is Financial Speculation with Agricultural Commodities Harmful or Helpful? A Literature Review of Empirical Research.” Journal of Alternative Investments, vol. 18, no. 3, (Winter):84-102.

Working, Holbrook. 1948. “Theory of Inverse Carrying Charge in Futures Markets.” Journal of Farm Economics, vol. 30, no. 1, (February):1-28.

Working, Holbrook. 1949. “The Theory of Price of Storage.” American Economic Review, vol. 39, no. 6, (December):1254-62.

31

8. Appendix A: Delivery in Grain Futures Markets

The first key aspect of the delivery process for grain futures contracts is that delivery is not satisfied directly by physical grain, but instead by delivering a warehouse receipt or a ship-ping certificate. The delivery instrument for grain futures markets was exclusively a ware-house receipt until fairly recently. The first markets to convert to a shipping certificate were CBOT corn and soybeans starting with the March 2000 and January 2000 contracts, re-spectively. CBOT wheat converted to a shipping certificate starting with the July 2008 contract and KCBOT wheat was converted beginning with the March 2018 contract. The use of warehouse receipts and shipping certificates contrasts with other commodity futures markets, such as WTI crude oil, that use a demand certificate system. In this alternative system, futures contracts are essentially transformed into short-term forward contracts dur-ing the delivery period and the seller (short) must make physical delivery of the commodity to the buyer (long). This is sometimes referred to as a “forced load out” delivery system.

A warehouse receipt is a legal document that provides proof of ownership (title) of a certain grade and quantity of a commodity at a given storage facility; e.g., 5,000 bushels of number one hard red winter wheat in a warehouse in Hutchinson, Kansas. Hence, a futures market that uses warehouse receipts ties the delivery process to grain in store at terminal elevators. It is important to recognize that warehouse receipts used in the futures delivery process are negotiable, and thus transferable, between parties. A shipping certificate is also a legal document, but rather than representing actual grain in storage, it gives the holder the right but not the obligation to demand load-out of the designated commodity from a particular shipping station; e.g., 5,000 bushels of number two yellow corn loaded on a barge at a shipping station on the Illinois River at LaSalle, Illinois.22 Rather than directly tying the delivery process to grain in store at terminal elevators, a shipping certificate system links delivery to the flow of grain through a marketing channel. Hence, shipping certificates offer flexibility to makers of delivery because the grain can be sourced over time and space, and this is the principal reason that grain futures contracts have been converted to this form of a delivery instrument.23 Like warehouse receipts, shipping certificates are transferable. Nei-ther warehouse receipts nor shipping certificates have expiration dates, and hence, can be held indefinitely.

22 In the case of a shipping certificate, title to the grain does not change hands until load out of grain occurs at the shipping station.

23 Shipping certificates also offer logistical and accounting advantages over warehouse receipts because certif-icates are created and managed solely by the futures exchange.

32

Only commercial firms approved by the CBOT and KCBOT as “regular” for delivery are allowed to issue warehouse receipts or shipping certificates. Firms must meet certain ex-change requirements to be eligible for regularity, such as a minimum net worth of $5 million, and have storage warehouses or shipping stations within the delivery territory of the futures contract. The CBOT and KCBOT also require that shipping certificates issued by regular firms be collateralized by a warehouse receipt for grain in the same location or a letter-of-credit. In practice, CBOT and KCBOT grain delivery locations with substantial storage (terminal elevators) typically use warehouse receipts because this is the cheaper form of collateral, whereas delivery locations without substantial storage (shipping stations) use a letter-of-credit since these facilities do not generally issue warehouse receipts for stored grain. The implication is that the CBOT and KCBOT shipping certificate delivery systems actually are hybrid systems in practice, with some shipping certificates functionally equiva-lent to warehouse receipts due to collateral requirements and others functioning as true shipping certificates.

Regular firms are the source of all delivery instruments for designated warehouses or ship-ping stations. This means that a regular firm that is short is the only party that has the ability to make an “original” delivery with a newly issued delivery instrument. The ex-change does not allow non-regular firms to issue delivery instruments because there is no guarantee that these other firms have access to sufficient physical commodity and financial resources to complete the delivery process. If firms were to promise delivery and not follow through, the contract would quickly fail. Regular firms are typically large commercial grain firms, such as Cargill, Bunge, and Archer Daniels Midland. For makers of delivery that are not a regular firm, the entity must buy a receipt or certificate from a regular firm, buy from non-regular firm holding a receipt or certificate, or have taken delivery on a previous long futures position.

Regular firms issuing delivery instruments must either: (i) have an equal quantity of grain in storage at the delivery location, (ii) have an equal amount of grain in storage at a facility near the delivery location, or (iii) be able to source the grain as needed to fulfill their contractual obligation. In the case of (iii), the exchange enforces this requirement by stip-ulating the maximum number of delivery instruments that a firm may issue. The maximum for a firm issuing warehouse receipts is determined by the amount of warehouse storage space it controls, while the maximum for a firm issuing shipping certificates is based on the loading capabilities at registered shipping stations. These restrictions imply that regular firms holding short futures positions cannot make unlimited deliveries.

Grain futures contracts traded at the CBOT and KCBOT specify a benchmark “par” de-livery grade and location for each contract. The par grade is No. 2 yellow corn for CBOT

33

corn futures and No. 2 yellow soybeans for CBOT soybeans futures, with premiums and discounts for grades above and below No. 2, respectively. The delivery territory for corn futures contracts is the section of the Illinois River from terminals in Chicago and Burns Harbor, Indiana south to Pekin, Illinois. For soybean futures contracts, the delivery terri-tory is extended an additional 200 miles along the Illinois and Mississippi Rivers to St. Louis, Missouri.24 Chicago and Burns Harbor deliveries occur at par for both corn and soybeans, with premiums for deliveries on the Illinois River running between 2 and 3 cents per bushel for corn and 2 and 3.5 cents per bushel for soybeans. St. Louis is at a 6 cent premium to par for soybeans.25

The par grades for CBOT wheat are No. 2 soft red winter wheat, No. 2 hard red winter wheat, No. 2 dark northern spring wheat, and No. 2 northern spring wheat. There is a 3 cent per bushel premium for the No. 1 grade for all classes of wheat that are deliverable. It is very rare for any other class than soft red winter wheat to be the cheapest-to-deliver for CBOT wheat, so the contract is universally regarded as a soft red winter wheat contract. In July 2009, the delivery territory for the CBOT wheat futures contract was expanded from facilities in Chicago, Toledo, and St. Louis to include facilities in a 12-county area of Northwest Ohio, the Ohio River from Cincinnati to the Mississippi River, and facilities on the Mississippi River from south of St. Louis to Memphis. Par locations for CBOT wheat are Chicago, Toledo, and Ohio River facilities. Deliveries in Northwest Ohio have a 10 cent per bushel discount and deliveries in St. Louis and Memphis have premiums of 10 cents and 20 cents, respectively.

The par grade for KCBOT wheat is No. 2 hard red winter wheat with 11 percent protein or greater, with a premium for No. 1 hard red winter wheat and discounts for lower protein content. In July 1996, the delivery territory for the KCBOT wheat futures contract was expanded from facilities in Kansas City to include warehouses in Hutchinson, Kansas. Fa-cilities in Salina/Abilene and Wichita were added in July 2008. Warehouses in Kansas City are par delivery locations for KCBOT wheat, with warehouses in Wichita, Hutchinson, and Salina/Abilene deliverable at discounts of 6 to 12 cents per bushel.

24 Starting with the March 2019 contract, the delivery territory for CBOT corn will be extended to St. Louis, making it the same as for soybeans. St. Louis was previously a delivery location for corn from December 1976 through December 1999.

25 Location differentials will be adjusted upward for corn and soybeans starting with the March and January 2019 contracts, respectively. See the CBOT rulebook at https://www.cmegroup.com/rulebook/CBOT/ for complete details

34

The initiation of the actual delivery process of CBOT and KCBOT grain futures contracts is made at the discretion of the short position holder. The long position holder, however, can force delivery by refusing to offset his or her futures position until expiration of the futures contract is imminent. The delivery process consists of a three-day sequence: 1) Intention Day where the short declares their intention for delivery to the clearinghouse, 2) Notice Day where the Clearinghouse notifies the oldest outstanding long position holder with an invoice for delivery, and 3) Delivery Day where the seller and the buyer exchange delivery instruments and payment. The first three-day sequence can be initiated two busi-ness days before the first business day of the expiration month and the last three-day se-quence can be initiated on the business day prior to the 15th calendar day of the expiration month. This results in a total delivery period of about 10 business days for each contract.

The preceding discussion highlights the flexibility built into the delivery process for CBOT and KCBOT grain futures contracts, as delivery can occur on multiple days, with different grades, and at various locations.26 Standard arbitrage theory predicts that delivery for a grain futures contract will occur at the cheapest-to-deliver location, grade, and date within the delivery period, as this will provide makers of delivery (shorts) the lowest cost alternative for sourcing the grain to satisfy delivery obligations (Stulz, 1982; Johnson, 1987). If futures are above the cash price, the cash commodity is bought, futures sold, and delivery made. If the cash price exceeds futures, then futures are bought and the buyer stands for delivery. This type of arbitrage should prevent the law of one price from being violated. However, both longs and shorts involved in the delivery process incur costs, which in turn determine arbitrage bounds for the convergence of cash and futures prices at cheapest-to-deliver times, grades, and locations. Irwin et al. (2011) estimate the direct cost of delivery for CBOT grain futures contracts to be 6 to 8 cents per bushel; so, a contract can be considered to have “converged” if cheapest-to-deliver date, grade, and location basis is above or below zero by 6 to 8 cents.

Despite the appeal of the cheapest-to-deliver arbitrage theory, there is very little empirical evidence that deliveries on grain futures markets actually occur in the manner predicted by the theory. To the best of my knowledge, only one study actually tests the cheapest-to-deliver theory. Peck and Williams (1991) examined the proportion of deliveries in Chicago versus Toledo in the 1970s and 1980s for CBOT corn, soybeans, and wheat, and found almost no relationship to cash price differences between Chicago and Toledo. In particular, Peck and Williams (p. 188) report, “The overall lack of explanatory power of the difference in price between Chicago and Toledo is striking and is one of the most surprising findings 26 The value of these delivery options to the short (timing, location, and grade) in grain markets may vary over time (Hranaiova and Tomek 2002; Hranaiova, Jarrow, and Tomek 2005).

35

of this study. For none of the commodities was this difference—the Chicago cash premium—related at all to the proportion delivered in Toledo. That is, delivery months with USDA's reported cash prices in Toledo a great deal below those in Chicago were no more associated with relatively large deliveries in Toledo than were ones with Toledo prices close to or even above those in Chicago.” These results are suggestive of measurement errors in publicly-reported USDA cash prices and/or the omission of other important factors that drive deci-sions regarding the location of deliveries on grain futures contracts.

One final feature of the delivery system for CBOT and KCBOT grain futures contracts that is important to discuss is storage rates. Because grain is costly to store, a long taking delivery on a grain futures contract incurs a storage cost for as long as the entity holds the delivery instrument. This is obvious in the case of a warehouse receipt because it represents title to grain in store. It is also true for a shipping certificate because grain must be held by the short or be readily sourced if load out is demanded by the long. The fee is assessed daily and the rate is set by the futures exchange rather than by the market. Historically, the storage rate on grain futures contracts has been fixed for long periods of time. The rate was fixed at 16/100 of a cent per bushel per day (4.8 cents per month) for CBOT corn, soybeans, and wheat contracts in the early 1980s and continued at this rate through the September 1993 corn and soybean contracts and the May 1993 wheat contract. The storage rate for all three grain markets in 1993 was lowered to 15/100 of a cent per bushel per day (4.5 cents per month) and remained at this level through the September 2008 contracts, with a brief period of lower rates for corn and soybeans in 2000-2001. The rate was increased to 16.5/100 of a cent (4.95 cents per month) beginning with the December 2008 contract for corn, the November 2008 contract for soybeans, and the July 2008 contract for wheat. The storage rates for CBOT corn and soybeans remain in place through 2018. Starting in the early 1980s, the storage rate for KCBOT wheat was fixed at 13.3/100 of a cent (3.99 cents per month) and then in July 2008 it was raised to 14.8/100 of a cent (4.44 cents per month).

The CBOT broke with its long practice of fixed storage rates and implemented a seasonal storage rate for wheat starting with the September 2009 contract. Under the seasonal system, the maximum storage rate for wheat was 26.5/100 cents per bushel per day (7.95 cents per month) for the harvest period beginning after the July contract expiration and ending with the expiration of the December contract. The storage rate for CBOT wheat reverted back to 16.5/100 cents (4.95 cents per month) for the “non-harvest” period. The higher seasonal rate was designed to account for the greater demand for storage during the soft red winter wheat harvest. Beginning with the September 2010 contract, the CBOT implemented a variable storage rate (VSR) rule for wheat (Seamon, 2009) that fully auto-mates adjustment of storage rates. Under VSR, if the average spread between the expiring

36

and next nearby contract during the specified averaging period is more than 80 percent (less than 50 percent) of “full financial carry” then the daily storage rate is increased (decreased) by 10/100 of a cent per day for the next nearby contract. If the average spread between the expiring and next nearby contract during the averaging period is between 50 and 80 percent of “full financial carry” then the daily storage rate remains the same.27 There is no upper limit for the maximum allowable storage rate under VSR, but the minimum rate is 16.5/100 of a cent per bushel per day (4.95 cents per month).

The KCBOT adopted a seasonal storage rate beginning with the September 2011 contract, with storage rates for the July and September contracts of 29.6/100 of a cent (8.88 cents per month) and 19.7/100 of a cent (5.91 cents per month) the rest of the year. The KCBOT converted to VSR starting with the May 2018 contract.

9. Appendix A: Data

To compute basis at delivery locations, we use the settlement price of the expiring futures contract on the first five days of the delivery month. The source for the futures prices is barchart.com. Cash prices for the first five days of the delivery month are collected for the following locations and years:

27 Spreadsheets containing VSR computations for the most recent CBOT and KCBOT wheat expirations can be found at https://www.cmegroup.com/trading/agricultural/grain-and-oilseed/variable-storage-rate.html.

37

With one exception, the source for all cash prices is the Agricultural Marketing Service of the U.S. Department of Agriculture (USDA). The USDA reports the range of spot bids at the specified location after 1:30 pm CST (soon after the close of the futures markets.) The data are generally available by 3:00 pm CST. Cash wheat prices for Hutchinson during 1996-2007 are those reported in the Wichita Eagle. Basis is calculated as the settlement futures price minus the cash bid. In all cases, we use the mid-point of the daily range of bids.

The price series collected by the USDA represents bid prices by terminal elevators for corn, soybeans, and wheat. Since actual transaction prices are not collected, reported cash prices may be contaminated by measurement errors (Pirrong, Haddock, and Kormendi 1993, pp. 16-17). For example, when elevators are near capacity they may drop their bids far below the prevailing spot price for grain in an area. The USDA at least partially accounts for this behavior by surveying multiple terminal elevators in a given area. Nonetheless, there is the possibility of a varying degree of bias in the basis levels computed for this study using USDA bid data. Unfortunately, there is a paucity of empirical evidence on the degree of bias in USDA price bid data. Heath (2009) compares USDA prices for soft red winter wheat in

Futures Contract Delivery Location Sample PeriodCBOT Corn Chicago 1986-2017CBOT Corn Toledo 1986-1999CBOT Corn Illinois River North of Peoria 2000-2017CBOT Soybeans Chicago 1986-2017CBOT Soybeans Toledo 1986-1999CBOT Soybeans St. Louis 1993-2017CBOT Soybeans Illinois River North of Peoria 2000-2017CBOT Soybeans Illinois River South of Peoria 2000-2017CBOT Wheat Chicago 1986-2017CBOT Wheat Toledo 1986-2017CBOT Wheat St. Louis 1993-2017CBOT Wheat Cincinnati 2009-2017CBOT Wheat Memphis 2009-2017KCBOT Wheat Kansas City 1996-2017KCBOT Wheat Hutchinson 1996-2017KCBOT Wheat Salina/Abilene 2008-2017KCBOT Wheat Wichita 2008-2017

38

Chicago, Toledo, and St. Louis with prices obtained from a private data vendor for individ-ual elevators that are regular for delivery. He finds that the median difference between the USDA and private elevator price is less than two cents per bushel. While this evidence suggests USDA price data are a good reflection of the market price for grain in delivery locations, the private elevator prices Heath uses still only represent bid data. Further evi-dence is needed to better understand the accuracy of USDA reported cash price bids.

Delivery location and grade differentials from the CBOT and KCBOT Rulebooks are ap-plied as necessary, with premiums added to expiring futures prices and discounts subtracted. Historical contract storage rates (i.e., the storage rate on delivery instruments) are collected from CBOT and KCBOT records. Like Garcia, Irwin, and Smith (2015), we do not add the grade premium for No. 1 yellow soybeans to expiring futures prices. Plots of delivery location basis clearly show that CBOT soybean futures have converged to the No. 1 yellow soybean price for decades even though No. 2 yellow soybeans is the par delivery grade. We have had extensive discussions with CBOT staff and have been unable to pinpoint exactly why this happens. The 3-month London Interbank Offered Rate (LIBOR) is used to meas-ure interest rates. It is the most widely used benchmark rate for short-term interest rates and is compiled by the British Bankers Association in conjunction with Reuters and released to the market shortly after 11am.

We start the sample for CBOT corn, soybeans, and wheat in 1986 because that is the starting date for the sample used by Garcia, Irwin, and Smith (2015), and our data and procedures follow from that study. Garcia, Irwin, and Smith show that delivery location basis (futures minus cash) for KCBOT wheat was consistently negative from 1990–1995, but then is almost always near zero or positive thereafter. Prior to 1996, Kansas City was the only delivery location and it appears this led to a market imbalance that favored long futures holders. Once Hutchinson was added as a delivery location in 1996 this imbalance disappeared. For this reason, we start the sample for KCBOT wheat in 1996.

39

Figure 1. Cheapest-to-Deliver Basis for CBOT Corn Futures Contracts, Average of First Five Days of Delivery, March 1986 - December 2017 Contracts (basis = futures - cash)

-10

0

10

20

30

40

50

60

70

1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016

Basis

(cen

ts/b

ushe

l)

Contract Year

40

Figure 2. Cheapest-to-Deliver Basis for CBOT Soybean Futures Contracts, Average of First Five Days of Delivery, January 1986 - November 2017 Contracts (basis = futures - cash)

-10

0

10

20

30

40

50

60

70

80

90

100

1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016

Basis

(cen

ts/b

ushe

l)

Contract Year

41

Figure 3. Cheapest-to-Deliver Basis for CBOT Wheat Futures Contracts, Average of First Five Days of Delivery, March 1986 - December 2017 Contracts (basis = futures - cash)

-50

0

50

100

150

200

250

300

1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016

Basis

(cen

ts/b

ushe

l)

Contract Year

42

Figure 4. Cheapest-to-Deliver Basis for KCBOT Wheat Futures Contracts, Average of First Five Days of Delivery, March 1996 - December 2017 Contracts (basis = futures - cash)

-25

0

25

50

75

100

125

1996 1999 2002 2005 2008 2011 2014 2017

Basis

(cen

ts/b

ushe

l)

Contract Year

43

44

45

46

Figure 8. Annual Commercial Shipments at Facilities Regular for Delivery of CBOT Corn Futures Contracts, 1975-2017

-

50

100

150

200

250

300

350

1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 2014 2017

Ship

men

ts (m

ilion

bus

hels)

Year

N. Illinois River

Toledo

Chicago

47

Figure 9. Annual Commercial Shipments at Facilities Regular for Delivery of CBOT Soybean Futures Contracts, 1975-2017

-

50

100

150

200

250

300

350

1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 2014 2017

Ship

men

ts (m

illio

n bu

shel

s)

Year

Illinois River - St. Louis

Toledo

Chicago

48

Figure 10. Annual Commercial Shipments at Facilities Regular for Delivery of CBOT Wheat Futures Contracts, 1975-2017

-

50

100

150

200

250

300

350

1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 2014 2017

Ship

men

ts (m

illio

n bu

shel

s)

Year

NW OH - OH River - MS River

St. Louis

Toledo

Chicago

49

Figure 11. Annual Commercial Shipments of Corn, Soybeans, and Wheat in Chicago at Facilities Regular for Delivery of CBOT Grain Futures Contracts, 1975-2017

-

50

100

150

200

250

300

350

1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 2014 2017

Ship

men

ts (m

illio

n bu

shel

s)

Year

50

Figure 12. Average Cheapest-to-Deliver Basis for Grain Futures Contracts When the Nearby Spread is Above and Below 80 Percent of Full Financial Carry, Average of First Five Days of Delivery, CBOT Corn and Wheat: March 1986 - December 2017 Contracts, CBOT Soybeans: January 1986 - November 2017 Contracts, KCBOT

Wheat: March 1996 - December 2017 Contracts.

8.010.9

21.9

11.0

22.3 21.0

51.8

40.2

0

10

20

30

40

50

60

70

CBOT Corn CBOT Soybeans CBOT Wheat KCBOT Wheat

Basis

(cen

ts/b

ushe

l)

Futures Market

Below 80% of Full Carry Above 80% of Full Carry

51

52

53

Figure 15. Estimated Market Value of Physical Storage and Storage Rate for CBOT Corn Futures Contracts, Average of First Five Days of Delivery, May 1986 - September 2017 Contracts

-5

0

5

10

15

1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016

Stor

age

Valu

e or

Rat

e (c

ents

/bus

hel/m

onth

)

Contract Year

Futures Storage Rate

Market Value of Physical Storage

54

Figure 16. Estimated Market Value of Physical Storage and Storage Rate for CBOT Soybean Futures Contracts, Average of First Five Days of Delivery, March 1986 - September 2017 Contracts

-5

0

5

10

15

20

1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016

Stor

age

Price

or

Rate

(cen

ts/b

ushe

l/m

onth

)

Contract Year

Futures Storage Rate

Market Value of Physical Storage

55

Figure 17. Estimated Market Value of Physical Storage and Storage Rate for CBOT Wheat Futures Contracts, Average of First Five Days of Delivery, May 1986 - September 2017 Contracts

-5

0

5

10

15

20

25

30

1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016

Stor

age

Valu

e or

Rat

e (c

ents

/bus

hel/m

onth

)

Contract Year

Futures Storage Rate

Market Value of Physical Storage

56

Figure 18. Estimated Market Value of Physical Storage and Storage Rate for KCBOT Wheat Futures Contracts, Average of First Five Days of Delivery, May 1995 - September 2017 Contracts

-5

0

5

10

15

1996 1999 2002 2005 2008 2011 2014 2017

Stor

age

Valu

e or

Rat

e (c

ents

/bus

hel/m

onth

)

Contract Year

Futures Storage Rate

Market Value of Physical Storage

57

Figure 19. Cheapest-to-Deliver Basis and Predicted Basis for CBOT Corn Futures Contracts, Average of First Five Days of Delivery, March 1986 - September 2017 Contracts

-10

0

10

20

30

40

50

60

70

1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016

Basis

(cen

ts/b

ushe

l)

Contract Year

Actual Basis

Predicted Basis

58

Panel A: Open Interest

Panel B: Volume

Figure 20. Percentage of Open Interest and Volume in Deferred CBOT Wheat Futures Contracts, 12-Month Moving Average, January 2002 - December 2017

10

15

20

25

30

35

40

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

% O

pen

Inte

rest

in D

efer

red

Cont

ract

s

Contract Year

VSR Initiated in September

2010

8

10

12

14

16

18

20

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

% V

olum

e in

Def

erre

d Co

ntra

cts

Contract Year

VSR Initiated in September

2010

59

60

Figure 22. Chicago Basis for CBOT Corn Futures Contracts, Average of First Five Days of Delivery, March 1970 - December 1979 Contracts (basis = futures - cash)

-25

-15

-5

5

15

25

1970 1971 1972 1973 1974 1976 1977 1978 1979

Basis

(cen

ts/b

ushe

l)

Contract Year

61

Figure 23. Chicago Basis for CBOT Soybean Futures Contracts, Average of First Five Days of Delivery, January 1970 - November 1979 Contracts (basis = futures - cash)

-25

-15

-5

5

15

25

1970 1971 1972 1973 1974 1975 1976 1977 1978 1979

Basis

(cen

ts/b

ushe

l)

Contract Year

62

Figure 24. Chicago Basis for CBOT Wheat Futures Contracts, Average of First Five Days of Delivery, March 1970 - December 1979 Contracts (basis = futures - cash)

-25

-15

-5

5

15

25

1970 1971 1972 1973 1974 1976 1977 1978 1979

Basis

(cen

ts/b

ushe

l)

Contract Year

63

Panel A: Corn

Panel B: Soybeans

Figure 25. Storage Rates on Corn and Soybeans for CBOT Futures and Central Illinois Country Elevators, 1986-2006 vs. 2007-2017 Average

1

2

3

4

5

6

7

J F M A M J J A S O N D

Stor

age

Rate

(cen

ts/b

ushe

l/mon

th)

Month

CBOT Futures: 2007-2017

Country Elevators: 1986-2006

Country Elevators: 2007-2017

CBOT Futures: 1986-2006

1

2

3

4

5

6

7

J F M A M J J A S O N D

Stor

age

Rate

(cen

ts/b

ushe

l/mon

th)

Month

CBOT Futures: 2007-2017

Country Elevators: 1986-2006

Country Elevators: 2007-2017

CBOT Futures: 1986-2006

64

Figure 26. Estimates of the Market Value of Physical Storage for CBOT Wheat and Stocks of Wheat at Delivery Locations in Chicago and Toledo, May 1986 - September 2017 Contracts

y = 4.1782ln(x) - 37.21R2 = 0.2224

-30

-20

-10

0

10

20

30

40

0 5 10 15 20 25 30 35 40 45 50

Mar

ket V

alue

of S

tora

ge (c

ents

/bus

hel/

mon

th)

Stocks (million bushels)

Red dots = Value > 5 centsBlue dots = Value < 5 cents