Passive Investors and Managed Money in Commodity Futures Part 4: Price Discovery

Passive Investors and Managed Money in Commodity Futures

Part 4:Price Discovery

Prepared for:

The CME Group

Prepared by:

October, 2008

2

Table of Contents

Section Slide Number

Objective 3

Price Pressure Measurement 4-10

Price Pressure Characteristics 11

Price Pressure Limitations 12

Price Discovery Concept 13-15

Price Pressure Results 16-33

Special Notes on Price Discovery 34-35

Granger Causality Analysis 36-37

Granger Causality Results 38-45

Vector Autoregession Model 46-47

Vector Autoregression Results 48-57

Summary 58-59

3

Objective

Part 4 is an investigation into the effect that large trader groups have on the price discovery function of the studied futures markets. The specific objective is to determine if one or more trader types routinely benefits or hinders price discovery. Particular focus is placed on the index trader and money manager groups.

4

Price Pressure Measurement

In order to evaluate the impact that different trader groups are having on the price discovery process, Informa utilized a method to quantify the amount of “pressure” that each trader group exerts in the market on each trading day.

In what follows, we illustrate the calculation of the price pressure measures for a single day in one contract. The example we use is for the March, 2006 soybean contract. We will demonstrate how price pressure is calculated for Jan 18, 2006

1

1 This technique was developed in: Murphy, Robert D. “The Influence of Specific Trader Groups on Price Discovery in Live Cattle Futures.” Ph.D. Dissertation, Dept of Agricultural and Applied Economics, Virginia Tech, 1995.

5

Price Pressure Measurement (cont’d)

Step 1. Measure the change in net position for each trader group and the change in price on the day in question. Negative numbers indicate the group was net short and positive numbers indicate when the group was net long.

1/17/2006 1/18/2006 Change

Commercial -51,906 -51,445 461Indexer 94,474 94,475 1

Money Manager -6,153 -5,962 191Non-Commercial -15,839 -16,104 -265

SmallTrader -20,576 -20,964 -388

Settle Price (cts/bu) 571.25 565.75 -5.5

Net Position

6


Step 2. Calculate the Initial Fractions for each group as the change in that group’s net position over the sum of the absolute value of all position changes that day. For example the initial fraction for the Commercial group is: 461 ÷ 1306 = 0.353.

Below we give the initial fractions for each group on Jan 18, 2006.

1/18/2006

Commercial 0.353Indexer 0.001

Money Manager 0.146Non-Commercial -0.203

SmallTrader -0.297

7


Step 3. Calculate the Initial Pressure for each group by multiplying the initial fraction and the absolute value of the price change for that day. For example the initial pressure for the Commercial group is: 0.353 x 5.5 = 1.941.

Below we give the Initial Pressures for each group on Jan 18, 2006.

1/18/2006



SmallTrader -1.634

8


Step 4. Calculate the Supplemental Fractions for each group by dividing the group’s net position change by the sum of the absolute values of the net position changes for all groups whose net position change was in the same direction as the price change. We refer to these groups as “mover groups”. On Jan 18, the Non-Commercial and Small Traders were both mover groups. The supplemental fraction for any non mover group is zero.

Here are the supplemental fractions for Jan 18, 2006.

1/18/2006



Small Trader -0.594

9


Step 5. Calculate the Supplemental Pressure for each group by multiplying the supplemental fraction and the absolute value of the price change for that day. For example the supplemental pressure for the Non-Commercial group is: -0.406 x 5.5 = -2.23.

Below we give the Supplemental Pressures for each group on Jan 18, 2006.

1/18/2006



Small Trader -3.268

10


Step 5. Calculate the Total Price Pressure for each group by adding the initial pressure and the supplemental pressure. For example, the total price pressure exerted by the Non-Commercial group on was -1.116 + -2.322 = -3.348

Below we give the Total Price Pressures for each group on Jan 18, 2006.

1/18/2006



Small Trader -4.902

11

Characteristics of the Price Pressure Measures

We can tell from the total price pressures that the Commercial, Indexer and Money Manager groups were all exerting positive pressure on price on this date. The Non-Commercial and Small Trader groups were exerting negative pressure.

When we sum the price pressures across all groups, we get the exact change in price for that day. This is an essential characteristic of the price pressure measures—the change in price on any given day can be explained completely as a function of the price pressure exerted by the five groups of traders.

1/18/2006



Small Trader -4.902

Sum of All Pressures: -5.500

12

Limitations of Price Pressure Measures

Intra-day pressure cannot be captured, we only get the net pressure for all of the activity that occurred during a session.

Every futures trade by a particular trader group is assumed to have equal impact on price pressure.

All unit changes in net position for the mover groups are given equal weight. It is impossible to tell which, if any, of the mover groups (Non-commercial and Small Trader in this example) are exerting more pressure per unit change in net position.

When there is no price change on a given day, this process will produce a total pressure of zero for all groups. In reality, we may have had two groups exerting large but offsetting pressure so the price did not change.

Despite its limitations, this process does provide a rule that can be applied consistently to all trading days and thus does not favor one group or another.

13

Price Discovery Concept

Time

$

Expir

ati

on

“True Value” of the Commodity

This Period is Beneficial to Price Discovery: (moving price back toward its true value)

This Period is Detrimental to Price Discovery (moving price away from its true value)

14

Price Discovery Concept

Price discovery in futures markets involves how well the futures price reflects the ultimate value of the commodity.

For example, the March 2006 soybean contract is doing a good job of price discovery when it accurately represents the price of cash soybeans at the delivery location in March of 2006. If it spends large amounts of time away from the “true value” of cash soybeans in March of 2006 then we conclude that its price discovery performance was poor.

In the section that follows, we will use the price pressure measures to identify which trader groups tend to help price discovery by exerting pressure consistent with bringing price back toward true value and which hinder price discovery by routinely pushing price away from true value.

15

Price Series Used to Represent Fundamental Value

Commodity Price SeriesCorn Chicago Yellow #2, Truck-Rail

Soybeans Chicago #1 Soybeans, Truck-Rail

Chicago Wheat Toledo Soft Red Winter #2, Truck

KC Wheat Kansas City Hard Red Winter Ordinary #1

MN Wheat Minneapolis Dark Northern Spring, 14% Protein, #1, Truck

Cotton North Delta Cash Price

Nat Gas Avg of futures - last 5 days before expirationCrude Oil Avg of futures - last 5 days before expiration

16

Results – CMEG Corn Futures

Sum of Beneficial and Detrimental Price Pressure (absolute value in cents/bu.)

Commercials Non-Commercials Indexers Money Managers Small Traders

Contract Beneficial Detrimental Beneficial Detrimental Beneficial Detrimental Beneficial Detrimental Beneficial Detrimental200503 19 23 27 10 1 15 38 16 13 17200505 41 34 34 33 33 33 45 58 36 18200507 109 69 60 62 44 35 118 98 47 48200509 137 123 133 114 76 89 67 44 70 82200512 117 85 110 95 45 104 167 140 108 81200603 158 149 161 191 57 42 101 145 200 99200605 179 216 228 207 65 42 101 102 196 172200607 237 244 228 261 69 72 138 151 229 159200609 242 200 253 254 54 132 103 65 197 153200612 195 290 319 249 161 136 350 197 208 228200703 178 463 538 250 214 73 297 144 193 282200705 366 440 544 280 113 172 274 188 294 254200707 336 480 537 464 193 172 499 336 335 376200709 450 461 555 455 136 289 298 153 291 318200712 366 521 514 342 360 199 722 501 305 395200803 373 836 938 491 225 105 423 227 393 429200805 487 785 863 531 320 86 452 303 463 581200807 490 837 786 648 560 202 831 585 484 453200809 681 507 830 626 123 464 442 359 489 490

Total: 5159 6763 7657 5563 2846 2462 5467 3810 4551 4637

Beneficial as a % of

Detrimental:

76% 138% 116% 143% 98%

17

Comments on Corn Results

The Commercial and Small Trader groups are the only groups that showed more detrimental pressure than beneficial over the study period.

Money managers had the highest ratio of beneficial to detrimental pressure.

We hypothesize that indexers, non-commercials and money managers did well because they correctly anticipated that expiration values would be high. In other words, those that persistently pursued long positions in corn over the last few years were often on the right side of the market given where commodity prices ended up. Whether this was luck or skill is debatable, but this upward pressure was correct in the end.

18

Results – CMEG Soybean Futures



Contract Beneficial Detrimental Beneficial Detrimental Beneficial Detrimental Beneficial Detrimental Beneficial Detrimental200501 55 5 7 12 0 0 3 1 0 43200503 67 89 87 37 20 24 120 97 107 62200505 163 131 175 108 109 72 212 115 72 112200507 370 392 201 238 185 69 373 159 134 164200508 372 331 450 312 53 49 235 165 285 306200509 416 338 363 486 33 29 152 121 473 393200511 391 400 226 245 150 209 601 407 310 379200601 621 466 380 415 125 67 238 334 595 547200603 714 606 387 460 159 95 339 264 455 508200605 657 466 531 545 52 127 334 215 536 492200607 736 512 552 537 139 142 276 367 518 472200608 339 479 477 489 9 9 124 111 697 457200609 387 443 408 299 13 26 135 131 473 334200611 552 355 652 373 170 133 569 503 382 519200701 319 616 344 342 262 7 261 116 423 451200703 297 681 554 353 159 15 253 153 474 375200705 425 524 483 443 127 111 284 198 375 285200707 399 796 712 581 247 42 500 247 479 409200708 299 545 896 569 35 59 140 137 443 362200709 364 544 822 548 90 97 148 198 554 312200711 745 1064 989 565 401 170 884 649 550 711200801 514 944 926 556 417 109 611 342 605 518200803 805 1081 1226 666 343 188 714 471 687 632200805 890 1371 1434 1153 479 328 943 398 973 606200807 1238 1261 1361 1296 833 398 1682 1084 949 1032200808 990 963 1548 1100 310 479 337 426 1143 778200809 827 756 1312 905 214 89 389 462 1053 1019

Total: 13,952 16,160 17,506 13,631 5,136 3,142 10,858 7,870 13,743 12,279Beneficial as

a % of Detrimental:

86% 128% 163% 138% 112%

19

Comments on Soybean Results

The Commercial group is the only group that showed more detrimental pressure than beneficial over the study period

Indexers had the highest ratio of beneficial to detrimental

Similar to corn, we hypothesize that indexers, non-commercials and money managers did well because they correctly anticipated that expiration values would be high. In other words, those that persistently pursued long positions in soybeans over the last few years were often on the right side of the market given where commodity prices ended up.

20

Results – CMEG Wheat Futures




Total: 8534 9067 9699 9234 6139 6659 8931 7660 6887 7255Beneficial as

a % of Detrimental:

94% 105% 92% 117% 95%

21

Comments on CMEG Wheat Results

Small Traders, Indexers and Commercials showed slightly more detrimental pressure than beneficial pressure over the study period.

Money Managers had the highest ratio of beneficial to detrimental price pressure.

Indexers had slightly more detrimental pressure than beneficial.

Indexers were particularly detrimental in the last two contracts, July and September, 2008. In these two contracts, prices were forced way above true value a few months prior to expiration (Spring, 2008). This is the time when Indexers are most active.

Small traders seem to show equal quantities of beneficial and detrimental pressure in each month.

22

Results – KC Wheat Futures





a % of Detrimental:

96% 121% 116% 133% 97%

23

Comments on KC Wheat Results

Small Traders and Indexers showed slightly more detrimental pressure than beneficial pressure over the study period.

Money Managers had the highest ratio of beneficial to detrimental price pressure.

Indexers had slightly more detrimental pressure than beneficial.

Indexers were particularly detrimental in the last three contracts, May, July and September 2008.

Index traders exert less pressure overall in this market than they do in the Chicago wheat market.

24

Results – MN Wheat Futures





a % of Detrimental:

81% 127% 123% 137% 98%

25

Comments on MN Wheat Results

Index traders have historically not been present in this market, but recently a small amount of activity has surfaced.

Commercials are the only group to exhibit more detrimental price pressure than beneficial.

Money managers have exhibited a greater level of beneficial price pressure in six of the last seven contracts in the study period.

26

Results – ICE Cotton Futures

Sum of Beneficial and Detrimental Price Pressure (absolute value in $/lb.)


Contract Beneficial Detrimental Beneficial Detrimental Beneficial Detrimental Beneficial Detrimental Beneficial Detrimental200503 13 5 2 2 1 4 12 7 5 8200505 21 16 7 11 9 6 24 9 8 9200507 36 19 18 21 1 12 25 20 15 12200512 48 29 35 21 9 12 33 52 30 29200603 58 37 49 34 13 8 22 32 38 41200605 35 40 55 44 8 13 15 13 56 54200607 50 40 58 54 11 9 14 19 57 63200612 64 36 41 48 15 19 37 33 47 56200703 48 25 33 33 8 19 24 32 26 27200705 35 22 44 37 8 13 12 14 34 34200707 61 37 55 51 9 17 17 20 38 46200712 51 53 68 40 29 18 37 47 31 37200803 60 71 48 53 33 17 43 28 42 51200805 57 70 64 65 23 23 32 28 53 43200807 77 74 81 70 36 44 47 37 53 54

Total: 714 574 659 584 212 233 395 390 534 566


Detrimental: 125% 113% 91% 101% 94%

27

Comments on Cotton Results

Index and Small Traders both exerted more aggregate detrimental pressure than beneficial pressure.

Index traders exerted less overall price pressure than did the Commercial, Non-commercial and Small Trader groups.

Money Managers displayed a near equal split between beneficial and detrimental price pressure.

28

Results – NYMEX Natural Gas Futures

Sum of Beneficial and Detrimental Price Pressure (absolute value in $/MMBtu)


Contract Beneficial Detrimental Beneficial Detrimental Beneficial Detrimental Beneficial Detrimental Beneficial Detrimental200502 0 0 0 0 0 1 2 0 0 0200503 0 1 1 0 2 1 2 1 1 0200504 1 1 1 1 2 1 2 2 1 1200505 1 1 1 0 3 0 2 3 2 1200506 2 1 1 2 1 2 3 2 2 2200507 2 2 2 1 2 2 3 3 3 2200508 2 2 2 3 3 2 4 4 4 2200509 2 4 5 3 6 3 6 5 4 2200510 7 5 8 4 5 5 8 5 4 4200511 6 7 9 4 8 5 8 6 6 5200512 6 6 8 8 6 6 11 8 8 5200601 11 9 11 9 9 7 12 12 7 6200602 11 11 10 10 7 11 17 11 7 7200603 11 8 13 10 7 16 17 11 8 9200604 9 7 6 8 6 7 12 10 8 5200605 11 6 6 6 7 10 10 10 6 5200606 8 9 8 8 4 6 11 8 7 7200607 9 8 8 8 5 4 9 8 6 9200608 10 8 8 10 3 5 9 6 7 8200609 8 8 7 7 5 3 9 7 7 10200610 9 9 7 7 5 5 9 8 5 7200611 7 9 8 7 3 4 10 7 6 7200612 8 7 7 7 5 5 6 7 8 7200701 7 7 6 8 6 5 6 9 6 6200702 8 6 6 9 5 2 4 9 7 7

29

Results – NYMEX Natural Gas Futures (cont’d)

Sum of Beneficial and Detrimental Price Pressure (absolute value in $/MMBtu)



Total: 234 220 239 238 165 156 253 232 220 212


Detrimental: 106% 101% 106% 109% 104%

30

Comments on Natural Gas Results

In this market, all of the groups showed a nearly equal split between beneficial and detrimental price pressure. This is indicative of a situation where no one trader group regularly percieves mis-pricing in the market.

Money managers had the highest beneficial/detrimental price pressure ratio.

31

Results – NYMEX Crude Oil Futures

Sum of Beneficial and Detrimental Price Pressure (absolute value in $/barrel)


Contract Beneficial Detrimental Beneficial Detrimental Beneficial Detrimental Beneficial Detrimental Beneficial Detrimental200502 4 1 1 1 0 3 3 1 3 0200503 6 7 4 2 9 5 6 4 6 2200504 5 12 4 5 9 4 17 2 4 3200505 15 11 10 6 19 12 8 10 7 8200506 24 12 12 14 18 17 11 12 10 11200507 27 25 21 14 20 20 17 10 15 10200508 24 30 20 23 35 15 27 11 16 16200509 26 36 27 27 35 15 32 17 18 17200510 36 39 32 25 36 25 35 16 27 21200511 52 35 35 32 37 33 23 27 32 22200512 38 27 43 37 45 37 33 26 35 34200601 53 43 46 34 40 38 30 25 36 34200602 58 41 52 43 39 35 25 16 46 44200603 52 39 53 47 57 48 32 30 44 36200604 78 44 51 57 43 45 28 18 61 40200605 57 39 58 76 50 37 31 23 60 53200606 58 60 74 56 62 61 56 35 45 41200607 76 51 67 74 58 55 29 21 63 58200608 76 55 74 91 56 37 29 25 66 49200609 71 62 92 75 61 63 37 23 60 52200610 72 53 92 80 49 58 24 19 70 63200611 77 53 88 96 41 49 21 23 82 68200612 72 62 93 81 70 67 60 60 80 73200701 79 73 77 76 65 35 31 35 63 55200702 51 68 92 96 43 36 20 13 68 56

32

Results – NYMEX Crude Oil Futures (cont’d)

Sum of Beneficial and Detrimental Price Pressure (absolute value in $/barrel)



Total: 1845 1589 2209 2092 1454 1336 937 729 1633 1466


Detrimental: 116% 106% 109% 128% 111%

33

Comments on Crude Oil Results

In this market, prices trended routinely higher often expiring near contract highs, this characteristic results in far more beneficial pressure than detrimental in aggregate.

Money managers had the highest beneficial/detrimental price pressure ratio, but they exerted less overall pressure than the other four groups.

Non-commercials had the lowest beneficial/detrimental price pressure ratio.

In the most recent contract, Sep 2008, indexers showed a high degree of detrimental price pressure. This contract trended sharply lower in the last couple of months of its existence.

34

Special Notes on the Price Pressure Analysis

It is possible that Commercial traders, if they are textbook hedging, do not care so much about the true value of the commodity as much as they do about shifting risk. Thus, Commercials often appear to exert detrimental pressure as a result of routine hedging activities.

The “true value” used in these analyses was the reported cash price in the days surrounding first notice day for all commodities except natural gas and crude oil. Because of the prolonged nature of delivery in these markets (a 30-day delivery window) spot prices were not considered to be indicative of the true value of the contract. In these instances, the average value of the futures in the final five days of trading was used as the true value. The assumption here is that these energy contracts converge correctly to the market’s estimate of their true value.

35

Some might argue that excessive buy and hold strategies became self-fulfilling prophecies in these storable commodities, forcing contracts to expire at values higher than the true cash value of the commodity.

However, all of these contracts are eventually settled by physical delivery and in most instances it appears the delivery process worked well, allowing participants to turn futures paper into physicals at expiration. The exceptions are the CMEG wheat contract and, to a lesser extent, the CMEG soybean and corn contracts in the latter part of the study period. Problems with contract design in these commodities is addressed in separate phase of this project.

It is difficult to argue that the futures are priced higher than the fundamental value of the commodity when market participants are actively exchanging futures for the physical commodity via the delivery process.

Special Notes on the Price Pressure Analysis

36

Granger Causality Analysis

Granger Causality is an econometric technique used to determine if one variable leads (“causes”) another.

In this exercise, we want to investigate:

Do changes in net position by trader groups cause price changes?

Or Do changes in prices cause traders to alter their positions?

To do this, two models are estimated, a unrestricted model and a restricted model. A simple F test is used to determine if the added variable in the unrestricted model results in significantly smaller sum of squared residuals. P-values from this F test are reported rather than the F statistic itself.

37

Granger Causality Analysis (cont’d)

tttt eicenNetPositioice 11 PrPr

ttt eiceice 1PrPr

The Unrestricted Model

The Restricted Model

If, by adding the change in net position as an explanatory variable, the sum of squared errors is significantly smaller than in the restricted model, then we conclude that changes in net position lead changes in price.A second set of equations is also estimated, but in these the change in net position is the dependent variable and change in price as the added independent variable in the unrestricted model. From these, we can test if a change in price leads a change in net position.This analysis used daily data on all contracts that traded between Jan 1, 2005 and June 30, 2008. This provided well over 8,000 degrees of freedom for nearly every commodity-trader group combo except index traders in Minneapolis wheat. Accordingly, the results are considered robust.

38

Granger Causality Results, Corn

Price seems to cause Commercials to take positions

No evidence that position-changing by any group leads price change.

CommercialNon-

CommercialIndexer Money Mgr

Small Trader

---- P Values -----Position Causes Price 0.306 0.974 0.466 0.577 0.312Price Causes Position 0.000 0.785 0.872 0.313 0.088

---- Significant at 5% Level -----Position Causes Price N N N N NPrice Causes Position Y N N N N

P-values are the probability that the sum of squared errors in the unrestricted model is not different from the sum of squared errors in the restricted model.

39

Granger Causality Results, Soybeans

Similar to corn, commercial positions appear to respond to changes in price.

No evidence that positions lead price for any group.

CommercialNon-


Small Trader


---- Significant at 5% Level -----Position Causes Price N N N N NPrice Causes Position Y N N N Y


40

Granger Causality Results, Chicago Wheat

Commercials and money managers respond to price by changing positions

No evidence that position changes influence price.

CommercialNon-


Small Trader


---- Significant at 5% Level -----Position Causes Price N N N N NPrice Causes Position Y N N Y N


41

Granger Causality Results, Kansas City Wheat

No significant effect in either direction

CommercialNon-


Small Trader


---- Significant at 5% Level -----Position Causes Price N N N N NPrice Causes Position N N N N N


42

Granger Causality Results, Minneapolis Wheat

The significance of the Indexer group could be an artifact of a very small number of observations compared to the other groups. Indexers have only recently moved into this market and currently hold minimal positions. Had the sample been larger, it is possible that no significant impact would have been detected.

No effect in either direction for the remaining groups.

CommercialNon-


Small Trader


---- Significant at 5% Level -----Position Causes Price N N Y N NPrice Causes Position N N Y N N


43

Granger Causality Results, Cotton

Small trader positions appear to respond to price.

No evidence that position changes by any group has a significant impact on price.

CommercialNon-


Small Trader


---- Significant at 5% Level -----Position Causes Price N N N N NPrice Causes Position N N N N Y


44

Granger Causality Results, Natural Gas

Large non-commercials display a simultaneous relationship between prices and net positions. A simultaneous relationship develops when two variables feed off one another. In this case, positions may cause price which then encourages further position changes.

Money manager and small trader net position responds to the previous period’s price change (trend followers).

CommercialNon-


Small Trader


---- Significant at 5% Level -----Position Causes Price N Y N N NPrice Causes Position N Y N Y Y


45

Granger Causality Results, Crude Oil

Three groups show strong reaction to price.

No indication that changes in position by any group has a significant impact on prices.

CommercialNon-


Small Trader


---- Significant at 5% Level -----Position Causes Price N N N N NPrice Causes Position N Y N Y Y


46

Vector Autogregression Analysis

To futher investigate the impact of trader groups on prices and volatility, a vector autoregression (VAR) analysis was performed.

This involves a system of statistical equations where the dependent variables are the change in price and the change in each trader group’s position in the current period and the explanatory variables are the same, but lagged one period.

This model is very similar to the Granger Causality models with difference being the inclusion of lagged net position variables for all trader groups.

47

VAR Functional Model

tttttteMMNPINPNCNPCNPt

1151141131111 12PrPr

tttttteMMNPINPNCNPCNPtCNP

1251241231121 22Pr

tttttteMMNPINPNCNPCNPtNCNP

1351341331131 32Pr

tttttteMMNPINPNCNPCNPtINP

1451441431141 42Pr

tttttteMMNPINPNCNPCNPtMMNP

1551541531151 52Pr

Where: Pr = Change in Price

CNP = Change in Commercial Net Position

NCNP = Change in Non-Commercial Net Position

INP = Change in Indexer Net Position

MMNP = Change in Money Manager Net Position

48

VAR Results, Corn

Parameter Estimates

Price CommercialNon-

Commercial Indexer Money Mgr

Price 0.030 0.000 0.000 0.000 0.000

Commercial 7.684 0.203 -0.112 -0.346 -0.158

Non-Commercial -3.058 -0.154 0.026 -0.341 -0.115

Indexer -2.882 -0.064 -0.013 0.778 -0.029

Money Mgr -6.792 -0.197 -0.096 -0.240 0.095(Red indicates significant at 5% level) *Change in the listed variable

P-values for Parameter Significance


Commercial Indexer Money Mgr

Price 0.002 0.274 0.557 0.522 0.231

Commercial 0.082 0.000 0.000 0.000 0.000

Non-Commercial 0.423 0.000 0.111 0.000 0.000

Indexer 0.459 0.000 0.429 0.000 0.083

Money Mgr 0.087 0.000 0.000 0.000 0.000

Cu

rren

t P

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d V

aria

ble

*

Lagged Variable (1 period)*

Cu

rren

t P

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d V

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Lagged Variable (1 period)

49

VAR Results, Soybeans

Parameter Estimates


Commercial Indexer Money MgrPrice 0.033 0.000 0.000 0.000 0.000

Commercial 1.296 0.060 -0.218 -0.540 -0.266Non-Commercial 0.268 -0.161 -0.040 -0.340 -0.157

Indexer -0.568 -0.044 0.023 0.820 0.007Money Mgr -2.187 -0.168 -0.075 -0.166 0.050

(Red indicates significant at 5% level) *Change in the listed variable




Commercial 0.199 0.001 0.000 0.000 0.000Non-Commercial 0.720 0.000 0.006 0.000 0.000

Indexer 0.416 0.001 0.089 0.000 0.620Money Mgr 0.017 0.000 0.000 0.000 0.005

Cu

rren

t P

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d V

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ble

*



Cu

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t P

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d V

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50

VAR Results, Chicago Wheat

Parameter Estimates



Commercial 1.372 0.137 -0.033 -0.351 -0.159Non-Commercial -0.515 -0.201 -0.087 -0.381 -0.250

Indexer 0.356 -0.178 -0.115 0.620 -0.045Money Mgr -1.144 -0.081 -0.079 -0.182 0.110







Cu

rren

t P

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d V

aria

ble

*



Cu

rren

t P

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d V

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ble

51

VAR Results, Kansas City Wheat

Parameter Estimates



Commercial -0.762 -0.196 -0.561 -0.898 -0.597Non-Commercial 0.394 -0.041 0.034 -0.012 -0.005

Indexer -0.140 -0.048 -0.010 0.694 0.009Money Mgr 0.600 -0.087 0.069 -0.074 0.205







Cu

rren

t P

erio

d V

aria

ble

*



Cu

rren

t P

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d V

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52

VAR Results, Minneapolis Wheat

Parameter Estimates


Commercial Indexer Money MgrPrice 0.217 0.001 0.001 -0.008 -0.001

Commercial -0.317 -0.047 -0.147 -0.022 -0.386Non-Commercial -0.066 -0.046 -0.117 -0.074 0.006

Indexer 0.170 0.005 0.009 0.100 0.001Money Mgr 0.110 -0.119 0.018 -0.061 0.172







Cu

rren

t P

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d V

aria

ble

*



Cu

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t P

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d V

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ble

53

VAR Results, Cotton

Parameter Estimates



Commercial -7.842 0.063 -0.106 -0.582 -0.469Non-Commercial -1.783 -0.169 -0.083 -0.247 -0.219

Indexer -5.192 -0.070 -0.018 0.742 -0.073Money Mgr 13.514 -0.082 -0.088 -0.187 0.422







Cu

rren

t P

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d V

aria

ble

*



Cu

rren

t P

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d V

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ble

54

VAR Results, Natural Gas

Parameter Estimates



Commercial 22.859 0.172 0.072 -0.151 0.002Non-Commercial 65.391 -0.180 -0.148 -0.219 -0.173

Indexer 7.145 -0.183 -0.052 0.615 -0.073Money Mgr 11.231 -0.148 -0.207 -0.567 -0.112







Cu

rren

t P

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d V

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ble

*



Cu

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t P

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d V

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55

VAR Results, Crude Oil

Parameter Estimates


Commercial Indexer Money MgrPrice -0.038 0.000 0.000 0.000 0.000

Commercial 3.798 0.239 0.103 -0.281 0.007Non-Commercial 21.852 -0.163 -0.131 -0.283 -0.184

Indexer -0.879 -0.275 -0.188 0.565 -0.128Money Mgr -15.384 -0.135 -0.085 -0.267 0.013







Cu

rren

t P

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d V

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*



Cu

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t P

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d V

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VAR Results, Discussion

In the price equation, none of the lagged position changes are significant except for Commericals in soybeans and natural gas and Indexers in MN Wheat. This is generally consistent with the Granger Causality results that found that changes in position frequently do not lead changes in price.

In a few instances, price changes lead position changes. These include Money Mgrs in soybeans, Indexers in MN wheat, Non-commercials in natural gas, Non-commercials and Money Mgrs in crude oil. This is also generally consistent with the Granger Causality findings.

In crude oil and natural gas, the coefficients on price change are large and positive for Non-commercials. This would be consistent with a trend-following behavior where higher prices cause this group to increase the length of its position. The negative coefficient on Money Mgrs in crude indicates that price increases cause them to become more short.

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VAR Results, Discussion (continued)

In all other commodities beside corn, the “between group” parameters are frequently significant, indicating that there is often interaction between the positions of trader groups—thus particular trader types rarely act in isolation.

There are many “own position” parameters (those on the diagonal) that are significant and positive. This indicates that an increase in a group’s long position on a particular day is often followed by a further increase the next day. This supports the idea of regular “position building” over many trading sessions.

As with the Granger Causality analysis, the results from the VAR models tend to support a market process where traders change positions in reaction to price rather than the other way around.

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Part 4 Summary

A metric was developed to measure the pressure that trader groups put on market prices each day

These pressures were categorized as beneficial if they moved price back in the direction of the final value. They were considered detrimental if they moved price away from its final value.

No clear pattern emerged. All groups had periods of both types of price pressure.

Index traders buy and hold strategy was strongly beneficial when contracts expired near their highs, which was often.

The results are indicative of a situation where no trader group routinely “knows” the true price of a commodity and therefore does not routinely help or hinder price discovery.

Passive investors and trend following speculators may have just been lucky in catching a prolonged trend in their favored direction.

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Part 4 Summary

The Granger Causality and VAR results were consistent with previous findings using these techniques. There is little evidence that any group has a sustained and significant influence on price.

There is no evidence that changes in the index trader net position has a significant price impact.

Money Mangers were more likely to react to price changes than to cause them.

None of the three analyses presented in this section (price pressure, Granger Causality, Vector Autoregression) supports the idea that any one trader group was routinely behaving in a manner that was detrimental to price discovery.

Passive Investors and Managed Money in Commodity Futures Part 4: Price Discovery

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