Passive Investors and Managed Money in Commodity Futures

1
Passive Investors and Managed Money in Commodity
Futures Part 4 Price Discovery
Prepared for   The CME Group          Prepared
by       October, 2008
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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
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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 (contd)

• 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.

Net Position
6
Price Pressure Measurement (contd)

• Step 2. Calculate the Initial Fractions for each
  group as the change in that groups 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.

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Price Pressure Measurement (contd)

• 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.

8
Price Pressure Measurement (contd)

• Step 4. Calculate the Supplemental Fractions for
  each group by dividing the groups 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.

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Price Pressure Measurement (contd)

• 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.

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Price Pressure Measurement (contd)

• 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.

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 measuresthe 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.

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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
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)
Expiration
Time
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.

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Price Series Used to Represent Fundamental Value
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Results CMEG Corn Futures
Sum of Beneficial and Detrimental Price Pressure
(absolute value in cents/bu.)
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.

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

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

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

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

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Results ICE Cotton Futures
Sum of Beneficial and Detrimental Price Pressure
(absolute value in /lb.)
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.

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Results NYMEX Natural Gas Futures
Sum of Beneficial and Detrimental Price Pressure
(absolute value in /MMBtu)
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Results NYMEX Natural Gas Futures (contd)
Sum of Beneficial and Detrimental Price Pressure
(absolute value in /MMBtu)
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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/detrimen
  tal price pressure ratio.

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Results NYMEX Crude Oil Futures
Sum of Beneficial and Detrimental Price Pressure
(absolute value in /barrel)
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Results NYMEX Crude Oil Futures (contd)
Sum of Beneficial and Detrimental Price Pressure
(absolute value in /barrel)
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/detrimen
  tal price pressure ratio, but they exerted less
  overall pressure than the other four groups.
• Non-commercials had the lowest beneficial/detrimen
  tal 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.

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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
  markets estimate of their true value.

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Special Notes on the Price Pressure Analysis
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.
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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.

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Granger Causality Analysis (contd)
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.
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Granger Causality Results, Corn

• Price seems to cause Commercials to take
  positions
• No evidence that position-changing by any group
  leads price change.

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.

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Granger Causality Results, Chicago Wheat

• Commercials and money managers respond to price
  by changing positions
• No evidence that position changes influence
  price.

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Granger Causality Results, Kansas City Wheat

• No significant effect in either direction

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.

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.

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 periods price change
  (trend followers).

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.

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 groups
  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
Where DPr Change in Price DCNP Change in
Commercial Net Position DNCNP Change in
Non-Commercial Net Position DINP Change in
Indexer Net Position DMMNP Change in Money
Manager Net Position
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VAR Results, Corn
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VAR Results, Soybeans
50
VAR Results, Chicago Wheat
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VAR Results, Kansas City Wheat
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VAR Results, Minneapolis Wheat
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VAR Results, Cotton
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VAR Results, Natural Gas
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VAR Results, Crude Oil
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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
  groupsthus 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
  groups 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.