Passive Investors and Managed Money in Commodity Futures. Part 3: Volatility. Prepared for: The CME Group. Prepared by:

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1 Passive Investors and Managed Money in Commodity Futures Part 3: Prepared for: The CME Group Prepared by: October, 2008

2 Table of Contents Section Slide Number Objective and Approach 3 Graphs 4-13 Correlation Analysis Summary

3 Objective and Approach The primary objective of this section is to determine if an association exists between the presence of each large trader group and price volatility. Correlation analysis is the tool employed Market presence of a particular trader group is defined as the percentage of total open interest held by that group. calculated as a ten-day rolling volatility, expressed as a percentage. 3

4 Graphs Graphs of historical price volatility were produced for each of the eight commodity futures contracts. These are 10-day rolling annualized volatilities 1 calculated using the nearby futures contract. Trendlines fit to these series reveal that, for most commodities, volatility was increasing over the study period. Cotton, Nat Gas and Crude Oil show much less evidence of increasing volatility than the grain contracts day = 10 Pt ln Pt t=

5 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% Historical, Corn 5 1/3/2005 3/3/2005 5/3/2005 7/3/2005 9/3/ /3/2005 1/3/2006 3/3/2006 5/3/2006 7/3/2006 9/3/ /3/2006 1/3/2007 3/3/2007 5/3/2007 7/3/2007 9/3/ /3/2007 1/3/2008 3/3/2008 5/3/2008 7/3/2008 9/3/2008 Date

6 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% Historical, Soybeans 6 1/3/2005 3/3/2005 5/3/2005 7/3/2005 9/3/ /3/2005 1/3/2006 3/3/2006 5/3/2006 7/3/2006 9/3/ /3/2006 1/3/2007 3/3/2007 5/3/2007 7/3/2007 9/3/ /3/2007 1/3/2008 3/3/2008 5/3/2008 7/3/2008 9/3/2008 Date

7 Historical, Chicago Wheat % 90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% 7 1/3/2005 3/3/2005 5/3/2005 7/3/2005 9/3/ /3/2005 1/3/2006 3/3/2006 5/3/2006 7/3/2006 9/3/ /3/2006 1/3/2007 3/3/2007 5/3/2007 7/3/2007 9/3/ /3/2007 1/3/2008 3/3/2008 5/3/2008 7/3/2008 9/3/2008 Date

8 90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% Historical, Kansas City Wheat 8 1/3/2005 3/3/2005 5/3/2005 7/3/2005 9/3/ /3/2005 1/3/2006 3/3/2006 5/3/2006 7/3/2006 9/3/ /3/2006 1/3/2007 3/3/2007 5/3/2007 7/3/2007 9/3/ /3/2007 1/3/2008 3/3/2008 5/3/2008 7/3/2008 9/3/2008 Date

9 Historical, Minneapolis Wheat % % % % 80.00% 60.00% 40.00% 20.00% 0.00% 9 1/3/2005 3/3/2005 5/3/2005 7/3/2005 9/3/ /3/2005 1/3/2006 3/3/2006 5/3/2006 7/3/2006 9/3/ /3/2006 1/3/2007 3/3/2007 5/3/2007 7/3/2007 9/3/ /3/2007 1/3/2008 3/3/2008 5/3/2008 7/3/2008 9/3/2008 Date

10 Historical, Cotton 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% 10 1/3/2005 3/3/2005 5/3/2005 7/3/2005 9/3/ /3/2005 1/3/2006 3/3/2006 5/3/2006 7/3/2006 9/3/ /3/2006 1/3/2007 3/3/2007 5/3/2007 7/3/2007 9/3/ /3/2007 1/3/2008 3/3/2008 5/3/2008 7/3/2008 9/3/2008 Date

11 Historical, Natural Gas % % % % 80.00% 60.00% 40.00% 20.00% 0.00% 11 1/3/2005 3/3/2005 5/3/2005 7/3/2005 9/3/ /3/2005 1/3/2006 3/3/2006 5/3/2006 7/3/2006 9/3/ /3/2006 1/3/2007 3/3/2007 5/3/2007 7/3/2007 9/3/ /3/2007 1/3/2008 3/3/2008 5/3/2008 7/3/2008 9/3/2008 Date

12 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% Historical, Crude Oil 12 1/3/2005 3/3/2005 5/3/2005 7/3/2005 9/3/ /3/2005 1/3/2006 3/3/2006 5/3/2006 7/3/2006 9/3/ /3/2006 1/3/2007 3/3/2007 5/3/2007 7/3/2007 9/3/ /3/2007 1/3/2008 3/3/2008 5/3/2008 7/3/2008 9/3/2008 Date

13 Comments on Historical Price volatility has been generally increasing over the study period (Jan 2005 to Aug 2008). Very tight stocks in grain markets during 2008 made for volatile markets, particularly spring wheat where quality concerns were paramount. The data indicate increasing participation by Indexers over the period. However, it is impossible to assign causality: Perhaps increased participation by these groups resulted in increased volatility. Alternatively, higher volatility may have attracted money from these groups into these markets. 13

14 Correlation Analysis Explanation Correlations were calculated between market volatility and the presence of each trader type. Market presence is calculated as the percentage of all open interest held by a trader group on a particular day. Historical volatility was calculated using the most recent 10-day period and then annualized. The number of observations was large (daily data for every contract between Jan 2005 and Aug 2008) which means that even very low correlations are statistically significantly different from zero. 14

15 Correlation Matrix, Corn appears positively related to the presence of three of the four large trader groups, but is negatively related to the presence of small traders. Index traders and money managers are unique in that their market presence tends to move together. Commercial Non-Commercial Indexer Money Mgr Small Trader 1.00 Commercial Non-Commercial Indexer Money Mgr Small Trader

16 Correlation Matrix, Soybeans appears positively related to the presence of all of the large trader types except money managers. Index traders and money managers are unique in that their market presence tends to move together. Commercial Non-Commercial Indexer Money Mgr Small Trader 1.00 Commercial Non-Commercial Indexer Money Mgr Small Trader

17 Correlation Matrix, Chicago Wheat appears positively related to the presence of index traders and money managers. A large small trader presence is associated with reduced volatility. Commercial Non-Commercial Indexer Money Mgr Small Trader 1.00 Commercial Non-Commercial Indexer Money Mgr Small Trader

18 Correlation Matrix, KC Wheat appears positively related to the presence of index traders and money managers. Index traders and Money Managers tend to be present together in the market. A large small-trader presence is associated with reduced volatility. Commercial Non-Commercial Indexer Money Mgr Small Trader 1.00 Commercial Non-Commercial Indexer Money Mgr Small Trader

19 Correlation Matrix, Minneapolis Wheat appears positively related to the presence of large commercials, index traders and money managers. However, index traders played only a minor role in this market. The high negative correlation between commercial and small trader presence suggests that commercials are the dominate large trader group when they are absent, small traders fill the gap. Commercial Non-Commercial Indexer Money Mgr Small Trader 1.00 Commercial Non-Commercial Indexer Money Mgr Small Trader

20 Correlation Matrix, Cotton appears positively related to the presence of index traders and money managers, but the relationship is weak. Index traders and money managers tend to expand their presence together. The high negative correlation between commercial and other trader groups suggests that commercials are the dominate large trader group when they are absent, these other traders fill the gap. Commercial Non-Commercial Indexer Money Mgr Small Trader 1.00 Commercial Non-Commercial Indexer Money Mgr Small Trader

21 Correlation Matrix, Natural Gas appears positively related to the presence of money managers, but negatively related to the presence of Index traders. In both cases the relationship is weak. Contrary to the agricultural markets, index trader and money manager presence displayed an inverse relationship. Money managers and Indexers are likely to fill the gap when commercial presence is reduced. Commercial Non-Commercial Indexer Money Mgr Small Trader 1.00 Commercial Non-Commercial Indexer Money Mgr Small Trader

22 Correlation Matrix, Crude Oil appears positively related to the presence of non-commercials and money managers, but the relationship is weak. Contrary to the agricultural markets, index trader and money manager presence displayed an inverse relationship. Commercials are the dominant group and growth in their presence tends to be inversely related to the presence of other groups. Commercial Non-Commercial Indexer Money Mgr Small Trader 1.00 Commercial Non-Commercial Indexer Money Mgr Small Trader

23 Part 3 Summary There are few clear patterns in correlation across commodities. For some commodities, the presence of index traders and money managers tends to be associated with higher volatility (corn, wheat, cotton) but for other commodities this association is not as clear (crude oil, natural gas). In most of the agricultural markets, index traders and money managers appear to be present together. This could be because money managers tended to favor the same go long strategy that indexers used over this period. 23

24 Part 3 Summary There is nothing in this analysis that points to the conclusion that index traders and/or money managers cause higher volatility. Instead, fundamental conditions (tight supplies, strong demand expectations) might have attracted certain trader types such as money managers who require volatility in order to profit. 24

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