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

2. 2 Table of Contents SectionSlide Number Objective and Approach3 Volatility Graphs4-13 Correlation Analysis14-22 Summary23-24

3. 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. Volatility calculated as a ten-day rolling volatility, expressed as a percentage.

4. 4 Volatility 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. 10-day Volatility = 1

5. 5 Historical Volatility, Corn

6. 6 Historical Volatility, Soybeans

7. 7 Historical Volatility, Chicago Wheat

8. 8 Historical Volatility, Kansas City Wheat

9. 9 Historical Volatility, Minneapolis Wheat

10. 10 Historical Volatility, Cotton

11. 11 Historical Volatility, Natural Gas

12. 12 Historical Volatility, Crude Oil

13. 13 Comments on Historical Volatility 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 2005-2006 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.

14. 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.

15. 15 Correlation Matrix, Corn Volatility 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.

16. 16 Correlation Matrix, Soybeans Volatility 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.

17. 17 Correlation Matrix, Chicago Wheat Volatility appears positively related to the presence of index traders and money managers. A large small trader presence is associated with reduced volatility.

18. 18 Correlation Matrix, KC Wheat Volatility 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.

19. 19 Correlation Matrix, Minneapolis Wheat Volatility 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.

20. 20 Correlation Matrix, Cotton Volatility 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.

21. 21 Correlation Matrix, Natural Gas Volatility 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.

22. 22 Correlation Matrix, Crude Oil Volatility 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.

23. 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.

24. 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.