Ongoing Interagency and CFTC 

Agricultural Advisory Committ

July 29, 2008

Jeffrey H. Harris

Chief Economist
Office of the Chief Eco

•

Research Objectives

–

Provide the Commission with info

knowledge base 

–

Systematic analysis of market func

–

Utilize statistical and econometric 

•

Support Role

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Interface with Market Surveillance 

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Provide independent advice for En

–

Expert testimony (primary and sec

opinions)
Interagency Task Fo

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Partner with USDA, Treasury, F

Reserve Board of Governors, FeCommission, and SEC 

•

Objective

–

Examine conditions in the commo

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Price concerns, Volatility Conce
Interim study of Crude Oil 

•

Demand

•

Supply

•

Macro factors

•

Functioning of Futures markets 2003

–

Role of various traders and speculators

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Term structure clues

•

Statistical Tests

–

Correlations (price changes and position 

•

Not consistent over time

–

“Granger causality”

•

Little evidence
Commodity Index Tr

•

Mimic returns on broad class of 

–

Similar to stock index funds

–

Purchased by pension funds, etc.

–

Identified separately in 12 agricult

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Flows estimated to be $100+Billio

•

Portfolio Theory

–

Enhance return/risk when added to 

stocks and bonds

Commodity Index Tra

•

Little evidence that CIT positions aff

–

No correlation between CIT market shar

•

price changes or

•

volatility 

–

Supply of futures contracts is not fixed

•

Some evidence that CIT positions red

conditional volatility (ARCH models 

–

Rolling behavior provides liquidity
Net Long Positions of CIT's for Commodities in the CO

All Futures and Options (Delta Adjusted) Combined on 

-100

100300500700900

11001300150017001900

CBT WHEAT HOGS CATTLE SUGAR COFFEE COTTON FEEDERS SOYBEANS KCBT WHEAT SOYBE

Thousand Contracts .

CIT Net AFOC Long Positio

% of AFOC Open Interest
Other Types of Specul

•

CFTC data identifies daily positions o

–

Managed Money Traders (MMTs)

•

Hedge Funds

• C

P

O

s

• C

T

A

s

–

Floor Brokers/Traders

–

Other Non-reporting Traders

•

Most Non-commercial traders are spr

–

Long September, Short December, for in

•

Do these “speculators”

have an overa

prices?
Net Speculative Positions (% of Open 

13-

N

o

v

26-

F

e

b 

C

r

ude Oil 5% 6% 

G

a

s

olin

e 23% 20% 

He

a

tin

g Oil 12% 13% 

Na

t

u

r

al G

a

s

-

5

%

-9%

Ele

ctricity 1% 1% 

Gold 34% 41% Su

g

a

r 9% 21% 

Co

c

o

a 35% 32% 

Co

f

f

e

e 21% 25% 

FC

O

J 21%

5

% 

Co

t

t

o

n 24% 27% 
Other Speculators

•

Some positive correlations betwe

daily position changes and price 

–

Causation?

•

Little evidence of Granger causa

•

Herding among MMTs

–

Counter-cyclical: 

•

Buy herding when prices rise

•

Sell herding when prices fall
THOUSAND CONTRACTS DOLLARS P

1/31/182/12/153/23/163/304/164/305/145/296/126/267/117/258/88/229/69/2010/4 10/1811/1 11/15 11/30 12/1

0

100200300400500

0

-100-200-300-400-500-600-700-800 

CBOT Corn Market Composition January 2007 Thro

Reportable AFOC Net Daily Positio

COMMERCIAL MERCHANTS = PHYSICAL HANDLER OF GRAINMANAGED MONEY FUNDS = REGISTRATION CODE OF CTA, CPO, OR MANAGED MONEYCIT = COMMODITY INDEX TRADER

Commercial Merchants Managed Money Funds CIT C
THOUSAND CONTRACTS DOLLARS PE

1/31/182/12/153/23/163/304/164/305/145/296/126/267/117/258/88/229/69/2010/4 10/1811/1 11/15 11/30 12/14 12

0

50

100150200250

0

-50

-100-150-200-250 

CBOT Wheat Market Composition January 2007 Throu

Reportable AFOC Net Daily Position

COMMERCIAL MERCHANT = PHYSICAL HANDLER OF GRAINMANAGED MONEY = USE CODE OF CPO/CTA/MMCIT = COMMODITY INDEX TRADER

Commercial Merchants Managed Money CIT CBO
Summary

• Strong worldwide demand leadin

prices

• Little evidence that speculative t

groups systematically leading to prices

• Some evidence that CIT position

volatility
For Further Study

•

How do open interest and prices re

buying and/or selling pressure?

•

What is interaction between future

market?

•

What about CIT positions in non-a

–

Special Call results due in Septembe

•

Basic Science—Are CFTC classifi

useful?