Index Funds Do Impact Agricultural Prices

Paper prepared for the workshop “Understanding Oil and Commodity Prices” organized by the Bank of England, the Centre for Applied Macroeconomic

Analysis, Australian National University, and the Money, Macro and Finance Study Group, London, 25 May 2012

Christopher L. Gilbert and Simone Pfuderer(University of Trento)

Outline• Why analyze agricultural contracts and prices?• Financialization of agricultural markets• Methodology and data• Results: Sanders and Irwin (2011) revisited• Results: Analysis of less liquid markets• Conclusions

Why analyze agricultural contracts and prices …

… and particularly soybean oil and livestock contracts?• Question we are interested in: Do index

positions impact prices?• Strong contemporaneous relationship but it is

uninformative about direction of causality• Standard tool for analyzing causal

relationships is Granger-causality analysis• Relies on lagged effects

Why analyze agricultural contracts and prices?

• No evidence of Granger causality in literature (e.g. Sanders and Irwin 2011)

• Not surprising in liquid markets given Efficient Market Hypothesis

• Need to look in less efficient (i.e. less liquid) markets

• If Granger-causality is found there it is likely that causality is present in liquid markets – both agricultural and others - but not detectable with Granger-causality tests

Financialization of agricultural markets

Financialization of agricultural markets

Financialization• Major influx of “non-commercial” players into

the futures markets for agricultural commodities

• “Non-commercials” have no direct exposure to the price of the physical

• Most of focus has been on index investors, a special type of non-commercial player

Financialization of agricultural markets

Index-based investment• Index investors hold portfolios of commodity

futures contracts• Aim is to replicate returns on a tradable

commodity futures index - mainly S&P GSCI and Dow Jones-UBS

• Motivated by standard Markowitzian portfolio diversification arguments (Stoll and Whaley, 2010).

• Index investors are new “non-commercials” actors that differ from conventional speculators

Financialization of agricultural markets

Index-based investment• Index investors differ from conventional

speculators.

Index investors Conventional speculatorsHold all commodities in the index

Hold selected commodities

Are always long May be long or shortHold for long periods of time

Hold for short period of times

Roll as contracts approach expiry

Seldom roll

Financialization of agricultural markets

Index-based investment

Source: CFTC, Supplemental Commitment of Traders report

Financialization of agricultural markets

Financialization and prices• Concerns that agricultural prices are being driven

by factors unrelated to physical market fundamentals

• Finance literature demonstrates that large trades can impact prices (e.g. Scholes (1972), Shleifer (1986) and Holthausen et al. (1987)

• These impacts may either be transient, permanent, or, more generally, part transient and part permanent (e.g. de Jong and Rindi (2009))

Financialization of agricultural markets

Financialization and pricesEmpirical findings in grain markets:• Some find that financial factors were partially

responsible for the 2007-08 grains spike (e.g. Gilbert (2010a,b))

• Others don’t find any evidence that financial factors impacted agricultural prices ( e.g. Sanders and Irwin (2010, 2011))

• We revisit Sanders and Irwin (2011). We argue that, although their analysis is correct for the liquid markets they consider, extension to less liquid markets qualifies their findings.

Methodology and data

Methodology and data

Granger-causality analysisTwo basic components (Granger, 1969):• The cause appears before the effect - variables in the

future cannot influence variables in the past lagged candidate causal variable• The cause contains information not available

elsewherelagged candidate causal variable is useful in forecasting the causal variable

Methodology and data

Granger-causality test

where rjt is the logarithm of the return for commodity j in period t and xj,t-1 is a measure of the change in futures position in period t-1 and ujt is a disturbance.

The Granger-causality test is the test of H0 : β = 0

Granger Causality test with one lag (lagged dependent and independent variable):

Methodology and data

Granger-causality test

where rjt is the logarithm of the return for commodity j in period t and x j,t-1 is a measure of the change in futures positions in period t-1 and v jt is a disturbance.

The Granger-causality test is the test of the hypothesis H0 :

Granger Causality test with n lags (lagged dependent and independent variable):

Methodology and data

Position data• The CFTC publishes weekly Commitments of Traders

(COT) reports.• Published on Fridays, contains a breakdown of the

previous Tuesday’s open interest into different categories.

• COT Supplemental Reports, also published weekly, breakdown into commercial, non-commercial and index provider (CIT) positions.

Methodology and data

Position variablesWe use the same two variables as Sanders and Irwin (2011):Absolute measure of index positionsnet long position of index traders i.e. long contracts minus short contracts held by index tradersNormalized measure of index positionsindex trader long positions divided by the total long positions in the market

Methodology and data

Price data and variable• Price data: daily closing prices from Norma’s

Historical Data• Tuesday to Tuesday price changes since position data

is available for Tuesdays• Returns are contract-consistent, i.e. exclude roll

returns• Use log returns

Results: Sanders and Irwin (2011) revisited

Results: Sanders and Irwin (2011) revisited

Contracts analyzed• Corn - Chicago Board of Trade (CBOT)• Soybeans – CBOT• Wheat – CBOT• Wheat – Kansas City Board of Trade (KCBT)• Sample:

Sanders and Irwin (2011): 6 Jan 04 to 1 Sep 06Our sample: 3 Jan 06 to 27 Dec 2011Sanders and Irwin argue 2004-06 data crucial for their analysis

Results: Sanders and Irwin (2011) revisited

Sanders and Irwin (2011) revisited

Sanders and Irwin (2011) Our analysis

Sample 6 Jan 04 to 1 Sep 09 3 Jan 06 to 27 Dec 11Absolute [0.413] [0.048]**Normalized [0.103] [0.035]**Absolute [0.446] [0.171]Normalized [0.171] [0.068]*Absolute [0.841] [0.232]Normalized [0.402] [0.703]Absolute [0.895] [0.689]Normalized [0.384] [0.616]

Sanders and Irwin (2011) Granger-causality analysis revisited

The table reports the p-value for the Granger-non-causality tests that index returns do not Granger-cause price returns. Rejections at the 5% level are denoted by **.

CBOT corn

CBOT soybeans

CBOT wheatKansas wheat

Results: Sanders and Irwin (2011) revisited

Efficient markets and Granger causality

• Semi-strong form of the Efficient Markets Hypothesis (EMH, Fama, 1965) suggests that the lack of evidence in the grains market could be due to limitations of the methods in liquid markets

• Prices should not be forecastable from publically available information

• Lagged index investor position changes should not predict current futures price changes

• This suggests extending the analysis to less liquid markets where EMH may not apply so tightly

Results: analysis of less liquid markets

Results: analysis of less liquid markets

Less liquid markets• Soybean oil - CBOT• Feeder cattle – Chicago Mercantile Exchange

(CME)• Live cattle – CME• Lean hogs – CME

Results: analysis of less liquid markets

Less liquid markets

CBOT corn CBOT soybeans CBOT wheat KCBT wheat

3 January 2006 996,901 364,625 339,284 143,5801 September 2009 1,262,635 526,575 390,847 103,02627 December 2011 1,558,918 639,929 451,421 141,900

CBOT soybean oil

CME feeder cattle

CME live cattle CME lean hogs

3 January 2006 195,952 38,228 225,130 132,4151 September 2009 269,212 32,245 292,765 175,21827 December 2011 334,218 37,346 396,315 281,093

Source: CFTC Supplemental Commitments of Traders Reports.

All open interest for grain and livestock contracts

The table reports all open interest on three dates: 3/1/06 (the initial date of the sample available to us), 1/9/09 (the final date in the sample employed by Sanders and Irwin (2011)) and 27/12/11 (the final date of our sample).

Additional contracts analyzed

Contracts included in Sanders and Irwin (2011)

Results: analysis of less liquid markets

Results: soybean oil market

The table reports the p-values for the Granger-non-causality tests that index returns do not Granger-cause price returns. Rejections at the at the 5% level are denoted by** and those at the 1% level by ***.

Granger-causality test p-value

Absolute CIT positions

3 [0.018]**

Normalized CIT positions

1 [0.009]***

1 [0.229]

Absolute soybean CIT

2 [0.026]**

Normalized soybean CIT

1 [0.005]***

Granger-causality test results (CIT positions) for soybean oil

Effect variable

Candidate causal variable

Lag

CBOT soybean oil price returns

Absolute soybean oil CIT

3 [0.028]**

Normalized soybean oil CIT

Results: analysis of less liquid markets

Results: livestock markets

The table reports the p-values for the Granger-non-causality tests that index returns do not Granger-cause price returns. Rejections at the at the 10% level are denoted by* and those at the 5% level by**.

Granger-causality test p-value

Lean hogs

Absolute CIT positions lean

1 [0.646]

Normalized CIT positions

4 [0.043]**

Live cattle

Absolute CIT positions live

2 [0.053]*

Normalized CIT positions

3 [0.045]**

Granger-causality test results (CIT positions) for livestock markets

Effect variable

Candidate causal variable

Lag

Feeder cattle

Absolute CIT positions

1 [0.523]

Normalized CIT positions

2 [0.229]

Results: analysis of less liquid markets

Granger-causality in less liquid markets

• Strong evidence that index positions in the soybean complex Granger-cause soybean oil price returns

• Granger-causality tests also show Granger-causality the livestock contracts

• The evidence is strong for live cattle and weak for lean hogs contracts

Index Funds also Impact Metals and Energy Prices

The CFTC does not publish weekly data on CIT positions in crude oil.Metals are a “London commodity”. The LME does not publish any CIT information.We have constructed a weekly volume index of CIT positions across all US agricultural markets.If CIT allocations are relatively constant across all markets, this is a surrogate for total CIT positions.

This index correlates well with energy and metals price changes

WTI (left) and LME copper (below)

Correlations for contemporaneous changes are around 0.4 and stable over time.

Granger-Causality TestsTest statistic Tail probability

WTI F2,305 = 5.93 0.003Aluminium F2,305 = 8.15 < 0.001Copper F2,305 = 6.07 0.003Nickel t = 2.26 0.024Lead t = 1.99 0.047Tin t = 3.13 0.002Zinc F2,305 = 4.53 0.012

Granger-causality is established in each case (either with 1 or 2 lags). In this case, results are clearer for the more liquid markets.

Conclusions• Granger-causality tests rely on the ability of lagged position

changes to predict price changes• Might not be an effective tool in the analysis of asset

returns in liquid markets as these markets are relatively efficient

• We have added less liquid markets (soybean oil, feeder cattle, live cattle and lean hogs)

• We find clear evidence that index investment does affect returns in these less liquid markets.

• There is also evidence (not in this paper) for effects in the metals and energy markets.

Conclusions• If index investment activity impacts less liquid

agricultural futures markets, we conjecture that it also has an impact in the more liquid markets

• However, it is not possible to say how important this impact has been during the recent price spikes

Thank you for your attention!

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