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Index Funds Do Impact Agricultural Prices Paper prepared for the workshop Understanding Oil and Commodity Prices organized by the Bank of England, the.

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Presentation on theme: "Index Funds Do Impact Agricultural Prices Paper prepared for the workshop Understanding Oil and Commodity Prices organized by the Bank of England, the."— Presentation transcript:

1 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)

2 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

3 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

4 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

5 Financialization of agricultural markets

6 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

7 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

8 Financialization of agricultural markets Index-based investment Index investors differ from conventional speculators.

9 Financialization of agricultural markets Index-based investment Source: CFTC, Supplemental Commitment of Traders report

10 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))

11 Financialization of agricultural markets Financialization and prices Empirical findings in grain markets: Some find that financial factors were partially responsible for the grains spike (e.g. Gilbert (2010a,b)) Others dont 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.

12 Methodology and data

13 Methodology and data Granger-causality analysis Two 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 elsewhere lagged candidate causal variable is useful in forecasting the causal variable

14 Methodology and data Granger-causality test where r jt 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 position in period t-1 and u jt is a disturbance. The Granger-causality test is the test of H 0 : β = 0 Granger Causality test with one lag (lagged dependent and independent variable):

15 Methodology and data Granger-causality test Granger Causality test with n lags (lagged dependent and independent variable):

16 Methodology and data Position data The CFTC publishes weekly Commitments of Traders (COT) reports. Published on Fridays, contains a breakdown of the previous Tuesdays open interest into different categories. COT Supplemental Reports, also published weekly, breakdown into commercial, non-commercial and index provider (CIT) positions.

17 Methodology and data Position variables We use the same two variables as Sanders and Irwin (2011): Absolute measure of index positions net long position of index traders i.e. long contracts minus short contracts held by index traders Normalized measure of index positions index trader long positions divided by the total long positions in the market

18 Methodology and data Price data and variable Price data: daily closing prices from Normas 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

19 Results: Sanders and Irwin (2011) revisited

20 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 06 Our sample: 3 Jan 06 to 27 Dec 2011 Sanders and Irwin argue data crucial for their analysis

21 Results: Sanders and Irwin (2011) revisited Sanders and Irwin (2011) revisited

22 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

23 Results: analysis of less liquid markets

24 Results: analysis of less liquid markets Less liquid markets Soybean oil - CBOT Feeder cattle – Chicago Mercantile Exchange (CME) Live cattle – CME Lean hogs – CME

25 Results: analysis of less liquid markets Less liquid markets

26 Results: analysis of less liquid markets Results: soybean oil market

27 Results: analysis of less liquid markets Results: livestock markets

28 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

29 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.

30 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.

31 Granger-Causality Tests Test statisticTail probability WTIF 2,305 = AluminiumF 2,305 = 8.15< CopperF 2,305 = Nickelt = Leadt = Tint = ZincF 2,305 = Granger-causality is established in each case (either with 1 or 2 lags). In this case, results are clearer for the more liquid markets.

32 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.

33 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

34 Thank you for your attention!

35 References de Jong, F., and B. Rindi (2009), The Microstructure of Financial Markets, Cambridge, Cambridge University Press. Fama, E.F. (1965), The behavior of stock market prices, Journal of Business, 38, Gilbert, C.L. (2010a), How to understand high food prices, Journal of Agricultural Economics, 61, Gilbert, C.L. (2010b), Speculative influence on commodity prices , Discussion Paper 197, UNCTAD, Geneva. Granger, C. W. (1969), Investigating Causal Relations by Econometric Models and Cross-spectral Methods, Econometrica 37 (2), Holthausen, R.E., R. Leftwich and D. Mayers (1987), The effects of large block transactions on security prices: a cross-sectional analysis, Journal of Financial Economics, 19, Sanders, D. R. and S.H. Irwin (2010), A speculative bubble in commodity futures prices? Crosssectional evidence, Agricultural Economics, 41, Sanders, D. R. and S.H. Irwin (2011), New Evidence on the impact of index funds in U.S. grain futures markets, Canadian Journal of Agricultural Economics, 59, 519–32. Scholes, M. (1972), The market for securities: substitution versus price pressure and the effects of information on share prices, Journal of Business 45, Shleifer, A. (1986), Do demand curves for stocks slope down?, Journal of Finance 41, Stoll, H.R., and R.E. Whaley, (2010), Commodity index investing and commodity futures prices, Journal of Applied Finance, 20, 7-46.


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