Betting on Food: Which Role for Market Fundamentals in Grain Price Boom

Transcription

1 117 th EAAE Seminar; November ; University of Hohenheim Betting on Food: Which Role for Market Fundamentals in Grain Price Boom Federico Ciani PHD Programme in Development Economics University of Florence

2 Outline of the Presentation 1. What happened to cereal prices in Structural weaknesses of world cereal markets 3. Possible contingent causes of the cereal price boom both on the demand and the supply side 4. The role of financial speculation 5. The role of supply and demand fundamentals 6. Conclusions

3 What About Cereal Prices? Prices reached a peak between February and July 2008 almost doubling their 2007 value Then prices decreased even if they remained still high

4 World Market: Structural Weaknesses Stagnation of per capita production after 2000 Intensive growth of production but Strong regional differences SSA and ex- USSR countries Low tradability of cereals Thin Markets

5 Contingent Causes: the Supply Side Low stocks prices under pressure Bad performances of strategic big net exporters (e.g. Australia) due to climatic aleas Climate Change Oil spills over grain prices fertilizers transport costs Explicit policy measures e.g. export bans or restrictions in India, Ukraine etc.

6 Contingent Causes: the Demand Side Who will feed China (and India)? Until now China and India themselves (Alexandratos [2008]) Biofuels: no agreement about their role cereals become a petro-commodity but empirical analyses give different results U.S. Dollar depreciation Quite obscure issue. Possible field for further analysis Financial speculation

7 The Role of Financial Speculation(i) Financial Markets Future Markets 2 important functions hedging risk mitigation price discovery Most transactions don t imply any material transaction call the margin Economic Theory: speculation is not a negative phenomenon Speculators drive prices towards their equilibrium Commercial traders and speculators are complementary Anyway speculative bubbles arise. Why? Various theories: rational bubbles, herd behaviour, irrational exuberance

8 The Role of Financial Speculation(ii) Sanders and Irwin (2009): it isn t possible to find any cross sectional impact of index investment on prices Gilbert (2008): no evidence of index investment effect (Granger Tests) Sanders et al. (2008): verify the existence of an excess of speculation on grain markets (Working s T). No evidence

9 The Role of Financial Speculation(iii) Robles, Torero and Von Braun (2009): strong evidence of speculation impact on food price boom (Granger Tests) Gilbert (2009): considers the price-speculation relationship in the wider framework of the causes food price boom. He concludes futures activity was the single most important factor which drove up agricultural prices in Caccavale (2009): put in place an event analysis, asserting that a connection exists between the perturbation of price formation processes and events such as panics, food riots etc..

10 The Role of Fundamentals (i) VARIATION IN SUPPLY AND DEMAND FUNDAMENTALS INFORMATION PROCESSED BY MARKETS IMPACT ON PRICES WASDE Reports presented by the USDA are considered in literature as one of the most important sources of information for market operators (Sumner et al.[1989]; Garcia et al. [1995]; Massa et al. [2008]) WASDE Monthly Reports concerning Estimates and Forecasts about Supply and Demand of Major Agricultural Products WASDE reports catch the effect of factors such as stock depletion, Chinese and Indian growing demand, biofuel production growth and climate change

11 The Role of Fundamentals(ii) Economic Theory agents are likely to take their decisions when they are more informed grain price variations are higher on days characterized by information releases Data: continuous series of future prices of the CBoT for corn, wheat and soybeans from 1979 to 2009 (8,089 observations per commodity) by DATASTREAM Average absolute daily price variation

12 avg price variation (%) The Relevance of WASDE Reports WASDE Report Release Impact ( ) release lag Corn Soybeans Wheat source: author s elaboration on data by DATASTREAM and USDA

13 TEST WASDE reports seems to have lost their ability to influence market operators during the period Not fully satisfying: Constant variance is a very long and etherogeneous time period So, what can be done?

14 An Alternative Approach Jones, Lamont and Lumsdaine (1998): use a GARCH(1,1) model to evaluate the impact of U.S. macroeconomic news releases on U.S. treasury bond returns In this case the model is based on two equations ( a) R ( b) h t t R 1 t 1 2 t 1 WASDE h 1 t 1 t WASDE WASDE t t 1 WASDE t t 1 Where: R t = returns (considered with their sign) ω and μ are two constant terms ε t is the disturbance term WASDE= dummy=1 if t corresponds to a WASDE dummy release h t is conditional variance heteroskedasticity robust s.e. (Bollerslev and Wooldridge[1992]) Data: the same as before but here focus on corn

15 ARCH and GARCH effects are significant WASDE effect is significant WASDE t+1 effect is significant ( calm after the storm effect )

16 Outlier Detection through the Model ^ 2 E ( T 1 IT ) ht 1 T where I t is the information set at time t made up by (i)arch effect; (ii)garch effects and (iii)wasde releases The model is estimated on three year subsamples starting from Jan1979-Dec1981 Each subsample is used to forecast the s.e. for the following month Through the s.e. we build a sort of confidence area as the value of returns in a given day of the following month will be included between ±1.96s.e.with a probability of 95% Then the observed returns are plotted with the upper and the lower border of the confidence area. If the real values are not included in this area they can be defined as outliers

17 Number of outliers by year source: author s elaboration on data by DATASTREAM and USDA a lower number of outliers would have been detected without considering WASDE releases

18 source: author s elaboration on data by DATASTREAM and USDA

19 Conclusions(i) WASDE reports have overall been considered a valid instrument by market agents During the period these reports lost their appeal to market agents as an information source Since 2008 (the most perturbed year for corn price trend) WASDE reports have no longer been a valid forecasting instrument So market agents seems to be influenced by factors not included in WASDE reports Why?

20 Conclusions (ii) U.S. dollar exchange rate and oil price not included in WASDE reports Index Investors (40-50% total open positions on CBoT) not (or less) interested by changes in demand and supply estimates interested in risk diversification Commodity Super Cycle Theory What about economic agents and institutions ability to cope with the potential impact of these long term trends? financial speculation induces a blowout between long term trends and present prices Prices changed before long term transformation had an effective impact on supply and demand

21 Conclusions (iii) This analysis is far from being exhaustive Anyway it seems hard to assert that the food price boom was caused by sharp variations of supply and demand estimates as described by the WASDE reports

22 Quis fortunatior, at stultior quoque rege Mida fuit? Nam Mida, avidissimus Phrygiae rex, a diis incredibilem facultatem obtinuit: quae tangebat, in aurum mutabat. [ ] Sed in aurum mutabat etiam cibos; qua re non solum victu carebat, sed suam felicitatem cito amisit. Who was such a lucky and a stupid man as king Mida? In fact Mida, the greed king of Frigia, got an incredible power from gods: he transformed in gold everything he touched [ ] But he transformed in gold food too. Consequently he was not only lacking in food, but he soon lost his happiness. Valerio Massimo

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