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10/4/2012
1
STOCKS-TO-USE RATIOS AS INDICATORS OF VULNERABILITY TO SPIKES IN
GLOBAL CEREAL MARKETS
Brian D. Wright
Department of Agricultural and Resource Economics College of Natural Resources
University of California, Berkeley
2nd Session of the AMIS Global Food Market Information Group
ROME, FAO HEADQUARTERS Wednesday, 3 October 2012
based on work presented in a paper of the same title:
STOCKS-TO-USE RATIOS AS INDICATORS OF VULNERABILITY TO SPIKES IN GLOBAL CEREAL
MARKETS
September, 2012
AMIS Paper IG-12/4
by
Eugenio Bobenrieth, Brian Wright, and Di Zeng
10/4/2012
2
Post- “Inside Job” I perceive a need for disclosure:
Recent or current grant support:
• AMIS initiative of G20
• Energy Biosciences Initiative (UC Berkeley, UIUC, LBL, BP, funded by BP) – researches cellulosic biofuels
• USDA
• NIH
• NSF
• USPTO
• Giannini Foundation
Disclosure (contd.) • Current consulting relationships:
– World Bank
– FAO
• No recent positions in commodity markets
• No investments in agricultural input or service providers, or significant commodity market or energy market participants.
• In past year, I was a consultant/expert witness engaged by an entity that produces and exports agricultural products. I was recently an expert witness in a case involving generic drug entry in pharmaceuticals
• I recently made a presentation at a major agricultural bank
• Last month I was compensated by a leading investment firm for a presentation at their head office
• I am happy to identify any of the firms involved in the above activities, should any audience member request that I do so.
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This presentation:
Focus: Needs of policy makers, not traders Time frame: • Crop year • Pre-biofuels market behavior*
*See Wright, B.D. Applied Economic Perspectives and Policy (2011) volume 33, number 1, pp. 32–58. doi:10.1093/aepp/ppq033 for effects of biofuels onmarkets
This presentation:
Questions: Are stocks data relevant to grain market shortages? Are grains stocks-to-use ratios useful additions to price and production information?
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Maize Real Price Deflator: Manufactures Unit Value (MUV)
Maize Real Price and Trend Deflator: Manufactures Unit Value (MUV)
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Co-movement: Real prices of wheat, rice, maize
and calories (natural logarithm scale)
Correlations: Real De-trended Price
Wheat Maize Rice Calories
Wheat 1.0000
Maize 0.7875 1.0000
Rice 0.5803 0.6280 1.0000
Calories 0.8318 0.8598 0.9133 1.0000
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• Assumption:
Price Spikes coincide with real shortages
First Question: Does price summarize all market
information?
• Is price a sufficient statistic of market conditions and outlook?
• If markets are “perfect,” yes
– Then we can all go home (with unemployment benefits?)
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Do prices predict spikes? Transition matrix for calorie price:
To (percentiles)
0-20 20-40 40-60 60-80 80-100
From
(percentiles)
0-20 0.500 0.250 0.000 0.250 0.000
20-40 0.300 0.300 0.200 0.200 0.000
40-60 0.111 0.222 0.333 0.222 0.111
60-80 0.000 0.300 0.100 0.200 0.400
80-100 0.111 0.000 0.333 0.222 0.333
The challenge for predicting spikes: Global data are problematic
• Price is at best an average of diverse prices consumers actually pay for grain consumed
• Production data vary in accuracy
• Stocks data are estimates, guesses and worse…
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Second Question If we had perfect production forecasts
one year ahead, would they explain what current prices do not reveal?
• Let’s take a look:
Do realized prices reflect current production? Was largest major grains production shortfall during 1970s
spikes?
Source: Calculated using Data from PSD Online, USDA.
Note. World grains = wheat + maize + milled rice. All quantities converted into Calories assuming, for wheat 3338Kcal per Kg,
for maize 3650 Kcal per Kg, and for milled rice 3656 Kcal per Kg).
2010/2011 is projection.
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Does Increasing Production Volatility explain spikes?Where is 1972/3? Shortfalls Increasing?
Source: Calculated using Data from PSD Online, USDA.
Third Question Can realized stocks add anything
realized production data cannot tell us?
• Let’s take a look:
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Maize Stocks/Use Ratios - with and without China
Maize Stocks/use ratio vs. de-trended real price
Inverse relation clear (cf. production)
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Rice: Stocks/Use Ratios - with and without China
Rice Stocks/use ratio vs. de-trended real price
Inverse relation less clear
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Calories: Stocks/use vs. de-trended real price
A clear inverse relation
Calorie stocks/use ratio has highest correlation with detrended real price of each major grain
Wheat
detrended real
price
Maize
detrended real
price
Rice
detrended real
price
Calories
detrended real
price
Wheat stock/use ratio
-0.4018 -0.4413 -0.3438 -0.4344
Maize stock/use ratio
-0.3971 -0.5034 -0.4356 -0.5156
Rice stock/use ratio
-0.2286 -0.2048 -0.1731 -0.2136
Calories stock/use ratio
-0.4996 -0.5723 -0.4729 -0.5792
Years: 1961-2007 Stocks/use ex-China
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Calorie stocks/use ratio has highest correlation with detrended real price of each major grain
Wheat
detrended real
price
Maize
detrended real
price
Rice
detrended real
price
Calories
detrended real
price
Wheat stock/use ratio
-0.4018 -0.4413 -0.3438 -0.4344
Maize stock/use ratio
-0.3971 -0.5034 -0.4356 -0.5156
Rice stock/use ratio
-0.2286 -0.2048 -0.1731 -0.2136
Calories stock/use ratio
-0.4996 -0.5723 -0.4729 -0.5792
Years: 1961-2007 Stocks/use ex-China
Can carryover stocks-use ratio help predict shortages?
Transition matrix for calorie SUR.
To (percentiles)
0-20 20-40 40-60 60-80 80-100
From
(percentiles)
0-20 0.625 0.125 0.125 0.125 0.000
20-40 0.200 0.300 0.300 0.200 0.000
40-60 0.222 0.333 0.111 0.333 0.000
60-80 0.000 0.300 0.200 0.100 0.400
80-100 0.000 0.000 0.222 0.333 0.444
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To understand relation of stocks to price spikes better, we need a little storage theory:
• Storers smooth out troughs in price and low-value consumption after high harvests by “buying low to sell high”
• Storers smooth expected shortages if cash is available:
– invest in stocks, raise current price, reduce expected shortage
Role of storage arbitrage
[ ( 1)]( ) cost of storage 0
1
[ ( 1)]( ) cost of storage 0
1
E P tP t if stocks
r
E P tP t if stocks
r
Key relations: Buy when low, sell when high
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Add demand for stocks (non-negative)
Demand for consumption plus stocks
Note: Not like a constant elasticity consumer demand
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Equivalent shocks
Demand for consumption
Market demand, inclusive of stocks
Quantity
Price
Different impact
on prices
With stocks
Without stocks
When stocks are low, price
becomes very sensitive to
disturbances in supply
Why is price much more sensitive to shocks when stocks are minimal?
Theory: Effects of storage.
• Storers smooth out peaks after unexpected shocks, but only until their stocks run out
• When stocks run out, price spikes are required, to force consumers to respond one-for-one to shocks
SPIKES OCCUR ONLY IF STOCKS ARE MINIMAL
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We estimated a model of each grain market using only price data
• Avoids using “bad” stocks data
• Follows estimation tradition in this area
– but used Max Likelihood approach
– Need detrended background model for consistency
– Messy details…
We got results like this, (for wheat)
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We got results like this, (for wheat)
• Then we reconstructed stocks and consumption at each price
We got results like this (for wheat)
e.g. RED/GREEN = Stocks/Use at Price=0.9
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Wheat SUR and reconstruction from model: - recalibrated for mean (to include essential working
stocks) and range
Evidence of relationship: De-trended price vs. SUR and SUR
reconstruction for Wheat
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Choice of “thresholds” is up to AMIS - one illustrative example relates to an
inflexion point:
Inflexion point 1.05
Rice SUR and reconstruction from model: - a less good fit
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Rice: De-trended price vs. SUR and SUR reconstruction
Problems!
Calorie SUR and reconstruction from model: - fits much better
muc=
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Evidence of useful relationship: De-trended price vs. SUR for Calories
SUR sometimes a better warning indicator than price (e.g. 1972)
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Conclusions
• Stocks data are useful additions to prices and production forecasts
• Econometric model helps explain market behavior as response to fluctuations in consumption demand and production as moderated by stockholding, without resorting to wild conjectures
• Grain calorie substitution is crucial to evaluating information on production, prices and stocks
Thanks to my colleagues:
• Carlo Cafiero, FAO
• Juan Bobenrieth, Universidad del Bio-Bio, Chile
• Yang Xie, ARE, University of California, Berkeley
• Josef Schmidhuber, FAO
• Phil Abbott, Purdue University
• Olivier Mahul, World Bank
• Will Martin, World Bank
• Wallace Tyner
• Marc Sadler, World Bank
• Stefan Tangerman
• Phil Verleger
• Jeff Williams, UC Davis
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Thanks to my coauthors:
• Professor Eugenio Bobenrieth
– Catholic University, Santiago, Chile
• Di Zeng – Doctoral Candidate, ARE, University of California,
Berkeley
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CALORIE PRICE TRANSMISSION MATRIX
To
80-100 60-80 40-60 20-40 0-20
From
80-100 0.500 0.250 0.000 0.250 0.000
60-80 0.300 0.300 0.200 0.200 0.000
40-60 0.111 0.222 0.333 0.222 0.111
20-40 0.000 0.300 0.100 0.200 0.400
0-20 0.111 0.000 0.333 0.222 0.333
Table 4. Correlation coefficient matrix between detrended real price and stock/use ratio
(ex-China), 1961-2007
Wheat
detrended real
price
Maize
detrended real
price
Rice
detrended real
price
Calories
detrended real
price
Wheat
stock/use ratio -0.4018 -0.4413 -0.3438 -0.4344
Maize
stock/use ratio -0.3971 -0.5034 -0.4356 -0.5156
Rice
stock/use ratio -0.2286 -0.2048 -0.1731 -0.2136
Calories
stock/use ratio -0.4996 -0.5723 -0.4729 -0.5792
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Correlations: Detrended real price and stock-to-use ratio,
Wheat
detrended real
price
Maize
detrended real
price
Rice
detrended real
price
Calories
detrended real
price
Wheat stock/use ratio
-0.4018 -0.4413 -0.3438 -0.4344
Maize stock/use ratio
-0.3971 -0.5034 -0.4356 -0.5156
Rice stock/use ratio
-0.2286 -0.2048 -0.1731 -0.2136
Calories stock/use ratio
-0.4996 -0.5723 -0.4729 -0.5792
Years: 1961-2007 Stocks/use ex-China
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Maize Real price and production:
deviations from trend
Rice Real price and production: Log deviations from trend