Price dynamics and
financialization effects in
corn futures markets with
heterogeneous traders
ZEF-IFPRI Workshop on Food Price
Volatility and Food Security
8/9 July 2014, Bonn
Stephanie Grosche, Thomas Heckelei
University of Bonn
Index funds remove market entry barriers for
new types of commodity traders
Futures market
Trader types
Speculators
Portfolio
managers
Index
fund
Commercial traders
(Source: CFTC)
9 July 2014
Grosche, Heckelei, ILR, University of Bonn
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Research question
How are price dynamics on corn futures
markets affected by the availability of index
funds?
9 July 2014
Grosche, Heckelei, ILR, University of Bonn
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Methodology
Few-type heterogeneous agent model (HAM):

Behavioral price process
X
Structural model of observable price levels and volatilities
1
Estimate parameters with simulated method of moments
(SMM) using base period before 2006
2
Financialization scenario simulates market entry of portfolio
managers trading via index funds
9 July 2014
Grosche, Heckelei, ILR, University of Bonn
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Prices emerge sequentially from supply/demand
disequilibria
Period t
Volume
trader type 1
Period t+1
Demand
Price (P)
Excess
demand
Volume
trader type 2
Excess
supply
…
Volume
trader type N
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P
Supply
Grosche, Heckelei, ILR, University of Bonn
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Trading volume of commodity market agents
is determined by their behavioral functions
Commodity traders
Stylized strategy
Behavioral model
Commercials
Fundamentalist
Trade
 COon difference
( PF  Pt )between market
  tCO
price and fundamental price
Speculators
Chartist
Portfolio managers
Weighted combination
(via index funds)
Short-selling
constraint
9 July 2014
S
Trade
this
 S on difference
( Pt  Pt 1 between
)
 and
t
last period’s market price
[ ( P  P ) 
Trade
 PMon bothF viaFindext funds
  tPM
Reaction
coefficients
Independent
stochastic
effects
C ( Pt  Pt 1 )]
Grosche, Heckelei, ILR, University of Bonn
Reaction function
6
The short-selling constraint is a result of the
index fund’s replication model
Portfolio manager
Index funds
Replication volume
Commodity share
Total futures position
1
Replication volume
Commodity share
Futures market
Long
Long
Short
Short
2
1
Total futures position
1
Net futures position
2
Net futures position
Long
Long
Short
Short
2
1
2
Short-selling constraint
9 July 2014
Grosche, Heckelei, ILR, University of Bonn
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Market weights of trader types depend on relative
attractiveness of their stylized strategies
Determinants of
attractiveness
Relative strategy
attractiveness
Herding
Traders’ response
Commercials
Speculators
Fundamentalist
Predisposition
Chartist
Price
misalignment
9 July 2014
Portfolio managers
Fundamentalist
Grosche, Heckelei, ILR, University of Bonn
Chartist
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The market prices emerge from the interaction of
fundamentalist and chartist volumes
Trading volume (period t)
Price impact (period t+1)
Demand
Excess demand
P
P
P
P
#
F
C
F
C
…
C
…
C
F
F
Market maker reduces
excess demand
Supply
1
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2
3
…
T-1
2
3
Grosche, Heckelei, ILR, University of Bonn
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…
T
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Price, volatility and volume development
(Source: Bloomberg)
9 July 2014
Grosche, Heckelei, ILR, University of Bonn
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Price, volatility and volume development
(Source: Bloomberg)
9 July 2014
Grosche, Heckelei, ILR, University of Bonn
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Parameter estimation
Reaction coefficients, parameters determining strategy
attractiveness (predisposition, reaction to herding,
reaction to price misalignment), variances of stochastic
volume components
Weight matrix uses estimated covariance matrix of
moments
9 July 2014
Grosche, Heckelei, ILR, University of Bonn
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Model validation
Generally compares empirical sampling distribution of
moments with distribution generated by the model
Percentage coverage of distributions (“p-values”)
Relative entropy
9 July 2014
Grosche, Heckelei, ILR, University of Bonn
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Base results
overview
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Grosche, Heckelei, ILR, University of Bonn
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Base results
volatility effects
9 July 2014
Grosche, Heckelei, ILR, University of Bonn
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Financialization scenarios
Here, only scenario (1) results are presented
9 July 2014
Grosche, Heckelei, ILR, University of Bonn
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Position holdings (scenario 1)
9 July 2014
Grosche, Heckelei, ILR, University of Bonn
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Price level effects (scenario 1)
9 July 2014
Grosche, Heckelei, ILR, University of Bonn
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Volatility effects (scenario 1)
9 July 2014
Grosche, Heckelei, ILR, University of Bonn
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Conclusions
Base period: higher reaction coefficients decrease
persistence of price deviations from fundamental value
Financialisation scenario: price levels are not inflated,
fluctuation even more closely around fundamental
value
Volatility increased as the new stochastic volume of
portfolio managers transports new information from
other markets
Effect of short-selling constraint minimal, certainly not
in the direction of increasing
9 July 2014
Grosche, Heckelei, ILR, University of Bonn
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Further research plans
Development and analysis of policy scenarios, e.g.
 position limits
 transaction taxes
 price limits/bands
Model extensions to physical market
9 July 2014
Grosche, Heckelei, ILR, University of Bonn
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Reference
For more details and full references see discussion paper under
http://www.ilr.uni-bonn.de/agpo/publ/dispap/download/dispap14_05.pdf
9 July 2014
Grosche, Heckelei, ILR, University of Bonn
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BACKUP
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Grosche, Heckelei, ILR, University of Bonn
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