Determinants
of Agricultural and Mineral
Commodity Prices
Jeffrey A. Frankel,
Harvard University, &
Andrew K. Rose,
University of California, Berkeley
Reserve Bank of Australia, August 2009.
2
Determination of Prices for Oil and Other
Mineral & Agricultural Commodities
Predominantly microeconomic.
Still, difficult to ignore macroeconomic
influences sometimes.
Examples: many commodity prices
move far in same direction at the
same time:
The decade of the 1970s.
The decade of the 2000s.
3
►
Increase in
oil price
can
be explained by “peak oil”
fears, a risk premium on
Gulf instability, or political
developments in Russia,
Nigeria or Venezuela...
►
Some
farm prices
might be explained by
drought in Australia,
shortages in China, or
ethanol subsidies in the US.
4
But it
Cannot be Coincidence
that
almost all commodity prices rose
together during much of the decade,
and peaked abruptly in mid

2008.
Our Innovation
Combine
Macro and Micro
Determinants
of Commodity Prices
Hope: Get macro swings nested inside
well

grounded micro model
Need Good Micro Data on Determinants
of Individual Commodities
5
6
Three “Aggregate” Theories Explain the
Recent Rise of Commodity Prices
1.
Destabilizing Speculation
.
Storability & Homogeneity
=> Asset

like Speculation
2.
Monetary:
Low Real Interest Rates
Or High Expected Inflation
3.
Global Demand Growth.
Actual/Future Growth (China …)
Issues Exist with All Three
“Explanations”
In Theory, Speculation may be Stabilizing
Empirical Issues with All Three Theories
7
8
Counter

Evidence to Claims of
Destabilizing Speculation
1. Futures price of oil initially lagged behind
spot price.
2. High volume of trading
≠
net short position
3. Commodities that lack futures markets are
as volatile as those that have them.
4. Historical efforts to ban speculative futures
markets have failed to reduce volatility.
9
Monetary Explanation
1.
Some argue that high prices for oil & other
commodities in the 1970s were not exogenous,
but rather a result of easy monetary policy.
Perhaps inflation directly raises commodity prices? Commodities
may be an inflation hedge.
2.
Conversely, a rise in US real interest rates in the
early 1980s
.
helped drive commodity prices down
.
3.
The Fed cut real interest rates sharply,2001

04,
and again in 2008

09. Did this help push prices
first up, then down?
10
High Interest Rates in Theory
1.
Lower inventory demand;
and
2.
Encourage faster pumping of oil,
mining of deposits, harvesting of crops, etc.,
because owners can invest the proceeds at interest
rates higher than the return to saving the reserves.
3.
Both channels
–
fall in demand and rise in
supply
–
work to lower commodity price.
11
But … Counter

arguments Exist
Inventories of oil & other commodities said to
be low in 2008, contrary to the theory
(Krugman, Kohn)
Perhaps inventory numbers
do not capture all inventories, or
are less relevant than (larger) reserves.
King of Saudi Arabia (2008):
“we might as well leave the reserves
in the ground for our grandchildren.”
How Important are Monetary Effects?
12
Global Boom Theory Reasonable?
Sub

prime Mortgage Crisis
hit US, August 2007.
Thereafter, Growth Forecasts Fell Globally
But Commodity Prices did not
Decline
; their
rise actually
Accelerated
.
Quick Peek at Aggregate Data: Little
``
13
But Perhaps Too Macro?
Need to Control for Micro Determinants
of Commodity Prices
Our Objective:
Integrate
Micro and Macro
Commodity Price Determination
Theory
Empirical Estimation
14
15
“Overshooting” Theory of
Real Commodity Prices
s
≡ the spot price,
S ≡ its long run equilibrium,
p
≡ the economy

wide price index,
q ≡ s

p
, the real price of the commodity,
and
Q
≡
the long run equilibrium real price of
the commodity;
all in log form.
16
Derive Relationship for Real
Commodity from Two Equations:
Regressive Expectations (can be Rational):
E (Δs) =

θ (q

Q)
+
E(
Δp
)
“Arbitrage

like” condition links Inventories & Bonds:
E Δs + c =
i
where
c ≡ cy
–
sc
–
rp
.
cy
≡
convenience yield from holding the stock (e.g., the insurance value of
having an assured supply of a critical input in the event of a disruption)
sc
≡
storage costs (e.g., rental rate on oil tanks, etc.)
rp
≡
E Δs
–
(f

s)
≡
risk premium,
>0 if being long in commodities is risky, and
i
≡
the interest rate
17
Combining:
q

Q =

(1/
θ
) (i

E(
Δ
p
)
–
c)
This inverse relationship between q & r
has already been somewhat studied
Event studies
(monetary announcements)
Regressions of
q
against
r
in Frankel (2008):
Significant for half of the individual commodities
and in a panel study
and for various aggregate commodity price indices
But much is left out of this equation.
Especially variation in
c
18
Observable Manifestations of
Convenience Yield, Storage Costs, &
Risk Premium (c)
1. Inventories
Storage costs rise with inventory
Measured with World inventories where possible, US
otherwise
Could also estimate an inventory equation
19
Other Determinants
2. Real GDP
Transactions Demand for Inventories,
determinant of convenience yield
cy
Measured with real World GDP,
Also try OECD output gap, de

trend, G

7, IP …
3. The spot

futures spread,
s

f
High spread (“normal backwardation:) signifies
low speculative return, hence negative effect on
inventory demand and prices
Measurement more straightforward
20
Uncertainty Measures
4. Medium

term volatility
(σ)
Volatility a determinant of convenience yield,
and so of commodity prices
May also be determinant of risk premium
Measured as standard deviation of spot price
Can also extract implicit forward

looking expected
volatility from options prices
21
5. Risk
(political, financial, & economic)
Theoretical effect ambiguous:
Risk a determinant of
cy
(fear of
supply disruption), should have
a
positive
effect on commodity prices
Also a determinant of
rp
, risk
premium, should have a
negative
effect on prices
Measured (e.g., for oil) by weighted average of
(inverse) political risk for 12 top (oil) producers
Data availability issues; hence not always included
22
Complete Equation
q = Q

(1/
θ
) r
+
(1/
θ
)
γ
(Y) + (1/
θ
)Ψ
(
σ
)

(1/
θ
)
Φ (
INVENTORIES)

δ(
s

f
)
Objective: Determine (log) real commodity
price
3 Micro determinants
Volatility; spread; inventories
2 Macro determinants
World GDP; real interest rates
23
Estimation Strategy
Gather, use dis

aggregated data on 11
commodity panel
Annual data from 1960s to 2008
Commodities, span, frequency chosen to
maximize data availability
24
Booms around 1974

75 and 2008
25
Table 3a: Panel Results,
for log
real commodity prices,
Ln
(World
Real GDP)
Volatility
Spot

Futures
Spread
Inven

tories
Real
US
interest
rate
.60
2.29**

.003*

.15**

.01
(.27)
(.40)
(.001)
(.02)
(.01)
** (*) => significantly different from zero at .01 (.05) significance level.
Robust standard errors in parentheses; Intercept & trend included, not reported.
Results Seem Sensible
Micro Factors all “correctly” signed
Statistically significant
Macro Factors correctly signed
World GDP: statistically marginal effect
Real Interest Rate
consistently unreliable
Biggest Disappointment
26
Results Also Robust
Results insensitive to exact econometric
specification, model of world activity
Many variants reported in Table 3a
Results from first

differences in Table 3b
Possibly relevant because of (lack of) co

integration
27
Reasonable Fit to Data
28
29
Table 4: To Look for Bandwagon
Expectations, Add Lagged Rate
of Commodity Price Rise
Ln
(World
Real GDP)
Volatility
Spot

Futures
Spread
Inven

tories
Real
US
interest
rate
Lag of
Nominal
Price
Growth
.50
1.84**

.004**

.13**
.00
.0061**
(.27)
(.40)
(.001)
(.02)
(.01)
(.0005)
** (*) => significantly different from zero at .01 (.05) significance level.
Robust standard errors in parentheses; Intercept & trend included, not reported.
Bandwagon Effects!
Commodity Prices Positively,
Significantly affected by Lagged Growth
in
Nominal
Commodity Price
Small but Insensitive Effect
Another Inefficiency in Commodity
Markets?
Helps Explain Recent Run

Up (somewhat)
30
31
Table 5: To Look for Another
indicator of Monetary Ease,
Add Aggregate Inflation
Ln
(World
Real GDP)
Volatility
Spot

Futures
Spread
Inven

tories
Real
US
interest
rate
Inflation

2.11**
2.12**

.003**

.14**
.02
.082**
(.61)
(.27)
(.001)
(.02)
(.01)
(.015)
** (*) => significantly different from zero at .01 (.05) significance level.
Robust standard errors in parentheses; Intercept & trend included, not reported.
Inflation Effects!
Commodity Prices Positively,
Significantly affected by Inflation
Again: Robust Results, but Small
Probably negligible effect for conduct of
monetary policy
Hedge against Inflation?
Doesn’t Explain Recent Run

Up
32
33
Other Tests: Indices
Construct Commodity Price Indices
Use 6 Weighting Schemes
Dow

Jones/AIG; S&P GCSI; CRB
Reuters/Jefferies;
Grilli

Yang; Economist; Equal
3 Different Periods of Time
Data availability => longer span has fewer
commodities
Similar (Weaker) Results
Micro work OK; poor real interest rate results
34
Other Tests: Hi

Tech
Unit root tests
Philips

Perron on individual commodities
Panel unit

root tests
Co

integration tests
Johansen on individual commodities
Panels too
Vector error correction results
35
Overall Model Performance
The commodity

specific explanatory factors
work (surprisingly) well:
Inventory holdings
Spot

futures spread
Volatility
Macroeconomic variables work (surprisingly)
poorly:
Economic activity
(Especially) Real interest rates
36
Possible Extensions
Survey data as direct measure of
expectations
Higher Frequency data (on fewer
commodities, shorter time

span)
Modeling non

linearities
Estimate simultaneous system in
inventories, expectations, and commodity
prices, tied directly to the theory
Conclusion
Model works reasonably:
Micro determinants work well
Macro phenomena
not
as important
Real growth raises real commodty prices
As does inflation
But real interest rate channel fails here.
Evidence of Bandwagon Effects
“Speculative Bubble” possible in Commodities
Helps explains 2007

9 boom and bust?
37
38
Appendices
Graphs of data
Why American interest rates?
Commodity

specific Results
Full Panel Results
39
40
41
42
43
Why American Real
Interest Rates?
Assume commodity markets integrated
If so, denomination doesn’t matter
Data availability issues for G

3/G

7
interest rates
Inevitable EMU issues
44
Table 2a: Commodity

Specific Results
Real World
GDP (+)
Volatility
+
Spot

Future
Spread (

)
Inventories
(

)
Real Interest
Rate (

)
Corn
1.53*
(.69)
1.52
(.89)

.003
(.003)

.18
(.17)

.01
(.02)
Copper
.03
(.68)
1.92**
(.54)

.005
(.003)

.21**
(.06_

.03
(.01)
Cotton
.66
(.85)
1.07
(.57)

.002
(.002)

.12
(.14)
.01
(.01)
Cattle
7.37**
(1.03)

.65
(.34)

.007
(.002)
2.37**
(.48)

.06**
(.01)
Hogs

.57
(1.64)
.64
(.71)

.004*
(.002)
.18
(.31)

.03**
(.01)
Oats
2.66**
(.71)
3.28
(1.69)

.006**
(.002)

.59**
(.11)

.02
(.01)
Oil
.05
(8.60)
.57
(1.69)

.003
(.003)

2.52
(5.02)

.01
(.07)
Platinum
1.22
(2.17)
1.78*
(.87)
.002
(.002)

.21**
(.03)
.08**
(.01)
Silver
2.69
(2.13)
3.32**
(.73)
.003
(.003)

.37*
(.18)
.01
(.03)
Soybeans
1.94**
(.70)
2.68**
(.55)

.001
(.002)

.05
(.07)

.01
(.01)
Wheat

5.98*
(2.79)
1.90**
(.47)
.008*
(.003)

1.42**
(.27)
.03
(.02)
45
Full Panel Results
Table 3a: Levels
Real World
GDP (+)
Volatility
(+)
Spot

Future
Spread
(

)
Inventories
(

)
Real Interest
Rate (

)
Basic
.60
(.27)
2.29**
(.40)

.003*
(.001)

.15**
(.02)

.01
(.01)
Drop Fixed Effects
.56
(.31)
2.65
(1.40)

.023**
(.006)

.20**
(.03)
.02
(.04)
Substitute Time
Effects
n/a
2.32
(1.80)

.026**
(.007)

.20**
(.01)
n/a
Time and Fixed
Effects
n/a
1.61**
(.29)

.002*
(.001)

.13**
(.01)
n/a
Drop Spread
.58
(.30)
2.36**
(.38)
n/a

.15**
(.02)

.01
(.01)
Growth (not log) of
World GDP

.01
(.01)
2.36**
(.40)

.003
(.001)

.15**
(.02)

.00
(.01)
OECD Output Gap
.01
(.01)
2.34**
(.44)

.002*
(.001)

.15**
(.02)

.01
(.01)
HP

Filtered GDP
2.35
(1.47)
2.32**
(.43)

.003*
(.001)

.14**
(.02)

.01
(.01)
Add Quadratic
Trend
.48
(.40)
2.30**
(.40)

.003*
(.001)

.15**
(.02)

.01
(.01)
46
Table 3b: Panel Results, First

Differences
Real
World GDP
+
Volatility
+
Spot

Future Spread

Inventories

Real Interest Rate

Basic
.03**
(.01)
.75**
(.24)

.002**
(.001)

.10*
(.05)
.00
(.01)
Drop Fixed Effects
.03**
(.01)
.78**
(.17)

.002**
(.001)

.11**
(.04)
.00
(.01)
Substitute Time
Effects
n/a
.55**
(.19)

.002**
(.001)

.08*
(.04)
n/a
Time and Fixed
Effects
n/a
.53**
(.18)

.002**
(.001)

.07
(.04)
n/a
Drop Spread
.04**
(.01)

.0020**
(.0005)

.10
(.05)

.00
(.01)
OECD Output Gap
.03**
(.01)
.77*
(.25)

.0018**
(.0005)

.12*
(.04)
.01
(.01)
HP

Filtered GDP
4.91**
(.97)
.78*
(.23)

.002**
(.001)

.12*
(.04)
.01
(.01)
Add Quadratic Trend
.03**
(.01)
.75**
(.24)

.002**
(.001)

.10*
(.05)
.00
(.01)
47
Table 4: Panel Results, Bandwagons
Real World
GDP (+)
Volatility
(+)
Spot

Future
Spread
(

)
Inventories
(

)
Real Interest
Rate
(

)
Lagged Price
Change (+)
Basic
.50
(.27)
1.84**
(.40)

.004**
(.001)

.13**
(.02)
.00
(.01)
.0061**
(.0005)
Drop Fixed Effects
.57
(.31)
1.92
(1.42)

.025**
(.006)

.19**
(.03)
.04
(.04)
.0104*
(.0044)
Substitute Time
Effects
n/a
1.84
(1.90)

.028**
(.007)

.19**
(.01)
n/a
.0101
(.0067)
Time and Fixed
Effects
n/a
1.37**
(.28)

.004**
(.001)

.12**
(.01)
n/a
.0050**
(.0008)
Drop Spread
.48
(.32)
2.01**
(.37)

.14**
(.02)

.00
(.01)
.0053**
(.0005)
Growth (not log) of
World GDP

.01
(.01)
1.90**
(.40)

.005**
(.001)

.13**
(.02)
.01
(.01)
.0061**
(.0005)
OECD Output Gap

.00
(.01)
1.90**
(.43)

.004**
(.001)

.13**
(.02)
.01
(.01)
.0063**
(.0005)
HP

Filtered GDP

.71
(1.58)
1.92**
(.42)

.004**
(.001)

.13**
(.02)
.01
(.01)
.0062**
(.0005)
Add Quadratic
Trend
.26
(.37)
1.85**
(.41)

.004**
(.001)

.13**
(.02)
.01
(.01)
.0062**
(.0005)
Drop post

2003 data
1.21**
(.28)
1.26
(.58)

.004**
(.001)

.11**
(.04)
.01
(.01)
.0049**
(.0005)
With AR(1)
Residuals
2.08*
(.81)
.89**
(.13)

.0033**
(.00004)

.10**
(.03)
.00
(.01)
.0031**
(.0004)
48
Table 5: Panel Results,
Adding Inflation
Real
World
GDP
+
Volatility
+
Spot

Future
Spread

Inventories

Real Interest
Rate

Inflation
Basic

2.11**
(.61)
2.12**
(.27)

.0032**
(.0007)

.14**
(.02)
.019
(.012)
.082**
(.015)
Drop Fixed
Effects
.70*
(.32)
2.25
(1.43)

.023**
(.006)

.19**
(.03)
.040
(.038)
.075
(.041)
Drop Spread

2.04**
(.63)
2.21**
(.26)

.15**
(.02)
.015
(.012)
.079**
(.015)
Growth (not log)
of World GDP
.02
(.01)
2.01**
(.32)

.0027**
(.0007)

.15**
(.02)
.006
(.011)
.058**
(.010)
OECD Output
Gap

.00
(.01)
2.09**
(.28)

.0030**
(.0007)

.15**
(.02)
.014
(.012)
.083**
(.014)
HP

Filtered
GDP
.19
(1.64)
2.03**
(.33)

.0031**
(.0008)

.15**
(.02)
.005
(.013)
.051**
(.009)
Add Quadratic
Trend

2.47**
(.76)
2.14**
(.27)

.0032**
(.0006)

.14**
(.02)
.017
(.011)
.085**
(.015)
49
50
Comments 0
Log in to post a comment