Download Macroeconomic Determination of Prices of Agricultural and Mineral

Determinants
of Prices of Agricultural
and Mineral Commodities
Jeffrey Frankel,
Harvard University, &
Andrew Rose,
University of California, Berkeley
First draft of a paper for the Reserve Bank of Australia.
To be presented at pre-conference, 16 June, 2009,
Westfälische Wilhelms University Münster, Germany;
Co-sponsored also by CAMA, Australia,
& VERC, Wilfred Laurier University, Canada
The determination of prices for oil and
other mineral & agricultural commodities
 falls predominantly in the province of
microeconomics.
 But in periods when many commodity
prices are moving far in the same
direction at the same time, it becomes
difficult to ignore the influence of
macroeconomics.
 The decade of the 1970s.
 The decade of the 2000s.
2
► A rise in the price of oil
might 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.
3
But it cannot be coincidence that
almost all commodity prices rose
together during much of the decade,
and peaked abruptly in mid-2008.
Commodity Price Index
50
100
150
200
Monthly data from Jan 2000 to Feb 2008
2000m1
Source: IMF
2002m1
2004m1
Time
2006m1
2008m1
4
Three theories competed to explain the
ascent of commodity prices in 2003-08.
1. Most standard: the global demand growth
explanation, emphasizing especially
growth in China, India, etc.
2. Also highly popular:
destabilizing speculation.
1. Storability & homogeneity
=> asset-like speculation.
2. But destabilizing?
3. Expansionary monetary policy
1. low real interest rates
2. expected inflation.
5
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.
6
The real interest rate 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. [1]
2. Conversely, a rise in US real interest rates in the
early 1980s. helped drive commodity prices down.[2]
3. The Fed cut real interest rates sharply,2001-04,
and again in 2008-09.
My claim: it helped push up commodity prices.[3]
[1] Barsky & Killian (2001).
[2] Frankel (1985).
[3] Frankel (2008).
7
High interest rates
Lower inventory demand;
and
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.
Both channels – fall in demand & rise in supply –
work to lower the commodity price.
A 3rd channel goes the same direction -trading in contracts (“the carry trade”):
Low interest rates induce a “search for yield”
among investors, who go long in commodities
(just as FX, emerging markets., etc.)
8
Inverse correlation between
real interest rate and real
commodity price index (DJ, 1950-2008)
Dow Jones Commodity Price Index vs.
Real Interest Rate
Annual, 1950-2008
1.5
Log Real Commodity Price Index
1
0.5
0
-0.5
-7.5%
-5.0%
-2.5%
0.0%
2.5%
Real Interest Rate
5.0%
7.5%
10.0%
9
Counter-argument that applies to both
the destabilizing-speculation & easymoney theories (Krugman, 2008, & Kohn, 2008):
 Inventories of oil & other commodities were
said to be low in 2008, contrary to the theory.
 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.”
10
But in 2008, enthusiasm for theories (2) & (3),
the speculation & interest rate theories, rose,
at the expense of theory (1), the global boom.
 The sub-prime mortgage crisis
hit the US in August 2007.
 Thereafter, forecasts of growth fell, not just
for the US but globally, including China.
 Meanwhile commodity prices, far from
declining as one might expect from the global
demand hypothesis, accelerated.
 For the year following August 2007, at least,
the global boom theory was not relevant.
 That left explanations (2) and (3).
11
Definitions
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.




12
Derive the relationship between
q & r from two equations:
 Regressive expectations:
 E (Δs) = - θ (q-Q) + E(Δp).
(2)
 Arbitrage condition between inventories & bonds:
 E Δs + c = i,
(3)
 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
13
Combining (2) & (3)
gives the relationship:

q - Q = - (1/θ) (i - E(Δp) – c) .
(5)
 This inverse relationship between q & r
has been supported by:
 Event studies (monetary announcements)
 The graphs
 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. Esp. variation in c.
14
Inverse correlation between real
interest rate and real commodity
price index (Moody’s, 1950-2008)
Moody's Commodity Price Index vs.
Real Interest Rate
Annual, 1950-2008
1.5
Log Real Commodity Price Index
1
0.5
0
-0.5
-7.5%
-5.0%
-2.5%
0.0%
2.5%
Real Interest Rate
5.0%
7.5%
15
10.0%
Translate convenience yield, storage
costs, & risk premium from equation
(6) into empirically usable form,
with 4 or 5 measurable factors:
1. Inventories.
Storage costs rise with the extent to which
inventory holdings strain existing storage
capacity:
sc = Φ (INVENTORIES).
 Can estimate an inventory equation:
INVENTORIES
= Φ-1 (sc) = Φ-1 (cy-i–(s-f))
(8)
16
Two more measurable
determinants
2. Real GDP
or industrial production,
representing the transactions demand for
inventories, is a determinant of the convenience
yield cy.
Call the relationship γ (Y).
3. The spot-futures spread, s-f.
A higher spot-futures spread (normal
backwardation) signifies a low speculative
return and should have a negative effect on
inventory demand and on prices.
17
The last two are uncertainy
measures
4. Medium-term volatility (σ), measured
either as the standard deviation of the spot
price or as the implicit forward-looking
expected volatility that can be extracted from
options prices.
 Volatility is a determinant of convenience yield,
cy; and so of commodity prices
 It may also be a determinant of the risk
premium.
18
5. Risk (political, financial, & economic),
in the case of oil, e.g., is measured by a weighted
average of (inverse) political risk
for 12 top oil producers.
 The theoretical sign is ambiguous:
 Risk is another determinant of cy (esp. fear of
disruption of availability), whereby it should have a
positive effect on commodity prices.
 But it is also a determinant of the risk premium rp,
whereby it should have a negative effect on prices.
19
The equation works for oil inventories:
INVENTORIES
=Φ
-1
(cy - i– (s-f))
 -----------------------------------------------------------------------------log_inventories
|
Coef.
Std. Err.
t
P>|t|
 -----------------------+----------------------------------------------------- Real interest rate| -.00056
.00033 -1.71
0.09
 Oil spot-forward | -.00079
.00013 -5.98
0.00
 Log industr.prod. | .05222
.01968 2.65
0.01
 risk
| .00013
.00018 0.69
0.491
 Lag log inv
| .93105
00976 95.39
0.000

counter
| -.00003
.00001 -2.21
0.027

counter2
| -2.78e-09
5.13e-09 -0.54
0.588

_constant
| .18380
.09458 1.94
0.052
 --------------------------------------------------------------------------------------------20
The same macro variables work
to determine real oil price:












-----------------------------------------------------------------------Log real oil p |
Coef. Std. Err.
t P>|t|
------------------+----------------------------------------------------Log ind.prod. | 3.445 .239
14.44 0.00
log inventory | .455
.119
3.82 0.00
Real int.rate | -.052
.004
-13.24 0.00
Oil risk
| .037
.002
16.25 0.00
s-f spread | .026
.002
15.94 0.00 .
counter | -.006
.0002
-34.82 0.00
counter2 | 2.84e-06 6.23e-08
45.52 0.00
constant | -19.673 1.143
-17.21 0.00
------------------------------------------------------------------------21
Complete equation,
from (5) and (8):
 q = Q - (1/θ) r + (1/θ) γ(Y) + (1/θ)Ψ (σ)
- (1/θ) Φ (INVENTORIES)
(9)
 We now test it on 12 commodities,
with data from 1960s to 2008.
22
-2.5
-3
-3.5
-4
1950
1975
2008
Corn
1975
2008
Copper
.5
0
-.5
1950
1.5
1
.5
0
-.5
1950
1975
2008
Platinum
1
-1
1950
-3.5
-.5
-4
-1
1950
1975
2008
-3
1950
1975
2008
-4.5
1950
1975
1975
Silver
2008
-1.5
-2
-2.5
-3
-3.5
1950
0
1950
1975
2008
1975
2008
1975
2008
Gold
2008
Oats
-2
2008
-.5
0
-1
1975
2
-3
1.5
1
1950
0
Cotton
Hogs
2
3
.5
Cattle
2.5
.5
-.5
-1
-1.5
-2
-2.5
1950
Oil
1975
2008
Soybeans
-2
-2.5
-3
-3.5
-4
1950
Wheat
Booms around 1974-75 and 2008
Log Real Spot Price
23
Table 3b -- Panel Results,
for ln of real commodity prices,
with risk included.
Ln(G-7 Volatility Risk
Real
GDP)
Pooled .82*
(.38)
.57*
2.24
(1.57)
.21
Annual data.
SpotFutures
Spread
Inventories
-.021** -.16**
Real
interest
rate
.02
(.11)
(.006)
(.04)
(.04)
1.75*
-.06
-.003*
-.15**
.00
(.58)
(.04)
(.001)
(.03)
(.01)
Commodity
effects
(.21)
** (*) => significantly different from zero at .01 (.05) significance level.
Robust standard errors in parentheses; Intercept & trend included, not reported.
24
Other tests
 6 Major commodity price indices.
 Unit root tests
 Philips-Perron on individual commodities
 & panel
 Co-integration tests
 Johanson on individual commodities
 Panel
 Vector error correction
25
Overall conclusions
(as of now)
 The commodity-specific explanatory factors
work surprisingly well:
 Inventory holdings
 Spot-futures spread
 Volatility
 In the latest results, the macroeconomic
variables work surprisingly poorly:
 Economic activity
 Real interest rates
26
Possible extensions
 Explore other measures of real interest
rate and economic activity.
 Try survey data as a direct measure of
expectations.
 Estimate simultaneous system
 in inventories, expectations or spread,
and commodity prices,
 tied directly to the theory.
27
Appendix
 Graphs of data
28
4
3
2
1
0
1950
1975
2008
Corn
.2
1975
2008
Cattle
1.5
1
2
1
.5
1
.5
0
1950
1975
2008
2
1
1975
Platinum
4
3
2
1
0
1950
1975
0
1950
2008
2
1.5
1
.5
0
1950
1975
2008
0
1950
Cotton
2008
2
1.5
1
.5
0
1950
Hogs
3
0
1950
3
Copper
.4
0
1950
1.5
1975
1975
2008
1975
2008
1975
2008
Gold
2008
4
3
2
1
0
1950
Oats
Oil
.3
4
.2
2
1975
2008
Silver
0
1950
.1
1975
Soybeans
Risk
2008
0
1950
Wheat
29
40
20
0
-20
-40
1950
1975
2008
Corn
20
0
-20
-40
-60
1950
100
0
50
-20
0
1975
2008
-50
1950
Cattle
1975
Platinum
2008
-50
1950
0
0
2008
2008
40
20
0
-20
-40
1950
1975
2008
Silver
1975
2008
1975
2008
1975
2008
Gold
100
50
0
1975
2008
-50
1950
2008
40
20
0
-20
-40
1950
Oats
1975
-20
1950
Cotton
0
0
20
40
20
0
-20
-40
1950
50
50
50
-50
1950
Hogs
100
-50
1950
1975
40
2008
Copper
20
-40
1950
1975
100
Oil
1975
Soybeans
Wheat
Future-Spot Spread
30
12.5
12
11.5
11
10.5
1950
1975
2008
Corn
11.6
11.4
1975
2008
Cattle
6
4
2
0
-2
1950
11
13
10.5
12
10
11
1950
1975
2008
Copper
11.8
11.2
1950
14
1975
Platinum
11.1
11
10.9
10.8
10.7
1950
2008
1975
2008
Cotton
2008
1975
1950
Gold
8.4
13
8.2
12
1975
2008
Hogs
8
7
6
5
4
1950
9.5
1950
1
.5
0
-.5
-1
8
11
1950
1975
2008
7.8
1950
Oats
1975
Silver
2008
11
10
9
8
7
1950
1975
2008
1975
2008
Oil
12.5
12
11.5
1975
Soybeans
2008
11
1950
Wheat
Log Inventory
31
.2
.15
.1
.05
0
1950
.4
.2
1975
2008
Corn
.2
.15
.1
.05
0
1950
1975
2008
Copper
1975
2008
Cattle
.2
.1
1975
Platinum
.2
.15
.1
.05
0
1950
1975
.3
.2
.2
.1
.1
0
1950
2008
.4
.3
.2
.1
0
1950
1975
2008
0
1950
Cotton
2008
.4
.1
.2
0
1950
1975
2008
0
1950
Oats
1975
Silver
2008
.3
.2
.2
.1
.1
1975
Soybeans
Volatility
2008
1975
2008
1975
2008
Oil
.3
0
1950
1975
Gold
.2
Hogs
.3
0
1950
0
1950
.3
2008
0
1950
Wheat
32
5
0
-5
1950
1975
2008
Real Interest Rate
33
31
30.5
30
29.5
29
1950
1975
2008
Log Real G-7 GDP
34
35