CommodityP RBA muensterjun16+

Determinants
of Prices of Agricultural
and Mineral Commodities
Jef fr ey Fr ankel,

Har var d Univer sity, &

Andr ew Rose,

Univer sity of Califor nia, Ber keley

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


Commodity Price Index

100

150

200

Monthly data from Jan 2000 to Feb 2008

50

All commodity price index in US$

But it cannot be coincidence
that almost all commodity
prices rose together during
much of the decade, and
peaked abruptly in mid-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)

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
between q
equations:

relationship
& r from two

 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

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 )

15

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
0

2008

1975

2008

1975

Platinum

2008

2

-.5

1

-1
1950

0

-3.5

-.5

-4

-1
1950

1975

2008

-3
1950

1975

0
1950

2008

-4.5
1950

1975

1975

Silver

2008

-1.5
-2
-2.5
-3
-3.5
1950

1975

2008

1975

2008

1975

2008

Gold
-.5
-1
-1.5
-2

-2.5
1950

2008

Oats

-2

1.5

0

-3

-1

2

3

Cotton

Hogs

2.5

.5

.5

Cattle

1
1950

1975

Copper

.5

-.5
1950

1.5
1
.5
0
-.5
1950

Oil

1975

-2
-2.5
-3
-3.5

-4
1950

2008

Soybeans

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. Annual data.
Ln(G-7 Volatility Risk
Real
GDP)

Pooled .82*
(.38)

.57*

2.24

(1.57)

.21
(.11)

1.75*

-.06

(.58)

(.04)

SpotFutures
Spread

Inventories

-.021** -.16**
(.006)

(.04)

-.003* -.15**

Real
interest
rate

.02

(.04)

.00

Commodity

effects

(.21)

(.001)

(.03)

(.01)

** (*) => significantly different from zero at .01 (.05) significance level.
24
Robust standard errors in parentheses; Intercept & trend included, not reported.


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