Determinants of Prices of Agricultural

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Oct 28, 2013 (3 years and 5 months ago)

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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

2

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.



3



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.





4

But it cannot be coincidence that
almost all commodity prices rose
together during much of the decade,
and peaked abruptly in mid
-
2008.

5

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
.

6

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.

7

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).



8

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 3
rd

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.)


9

Inverse correlation between

real interest rate and real
commodity price index
(DJ, 1950
-
2008)

10

Counter
-
argument that applies to both
the destabilizing
-
speculation & easy
-
money 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.”

11

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).

12

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.

13

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

14

Combining (2) & (3)

gives the relationship:



q
-

Q =
-

(1/
θ
) (i
-

E(
Δ
p
)


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.



15

Inverse correlation between real
interest rate and real commodity
price index

(Moody’s,
1950
-
2008
)

16

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)

17

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.

18

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.


19

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.

20

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



---------------------------------------------------------------------------------------------

21

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


-------------------------------------------------------------------------

22

Complete equation,


from (5) and (8):



q = Q
-

(1/
θ
) r
+

(1/
θ
)

γ
(Y) + (1/
θ

(
σ
)



-

(1/
θ
)
Φ
(INVENTORIES)

(9)



We now test it on 12 commodities,

with data from 1960s to 2008.


23

Booms around 1974
-
75 and 2008

24

Table 3b
--

Panel Results,

for
ln

of real commodity prices,

with risk included. Annual data.

Ln(G
-
7
Real
GDP)

Volatility

Risk

Spot
-
Futures
Spread

Inven
-
tories


Real

interest
rate

.82*

2.24

.21

-
.021**

-
.16**

.02

(.38)

(1.57)

(.11)

(.006)

(.04)


(.04)


.57*

1.75*

-
.06

-
.003*

-
.15**

.00

(.21)

(.58)

(.04)

(.001)

(.03)


(.01)


Pooled



Commodity

effects

** (*) => significantly different from zero at .01 (.05) significance level.


Robust standard errors in parentheses; Intercept & trend included, not reported.

25

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

26

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

27

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.

28

Appendix


Graphs of data

29

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