Determinants of Agricultural and Mineral Commodity Prices Jeffrey A. Frankel, Harvard University,

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Determinants of Agricultural and
MineralCommodity PricesJeffrey A. Frankel,
Harvard University, Andrew K. Rose,
University of California, Berkeley

• Reserve Bank of Australia, August 2009.

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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.

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? 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.

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

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Three Aggregate Theories Explain the Recent
Rise of Commodity Prices

• Destabilizing Speculation.
• Storability Homogeneity gt Asset-like
  Speculation
• Monetary
• Low Real Interest Rates
• Or High Expected Inflation
• Global Demand Growth.
• Actual/Future Growth (China )

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Issues Exist with All Three Explanations

• In Theory, Speculation may be Stabilizing
• Empirical Issues with All Three Theories

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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.

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Monetary Explanation

• 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.
• Conversely, a rise in US real interest rates in
  the early 1980s. helped drive commodity prices
  down.
• The Fed cut real interest rates sharply,2001-04,
  and again in 2008-09. Did this help push prices
  first up, then down?

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High Interest Rates in Theory

• 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 and rise in supply
  work to lower commodity price.

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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?

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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.

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Quick Peek at Aggregate Data Little

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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

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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.

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Derive Relationship for Real Commodity from Two
Equations

• Regressive Expectations (can be Rational)
• E (?s) - ? (q-Q) E(?p)
• Arbitrage-like condition links Inventories
  Bonds
• E ?s 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 ?s (f-s)
  risk premium, gt0 if being long in commodities
  is risky, andi the interest rate

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Combining

• q - Q - (1/?) (i - E(?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

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Observable Manifestations of Convenience Yield,
Storage Costs, Risk Premium (c)

• 1. InventoriesStorage costs rise with inventory
• Measured with World inventories where possible,
  US otherwise
• Could also estimate an inventory equation

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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

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Uncertainty Measures

• 4. Medium-term volatility (s)
• 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

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• 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

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Complete Equation

• q Q - (1/?) r (1/?) ?(Y) (1/?)? (s)
  - (1/?) F (INVENTORIES)-d(s-f)
• Objective Determine (log) real commodity price
• 3 Micro determinants
• Volatility spread inventories
• 2 Macro determinants
• World GDP real interest rates

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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

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Booms around 1974-75 and 2008
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Table 3a Panel Results, for log real commodity
prices,
Ln(World Real GDP) Volatility Spot-Futures Spread Inven-tories Real USinterest rate
.60 2.29 -.003 -.15 -.01
(.27) (.40) (.001) (.02) (.01)
() gt significantly different from zero at
.01 (.05) significance level. Robust standard
errors in parentheses Intercept trend
included, not reported.
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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

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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

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Reasonable Fit to Data
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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 USinterest rate Lag of NominalPrice Growth
.50 1.84 -.004 -.13 .00 .0061
(.27) (.40) (.001) (.02) (.01) (.0005)
() gt significantly different from zero at
.01 (.05) significance level. Robust standard
errors in parentheses Intercept trend
included, not reported.
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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)

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Table 5 To Look for Another indicator of
Monetary Ease, Add Aggregate Inflation
Ln(World Real GDP) Volatility Spot-Futures Spread Inven-tories Real USinterest rate Inflation
-2.11 2.12 -.003 -.14 .02 .082
(.61) (.27) (.001) (.02) (.01) (.015)
() gt significantly different from zero at
.01 (.05) significance level. Robust standard
errors in parentheses Intercept trend
included, not reported.
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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?
• Doesnt Explain Recent Run-Up

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Other Tests Indices

• Construct Commodity Price Indices
• Use 6 Weighting Schemes
• Dow-Jones/AIG SP GCSI CRB Reuters/Jefferies
  Grilli-Yang Economist Equal
• 3 Different Periods of Time
• Data availability gt longer span has fewer
  commodities
• Similar (Weaker) Results
• Micro work OK poor real interest rate results

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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

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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

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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

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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?

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Appendices

• Graphs of data
• Why American interest rates?
• Commodity-specific Results
• Full Panel Results

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Why American Real Interest Rates?

• Assume commodity markets integrated
• If so, denomination doesnt matter
• Data availability issues for G-3/G-7 interest
  rates
• Inevitable EMU issues

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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)
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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)
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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)
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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)
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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)
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