Reserve Bank of Australia, August 2009.

1. Determinants of Agricultural and MineralCommodity PricesJeffrey 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.

5. 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

6. Three “Aggregate” Theories Explain the Recent Rise of Commodity Prices • Destabilizing Speculation. • Storability & Homogeneity => Asset-like Speculation • Monetary: • Low Real Interest Rates • Or High Expected Inflation • Global Demand Growth. • Actual/Future Growth (China …)

7. Issues Exist with All Three “Explanations” In Theory, Speculation may be Stabilizing Empirical Issues with All Three Theories

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 • 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?

10. 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.

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.

13. Quick Peek at Aggregate Data: Little ``

14. 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

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, andi≡ the interest rate

17. Combining: • q - Q = - (1/θ) (i - E(Δp) – c) • This inverse relationship between q & r has already been somewhat studied • Event studies (monetary announcements) • Regressions of q against rin 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. InventoriesStorage 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 logreal commodity prices, ** (*) => significantly different from zero at .01 (.05) significance level. Robust standard errors in parentheses; Intercept & trend included, not reported.

26. 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

27. 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

28. Reasonable Fit to Data

29. Table 4: To Look for Bandwagon Expectations, Add Lagged Rate of Commodity Price Rise ** (*) => significantly different from zero at .01 (.05) significance level. Robust standard errors in parentheses; Intercept & trend included, not reported.

30. 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)

31. Table 5: To Look for Another indicator of Monetary Ease, Add Aggregate Inflation ** (*) => significantly different from zero at .01 (.05) significance level. Robust standard errors in parentheses; Intercept & trend included, not reported.

32. 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

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

37. 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?

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

44. 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

45. Table 2a: Commodity-Specific Results

46. Full Panel Results Table 3a: Levels

47. Table 3b: Panel Results, First-Differences

48. Table 4: Panel Results, Bandwagons

49. Table 5: Panel Results, Adding Inflation