The behavior of base metals prices

Transcription

1 The behavior of base metals prices São Paulo November 5,

2 Agenda Unique features The price behavior Facts and fantasies Minerals and metals for the long run 2

3 Unique features 3

4 Unique features Commodity investing Commodities and the weather Commodities and inflation Price elasticities Cyclical drivers 4

5 The price behavior 5

6 Price performance Key features Asymmetric volatility Price co-movement 6

7 Fat tails: large numbers of extreme values Aluminum Nickel Copper Freight Kurtosis The kurtosis coefficient measures the magnitude of the extreme values of the distributions. If returns are normally distributed, then the kurtosis should be three. 7

8 Unpredictability and volatility clustering Aluminum prices 7,000 12% US$/metric ton 6,000 5,000 4,000 3,000 2,000 1,000 Source: Vale and LME % 0% -6% -12% -18% -24% -30% -36% -42% -48% Daily returns 8

9 Unpredictability and volatility clustering Copper prices 20,000 20% 18,000 US$/metric ton 16,000 14,000 12,000 10,000 8,000 6,000 4,000 2, Source: Vale and LME % 0% -10% -20% -30% -40% Daily returns 9

10 Unpredictability and volatility clustering Nickel prices 20% US$/metric ton 90,000 75,000 60,000 45,000 30,000 15, Source: Vale and LME % 0% -10% -20% -30% -40% -50% Daily returns 10

11 Brief summary of econometric tests Autocorrelation in returns are not significant unpredictability Autocorrelation in square returns are significant volatility clustering 11

12 Stock prices are inversely related to volatility S&P 500 x VIX 1,600 1,500 1,400 1,300 1,200 1,100 1, S&P 500 VIX Source: Bloomberg 12

13 Volatility asymmetry: base metals prices are positively related to volatility Volatility asymmetry¹ S&P 500 Aluminum Copper Nickel Up cycle 0.86% 2.06% 2.08% 2.97% Down cycle 1.73% 1.64% 1.90% 2.40% ¹One month moving average of the standard deviation of daily returns Source: Vale 13

14 Prices co-movement in the short-term influenced by common factors Co-movement between returns: simple correlation Jan/04 - Oct/09 Copper Aluminum Iron ore Thermal coal Oil Corn Soybeans Wheat Nickel Copper Aluminum Iron ore Thermal Coal Oil Corn Soybeans 0.57 Source: Vale 14

15 Price co-movement in the long term: specific market fundamentals prevail Co-integration analysis March 1987 October 2009 All commodities Energy and metals prices Food and metals prices Metals prices High degree of co-movement Low degree of co-movement 1 X X X X 1- We can say that the degree of the co-movement is low because there is only one co-integration vector. To find a strong co-movement between n variables it would be necessary to have n-1 co-integration vectors. Source: Vale 15

16 The relationship between iron ore and maritime freight prices There is a high correlation between maritime freight and iron ore prices. They also behave in a similar manner over the long term. There is a common trend which influences the behavior of both prices in the same direction. 16

17 The relationship between iron ore and maritime freight prices Analysis of common trend influences Horizon 1-year 2-year 5-year 10-year % of freight price variations explained by common trend 91.7% 93.5% 93.1% 94.6% % of iron ore price variations explained by common trend 33.7% 69.8% 85.8% 87.9% 17

18 Price co-movement is likely to increase during financial crises Base metals and oil Co-movement¹ 1.0 Base metals and food Measured by 1-year correlation of price returns Source: Vale Base metals LMEX Food CRB food 18

19 Facts and fantasies 19

20 Facts and fantasies about minerals and metals prices Mean reversal Financial investments The Chinese iron ore stockpiling 20

21 Iron ore prices are not doomed to revert to the mean 140 Real iron ore prices¹ US$ cents/fe-dmt ¹ Nominal prices adjusted by the September 2009 US PPI Source: Vale 21

22 Base metals prices are not doomed to revert to the mean 6,000 5,500 5,000 4,500 4,000 Aluminum price¹ US$/ton 9,000 8,000 7,000 6,000 Copper price¹ US$/ton 60,000 50,000 40,000 Nickel price¹ US$/ton 3,500 5,000 30,000 3,000 2,500 2,000 1,500 4,000 3,000 2,000 20,000 10,000 1, , ¹ Monthly nominal prices adjusted by the September 2009 US PPI Sources: Vale and LME 22

23 Brief summary of econometric tests Unit root tests do not support the mean reversal hypothesis Unit root test: Augmented Dickey-Fuller Null hypothesis: price has a unit root Prices 1 Iron ore Nickel Copper Aluminum Augmented Dickey- Fuller test P-value ¹ Annual real prices adjusted by US PPI Sources: Vale 23

24 However, metals price volatility reverts to the mean 18% Nickel price volatility¹ 16% 14% 12% 10% 8% 6% 4% 2% 0% ¹ Standard deviation of 20-trading day moving average of daily returns. Sources: Vale and LME 24

25 Metal prices are determined by fundamentals Copper: prices and long positions Net long positions Spot price 10,000 9, , number of contracts ,000 6,000 5,000 4,000 3,000 US$/ton -30 2, ,000 Source: Vale, NYMEX and CFTC 25

26 Metals prices are determined by fundamentals Financial investments are caused by prices, it does not cause prices. Granger causality test March 1995 October 2009 Null hypothesis Obs F-Statistic Probability Long positions does not Granger causes spot price Spot price does not Granger causes long positions

27 There is no evidence of stockpiling Days of iron ore consumption Days of iron ore imports Mt Days Mt Days Stocks at the ports Stocks at the ports Sep-08 Nov-08 Jan-09 Mar-09 May-09 Jul-09 Aug-09 Oct Sep-08 Nov-08 Jan-09 Mar-09 May-09 Jul-09 Aug-09 Oct

28 Minerals and metals for the long run 28

29 Is there a bubble? Iron ore Aluminum Copper Nickel Platinum² Oil³ Thermal coal Soybeans 4 Wheat 4 US$/ton All-time high Current price¹ ,290 1,903 8,985 6,575 54,200 18,465 2,251 1, , , Δ% ¹ October 30, 2009 ² US$ per oz ³ US$ per barrel 4 US$ per bushel 29

30 This time metal prices were much more volatile than in the recessions of the last 40 years Commodity prices in global recessions and recoveries¹ Aluminum -46% -26% 5% 38% Copper -56% -30% 5% 102% Nickel -65% -32% 13% 80% Current recession Average of last 5 recessions Current recovery² Average of last 5 recoveries ¹ Recessions are measured as the percentage change of metal prices from the beginning of each recession to trough; recoveries are measured as the percentage change of metal prices from the trough to the end of each recession. Global recessions:1974, , , 1998 and ² Percentage change from last trough until September Source: Vale and IMF 30

31 The sharp drop of metal prices was caused by the steep decline of manufacturing output 20 Global industrial production growth % 3mma, saar¹ ¹ 3-month moving average, seasonally adjusted annualized rate Source: Vale and JP Morgan 31

32 while the strong recovery from the recessions was determined by the fast EM-led growth of IP 8 Industrial production % 3mma, sa¹ Emerging economies Developed economies Jan-08 May-08 Sep-08 Jan-09 May-09 ¹ Seasonally adjusted Source: Vale and JP Morgan 32

33 Emerging economies have become the dominant consumers of commodities Share in global consumption % Aluminum Copper Nickel Iron ore¹ ¹ Seaborne trade Chinese market share Source: Vale and WBMS 33

34 Emerging economies have been the drivers of global consumption growth of minerals and metals Contribution to growth in iron ore seaborne trade Contribution to global consumption growth Developed economies Emerging market economies China Rest of the world 5% Steel Aluminum 3.5% 3% 62.2% 74% 96.5% 97% China 95% Copper -61% 106% 161% Nickel -10% 106% 110% Sources: Vale, Tex Report and WBMS 34

35 Unless economic policies deteriorate, we expect convergence to go on, thus underpinning a long minerals and metals cycle 9 Decoupling Real GDP growth % annual Decoupling 8 Developed economies 7 Emerging economies E 2014E Source: Vale and IMF 35

36 Vale: a global leader 36