commodity price volatility: causes and impact on low ... · particular, they point to the greater...

PRELIMINARY AND INCOMPLETE DRAFT NOT FOR QUOTATION

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13 October 2010

Paper prepared for the conference on “Impacts, responses & initial lessons of the financial crises for low income countries”

Danish Institute for International Studies, Copenhagen, 14–15 October 2010

Commodity price volatility: causes and impact on low-income countries

Jörg Mayer1 (UNCTAD) and Johannes Gareis (Würzburg University)

Abstract Commodity price volatility has increased significantly over the past few years. There is some evidence that part of this increase is due to the financialization of commodity exchange trading and the associated volatility spillovers from financial markets. In countries where commodity exports account for a large share in national income, commodity price volatility hampers economic growth such that it may more than compensate for the otherwise positive growth effects of natural resource abundance. In low-income food-deficit countries, especially those with low foreign exchange reserves and little fiscal space, commodity price volatility poses the risk of food security. Commodity price volatility is likely to remain a recurrent feature in the years to come. Policy measures to address commodity price volatility must be taken at the national and international levels and address both fundamental factors and the financialization of commodity trading. Acknowledgements: The authors are grateful to Flavine Creppy and Yumiko Mochizuki for data provision. The opinions expressed are solely those of the authors and do not necessarily reflect the views of UNCTAD or its Member States.

1 Corresponding author: Jörg Mayer, United Nations Conference on Trade and Development (UNCTAD), Palais des Nations E.10020, CH-1211 Geneva 10, Switzerland, [email protected], ++41 22 9175722.


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I Introduction Commodity price developments have re-emerged as a hotly debated issue. Much of this new interest is related to the strong increase in commodity prices in the run-up to the current crisis and their collapse in the second half of 2008, when the crisis started to spread globally. Wide swings in commodity prices had significant adverse effects for economic activity and posed problems for macroeconomic management. Particularly affected were countries where commodity exports account for a large share of domestic output, as well as net food and energy importing countries with few foreign-exchange reserves and little fiscal space. The debate on what caused the commodity price boom between 2003 and 2008 is ongoing. Some authors attribute all price pressure to changes in supply and demand (for a survey, see Irwin and Sanders, 2010) stemming in particular from rapidly rising demand in China and a few other emerging economies. Price pressure from growing demand in emerging economies, which occurred over a wide range of commodities, was reinforced by more sector-specific factors. For food commodities, policy measures supporting the use of biofuels (e.g., Mitchell, 2008) and decades of underinvestment in agriculture (e.g., UNCTAD, 2008a) played an important role, whereas in the case of mineral and energy commodities strongly increased exploration and extraction costs, previous underinvestment in exploration and long lead times for the development of new projects were some additional factors. According to this view, prices fell sharply in 2008 and 2009 when the global economic downturn set in and demand was fading. Other observers have argued that beyond these changes in fundamental supply and demand relationships, broader macroeconomic and financial factors need to be considered to fully understand recent commodity price developments (e.g., UNCTAD, 2009a, 2009b; Mayer, 2009; Gilbert, 2010). In particular, they point to the greater importance of financial investment on commodity exchanges and the ensuing greater correlation between prices of financial assets and commodity prices (e.g., CFTC, 2008, and Tang and Xiong, 2010). This view attributes an important part of the price collapse in 2008 to a synchronized unwinding of speculative positions across financial and commodity markets. This debate on the reasons behind the recent large changes in the level of commodity prices overlaps with concerns that commodity price developments may have entered a new era in which substantial price volatility has become a frequently observed phenomenon. To the extent that changes in fundamental supply and demand factors are considered the only forces behind recent price changes, such concerns may be misguided and policy measures leading to a sustained increase in supply and stepped-up inventory holdings would be largely sufficient to avoid recurrent episodes of high price volatility. Indeed, commodity prices have remained at a relatively high level. This fact alone should be expected to provide incentives for stepped-up investment that will expand supply in a sustained manner. Sustained supply expansion could replenish inventories and extend extraction capacities so that short-term supply shortages (e.g., caused by bad harvests or extreme weather events) can be met without risking price spikes. However, to the extent that portfolio rebalancing by financial investors causes price volatility to spill over from financial onto commodity markets, and thus contributes to greater commodity price volatility, such supply-focused measures will be insufficient.


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Indeed, many policymakers in the United States and Europe are actively considering the adoption of specific measures targeted at making financial investment in commodities more transparent and tightening regulation on commodity exchange trading (e.g., Chevalier, 2009). Others go even further and make the case for more direct intervention in commodity exchange trading (e.g., von Braun and Torero, 2008; Nissanke, 2010). The main contribution of this paper is to examine the impact on commodity price volatility, rather than on price levels, that the greater importance of financial investors in commodity markets and the associated increased spillovers of price development from equity markets may have had. The only study in this area available to date (Gilbert and Morgan, 2010) is limited to food commodities. Moreover, Gilbert and Morgan (2010) infer the impact of financialization on price volatility from its impact on the level of commodity prices, while this study provides direct evidence on the effects of the financialization of commodity exchange trading for price volatility of a wide range of commodities. This paper also extends recent work by van der Ploeg and Poelhekke (2009, 2010). These authors argue that the adverse association between natural resource and economic growth (the so-called “resource curse”) results from swings of resource prices that “translate into severe shocks to GDP in countries that depend on natural resource exports … so that the quintessence of the resource curse appears to be the notorious volatility of commodity prices” (van der Ploeg and Poelhekke, 2010: 50, 52). Van der Ploeg and Poelhekke (2009, 2010) use the volatility of countries’ share of resource exports in GDP as a measure for commodity price volatility, but they provide no evidence for the adverse impact on output growth of price volatility itself. This study uses the volatility of country-specific commodity export price indices to explore this link directly. Section 2 takes a commodity-specific perspective to assess the development of commodity volatility over the past two decades. It shows that price volatility has increased significantly since the early 2000s, i.e. when financial investment started to become a key characteristic of commodity trading. The section also examines whether shocks from equity markets became positively correlated with commodity price volatility in the mid-2000s. Taken together this section provides some indication that the increased participation of financial investors in commodity markets has been associated with an increased positive correlation between price volatilities in equity and commodity markets. Section 3 adopts a country-specific perspective and examines the development of country-specific commodity price indices for the past five decades. The examination related to countries’ export prices reveals that commodity price volatility has varied widely, depending on the composition of countries’ commodity export baskets and in particular the extent to which commodity exports are diversified. The section then extends the work by van der Ploeg and Poelhekke (2009, 2010) to explore the link between commodity price volatility and economic growth. Section 4 examines country-specific evidence related to import prices and demonstrates an increase in volatility for the vast majority of net food and energy importing developing countries. Most adversely affected are countries for which wheat and oil account for an important share of commodity imports. Section 5 concludes.


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II Commodity price volatility: recent evidence and effects from financial markets Substantial price volatility has long been recognized as one of the characteristics that distinguish commodities from other kinds of products. Price indices for each of the major commodity groups fluctuate much more than those for unit value indices of manufactures (UNCTAD, 2008: 40) (add chart). However, periods of relatively high price volatility have often been short-lived and interspaced with longer periods of relatively stable prices. This section adopts a commodity-specific perspective. Its objectives are, first, to assess the development of commodity prices since the early 1990s and, second, to examine whether the financialization of commodity trading has altered the dynamics of commodity price volatility. 1 Assessing recent commodity price volatility The commodity-specific analysis of price volatility in this section is based on a sample of 18 commodities, comprising 13 agricultural products (cocoa, coffee, cotton, feeder cattle, live cattle, maize, rice, soybean meal, soybean oil, soybeans, sugar, Chicago wheat and Kansas wheat), three metals (copper, gold silver) and two energy commodities (crude oil and natural gas). Following standard practice (see text box 1 for further discussion), price volatility is measured as the annualized standard deviation of changes in logarithmic prices. Price changes are calculated as follows:

,,

, 1

*100i ti t

i t

PR ln

P

where Ri,t represents logarithmic price changes and Pi,t is the price of the first-month futures contract of commodity i. The analysis is based on daily data for the period January 1992 – December 2009. The full sample period may be divided into three sub-periods (1992–1996, 1997–2002 and 2003–2009). The last of these three sub-periods may be considered as covering the financialization of commodity trading.2

2 The year 2003 has been chosen as the cut-off date because evidence on both over-the-counter commodity derivatives and futures and options contracts outstanding on commodity exchanges indicates this year to mark the beginning of the financialization of commodity trading (UNCTAD, 2009: chart 2.1).


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_____________________________________________________________________

Text box 1: Commodity price volatility: definition, measurements and fundamental reasons

Price volatility refers to the variability of a price. It is commonly measured by the standard deviation of changes in logarithmic prices, which is a unit free measure. Price volatility is generally reported at annual rates. Assuming individual observations of price volatility to be independent over time, i.e. assuming the efficient market hypothesis to hold, price volatility measured on the basis of monthly data can be annualized by multiplying monthly volatility measures by √12; accordingly, daily volatilities are annualized by multiplying by √250 (since a calendar year has approximately 250 working days). The particular reasons for commodity price volatility differ by commodity, and may change over the course of time. But in general, it is related to variation in commodity supply and demand and to the low responsiveness of producers and consumers to changes in supply and/or demand. Changes in supply are determined by the amount of resources that are exploited (e.g., the surface of planted areas and the capacity of mines and oil wells) and the productivity with which this is done. In agriculture, yield is influenced, in the short run, by weather conditions and, in the longer run, by investment in exploitation methods, including fertilizer use and mechanization, and impacts from climate change. In extractive industries, productivity is influenced by the purity of extracted minerals and the cost of discovery and exploitation of underground resources. Changes in demand are influenced by prices of substitutes (biofuel), shifts in tastes and, especially changes in income. Global demand growth – and related changes in the business cycle – is the single most important element in demand for industrial raw materials and energy commodities, but the responsiveness of commodity demand to global output growth depends on the level of industrial development of the countries that drive global growth. This is because the intensity of a country’s raw material use depends on its level of development, as further discussed below). Sizeable changes in supply and/or demand conditions can provoke considerable price volatility because of low short-term price elasticity of producers and consumers. Producers can alter the amount of resources under exploitation only with a lag of several months. Consumers usually change habits only in response to substantial price changes because they cannot reduce food intake, and may be unwilling to change diets, and because commodity prices often account for a small share in the overall value of a consumed product (e.g., cocoa in chocolate, or raw materials in consumer durables). Stockholding and spare production capacities that can be easily activated (such as often the case in oil production) can, within certain limits, rapidly augment market supply and, thus, moderate the impact of short-term supply and demand shocks on price fluctuations. This means that augmented stockholding and globally better coordinated inventory management can go a long way in mitigating price volatility to the extent that prices fluctuate only in response to changes in fundamentals. By contrast, additional


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measures will be required if financial investment has a sizeable impact on price volatility. _____________________________________________________________________ Price volatility, calculated as the standard deviation of changes in logarithmic prices, is an unconditional measure of price variability that is generally assumed to be constant over time. In some cases, however, price volatility exhibits periods of large volatility followed by periods of relative tranquillity. In such circumstances, price volatility may be modelled on the basis of the variability of prices in the past. By conditioning price volatility on factors that are able to predict price movements, such as past volatility levels and price developments, price volatility becomes time dependent and may be greater or lower than unconditional volatility at any point in time. Looking at unconditional price volatility first, annualized unconditional price volatility based on daily observations can be calculated according to the following equation:

T

titiannualizedi RR

T 1

2_

,, )(1*250

Unconditional price volatility has varied significantly across commodities. While the prices of most commodities in the sample have fluctuated by an annual average of about 20–30 per cent, the prices of the two energy commodities and coffee have been the most volatile in the sample, with prices of natural gas fluctuating by an annual average of about 55 per cent, followed by coffee and oil, whose prices fluctuated by about 35–40 per cent (figure 1). This contrasts sharply with the price volatility for the two commodities in the livestock sector, as well as that for gold, which is slightly more than 10 per cent by year, i.e. only about one fifth of that for natural gas.

Source: Authors' calculations based on data from Bloomberg.

Figure 1Unconditional price volatility, selected commodities, 1992–2009

0

10

20

30

40

50

60

Coco

a

Cof

fee

Cott

on

Suga

r

Mai

ze

Soyb

ean

s

Soyb

ean

Mea

l

Soyb

ean

Oil

Ric

e

Whe

at C

hica

go

Whe

at K

ansa

s

Cop

per

Gol

d

Silv

er

Feed

er C

attl

e

Live

Cat

tle

Oil

Gas

%

1992-1996

1997-2002

2003-2009


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Unconditional price volatility also exhibits significant variation over time. Perhaps most importantly, for 16 of the selected 18 commodities price volatility peaked in the period 2003–2009, i.e. when financial investment became a key characteristic of commodity trading (figure 1). This increase can be observed in particular for metals and grains. It was strongest for copper and silver whose price volatility increased roughly by 10 percentage points to peak at about 34 per cent in the period 2003–2009. It is also interesting to note that for a wide range of commodities (including maize, soybeans, soybean meal, soybean oil, Chicago wheat, Kansas wheat, copper and silver) price volatilities were similar in the first and second sub-periods, but experienced a sharp increase in the third sub-period. On the other hand, there are five commodities (cocoa, coffee, natural gas, rice and sugar) whose price volatility peaked in the first sub-period or in the second one. Turning to conditional price volatility, the Generalized Autoregressive Heteroskedasticity (GARCH) model proposed by Bollerslev (1986) can be written as:

k

mtimtimiiti RcR

1,,,0,,

p

j

q

nntinijtijiiti c

1 1

2,,

2,,1,

2,

In this model, henceforth referred to as GARCH model 1, the first equation specifies the mean equation for each commodity i, where 1, tti I is conditional normal

distributed with mean zero and variance 2,i t , and 1tI is the information available up

to time 1t . The second equation specifies the variance equation for each commodity i, where 2

,i t is the conditional variance whose lags represent the GARCH (q) effects,

and 2,ti is the squared shock term from the mean equation whose lags represent the

ARCH (p) effects. The parameter ,1ic is a constant, and the parameters ,i t j and

,i t n capture the persistent effects of past shocks and past volatilities. Measuring conditional price volatility on the basis of the GARCH model 1 leads to slightly lower results (figure 2) than those for the unconditional price volatility measure, which were reported in figure 1.3 This applies both across commodities and across sub-periods. Nevertheless, the results for conditional price volatility also indicate a sharp increase in price volatility for the period 2003–2009.

3 For each commodity i the GARCH model 1 is estimated for the period 1992–2009 via maximum likelihood using the Bernd-Hall-Hall-Hausman algorithm. An AR(k) model for the mean equation is fitted to reduce serial correlation. To test the adequacy of the model, the ARCH-LM test and the Ljung-Box Q-statistics are utilized. The time series plots of fitted annualized conditional standard deviations and the model adequacy check for all 18 commodities are available from the authors on request.


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Source: Authors' calculations based on data from Bloomberg.

Figure 2Mean conditional price volatility, selected commodities, 1992–2009

0

10

20

30

40

50

60

Coco

a

Cof

fee

Cot

ton

Suga

r

Mai

ze

Soyb

eans

Soyb

ean

Mea

l

Soyb

ean

Oil

Ric

e

Whe

at C

hic

ago

Wh

eat

Kans

as

Cop

per

Gol

d

silv

er

Feed

er C

attl

e

Live

Cat

tle

Oil

Gas

%

1992-1996

1997-2002

2003-2009

However, to examine whether the increase in volatility in 2003–2009 is a long-run phenomenon, rather than just a short-lived period of relatively high volatility, it is useful to test the following model, henceforth referred to as GARCH model 2, is as follows:

q

nntini

p

jjtijidummyiiti dummycc

1

2,,

1

2,,03,1,

2, 03

where dummy03 takes the value zero before 2003 and one from 2003 onwards, and ci,dummy03 measures the magnitude of the change in the constant of the conditional variance from 2003 onwards. If an estimation of the equation results in the coefficient of this variable to be statistically significant and have a positive sign (i.e. ci,dummy03>0), it indicates that the increase in volatility in 2003–2009 marks a structural change in the development of commodity price volatility. The estimation results shown in table 1 indicate that this is indeed the case for feeder cattle, live cattle, coffee, cotton, copper, maize, silver, soybeans, soybean oil, soybean meal, Chicago wheat, and Kansas wheat.4 These results suggest that the financialization of commodity trading resulted in an increase in price volatility, particularly of food commodities.

4 The lag structure of the estimation results shown in table 1 differs across commodities because post-estimation test statistics indicated remaining ARCH-effects for some of the commodities. In these cases (i.e. for coffee, sugar, soybean meal, silver, crude oil, and natural gas), the estimation was repeated by extending the lag structure until no ARCH-effect was left in the variance process.


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Cocoa 0.01 * 0.01 0.98 *** 0.02 ***

Coffee 0.19 *** -0.07 * 0.93 *** 0.10 *** -0.06 **

Cotton 0.03 *** 0.02 * 0.96 *** 0.03 ***

Sugar 0.03 *** 0.00 0.97 *** 0.20 *** -0.18 ***

Maize 0.05 *** 0.06 *** 0.90 *** 0.07 ***

Soybeans 0.02 *** 0.02 ** 0.93 *** 0.06 ***

Soybean 0.02 *** 0.03 ** 0.94 *** 0.12 *** -0.08 ** meal

Soybean 0.03 *** 0.02 ** 0.94 *** 0.04 *** oil

Rice 0.11 * 0.01 0.89 *** 0.08 ***

Wheat 0.12 *** 0.10 *** 0.89 *** 0.06 *** Chicago

Wheat 0.06 *** 0.06 ** 0.91 *** 0.05 *** Kansas

Copper 0.03 *** 0.02 ** 0.95 *** 0.04 ***

Gold 0.00 0.00 0.96 *** 0.04 ***

Silver 0.01 *** 0.01 * 0.97 *** 0.08 *** -0.05 **

Feeder 0.01 *** 0.01 *** 0.92 *** 0.05 *** cattle

Live 0.03 *** 0.01 *** 0.95 *** 0.03 *** cattle

Oil 0.02 *** 0.01 0.97 *** 0.06 ** -0.04 0.03 0.02 0.06 -0.10 ***

Gas 0.23 *** -0.01 0.92 *** 0.08 *** 0.01 -0.02 -0.03 0.07 ** -0.04 *

Note: The maximum likelihood estimator based on the Bernd-Hall-Hausman algorithm was used for the GARCH model 2.The sample comprises 4693 observations of daily returns for the period 1992–2009. Bolleslev-Wooldridge robust t-statistics in parenthesis. *, **, *** denotes statistcial significance at the 10%, 5% and 1% level, respective ly.

(191.68)

(53.45)

(1.58)

(2.92)

(-3.29)

(-1.91)(-0.73)

(1.08) (1.04)

(-0.98)(0.21)

(-1.14)

(5.17)

(5.01)

(-2.07)

(-13.74)

(-2.30)

(-2.16)

(2.99)

(4.28)

(2.37)

(3.11)

(1.16)

(-0.16)

(15.97)

(7.48)

(6.72)

(3.79)

(3.24)

(5.71)

(5.45)

(5.51)

Arch(-6)

(1.07)

(-1.95)

(1.86)

(4.76)

(3.14)

(5.78)

Arch(-2) Arch(-3) Arch(-4) Arch(-5)

(58.67)

Dummy 03 Garch(-1) Arch(-1)

(0.01)

(2.87)

(2.16)

(2.05)

(2.15)

(0.46)

(119.81)

(166.94)

(76.58)

(2.81)

(2.02)

(2.19)

(0.74)

(1.74)

(9.00)

(170.86)

(35.68)

(26.37)

(41.77)

(96.84)

(2.67)

(3.03)

Impact on commodity price volatility: GARCH estimations

Table 1

(120.40)

(142.12)

(65.93)

(91.81)

(95.60)

(81.11)

(2.78)

(2.77)

(1.94)

Constant

(2.72)

(2.93)

(3.46) (2.09) (88.65) (5.04)

(2.73)

(10.05)

(6.02)

(3.88)

(3.83)

(2.74)

(3.41)

(1.55)

(3.49)

(1.66)


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2 The impact of financial investors on commodity price volatility Much of the recent commodity price developments have been attributed to changes in fundamental supply and demand relationships (see, e.g., Krugman, 2008; and Hamilton, 2009). From this perspective, rapid growth in emerging economies stimulated world demand for all major commodities and led to a synchronized price boom of these commodities. With the beginning of the financial crises in mid-2008, which worsened economic performance in all major countries and led to global recession in 2009, world commodity demand plunged and commodity prices collapsed. However, the increasing presence of financial investors on commodity exchanges since the early 2000s raised concerns among policymakers and academics that financial factors may have significantly contributed to the recent commodity price developments. To the extend that financial investment has a price effect on commodity exchanges, financial investors (who regard commodities as an asset class and do not necessarily trade on the basis of supply and demand relationships) add a broad set of financial factors as new components to the determinants of commodity price variability. Gorton and Rouwenhorst (2006), who investigated the properties of commodity futures as an asset class, show that commodity futures returns exhibit a small but negative correlation with equity returns before the early 2000s. When the stock market crashed in 2000, commodity markets attracted a huge inflow of financial investments as the tripling of futures and options contracts outstanding on commodity exchanges between 2002 and mid-2008 proofs (BIS, 2009). Since financial investors generally hold a large diversified portfolio across different asset classes and usually invest a fraction of their portfolio in stocks, the financialization of commodities may result in increased co-movement between commodity and equity prices. Due to portfolio rebalancing, financial investors may cause price movements to spill over from equity to commodity markets (Tang and Xiong, 2010; Kyle and Xiong, 2001). This subsection looks at the link between commodity price volatility and price volatility on equity markets for the period January 1992 – December 2009 in order to examine whether this link has changed with the increasing presence of financial investors on commodity exchanges in the early 2000s. To test whether the increasing presence of financial investors on commodity exchanges resulted in larger spillovers of stock market volatility on commodity price volatility, we utilize the standard GARCH model of the previous subsection and test the following specification, henceforth referred to as GARCH model 3,

q

ntdummyiintini

p

jjtijidummyiiti EQdummydummycc

1103,

2,,

1

2,,03,1,

2, )03(03

where EQt-1 stands for the absolute value of the log return of the S&P 500 equity index, δi is the average volatility spillover effect from equity to commodity prices, and δi,dummy03 measures the change of this spillover effect after 2003.


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The results from this estimation suggest that only few commodities are subject to spillover effects from equity markets (table 2). Price volatility of only six of the selected 18 commodities is positively correlated with equity price volatility. These commodities are: cocoa, gold, maize, oil, soybean meal, Chicago wheat, and Kansas wheat. The table also provides some evidence on the influence of financial investors on commodity price volatility. The coefficient which measures the change of the spillover effect of price variability from the equity market after 2003 is positive and significant at the 10 percent level for Chicago wheat as well as for Kansas wheat. This result suggests that the volatility dynamics of those commodities fundamentally changed in the early 2000s, such that in the wake of financialization commodity price volatility has become more exposed to variability on equity markets.

Cocoa 0.00 0.00 0.98 *** 0.02 *** 0.01 * 0.00

Coffee 0.23 *** -0.05 0.90 *** 0.05 *** 0.10 -0.09

Cotton 0.03 ** 0.00 0.95 *** 0.03 *** 0.01 0.04

Sugar 0.04 ** -0.02 0.97 *** 0.21 *** -0.18 *** -0.01 0.03

Maize 0.03 *** 0.08 *** 0.90 *** 0.07 *** 0.04 ** -0.02

Soybeans 0.02 ** 0.03 0.92 *** 0.06 *** 0.01 0.00

Soybean 0.01 0.04 0.94 *** 0.12 *** -0.07 ** 0.03 ** -0.01 meal

Soybean 0.03 *** 0.01 0.94 *** 0.04 *** 0.00 0.02 oil

Rice 0.11 -0.01 0.89 *** 0.07 *** -0.01 0.03

Wheat 0.13 ** 0.04 0.87 *** 0.06 ** 0.06 ** 0.16 * Chicago

Wheat 0.07 ** 0.01 0.90 *** 0.05 *** 0.02 0.10 * Kansas

Copper 0.04 *** 0.00 0.95 *** 0.04 *** -0.01 0.03

Gold -0.00 0.01 0.96 *** 0.03 *** 0.01 ** 0.00

Silver 0.02 ** 0.01 0.97 *** 0.08 *** -0.05 ** -0.01 0.01

Feeder 0.01 *** 0.01 * 0.93 *** 0.05 *** -0.00 0.00 cattle

Live 0.03 *** 0.00 0.94 *** 0.03 *** 0.00 0.01 cattle

Oil 0.01 0.01 0.96 *** 0.05 ** -0.03 0.02 0.02 0.07 * -0.10 *** 0.04 ** 0.01

Gas 0.22 *** -0.04 0.92 *** 0.08 *** 0.01 -0.02 -0.03 0.07 *** -0.04 * 0.02 0.03

Note: The maximum likelihood estimator based on the Bernd-Hall-Hausman algorithm was used for the GARCH model 3.The sample comprises 4693 observations of daily returns for the period 1992–2009. Bolleslev-Wooldridge robust t-statistics in parenthesis. *, **, *** denotes statistcial signif icance at the 10%, 5% and 1% level, respectively.

Table 2

Impact of equity price volatil ity on commodity price volatility: GARCH estimations

(-1.41) (0.52)

(1.97) (1.78)

(0.95) (1.78)

Dummy03

(1.92)

(-0.19) (0.58)

(-0.45) (0.43)

(-1.31) (0.84)

(0.28) (0.33)

(1.99)

(2.33) (-0.06)

(0.62) (0.64)

(-0.35)

(0.35) (0.76)

(0.33)

(1.57) (-1.32)

(0.74) (1.34)

(-0.51)

EQ(-1) EQ(-1)*

(2.27) (0.41)

(1.23)

(2.07) (-0.34)

(0.79) (-0.08)

(-3.26)

(2.43) (-0.40) (57.84) (3.06) (0.16) (-0.63) (-1.01) (2.87) (-1.89)

(-0.90) (0.89) (0.56) (1.74)(0.67) (0.33) (130.87) (2.17)

(3.15) (0.84) (74.18) (4.96)

(-2.15)

(3.61) (1.93) (82.77) (5.79)

(2.39) (0.47) (164.14) (3.25)

(-1.17) (1.00) (107.78) (4.89)

(3.03) (0.17) (95.84) (5.13)

(2.44) (0.17) (33.95) (3.97)

(2.12) (0.76) (19.00) (2.40)

(1.33) (-0.24) (37.56) (5.44)

(-2.12)

(2.95) (0.52) (75.80) (5.31)

(1.25) (1.62) (75.85) (3.63)

(2.14) (1.21) (85.30) (6.58)

(-4.60)

(2.03) (1.94) (57.42) (7.06)

(2.14) (-1.11) (137.76) (5.25)

(2.08) (0.15) (111.25) (5.44)

Arch(-2) Arch(-3)

(2.85) (-0.82) (4.04)(38.39)

(196.42)

Arch(-4) Arch(-5) Arch(-6)

(0.53) (0.48) (4.53)

Constant Dummy 03 Garch(-1) Arch(-1)


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III Commodity export price volatility and developing country growth 1 Commodity export price volatility: recent evidence The preceding section looked at price volatility of individual commodities. This section examines the incidence of commodity price volatility for specific countries and country groups and the period 1960–2010, based on country- or group-specific commodity price indices. Each such index refers to monthly prices of 48 commodities, including 16 food commodities, 13 agricultural raw materials, 13 metals, and 6 energy commodities (for a list, see Annex table 1).5 Each commodity represents a product group at the three-digit level of the Standard International Trade Classification (SITC).6 The 48 commodities represent half of the 96 commodity product groups in the SITC-classification and cover about 85 per cent of developing countries’ (and an average of about 75 percent of world) commodity exports over the past 15 years. The price indices are geometric Laspeyres (i.e. base-weighted) indices, as introduced by Deaton and Miller (1995) and subsequently also used by Dehn (2000) and Dehn, Gilbert and Varangis (2005).7 They are constructed as follows:

iWii

PIndex

where Pi is the international price for commodity i expressed in dollar and the weighting item Wi is the value of commodity i as a share of the total value, n, of a country’s (or country group’s) exports of the 48 commodities in the base period j. The base period is set to 1995.8 The percentage change in the index is a weighted average 5 For 12 of these 48 commodities, price data are available only starting in January 1970. These commodities (other meat, fish, crustaceans, barley, wood, sawn wood, coal, refined petroleum, petroleum products, liquefied gas, natural gas, and silver) are not included in the calculations for the 1960s. Not included in this list of 48 commodities are diamonds and gold, i.e. two product groups that may be considered as commodities, although they are not usually categorized as such. However, there is no world price for diamonds and the price of gold is strongly influenced by inflation expectations and the use of a store of value by both individuals and central banks, rather than by fundamental production and consumption relationships. 6 Previous studies that used country-specific commodity price indices (e.g., Dehn (2000), Dehn, Gilbert and Varangis (2005), and Gilbert and Morgan (2010)) are based on trade data at the level of narrow individual commodities. Proceeding at the 3-digit level of the SITC classification, as done in this study, allows for a significant widening in the country coverage. For many low-income countries, i.e. those for which commodity exports often are of key importance, trade data are either incomplete or simply unavailable from standard trade databases, such as the United Nations Comtrade database, which rely on reported data. This study uses trade data from the UNCTAD database, which makes reported data mutually consisted (divergences may arise from cross-country differences in the treatment of trade related to export processing zones, variations over time in the categorization of trade as ‘special transaction’, etc.), and includes estimates to guarantee wide data coverage. 7 The advantages of using geometrically weighted indices are that each country-specific index is (i) unique, as it reflects the composition of a country’s export basket, and (ii) exogenous, i.e. it does not include any supply responses to changes in world prices. The disadvantage of using the same set of world prices for all countries is that doing ignores the fact that different countries may receive different prices for their products, for example because of quality differences. However, there is no reason to believe that such quality-related price differences introduce an additional element to price volatility. 8 Given that the recent commodity price boom was broad based, setting the base period to 2008 would affect the weights of individual commodities only marginally. Sharp changes in export basket weights


13

of the percentage change in the prices of the 48 commodities. Choosing country- or group-specific weights ensures that each index is unique. The index can be calculated in terms of nominal prices (expressed in dollar) or in real terms, e.g. deflated by a standard dollar deflator (such as the United States producer price index).9 The volatility of nominal commodity price indices has increased over the past 50 years. In the period 2003–2010, it was significantly higher than in earlier decades. This is true for developing countries as a group, as well as for all major groupings of developing countries (figure 5).10 Commodity price volatility in the period 1974–1990 was generally higher than during 1960–1972. However, this increase is not surprising, given that the commodity price booms that occurred in 1973–74 and 1979–81, as well as the two major oil price shocks of the 1970s, are part of the second period. Volatility subsequently declined and during 1991–2002 was at its lowest level across the four sub-periods for the group of developing countries taken together, as well as for the vast majority of the major developing country groupings. This period broadly covers the years of the great moderation, i.e. the period during which the world economy experienced high, stable and non-inflationary rates of economic growth.11

arise only with substantial diversification at the extensive margin, such as through the discovery and exploitation of oil or mineral resources. 9 Deflating may also be based on import unit values, as proposed by Dehn, Gilbert and Varangis (2005). However, monthly data on import unit values are available (e.g., from the IMF’s International Financial Statistics database) only for a small number of countries. Dehn (2000) uses quarterly indices and deflates by the unit value index of developed country manufactured exports, which is similar to Deaton and Miller’s (1995) use of the index of imports of manufactured goods by developing countries. Collier and Goderis (2010) use each country’s export unit value index, and Molina (2010) deflates by using each country’s consumer price index. 10 Central America and South America are the two exceptions to this general pattern. However, high price volatility measured for these two regions are largely due to sharp price declines in January 1965, August 1966 and April 1968 of coffee, copper and sugar, i.e. three commodities that are important elements in these countries’ export baskets. 11 The period 1991–2002 also covers the, albeit relatively modest, commodity price boom of the mid-1990s.


14

Source: Authors' calculations based on data from the UNCTAD database.

Figure 5Volatility of nominal commodity price indices, selected country groups. 1960–2010

0

5

10

15

20

25

30De

velo

ping

cou

ntri

es

Carib

bean

Cen

tral

Am

eric

a

Sou

th A

mer

ica

Wes

t Asi

a

LDCs

Tran

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n ec

onom

ies

Nor

th A

frica

Sub

-Sah

aran

Afri

ca

SSA

ex

Sout

h A

fric

a

S SE

E A

sia

Dev

elop

ing

Amer

ica

Dev

elop

ing

Asia

MEN

A

Per c

ent

1960-1973 1974-1990 1991-2002 2003-2010

Variability in the value of the dollar may explain part of the variability of the nominal price indices, particularly for the post-1973 period, i.e. following the breakdown of the Bretton-Woods exchange-rate regime and the adoption of widespread floating. Deflating the nominal commodity price indices by the United States producer price index gives a set of real commodity price indices. While deflation generally reduces the contrast between the period 2003–2010 and the earlier periods, real commodity price indices also document the large increase in commodity price volatility over the past few years (figure 6).


15

Note: Nominal price indices def lated by the United States producer price index.Source: Authors' calculations based on data from the UNCTAD database and IFS.

Figure 6Volatility of real commodity price indices, selected country groups. 1960–2010

0

5

10

15

20

25

30D

evel

opin

g co

untr

ies

Car

ibbe

an

Cen

tral A

mer

ica

Sout

h A

mer

ica

Wes

t Asi

a

LDC

s

Tran

sitio

n ec

onom

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Nort

h A

frica

Sub-

Saha

ran

Afri

ca

SSA

ex S

outh

Afri

ca

S SE

E A

sia

Deve

lopi

ng A

mer

ica

Dev

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ing

Asia

MEN

A

Per c

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1960-1973 1974-1990 1991-2002 2003-2010

The level of commodity price volatility differs across countries, depending on the composition of a country’s commodity exports and especially the degree to which these exports are diversified. However, contrary to what might be expected, figures 5 and 6 do not suggest that a more diversified commodity export basket is associated with a lower level of commodity price volatility. In the period 2003–2010, volatility in those country groups – North Africa, sub-Saharan Africa excluding South Africa, and West Asia – in which oil and gas account for a very large proportion of exports was lower than in those groups of countries whose export baskets are more diversified, such as Developing America and Developing Asia. Country-specific evidence further nuances the picture and shows that the incidence of price volatility strongly depends on the importance of specific products in a country’s commodity export basket, independently of the concentration of that basket on one or two commodities (figure 7). Countries (such as Bolivia, Chile, Nigeria and Zambia) for which copper, oil or gas account for a large share of commodity exports have experienced high price volatility, with the peak being in the period 2003–2010. Countries (such as Ethiopia and Uganda) whose main export commodity is coffee have also experienced relatively high levels of price volatility, though with a sharp drop for the period 2003–2010. The same is true for Bangladesh whose main export commodities are crustaceans and jute. By contrast, countries (such as Argentina, Brazil and the United Republic of Tanzania) that have a more diversified commodity export basket experienced relatively low levels of price volatility, but also with a peak in the period 2003–2010. This country-specific evidence on the relationship between diversification and price volatility contradicts that regarding country groups, referred to above. On the other hand, it supports the finding that volatility of an export basket


16

is strongly determined by what commodities are included, rather than by how concentrated that basket is.

Note: Nominal price indices def lated by the United States producer price index.Source: Authors' calculations based on data from the UNCTAD database and IFS.

Figure 7Volatility of real commodity price indices, selected countries, 1960–2010

0

5

10

15

20

25

30

Chi

le

Tanz

ania

Arg

entin

a

Braz

il

Burk

ina

Faso

Mal

aysi

a

Zam

bia

Sout

hAf

rica

Nig

eria

Sene

gal

Boliv

ia

Ban

glad

esh

Ethi

opia

Ugan

da

Per c

ent

1960-1973 1974-1990 1991-2002 2003-2010

2 Commodity export price volatility and the resource curse Developing countries for which commodities account for a sizeable part of their export earnings have shared the experience of rapid economic growth that characterized the group of developing countries as a whole prior to the outbreak of the current crisis. Moreover, developing countries with higher shares of manufactures in their export baskets have tended to be affected worse by the global economic downturn and their economic rebound was generally slower than that in natural resource economies, which have benefited from continued relatively high prices and buoyant commodity imports by China and other emerging economies (add chart or table). This observation contrasts with the often made claim that there is an inverse relationship between natural resources and economic performance. This so-called “resource-curse hypothesis” is usually based on the observation that many countries with important natural resource sectors have tended, on average, to do less well than other countries in terms of economic growth, the incidence of poverty and the distribution of income. In an influential series of studies, Sachs and Warner (1995, 1997, 1999) provide evidence suggesting that economies with a high share of natural resource exports in GDP in 1970 subsequently had a worse average growth performance than economies with little or no natural-resource exports.


17

This inverse relationship between natural resources and economic growth may relate to three effects. First, substantial earnings from commodity exports tend to result in an appreciation of the real exchange rate and to attract resources from other activities, thus discouraging diversification into non-commodity production and export activities. This effect is generally referred to as the ‘Dutch disease’.12 Second, the fact that commodities can often be easily extracted at low cost and generate large rents can be conducive to rent seeking and undermine the development of good institutions, necessary for long-term growth. Mehlum, Moene and Torvik (2006), for example, argue that the growth effect of natural resources varies with institutional quality: natural resources affect economic growth negatively only in economies with bad institutions but boost growth once their quality exceeds a critical threshold.13 However, political tensions and governance problems in poor countries may be related to the fact that commodity production, especially extractive industries, is geographically concentrated. This immobility of resource exploitation may cause local populations and governments dispute attempts by central governments to tax and distribute resource rents more widely. Third, price volatility can give rise to the resource curse because strongly fluctuating prices increase uncertainty and risk, which discourages investment. For example, Blattman et al (2007) show that “countries that specialize in commodities with substantial price volatility have more volatility in their terms of trade, enjoy less foreign direct investment, and experience lower growth rates than countries that specialize in commodities with more stable prices or countries that are industrial leaders. Countries in the periphery with volatile commodity prices and undiversified economies fall behind in economic development.” The adverse effects of commodity price fluctuations on non-commodity sectors is heightened if such volatility occurs in periods of commodity price booms that are combined with real exchange rate appreciations. In this case, export baskets may be highly concentrated in commodities and government budgets may strongly depend on fiscal revenues from commodity activities. This causes substantial difficulties in macroeconomic management and increases the share of economic activity and fiscal revenues exposed to adverse price shocks. The result may be unanticipated and abrupt changes in output growth.14 In a recent study, van der Ploeg and Poelhekke (2009) combine the natural resource literature with Ramey and Ramey’s (2005) investigation of the link between volatility of unanticipated output growth on the one, and growth performance on the other hand. In a first step, they show that the significance of the variable that reflects natural resource dependence in studies that follow the lines of Sachs and Warner (1995, 1997,

12 Specializing in commodity production is often seen as a problem because its potential for productivity growth, technological growth and linkages with other economic activities is lower than for manufacturing. Moreover, there may be a secular trend for commodity prices to decline relative to those of manufactures, implying that countries for which commodities account for the bulk of their exports earnings will suffer from declining terms of trade, as postulated by the Prebisch-Singer hypothesis. 13 It should be noted, however, that indicators of institutional quality – such as the rule of law, a functioning bureaucracy, and the absence of corruption – may be an outcome of economic development rather than a pre-condition for sustained economic growth. 14 It may also be price cycles that cause the governance problems observed by studies such as Mehlum, Moene and Torvik (2006)


18

1999) disappears once a variable is introduced that controls for output volatility.15 This fact is shown in table 3 where the coefficient on the variable “natural resource exports / GDP in 1970” is no longer significant when the standard deviation of GDP per capita growth is introduced as an additional variable. Moreover, the coefficient on this variable is strongly significant and has the expected negative sign.

Dependent variable (constant 2005international dollars, PWT 6.3)

Natural resource exports / GDP in 1970 -2.60 ** -1.43 -1.58 * -0.27(-2.41) (-1.14) (-1.77) (-0.25)

Log of real GDP per capita in 1970 -0.65 * -0.76 ** -1.03 *** -1.10 ***(-1.89) (-2.21) (-2.74) (-3.00)

Fraction of years open to trade (1970–2007) 8.64 ** 7.68 ** 10.69 *** 9.65 ***(2.60) (2.31) (3.47) (3.16)

Institutional quality in 1982 0.33 *** 0.30 *** 0.28 *** 0.27 ***(3.03) (2.76) (2.91) (2.86)

Average investment share of GDP (1970–2007) 0.10 *** 0.10 *** 0.07 *** 0.07 ***(5.37) (5.56) (4.17) (4.31)

Interaction term -1.07 ** -0.96 * -1.30 *** -1.18 **(-2.10) (-1.89) (-2.79) (-2.55)

Distance to coast or nearest navigable river -0.00 ** -0.00 **(-2.49) (-2.57)

Average population growth rate (1970–2007) -24.16 -21.45(-1.21) (-1.09)

Human capital in 1970 0.18 ** 0.16 *(2.13) (1.95)

Standard deviation of -0.11 * -0.11 ** GDP per capita growth (1970–2007) (-1.82) (-2.01)

Constant 2.22 3.59 5.26 ** 6.28 **(1.00) (1.55) (2.09) (2.49)

R-squared 0.53 0.55 0.70 0.71Number of countries 92 92 78 78

Interaction term: Log of real GDP per capita in 1970 * fraction of years open

Average GDP growth per capita 1970–2007

Table 3

Overall, van der Ploeg and Poelhekke (2009) show that the share of natural resources in GDP has a positive effect on economic growth, while the volatility of this share has a negative growth effect. They argue that commodity price volatility drives the volatility of the share of natural resource exports in a country’s GDP which, in turn, results in volatility of unanticipated output growth and depresses output per capita growth in countries that heavily depend on natural resources. While van der Ploeg and Poelhekke (2009, 2010) argue that the volatility of output is caused by commodity price volatility, they use the volatility of countries’ share of resource exports in GDP as a measure for commodity price volatility, but provide no evidence for the adverse impact on output growth of price volatility itself. The 15 This result mirrors a more general finding in the recent literature, namely that resource abundance, i.e. resource wealth, has a positive effect on economic growth (e.g., Brunnschweiler and Bulte, 2008).


19

remainder of this section uses the volatility of country-specific commodity export price indices to explore this link directly. It combines the share of commodity revenues in GDP (i.e. the value at risk) with commodity price volatility (i.e. the incidence of risk) based on changes in world prices (i.e. the probability of risk is the same for all countries).

Average resource dependence (1980) Average Export priceresource volatility

96 countries dependence(1980) (1980–2007)

Mean Mean Mean Averagevolatility

1st quartile total (?20.5 ) 38.2 15.1 1.0 6.24th quart ile total (?5.4 ) 3.3 13.3 2.4 3.4

1st quartile point resources (?8.3 ) 17.1 14.2 1.2 4.94th quart ile point resources (?2.6 ) 1.5 14.0 2.3 4.1

1st quartile dif fuse resources (?12.9 ) 29.0 15.5 1.2 6.64th quart ile diffuse resources (?0.7 ) 0.2 12.9 1.4 4.5

1st quartile food (?6.9 ) 12.4 13.9 1.5 4.04th quart ile food (?1.7 ) 0.8 14.1 2.1 4.2

1st quartile agri (?1.7 ) 5.7 13.5 1.2 5.24th quart ile agri (?0.2 ) 0.1 16.1 1.1 5.7

1st quartile ore (?3.0 ) 13.0 15.5 0.8 6.04th quart ile ore (?0.1 ) 0.0 14.6 1.3 5.4

1st quartile fuels (?4.1 ) 20.3 14.3 1.7 4.74th quart ile fuels (?0.1 ) 0.0 14.0 1.1 5.3

1st quartile share of most important commodity (? )4th quart ile share of most important commodity (? )

1st quartile share of most important 3 commodities (? )4th quart ile share of most important 3 commodities (? )

Table 4

Per capita

(1980–2007)

income growth

Resource dependence and volatility

Overall, table 4 shows that higher resource dependence (measured as the share of resource exports in GDP) in 1980 was associated in the period 1980–2007 with (i) higher export price volatility, (ii) lower average per capita income growth, and (iii) greater volatility of annual rates of per capita income growth. This evidence also holds if total resource dependence is disaggregated in dependence on point or diffuse resources. However, it no longer holds once resource dependence is further disaggregated into the four main commodity categories. This suggests that beyond resource dependence as a whole, the country-specific composition of export baskets determines the impact of resource dependence on economic growth. To be completed


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IV Commodity import price volatility and net food-importing developing countries While the above concentrated on export-based commodity price indices, the price volatility of commodity imports may be of greater importance for low-income food-deficit countries (LIFDCs).16 Domestic and import liberalization that many developing countries have adopted since the 1980s have significantly increased the exposure of domestic food markets to world commodity price shocks. Food security used to be narrowly defined in terms of self sufficiency while with import liberalization the supply potential from international trade has increasingly played an important role. A major objective of market liberalization was to develop domestic markets and infrastructure. But it is precisely those countries that have been successful in doing so that are now exposed most to world market disruptions. LIDFCs have experienced a significant deterioration of their agricultural trade balance over the past two decades and the growth of these countries’ food import bills has consistently outstripped that of GDP and of total merchandise exports. Liberalization and integration into world food markets has often also been accompanied by a tendency of households, especially in urban areas, to diversify their diets and move away from a dominant domestically produced staple. Such diversification may provide greater flexibility in reacting to sharp price spikes to the extent that such spikes are not correlated across different food products. Liberalization and greater reliance on global food markets may improve resource allocation in world food production. However, reductions in food stocks of major producers, including the United States and the European Union, combined with rapid demand growth particularly in Asia, has induced higher and more unstable prices. It is well known that food price volatility particularly hurts poor households the majority of which are net food purchasers, jeopardizing household food security and nutrition. Since they usually also need to purchase energy, they are double hurt. Underdeveloped physical and market infrastructure and the attendant high transport and transaction costs hamper the transmission of international prices into domestic markets. And ample foreign exchange reserves and the fiscal space required for the adoption of compensatory measures, such as the reduction of import tariffs and the deployment of targeted subsidies to protect the poorest and most vulnerable households, allow improved absorptive capacity of external price shocks. All of the countries selected from the group of LIFDCs experienced the highest (or in the case of Nepal and Uganda almost the highest) level of price volatility in the period 2003–2010 (figure 8). Among these countries, price volatility is highest where oil and wheat account for an important share of commodity imports.

16 The FAO determines the category of low-income food-deficit countries, which currently includes 77 countries, on the basis of three criteria: …


21

Note: Nominal price indices def lated by the United States consumer price index.Source: Authors' calculations based on data from the UNCTAD database and IFS.

Figure 8Volatility of real commodity price indices for imports,selected low-income food-deficit countries, 1960–2010

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5

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15

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25Ta

nzan

ia

Bang

lade

sh

Burk

ina

Faso

Bur

undi

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Hond

uras

Keny

a

Mal

i

Nepa

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Togo

Uga

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Egyp

t

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bia

Per c

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1960-1973 1974-1990 1991-2002 2003-2010

High unpredictability of commodity prices can cause an increase in the unpredictability of import prices if import positions are hedged through options, as shown by Sarris, Conforti and Prakash (2010) for maize imports and the period 2006–2008. This means that to the extent that the greater involvement of financial investors in commodity trading results in an increase in price uncertainty, low-income food-deficit countries may suffer greater import bill unpredictability. V Conclusions Commodity price volatility is unlikely to disappear. This may improve the probability that the calls for improved national and international mechanisms to contain commodity price volatility remain on the agenda of policymakers. Similar calls that arose after earlier price spikes were mostly abandoned once the spikes had passed. However, rather than aiming at complete price stability, the objective should be to remove extreme price changes and attain a tolerable level of price variability. This would require a reconsideration of the relative importance of domestic production, imports and reserves in stabilizing commodity (food) prices. The management of commodity price volatility must be part of a holistic strategy that combines long-term supply development (to address ongoing changes in fundamental supply and demand relationships) with measures that reduce the impact of financial market fluctuations on commodity prices (to address adverse impact from financialization). National policies adopted by developing countries clearly play an important role in the former, while the latter is better addressed by multilaterally coordinated international policies. National policies aiming at stabilizing prices in the


22

short run – including export bans, reductions in import tariffs and the adoption of subsidies to offset import price spikes – are desirable as emergency measures. However, in the long term they are likely to reinforce, rather than dampen, international price volatility and to undermine incentives for long-term investment in market development and productivity growth. Three-pronged approach: Increasing production and transparency in physical market:

o Addressing the long-term neglect of investment in developing country agriculture (research, examining the role of ‘land grabbing’ on food markets), reconsidering agricultural subsidies in developed countries, and speeding up exploration in extractive industries.

o Improving the transparency of physical commodity markets. This may

counter short-term national policies that reduce the supply of commodities available on world markets (such as export bans).

o For food commodities, reconsidering the establishment of international

grain reserves, i.e. an issue that the 2008- and 2009-meetings of the G8 had discussed but that has not been explored further.

Improving the transparency and regulation of commodity exchanges:

o The recently adopted tighter regulation of financial markets should be complemented by measures to improve transparency and increase the cost of speculation (e.g., capital deposits, position limits) for non-commercial traders.

Improving the mechanisms designed to deal with adverse effects of volatility,

particularly as it affects food security


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References Aizenman J and N Marion (1999). Volatility and investment: interpreting evidence form developing countries. Economica. 66( ): 157–179. Aulerich NM, Irwin SH and Garcia P (2010). The price impact of index funds in commodity futures markets: evidence from the CFTC’s daily large trader reporting system. Available at: farmdoc.illinois.edu/irwin/research/PriceeImpactIndexFund,%20Jan%202010.pdf. BIS (2009). Quarterly Review. September 2009. Bollerslev T (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics. 31 ( ): 307–327. CFTC (2008) Chevalier JM (2010). Rapport du groupe de travail sur la volatilité des prix du pétrole. Paris: Ministère de l’Economie, de l’Industrie et de l’Emploi. Collier P and B Goderis (2009). Commodity prices, growth and the natural resource curse. Deaton A and R Miller (1995). International commodity prices, macroeconomic performance, and politics in sub-Saharan Africa. Princeton Studies in International Finance No. 75. October. Dehn J (2000). Commodity price uncertainty in developing countries. Working Paper No. 2426. World Bank. Washington DC. August. Dehn J, CL Gilbert and P Varangis (2005). Agricultural commodity price volatility. In: Aizenman J and Pinto B, eds. Managing Economic Volatility and Crises: A Practitioner’s Guide. Cambridge and New York, Cambridge University Press. Gilbert CL (2010). Speculative influences on commodity futures prices 2006–2008. Discussion Paper No 197. UNCTAD: Geneva. Gilbert CL and CW Morgan (2010). Food price volatility. Philosophical Transactions of the Royal Society B. No. 365: 3023–3034. Gorton G and G Rouwenhorst (2006). Facts and Fantasies about commodity futures. Financial Analysts Journal 62 (2): 47–68. Hamilton J (2009). Causes and consequences of the oil shock of 2007-2008. Working paper, UC San Diego. Irwin SH and DR Sanders (2010). The impact of index and swap funds on commodity futures markets. OECD Food, Agriculture and Fisheries Working paper No. 27. Paris: OECD.


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Koren M and S Tenreyro (2007). Volatility and development. Quarterly Journal of Economics. 122(1): 243–287. Krugman P (2008). Fuels on the hill. The New York Times. June. Kyle A and W Xiong (2001). Contagion as a wealth effect. Journal of Finance. 56( ): 1401–1440. Mayer J (2010). The growing interdependence between financial and commodity markets. Discussion Paper No 195. UNCTAD: Geneva. Medina L (2010). The dynamic effects of commodity prices on fiscal performance in Latin America. Working Paper No. 09/56. International Monetary Fund. Washington DC. August. Mitchell D (2008). A note on rising food prices. Working Paper No. 4682. World Bank. Washington DC. July. Nissanke M (2010). Ramey G and VA Ramey (1995). Cross-country evidence on the link between volatility and growth. American Economic Review. 85(5): 1138–51. Sarris A, P Conforti and A Prakash (2010). The use of organized commodity markets to manage food import price instability and risk. Agricultural Economics. Forthcoming. Tang K and W Xiong (2010). Index investment and financialization of commodities. Princeton University. Working Paper 16385. National Bureau of Economic Research. Cambridge (Mass). September UNCTAD (2005). Trade and Development Report 2005. United Nations publication, New York and Geneva. UNCTAD (2008a). Report on Food Crisis. UNCTAD (2008b). Trade and Development Report 2008. United Nations publication, New York and Geneva. UNCTAD (2009a). The Global Economic Crisis: Systemic Failures and Multilateral Remedies. United Nations publication, New York and Geneva. UNCTAD (2009b). Trade and Development Report 2009. United Nations publication, New York and Geneva. United Nations (2009). World Economic and Social Survey 2009 — Promoting Development, Saving the Planet. New York: United Nations. United States Department of Agriculture (2009). 2009–18 Long-Term Agricultural Projections. Long-term projections Report OCE-2009-1)


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van der Ploeg F and S Peolhekke (2009). Volatility and the natural resource curse. Oxford Economic Papers. 61 (4): 727–760. van der Ploeg F and S Peolhekke (2010). The pungent smell of “red herring”. Subsoil assets, rents, volatility and the resource curse. Journal of Environmental Economics and Management. 60(1): 44–55. von Braun J and M Torero (2008). Physical and virtual global food reserves to protect the poor and prevent market failure. Policy Brief 4, International Food Policy Research Institute, Washington, DC, June. World Bank (2009). World Development Report 2010: Development and Climate Change. Washington DC: World Bank.


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DataNumber SITC code Commodity source

1 011 Beef CPB 0.7 0.52 012 Other meat (lamb, pork, poultry) IMF+Japan 1.3 0.63 034 Fish (tuna, salmon) IMF 1.0 0.84 035 Fishmeal CPB 0.1 0.15 036 Crustaceans (shrimp) IMF 0.5 0.76 041 Wheat CPB 1.0 0.27 042 Rice CPB 0.4 0.78 043 Barley IMF+Japan 0.2 0.09 044 Maize CPB 0.6 0.3

10 046 Meal and flour of wheat and flour of meslin CPB 0.1 0.111 057 Fruits and nuts (bananas and oranges) CPB+IMF 1.4 1.412 061 Sugar CPB 0.5 0.713 071 Coffee CPB 0.6 0.814 072 Cocoa CPB 0.3 0.415 074 Tea CPB 0.1 0.216 075 Spices CPB 0.1 0.217 121 Tobacco, unmanufactured CPB 0.2 0.318 211 Hides and skins CPB 0.1 0.019 222 Oil seeds for soft fixed oils CPB 1.1 1.020 223 Oil seeds for other fixed oils CPB 0.1 0.021 231 Rubber CPB 0.4 0.922 247 Wood, rough CPB 0.3 0.223 248 Wood, sawn CPB 0.8 0.424 263 Cotton CPB 0.3 0.225 264 Jute CPB 0.0 0.026 265 Vegetable textile fibres (sisal) CPB 0.0 0.027 268 Wool CPB 0.1 0.128 272 Crude fertilizers CPB 0.2 0.329 281 Iron ore and concentrates CPB 1.5 1.430 283 Copper ores and concentrates CPB 0.8 1.331 284 Nickel ores & concentrates; nickel mattes, etc. CPB 0.2 0.232 285 Aluminium ores and concentrates (incl. alumina) CPB 0.3 0.233 287 Ores and concentrates of base metals, n.e.s. CPB 0.7 0.934 321 Coal IMF 2.0 1.235 333 Petroleum oils, crude CPB 30.4 45.136 334 Petroleum oils, refined CPB 15.5 13.537 335 Residual petroleum products Japan+US 0.8 0.638 342 Liquefied propane and butane IMF+Japan 0.7 0.839 343 Natural gas IMF+Japan 5.9 4.440 421 Fixed vegetable fats & oils CPB 0.8 0.641 422 Other fixed vegetable fats & oils CPB 0.8 1.542 681 Silver CPB 1.1 1.043 682 Copper CPB 2.7 2.844 683 Nickel CPB 0.5 0.145 684 Aluminium CPB 2.6 1.646 685 Lead CPB 0.1 0.147 686 Zinc CPB 0.2 0.248 687 Tin CPB 0.1 0.2

SUM 80.5 88.9

Includes the 21 most important export commodities of developing countries in 1995(the date of the index weights) except054 Vegtb etc fresh, simply prsrvd and 037 Fish etc prepd, prsrvd nesThe products combined account for 83-89% of developing country commodity exportsand 70-81% of world commodity exports in the period 1995–2008

Table A1

Commodities included in country-specific indices

countriesWorld

2008 share incommodity exports from

Developing

commodity price volatility: causes and impact on low ... · particular, they point to the greater...

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