Do oil prices drive agricultural commodity prices? Further evidence in a global bio-energy context

Accepted Manuscript Do Oil Prices Drive Agricultural Commodity Prices? Further Evidence in a Global Bio-energy Context

Chi Wei Su, Xiao-Qing Wang, Ran Tao, Oana-Ramona LobonŢ PII:

S0360-5442(19)30217-8

DOI:

10.1016/j.energy.2019.02.028

Reference:

EGY 14670

To appear in:

Energy

Received Date:

19 July 2018

Accepted Date:

03 February 2019

Please cite this article as: Chi Wei Su, Xiao-Qing Wang, Ran Tao, Oana-Ramona LobonŢ, Do Oil Prices Drive Agricultural Commodity Prices? Further Evidence in a Global Bio-energy Context, Energy (2019), doi: 10.1016/j.energy.2019.02.028

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT

Do Oil Prices Drive Agricultural Commodity Prices? Further Evidence in a Global Bio-energy Context

Chi-Wei Su School of Economics, Qingdao University

Xiao-Qing Wang Department of Finance, Ocean University of China

Ran Tao Qingdao Municipal Center for Disease Control & Preventation

Oana-Ramona LOBONŢ Department of Finance, West University of Timisoara



Corresponding author: Xiao-Qing Wang, Department of Finance, Ocean University of China, Qingdao, Shandong, China. TEL: 86-18661491158. Address: 238, Songling Rd., Qingdao, Shandong, China. E-Mail: [email protected].

ACCEPTED MANUSCRIPT

Abstract This study explores the causalities between oil and agricultural commodity prices to examine whether the vertical market integration model holds for global market. Considering structural changes, the long-run nexus using full-sample data is found to be unstable, suggesting the causality test is not reliable. Instead a time-varying rolling-window technique is further employed to reexamine the dynamic causal relationships. The empirical results illustrate that the time-varying positive bidirectional causality exists between oil and agricultural prices over certain sub-periods, which supports the vertical market integration model that energy and agricultural prices can interact through direct biofuel channel and indirect input channel. Furthermore, our findings demonstrate that price transmission between two series occurs to agricultural commodities used both directly and indirectly in bio-energy productions. In order to support a relatively stable price level of oil and agricultural commodities, the system mandating global cooperation and concerted action should be expanded to maintain a strategic petroleum reserve. In addition, authorities should curb speculations in the commodity derivatives market. Moreover, the subsidy measure for particular commodities should be implemented, which is propitious to curb the contagious effect of sudden change in prices.

Keywords: Crude Oil Price; Agricultural Commodity Price; Rolling Window; Bootstrap; Time-Varying Causality JEL: C32; Q18; Q42

1

ACCEPTED MANUSCRIPT

1. Introduction1 Crude oil prices (COP), as well as agricultural commodity prices (ACP), are momentous determinants of the global economic performance. Specifically, as a vital commodity in the world, crude oil significantly influences different sectors of economy through direct and indirect transmissions [1,2]. For both developing and industrialized economies, ACP has significant welfare and policy implications [3]. On one hand, as crude oil is a fairly significant input for transportation and processing in the agricultural sector, the increasing prices of oil boosts crop prices by pushing the costs of production [4,5]. On the other hand, changes in commodity prices alter agricultural fuel demand, which involves an adjustment in COP [6]. As the importance of COP and ACP for the economy becomes increasingly prominent, price linkages between the two groups of commodities have attracted widespread attention [7]. Surges in crude oil prices, coupled with concern about increasingly prominent environmental problems triggered by excessive emissions, have stimulated demand for biofuels as substitutes for fossil fuels, leading to an increasingly wide attention to the stronger linkages between the two sectors [8,9,10]. The soaring agricultural commodity prices since the global food crisis in 2008 expose producers and consumers to additional risk, causing considerable stress especially in food-insecure, developing countries [11]. It is crucial for policymakers to be aware of the links between

Abbreviations ACP COP EISA 2007 EPAct 2005 FAO LR RB VAR WTI 1

Agricultural commodity price Crude oil price Energy Independence and Security Act of 2007 Energy Policy Act of 2005 Food and Agriculture Organization Likelihood ratio Residual-based bootstrap Vector auto-regression West Texas Intermediate

2

ACCEPTED MANUSCRIPT

the two sectors in order to adopt an efficient set of policy instruments to maintain the prices stable. Global crude oil prices have experienced dramatic booms and busts over the past decade. Owing to the weakening of the U.S. dollar, geopolitical events like Iraq war and Israel war, and the growing oil demand in the U.S. and China, oil prices have increased sharply since 2003 [12]. Through to mid-2008, crude oil increased its price four-fold from $29 per barrel to $147 per barrel, reaching a record high. Then following the global financial crisis it plummeted dramatically to $40 per barrel in December 2008. The political and macroeconomic events in relation to fuel producers such as Libya, Yemen and Egypt again drove price up to $100 per barrel in 2011 and 2012 [2]. As a consequence, the complex behavior of crude oil prices has brought substantial impact to agricultural commodity prices through production and transportation costs [13,14]. Besides, the accelerating process of biofuel production has put pressure on ACP, which results in their rapid rise [15]. It is observed that the tendency of main agricultural commodity prices has roughly followed the same pattern as oil prices [16]. The prices of major commodities, including maize and soybeans, more than doubled during the food crisis from 2006 to mid2008, and then experienced a drastic downward trend during the global financial crisis [5,14]. The World Bank’s nominal monthly food price index reached its highest level in 2011. As reported by Food and Agriculture Organization (FAO), due to the continuous growth of ACP, developing economies which primarily rely on agricultural imports are confronted with the payment of extra costs of 324 billion dollars [17]. The fluctuating agricultural commodity prices have put pressure on the household sector and thus exacerbated global hunger problems, posing a serious policy challenge [10,15].

3

ACCEPTED MANUSCRIPT

Considering the structural changes induced by political and macroeconomic events, it is vital to investigate the time-varying interdependences between COP and ACP, which can provide significant implications for accurate policy adjustments based on dynamic market conditions. More recently, the rapid growth of bio-energy production has caused an extra positive demand shock on agricultural commodity market, which profoundly spurs the food versus fuel debate [6]. Particularly, government subsidies and tax incentives, such as the EPAct 2005 and the Renewable Energy Directive of 2009, widely encourage biofuel production, strengthening the interrelation between energy and agricultural commodity markets [14]. Following the vertical market integration model developed by Ciaian and Kancs [6], price transmission mechanism between COP and ACP is confirmed to produce effect through direct biofuel channel and indirect input channel. Taking the case of direct biofuel channel, on the one hand, the usage of biofuels increases the demand and price of agricultural commodities which are essential feedstocks for bio-energy production [3]. On the other hand, the expansion of production in biofuels which are regarded as an alternative to conventional fuels, exert a downward pressure on high oil prices through the substitution effect between fuel and bio-energy. Moreover, the biofuel channel is demonstrated to play a stronger role in the interdependencies between energy and agricultural markets than the input channel [6], which highlights the significance of formulating appropriate bio-energy policies in relation to stable oil and food prices. Taking structural changes into account, we examine the causalities between COP and ACP utilizing bootstrap full- and sub-sample rolling window technique. Based on this method, the contribution of this study includes three main aspects. First, we analyze price

4

ACCEPTED MANUSCRIPT

transmission not only for agricultural commodities directly employed but also for commodities not used in bio-energy productions. Existing researches mainly focus on agricultural commodities in relation to ethanol and biodiesel production. Nevertheless, considering the high integration of agricultural sectors, shocks in one sector may be transmitted to other related sectors [18]. The inclusion of agricultural commodities not used for biofuel producing process, together with the identification of the direct or indirect mechanism of price transmission, would be conducive to a more comprehensive analysis on the interaction between overall agricultural and oil markets. Second, we explicitly examine the time-varying dynamics of the causalities by using rolling window estimation. Due to political and economic turmoil or policy changes the causal nexus may shift with time [19]. According to Balcilar et al. [20], the existing studies, considering only fullsample causalities, are prone to misguided conclusions due to parameter instability caused by structural changes. The rolling sub sample estimation in fixed-size windows possesses strong power in capturing the structural breaks as well as the evolution of causalities between the two series. Finally, this study applies bootstrap causality tests to each sub sample and thus provides remarkably effective analysis, especially when the regularity conditions, including parameter constancy and stationarity, are violated. Based on this method, we document several important findings. The empirical results show a bidirectional positive causality between COP and ACP in certain sub-periods, which is consistent with the vertical market integration model that energy and agricultural commodity prices can interact through direct biofuel channel and indirect input channel. In addition, the price transmission between the two series occurs in agricultural commodities both directly and indirectly used in biofuel production, indicating the strong

5

ACCEPTED MANUSCRIPT

correlation between fuel and the overall agricultural commodity markets. This paper proceeds as follows: section 2 reviews the related literature, the vertical market integration model is introduced in section 3, the methodology is explained in section 4, followed by section 5 which describes the corresponding data and provides the empirical results, concluding with section 6.

2. Literature Review The literatures on the relationships between energy prices and agricultural commodity markets have expanded rapidly over the last decade. So far, most part of the past research has concentrated on two aspects: price-level interactions [3,5,12,14,18,21] and volatility spillover effects in related markets [2,4,9,13,15,19,22]. In view of the energy substitutions promoted by biofuel policies, the issue on the energy-food nexus taking biofuel market into account has gained considerable interest among researchers [7,23,24]. Most biofuel-related studies analyze data representative of the U.S. and Brazil, since they are the major producers of biofuel [4]. Fernandez-Perez et al. [7] argue that the outbreak of biofuels industry further accelerates the linkage between agricultural and energy markets. Nazlioglu et al. [13] state that the usage of biofuels has increased the volatility spillovers between energy and biofuel-related commodity markets. Consequently, assessing price transmission between energy and agricultural markets along the biofuel and crop chain is of critical importance. Causalities between COP and ACP have become the subject of ongoing debate among researchers [5,14,25,26]. There are quite diverse conclusions in the prior studies. Specifically, the unidirectional causality between COP and ACP is illustrated in numerous

6

ACCEPTED MANUSCRIPT

investigations [27]. Overwhelming support for the argument that ACP responds to oil price movements is found [5,28,29]. Chang and Su [30] and Saghaian [31] confirm the price transporting mechanism from COP to ACP for both short- and long-run, respectively. As indicated by Chen et al. [12], the continuous rise in COP produces profound influence on price dynamics of agricultural commodities. Ciaian and Kancs [6] provide evidences that support the positive impact of COP on prices of nine major agricultural commodities through direct biofuel and indirect input channels. Myers et al. [28] demonstrate the contemporaneous unidirectional causality from COP to ACP and summarize a rule that crude oil exerts greater influence at high price level. Fernandez-Perez et al. [7] suggest a constant nonlinear price linkage from COP to ACP. Pal and Mitra [5] argue that the increases in oil prices expand the demand for feedstock of biofuels, thus driving up the agricultural commodity prices. There are also many academic researches confirming the causality running from ACP to COP. As demonstrated by Zhang et al. [32], the price of an agricultural commodity like sugar, as a signal to restore market equilibrium, produces a positive impact on COP. Natalenov et al. [25] find that two agricultural commodities (wheat and coffee) precede oil prices due to the uncertainty of politics and economy. Vacha et al. [33] argue that agricultural commodity prices lead energy prices at high-frequency dimension during the food crisis period. Avalos [34] confirms the price transmission running from ACP to COP after the Energy Policy Act of 2005 (EPAct 2005). Moreover, the bidirectional causality between the two series is expounded in certain investigations. Nazlioglu and Soytas [18] find evidence of a positive interaction between COP and twenty-four groups of ACP in the short term, which may be triggered by the

7

ACCEPTED MANUSCRIPT

financialization of commodity markets and diversification of portfolio. Pala [35] points out that the causality between price indexes of Brent crude oil and agricultural commodity is bidirectional over the long term. Rezitis [29] presents in his work that the increases of oil prices push up agricultural prices, and vice versa. Apergis et al. [36] support the hypothesis that bidirectional causalities do exist between COP and ACP series in the process of achieving long-term equilibrium, indicating the informational benefits across markets. Nevertheless, there are certain researches supporting the hypothesis that there is no correlation between the two series [11]. Reboredo [37] provides evidence for weak tail dependence, which implies that extreme oil price fluctuations have no effect on agricultural markets. Gilbert [38] attributes changes in ACP to the effects of other related factors, such as monetary expansion and exchange rate fluctuations, which are disassociated from oil prices. Qiu et al. [39] indicate that COP has no effect on agricultural prices and the major drivers of commodity prices are market forces of demand and supply. Fowowe [14] demonstrates that ACP in South Africa does not respond to the world COP, thus indicating the neutrality of commodity markets. Existent theoretical investigations have confirmed the price transmission mechanisms from COP to ACP [11,17,36]. Under the indirect transmission mechanism, oil has significant effects on agricultural prices through two channels – fertilizer and transportation costs [14]. A rise in COP pushes up production cost by increasing the cost of various key energy-intensive inputs like fertilizers and chemicals, which ultimately results in the uptrend of agricultural commodity prices [15]. Besides, the increase in COP makes the cost of transportation higher, thus boosting agricultural prices [17]. Under the direct transmission mechanism, the sharp increases in oil prices stimulate production of biofuels

8

ACCEPTED MANUSCRIPT

which are regarded as cost-effective alternatives to conventional fuels [30]. In consideration of the profitability of the available alternatives, larger parts of the lands are allocated for biofuels rather than for foodstuff production [5,32]. As biofuels are primarily extracted from crops like maize and soybeans, the expansion of production scale in biofuels triggers the increase in demand and also prices of agricultural commodities [3,34]. Moreover, COP and ACP are likely to be indirectly linked through exchange rate where the rising trend of oil prices result in the depreciation of local currency and consequently affecting the local prices of commodities [18]. In respect to methodology, the majority of previous empirical studies, estimating the existence and directions of the correlations between the two series, employs the cointegration techniques and its derivatives, such as Vector auto-regression (VAR) and Granger causality test [25,29,36]. Nevertheless, the results derived from these empirical studies utilizing full-sample data may neglect time-varying features which can provide significant implications for policymakers [34]. Over the last decades, the global financial crisis, bio-energy policies and regulations, and food crisis may have led to certain structural breaks in oil and agricultural markets, thus indicating the dynamic nexus between the two series [2,13]. Du and McPhail [26] regard March 2008 as a structural shift, and show a reinforced link after the breakpoint. Pala [35] also confirms two structural breaks in 2008, implying an unstable relationship between COP and ACP. Han et al. [8] reveal the structural heterogeneity in the causality between the two series during the biofuel policies and the global financial crisis periods. Therefore, this paper employs the rolling-window technique on the basis of modified bootstrap estimations to test for causality across subsamples with fixed-size windows. The method is more conducive to capturing structural

9

ACCEPTED MANUSCRIPT

shifts as well as the evolution of causality between two series, which is more appropriate for relevant policy formulation and adjustment under the tough background of food security.

3. The Vertical Market Integration Model We consider the vertical market integration model from Ciaian and Kancs [6] to explain the potential price transmission mechanism between COP and ACP. The model assumes that the representative farm can make a substitutable selection between producing two agricultural commodities (biomass and food)2 based on two inputs: crude oil and other inputs like land. The representative farm is assumed to maximize the standard profit function: 𝛱 = ∑𝑃𝑖𝑄𝑖(𝑁𝑖,𝐾𝑖) - 𝑤𝑁𝑖 - 𝑟𝐾𝑖. The equilibrium conditions are expressed as follows: 𝑃𝑖∂𝑄𝑖/∂𝑁𝑖 = 𝑤

for i =AB, AN

(1)

𝑃𝑖∂𝑄𝑖/∂𝐾𝑖 = 𝑟

for i =AB, AN

(2)

where Q represents production function; N represents non-fuel input (land); K represents crude oil input; P represents output price; w represents the price of land lease, and r is crude oil price. The indexes AB and AN represent biomass and food commodities, respectively. Equations (1) and (2) reveal the marginal conditions for the two kinds of inputs, respectively. Biofuel production is then considered. Each biomass unit can yield 𝛽 units of biofuels and bio-energy productions can yield feed by-product 𝛾. To simplify the analyses,

2Ciaian

and Kancs [6] present two categories of agricultural commodities: one can be used in biofuel productions (described as ‘biomass’) and one cannot be used in biofuel productions (described as ‘food’).

10

ACCEPTED MANUSCRIPT

we make the assumption that the constant values of unit processing costs, c, incur to bioenergy production from a unit of biomass. In doing so, bio-energy profitability primarily depends on the biomass and by-product prices net of processing costs. Besides, biofuel, 𝑆𝐵 (𝑟), and by-product, 𝑆𝑂(𝑃𝑂), supplies stand for the excess supply of biomass over demand, 𝑆𝐵 = 𝛽(𝑆𝐴𝐵 - 𝐷𝐴𝐵) and 𝑆𝑂 = 𝛾(𝑆𝐴𝐵 - 𝐷𝐴𝐵), respectively. Thus, we summarize the following market equilibrium conditions: 𝐵 𝑂 𝐴𝐵 𝑂 if 𝑃𝐴𝐵 = 𝑆𝐴𝐵 𝑜 ≥ 𝛽𝑟 + 𝛾𝑃𝑜 ―𝑐 ⟹ 𝑆 = 𝑆 = 0 ⟹ 𝐷 𝐵 𝑂 𝑂 𝐴𝐵 if 𝑃𝐴𝐵 ―𝐷 𝑜 < 𝛽𝑟 + 𝛾𝑃𝑜 ―𝑐 ⟹ 𝑆 > 0, 𝑆 > 0 ⟹ 𝑆

𝐴𝐵

(3)

>0

⟹ 𝑃𝐴𝐵 = 𝛽𝑟 + 𝛾𝑃𝑂 ―𝑐 𝑆𝐴𝑁 = 𝐷𝐴𝑁

(4) (5)

𝑂 where 𝑃𝐴𝐵 𝑜 is equilibrium price of biomass without regard to bio-energy production, 𝑃𝑜

is by-product price without regard to by-product production based on biomass. Equation (3) and Equation (4) determine the equilibrium condition of biomass. Its price level depends on the supply and demand situation of biomass, if the bio-energy return is lower than 𝑃𝐴𝐵 𝑜 . Contrarily, if the return is higher, the bio-energy supply will be positive. The model implies that price transmission mechanism occurs in both directions: from COP to ACP and vice versa. The transfer processes of price signals in the two directions also occur through two channels: direct biofuel and indirect input channels. First, crude oil price rise can be transmitted to ACP through the increase of production cost as well as the reduction of commodity supplies (indirect input channel). Second, the development of biofuel gives strong impetus to the rise in biomass price (direct biofuel channel). Since the demand for biomass in bio-energy producing process increases competition of all inputs, the continuously rising prices of inputs contribute to a further upward trend of food prices.

11

ACCEPTED MANUSCRIPT

To sum up, both channels imply a positive price transmission from crude oil to agriculture. Conversely, transmissions from agricultural to oil prices depend crucially on food demand elasticities. For the inelastic food demand, the decline of fuel demand in agriculture leads to lower oil prices [6]. For the elastic food demand, the indirect input channel implies that higher agricultural productivity provides internal impetus to the demand for oil in agriculture, thus pushing oil prices up. In regard to the direct biofuel channel, on one hand, biofuels boost COP, since the increase of biomass production triggered by bio-energy demand contributes to more agricultural demand for fuel. On the other hand, biofuels exert a downward pressure on COP by increasing the aggregate supply of fuel. To summarize, the indirect input channel indicates positive price transmitting mechanism between two series for inelastic food demand, and negative link between series for elastic food demand. Bio-energy could strengthen, weaken, or offset the impacts.

4. Methodology Based on the underlying theoretical framework, the bidirectional price interdependencies between energy and agricultural commodity prices imply that both COP and ACP are endogenous. However, in standard regression models by placing particular variables on the right hand side, the endogeneity of all variables sharply violates the exogeneity assumption of a regression equation. The problem can be circumvented by specifying VAR models on a system of variables, because no such conditional factorisation is made a priori in VAR framework. Therefore, the estimation of price interaction between COP and ACP series is conducted on the basis of a bivariate VAR model. Furthermore, whether the oil price would be driving agricultural prices or vice versa is also a central

12

ACCEPTED MANUSCRIPT

question in our research. The consideration motivates our choice of modified causality tests in order to study the direction of causalities between COP and ACP series. Bootstrap full-sample causality test The Granger causality tests make an assumption that underlying series are stationary. According to Toda and Phillips [40], if the stationary hypothesis does not hold, the Wald and Likelihood Ratio (LR) techniques may possess non-standard asymptotic distribution. The modified Wald test proposed by Toda and Yamamoto [41] performs satisfactorily in obtaining standard asymptotic distribution through the estimate of augmented VAR models with I(1) variables. We utilize modified Granger non-causality tests on the basis of a bivariate VAR framework. Another issue with standard causality tests is that they may cause specification bias and non-asymptotic critical values especially when dealing with a small sample. Shukur and Mantolos [42] proves that the Toda and Yamamoto [41] test possesses incorrect size in a small or medium sample. By means of employing the residualbased bootstrap (RB) technique and critical values, Shukur and Mantalos [43] achieve definite improvements with regard to the power and size properties. In this sense, regardless of cointegration properties, the outstanding performance of RB test has been demonstrated in a number of researches [20,44]. Moreover, the modified-LR test exhibits better size and power features, even when dealing with a small sample [45]. This paper turns to the RB-based modified-LR technique to investigate causalities between the series of COP and ACP. The bivariate VAR (p) process is given by: 𝜙 𝜙 [𝐶𝑂𝑃 𝐴𝐶𝑃 ] = [𝜙 ] + [𝜙 1𝑡

10

2𝑡

20

11(𝐿)

][

] [ ]

𝜙12(𝐿) 𝐶𝑂𝑃1𝑡 𝜀1𝑡 + 𝜀 (𝐿) 𝜙 (𝐿) 𝐴𝐶𝑃 21 22 2𝑡 2𝑡

13

t=1, 2, … , T

(6)

ACCEPTED MANUSCRIPT

where 𝐶𝑂𝑃1𝑡 and 𝐴𝐶𝑃2𝑡 indicate crude oil and agricultural prices, respectively. 𝜀𝑡 = ( 𝜀1𝑡,𝜀2𝑡)' is a white noise process with zero mean and covariance matrix ∑. 𝜙𝑖𝑗(𝐿) = 𝑝+1

∑𝑘 = 1𝜙𝑖𝑗,𝑘𝐿𝑘 , i, j =1, 2 and L is the lag operator defined as 𝐿𝑘𝑥𝑡 = 𝑥𝑡 ― 𝑘. On the basis of Equation (6), the null that COP does not Granger cause ACP is tested by imposing the restriction 𝜙12,𝑘 = 0 for k=1, 2, …, p. Analogously, the null hypothesis that ACP does not Granger cause COP is examined through the restriction 𝜙21,𝑘 = 0 for k=1, 2, …, p. In respect to the full-sample causality test, we rely on RB based p-values and modified-LR statistics. The p-value is defined as the probability, calculated under the null hypothesis, of having outcome as extreme as the observed value in the sample. In this regard, the significant causality running from COP to ACP can be confirmed when the first null is rejected, implying that price oscillation of oil is induced by policies would transform agricultural commodity market performance. Contrarily, if the second null can be rejected, ACP can predict the movements in COP. Parameter stability test The full-sample causality tests generally indicate a single causal nexus between COP and ACP in the sample period, due to the assumption that parameters are constant over time. Nevertheless, the estimation result would be unreliable and the nexus would show instability, if the underlying series exhibit structural breaks [46]. Consequently, the Sup-F, Mean-F and Exp-F tests are conducted to examine shortterm parameter stability [47,48]. Following Nyblom [49] and Hansen [50], we adopt the Lc test to examine long-term parameters stability. The four test statistics are defined in Appendix A. These tests are calculated according to the sequence of LR statistics that aims

14

ACCEPTED MANUSCRIPT

to check the parameter stability against the alternative of a single structural shift at an unknown time. Following Andrews [47], given that the tests for short-term parameter stability require 15 percent trimming from both ends of samples, we use samples in the fraction of (0.15, 0.85). Sub-sample rolling-window estimation Following Balcilar et al. [20], this paper utilizes the rolling-window bootstrap estimations to avoid pre-test bias and overcome the parameters inconsistence. There are two advantages of the method. Firstly, the rolling window allows for changes in causalities between variables. On the basis of the underlying theoretical analysis, the dynamic interrelation between COP and ACP as well as the possible changes in price transmission between series through direct biofuel and indirect input channels can be robustly studied through the rolling window technique. Secondly, in the presence of structural breaks, the rolling estimations can provide suitable tools for observing instability across different subsamples. The rolling window method is based on fixed-size sub-samples rolling sequentially from the beginning to the end of sample. Consequently, given a fixed-size rolling window including l observations, the full-sample can be separated into T-l sub-samples, that is, τl+1, τ-l,..., T for τ=l, l+1,…,T. Possible time variations in the causality between COP and ACP can be intuitively identified by calculating the bootstrap p-values of observed LRstatistic rolling through T-l sub-samples. The impact of COP on ACP is measured by 𝑁𝑏―1 𝑝

∗ ∑𝑘 ― 1𝜙21,𝑘 , where Nb represents the number of bootstrap repetitions. Analogously, the 𝑝

∗

∗

∗

impact of ACP on COP is measured by 𝑁𝑏―1∑𝑘 ― 1𝜙12,𝑘. Both 𝜙21,𝑘 and 𝜙12,𝑘 are

15

ACCEPTED MANUSCRIPT

bootstrap estimates from the VAR models. According to Balcilar et al. [20], the 90% confidence intervals are computed, where the lower and upper limits equal 5th and 95th ∗

∗

quantiles of each of 𝜙21,𝑘 and 𝜙12,𝑘, respectively. In the rolling-window estimation, the representativeness of models in sub-samples and accuracy of parameter estimates are two conflicting targets. A large window size could improve parameter accuracy but reduce representativeness of models. On the contrary, a small size could improve representativeness but reduce the accuracy. Based on Monte Carlo simulations, Pesaran and Timmerman [51] suggest that the window size should be larger than 20 when there are frequent shifts.

5. Data and Empirical Results To detect the causal links between COP and ACP, monthly data are employed covering the period from January 1990 to February 2017. In this sample period, possible structural breaks and the evolution of causality between series can be identified because our data set comprises geopolitical events, technological changes, speculations and crisis. The West Texas Intermediate (WTI) crude oil spot price is acquired from the Energy Information Administration. WTI crude oil is produced and traded around the world, thus reflecting the global supply and demand situation [52]. The WTI spot price is extensively regarded as one of the major international benchmarks for petroleum price, which is widely adopted as representative price in oil business and financial trading [53]. Oil prices are susceptible to policies and military actions, which has significant implications for investors to seek diversified portfolio. Since the Gulf War in 1990, WTI oil price has experienced substantial fluctuations, increasing the vulnerability of global economy. Market

16

ACCEPTED MANUSCRIPT

participants respond strongly to the price movements, which generate considerable economic uncertainty [39]. In the early 2000s, price of WTI presents a continuous sharp uptrend due to the growing demand from the U.S. and China and the stagnation of oil production as a result of geographical tensions such as Iraq war and Israel war, transforming the global commodity market performance [12]. The soaring and crashing tendency of WTI oil price before and after the global subprime crisis can also contribute to the structural changes. In addition, we obtain the world prices of four agricultural commodities (maize, soybeans, tea and cocoa beans) from the Commodity Prices Database of International Financial Statistics. The four variables of ACP are world benchmark series of prices, representing the global market [18]. Furthermore, maize and soybeans are energy-intensive product and used for production of biofuel, while tea and cocoa beans are not employed in bio-energy production, which is propitious to the comparative analysis for price transmission of agricultural commodities used and unused in biofuel productions [6]. The sample period contains the food crisis, the global financial crisis, and a series of energy and biofuel policy adjustments, which may account for the structural breakpoints. Among them, the EPAct 2005 particularizes bio-products as substitutes for conventional fuels, enhancing economic stability. The Energy Independence and Security Act of 2007 (EISA 2007) is signed into law, which attempts to explore further the development of bio-products and actualize greater energy independence [9]. To rectify potential heteroscedasticity and dimensional difference, the original series are transformed into natural logarithms. Based on three univariate tests for testing the stationarity of COP and ACP, including the ADF, PP, and KPSS tests, the variables are both stationary and integrated at level I(1). On the basis of Schwarz information criterion, the optimal lag orders based on of the VAR

17

ACCEPTED MANUSCRIPT

models for the four agricultural commodities are 2. The full-sample estimation results are indicated in Table 1. On the basis of the bootstrap p-values, the null hypotheses that COP does not Granger cause ACP are not rejected for the four agricultural commodities, implying that COP has no impact on ACP. Conversely, the null hypothesis that ACP does not Granger cause COP is rejected, suggesting that agricultural prices have predictive ability for oil price movements. These findings identify unidirectional causalities running from AOP to COP, which is inconsistent with some of the existent studies [5,28,29,38]. < Table 1 is inserted about here> The assumption of causality test denotes that parameters of VAR models should be constant over time. Considering the presence of structural breaks, the causalities between COP and ACP may be unstable, since the parameters of full-sample estimates shift with time. Therefore, when the assumption of parameter constancy is invalid, the full-sample causality tests may provide misleading conclusions due to structural breaks or regime shifts [46]. To prevent the possible inaccuracy in identifying the correlation between series, this study proceeds to examine the existence of structural breaks by performing parameter stability tests. Table 2 presents the corresponding results. The Sup-F test under the null hypothesis of parameter constancy against a one-time sharp shift is presented in the first row of each commodity series. The results imply the existence of a one-time sharp shift in ACP, COP and VAR systems for four series. The Mean-F and Exp-F approaches test the null that parameters follow a martingale process against a gradual evolution in parameters. On the basis of corresponding results, equations of ACP, COP and VAR systems evolve gradually over time for the four series. The Lc statistics test against that parameters follow a random walk process, which indicates the parameter non-constancy. Accordingly, the

18

ACCEPTED MANUSCRIPT

results suggest that parameters of the VAR models exhibit parameter instability. On the basis of parameter stability tests, VAR models estimated using full-sample data are unstable due to structural breaks during the sample period, which indicates that the result of a unidirectional causality is unreliable. Therefore, we take structural breaks into consideration by utilizing the rolling-window technique to examine causalities between AOP and COP. In consideration to the time-varying parameters across sub samples, this technique is superior in examining the links between series more accurately. Utilizing the RB-based modified-LR causality test, the bootstrap p-values of LR-statistics are estimated from VAR models using the rolling sub-sample data including 36-months observations3. Figs. 1-4 reveal the plots of the bootstrap p-values of LR statistics based on subsample data and the magnitude of the impact between series for maize, soybeans, tea and cocoa beans, respectively. Panel (a) and (c) in each figure display the bootstrap p-values testing the null hypothesis that COP does not Granger cause ACP and vice versa, respectively. In addition, the bootstrap estimates of sum of the rolling coefficients are presented in panel (b) and (d) in each figure, measuring the effect of COP on ACP and vice versa, respectively. Fig. 1 and Fig. 2 display the rolling window estimation results of maize and soybeans, which are directly used in bio-energy productions. According to panel (a) of Fig. 1 and Fig. 2, we can clearly see that the null that COP does not Granger cause ACP is rejected at 10% in some periods, including 1997:02–1999:06, 2003:09–2004:09, 2007:11–

3We

also performed the bootstrap rolling-window causality test in the window size of 24, 32, 48 months and computed the effect of COP on ACP and vice versa. Our findings show similarity with those from the tests in the 36-months window size, indicating that the results in the window size of 36 months are robust.

19

ACCEPTED MANUSCRIPT

2009:01 and 2012:02–2013:04. Panel (b) of Fig. 1 and Fig. 2 reveal that COP exerts a positive impact on ACP during the sub-periods mentioned above. After the Gulf war, COP presented periodic uptrend in the mid-1990s, due to the strong economic growth in the U.S. and the boom of the Asia-Pacific region, leading to the cyclical rising trend of ACP [16]. Prior to the Asian financial crisis, COP showed a modest rising trend in mid-1997, as a result of the alteration in the cost of storage and the general expectation among traders, triggering a temporary rise in ACP for a short time [37]. Afterwards, when the crisis erupted together with an increasing OPEC production, COP switched to a sharp downtrend, swiftly followed by the decline of ACP through the indirect input channel. However, during the period of early-1999, the rise in COP due to the reduction of OPEC output brought about the rapid increase of ACP. Iraq war, launched in March 2003, triggered higher precautionary demand related to the concern about oil supply, which pushed up the COP and had a potentially huge impact on ACP. The rapid development of emerging economies, especially since the second half of 2003, drove greater international demand for crude oil, leading to a dramatically high level of COP. Through the indirect input channel shown in the theoretical framework, the huge increase of COP caused general escalation in the costs of fertilizers and transportation, thus accounting for the rising agricultural commodity prices [7]. Meanwhile, the expansion of biofuels industries since the early 2000s in response to the high oil prices has created a positive link from COP to ACP through the biofuel channel. Under the direct biofuel transmission mechanism, increases in COP stimulate production of bio-energy due to the substitution effect on conventional fuels. Then the expansion of production scale in biofuels triggers the increase in demand and also prices of agricultural commodities, since biofuels are primarily extracted from crops like

20

ACCEPTED MANUSCRIPT

maize and soybeans [34]. Furthermore, speculative funds put substantial amounts of money into the oil futures markets, of which about 70% was associated with energy like crude oil. The rapid increase of index investment in oil market led to excess oil demand and further pushed up the COP, resulting in the upward trend of ACP by increasing the production costs [29]. After the global financial crisis, a collapse in demand for oil, due to the sharp decreasing global economic activities and speculative investments gradually withdrawing from the oil markets, have caused a crash in COP [32]. By reducing the costs of related agricultural resources, the downward trend of COP stimulates the ACP to decrease and strengthens the links between two series. Forward, around 2012, continuous geopolitical events associated with oil producing countries such as Libya and Yemen drove up the COP once again, resulting in transformation of the ACP movements. Nevertheless, the decline of COP, as a result of continuous production for crude oil by OPEC members and high production of shale by the U.S., leads to the drop in ACP since 2013 [2]. In summary, the above results imply that COP has time-varying positive impact on ACP during the sample period. On the basis of the underlying theoretical framework, during the early period when the biofuel sector was still at the initial stage, COP and ACP are expected to interact only through indirect input channel. With the rapid development of biofuels industries, price signals are further transmitted through direct biofuel channel. These results are consistent with the findings of Pal and Mitra [5], who investigated the causality between world food prices (cereals, vegetable oils, and sugar) and energy prices, and found that ACP co-moves with and is led by COP. Based on that, the price transmission from COP to ACP through direct biofuel channel is further confirmed by Pal and Mitra [5] that fluctuations in COP could change demand for agricultural commodities as feedstocks of biofuels, which leads

21

ACCEPTED MANUSCRIPT

to the movements of ACP. Also, our findings are consistent with Rezitis [29] and Saghaian [31] that agricultural prices respond positively to energy price changes. Conversely, the null hypothesis that ACP does not Granger cause COP can be rejected at 10% level in 1998:09–1999:08 and 2008:03–2009:06, according to panel (c) of Fig. 1 and Fig. 2. Panel (d) reveals ACP exerts positive impact on COP during the two sub-periods. With the spreading and deepening of the Asian financial crisis all over the world due to financial contagion, ACP presented a downward trend as a result of economic fluctuations, causing a significant effect on the movement of COP. However, in the wake of the EISA promulgated in December 2007 in order to reduce energy dependence and achieve supply security, farmers were encouraged to produce large amounts of biofuels, like bioethanol and biodiesel. Under the indirect input transmission mechanism, COP showed a continuous rising trend, due to the increasing demand for fuel in agricultural sector caused by greater biomass production. Furthermore, during the food crisis, the prices of major crops soared through direct biofuel and indirect input channels, together with the increase in COP until the middle of 2008 because of the increased purchasing power and the decline in production and stock balance [5]. Then the shock of the global financial crisis accounted for the concurrent drop in ACP and COP during the period from late-2008 to mid-2009. Accordingly, the positive causality running from ACP to COP is demonstrated. As implied by the indirect input channel in the theoretical framework, the increase of fuel demand in agriculture leads to a higher level of COP [6]. In regard to the direct biofuel channel, the bio-energy demand is expected to strengthen the impacts. In summary, the rolling window estimation results shown in Fig. 1 and Fig. 2 indicate the positive bidirectional causality between COP and ACP directly used in bio-energy production during certain specific

22

ACCEPTED MANUSCRIPT

periods, which supports the vertical market integration model that price transmission mechanism occurs in two directions – from COP to ACP and vice versa [6]. Nevertheless, in other periods, no significant correlations between the two series are confirmed, supporting the neutrality hypothesis that ACP does not respond to COP. The result is in accordance with the findings of Nazlioglu and Soytas [11] and Fowowe [14] that agricultural prices are neutral to global energy price. < Fig. 1 and Fig. 2 are inserted about here> Fig. 3 and Fig. 4 point out the rolling window estimations in the case of tea and cocoa beans, which are not employed in bio-energy production. As shown in panel (a) of Fig. 3 and Fig. 4, the null is rejected at 10% level in 1996:11–1997:03, 2005:01–2005:07 and 2011:05–2011:09, indicating that COP has the ability to explain the movements in ACP. Panel (b) presents that COP exerts positive impact on ACP series during the sub-periods, which is consistent with the results deriving from the rolling-window estimation for maize and soybean series. During the period of 1996-1997, the contradictions between excess production and insufficient demand triggered the decline of COP, thus leading to a temporary downward trend of ACP. In the wake of the rapid development of biofuels under the encouragement of governments to expand the production of renewable energy sources, surges in COP stimulated the production of biofuels which were primarily extracted from crops like maize and soybeans [2,3]. As the global cropland endowment was limited, the planting acreages for energy-related commodities were expanded due to the increased demand, leading to the decrease in supply and the rise in prices of other agricultural commodities [7]. Besides, as shown in theoretical framework, prices of the agricultural commodities which are not directly used in bio-energy productions are also affected

23

ACCEPTED MANUSCRIPT

through agricultural factor prices. With the expansion of biofuels industries, the increase in production of agricultural commodities directly used in bio-energy productions pushes up agricultural factor prices, which ultimately raises the non-biofuel agricultural prices. Analogously, as shown in panel (c) and panel (d) of Fig. 3 and Fig. 4, ACP has a positive impact on COP in 1993:02–1993:09, 1999:04–2000:07 and 2009:08–2011:10. Due to anomaly in climate and changes in global demand, international output of agricultural commodities experienced a series of fluctuations, which transformed the trend of ACP and further triggered the price linkage of crude oil [32]. In summation, the results in Fig. 3 and Fig. 4 confirm the positive bidirectional causality between COP and ACP which are not employed in bio-energy production. Consequently, the results from rolling window estimates in Figs. 1-4 indicate three main conclusions. First, the time-varying positive bidirectional causality exists between COP and ACP in certain sub-periods, supporting the vertical market integration model that COP and ACP can interact through direct biofuel channel and indirect input channel. Second, no significant correlations between the two series are confirmed in other periods, supporting the neutrality hypothesis that price signals cannot be transmitted between oil and agricultural markets. Third, price transmission between COP and ACP occurs to agricultural commodities both directly and indirectly used in bio-energy productions. The result is consistent with the findings of Ciaian and Kancs [6] that price signals are transmitted between fuel and the overall agricultural markets, including agricultural commodities indirectly used for biofuels industries. To stabilize the price level of oil and agricultural commodities, the subsidy measure for particular commodities should be designed and implemented to minimize the effect of sudden changes in COP and ACP, particularly in the period of political and

24

ACCEPTED MANUSCRIPT

economic turmoil. < Fig. 3 and Fig. 4 are inserted about here> In this paper, the bootstrap rolling-window estimations offer supplementary perspective on the causality between COP and ACP. The correlation between COP and ACP is basically consistent with the vertical market integration model in certain subperiods [6]. The nexus is not stable over time, which is actually very suitable as structural breaks in COP and ACP, such as geopolitical events, technological changes, speculations and crisis, have resulted in deviation over the past decades. Since the Gulf War in 1990, the political, economic and geopolitical events intensively affect price level of crude oil and agricultural commodities, which has a significant effect on supply-demand activities and appropriate policy adjustments [19]. Concerns about energy security due to political uncertainty in the Middle East have pushed COP up and facilitated the run-up in ACP. With the rapid expansion of biofuel sector in the 2000s, the agricultural production system has turned into an energy producer, firming the interaction between two series. During the 2008 financial crisis, the agricultural and energy markets have experienced synchronized boom and bust cycles. The financialization of commodity market has further strengthened the integration of agricultural and energy markets [13]. All of these events could bring temporary shocks or induce structural changes to the correlation between COP and ACP, which causes the global commodity markets to become more vulnerable to external shocks. Therefore, it is critical to support a relatively stable price level and enhance stability of global commodity market. In consideration of ACP being tied to COP, policymakers must incorporate these ACP-COP linkages into policy formulation and adjustment. On the supply side, it is vital to strengthen and expand the system mandating global cooperation

25

ACCEPTED MANUSCRIPT

and concerted action, like the IEA collective action framework, to maintain strategic petroleum reserve when oil supply is disrupted [15]. On the demand side, it is fundamental to readjust the orientation of the commodity derivatives market towards the initial purpose – hedging. Moreover, the subsidy measure for particular commodities should be designed and implemented to minimize the influence of abrupt changes during the period of commodity price turbulence, which is propitious to curb effect of increase in commodity prices on general public.

6. Conclusions This study explores the correlation between COP and ACP to test whether the vertical market integration model is reliable for global market. The bootstrap full-sample causality tests report unidirectional causalities running from AOP to COP. However, taking structural changes into consideration, this paper finds that the long-run relationships are unstable, implying that the causality test is not reliable. We proceed to the bootstrap rollingwindow technique and obtain the following conclusions. First, we confirm the existence of time-varying positive bidirectional causalities between COP and ACP for certain subperiods. The findings are consistent with the vertical market integration model that energy and agricultural commodity prices can interact through direct biofuel channel and indirect input channel. Second, no significant correlations between the two series are confirmed in other periods, supporting the neutrality hypothesis. Third, this paper demonstrates that price transmission between two series occurs to agricultural commodities both directly and indirectly used in bio-energy productions. Although the relationship between COP and ACP is not stable over time, it actually fits well with the fact that the world has undergone

26

ACCEPTED MANUSCRIPT

multiple political shifts and economic structural changes in commodity markets. With regard to limitations of the assumptions, a constant Leontief transformation technology is assumed when considering biofuel production. However, the constantly updated bioenergy technology may alter the extraction coefficient and exerts an effect on transmission process of price signals. In addition, perfect market adjustments are assumed in the theoretical framework of price transmission to the non-biofuel agricultural commodities. But the market rigidity and imperfection existing in reality could reduce or delay price adjustments [6]. These findings assure a further proof of the value of maintaining a relatively stable price level of oil and agricultural commodities especially in terms of political and economic structural changes. Considering the importance of direct biofuel transmission mechanism between COP and ACP, relevant policy formulation and implementation aimed at biofuel productions provide efficacious approach for policymakers to monitor and regulate oil and agricultural prices. Our findings further provide important implications for the authorities that much broader influence of oil prices on agricultural commodities both directly and indirectly used in biofuel productions should be taken into account in the process of price policy adjustments. Besides, it is crucial to strengthen and expand the system mandating global cooperation and concerted action, like the IEA collective action framework, to maintain strategic petroleum reserve when oil supply is disrupted. Apart from that, authorities should curb speculations in the commodity derivatives market. Moreover, the subsidy measure for particular commodities should be implemented, which is propitious to curb effect of sudden change in commodity prices on general public. According to the research question addressed in this paper, future empirical studies

27

ACCEPTED MANUSCRIPT

can focus on the price transmission between oil and agricultural commodities at a national level or cross countries. The applications of this empirical framework can also be extended by discussing about structural changes and the time-varying relationships between broader energy prices and other bulk commodities. Moreover, future research can assign increased priority to the different driving factors of commodity prices (macroeconomic determinants, financialization and weather variables, as example) and quantify their impacts.

Appendix A In order to examine the parameters stability, the Sup-F, Mean-F, Exp-F, and Lc statistics are calculated. Given a time series 𝑥𝑡 (t =1, 2, … , n), the parameter 𝛽 takes the value 𝛽1 for t < i and the value 𝛽2 for t ≥ i. Let 𝐹𝑖 denote LR statistic of the hypothesis of no structural change (𝛽1 = 𝛽2) for given i. When i (the date of structural change) is known only to lie in the interval [𝑖1, 𝑖2], the Sup-F statistic is: 𝑆𝑢𝑝𝐹 = sup 𝐹𝑖 𝑖1 ≤ 𝑖 ≤ 𝑖2

Following Andrews and Ploberger [48], the Mean-F statistic is:

28

(A.1)

ACCEPTED MANUSCRIPT

1

𝑖

(A.2)

𝑀𝑒𝑎𝑛𝐹 = 𝑖2 ― 𝑖1 + 1∑𝑖2= 𝑖 𝐹𝑖 1

Besides, the Exp-F statistic is: 1

1

𝑖

𝐸𝑥𝑝𝐹 = ln (𝑖2 ― 𝑖1 + 1∑𝑖2= 𝑖 exp (2𝐹 )) 𝑖

1

(A.3)

According to Nyblom [49] and Hansen [50], the Lc statistic is:

{

𝑛

}

―1∑ 𝐿𝑐 = tr 𝑀𝑛𝑛 𝑆 Ω ―1 𝑆' 𝑖=1 𝑖 1∙2 𝑖 𝑖

(A.4)

𝑖

where 𝑀𝑛𝑛 = ∑𝑡 = 1𝑥𝑡𝑥'𝑡 and 𝑆𝑖 = ∑𝑡 = 1𝑠𝑖. Note that, although the null hypothesis of SupF, Mean-F, Exp-F and Lc tests is constant, their alternative hypotheses are different [48,50]. Among these four statistics, the Sup-F tests whether a one-time sharp shift in regime occurs, while the Mean-F and Exp-F test the gradual stability of the model over time and assume that the parameters follow a martingale process. In addition, the Lc tests the parameters constancy against the alternative hypothesis that parameters follow a random walk process.

References [1] Nikkinen J, Rothovius T. The EIA WPSR release, OVX and crude oil internet interest. Energy 2019;166:131–41. [2] Kumar D. On Volatility Transmission from Crude Oil to Agricultural Commodities. Theor Econ Lett 2017;7:87–101. [3] Lucotte Y. Co-movements between crude oil and food prices: A post-commodity boom perspective. Econ Lett 2016;147:142–7. [4] Cabrera BL, Schulz F. Volatility linkages between energy and agricultural commodity prices. Energy Econ 2016;54:190–203. [5] Pal D, Mitra SK. Time-frequency contained co-movement of crude oil and world food

29

ACCEPTED MANUSCRIPT

prices: A wavelet-based analysis. Energy Econ 2017;62:230–9. [6] Ciaian P, Kancs D. Food, energy and environment: is bioenergy the missing link? Food Policy 2011;36(5) :571–80. [7] Fernandez-Perez A, Frijns B, Tourani-Rad A. Contemporaneous Interactions among Fuel, Biofuel and Agricultural Commodities. Energy Econ 2016;58:1–10. [8] Han L, Zhou Y, Yin L. Exogenous impacts on the links between energy and agricultural commodity markets. Energy Econ 2015;49(1):350–8. [9] Jiang J, Marsh TL, Tozer PR. Policy induced price volatility transmission: Linking the U.S. crude oil, corn and plastics markets. Energy Econ 2015;52:217–27. [10] Xu J, Yuan ZH, Chang SY. Long-term cost trajectories for biofuels in China projected to 2050. Energy 2018;160:452–65. [11] Nazlioglu S, Soytas U. World oil prices and agricultural commodity prices: Evidence from an emerging market. Energy Econ 2011;33(3):488–96. [12] Chen ST, Kuo HI, Chen CC. Modeling the relationship between the oil price and global food prices. Appl Energy 2010;87(8):2517–25. [13] Nazlioglu S, Erdem C, Soytas U. Volatility spillover between oil and agricultural commodity markets. Energy Econ 2013;36(3):658–65. [14] Fowowe B. Do oil prices drive agricultural commodity prices? Evidence from South Africa. Energy 2016;104:149–57. [15] Gardebroek C, Hernandez MA. Do energy prices stimulate food price volatility? Examining volatility transmission between US oil, ethanol and corn markets. Energy Econ 2013;40(40):119–29. [16] Gohin A, Chantret F. The long-run impact of energy prices on world agricultural

30

ACCEPTED MANUSCRIPT

markets: The role of macro-economic linkages. Energy Policy 2010;38(1):333–9. [17] Burakov D. Oil Prices, Exchange Rate and Prices for Agricultural Commodities: Empirical Evidence from Russia. AGRIS On-line Pap Econ Inform 2016;08(2):33– 47. [18] Nazlioglu S, Soytas U. Oil price, agricultural commodity prices, and the dollar: A panel cointegration and causality analysis. Energy Econ 2012;34(4):1098–104. [19] Kang SH, Mciver R, Yoon SM. Dynamic spillover effects among crude oil, precious metal, and agricultural commodity futures markets. Energy Econ 2017;62:19–32. [20] Balcilar M, Ozdemir ZA, Arslanturk Y. Economic Growth and Energy Consumption Causal Nexus Viewed Through a Bootstrap Rolling Window. Energy Econ 2010;32(6):1398–410. [21] Koirala, KH, Mishra, AK, D'Antoni, JM, Mehlhorn, JE. Energy prices and agricultural commodity prices: Testing correlation using copulas method. Energy 2015;81:430–6. [22] Ji Q, Bouri E, Roubaud D, Shahzad S. Risk spillover between energy and agricultural commodity markets: A dependence-switching CoVaR-copula model. Energy Econ 2018;75:14–27. [23] Paris A. On the link between oil and agricultural commodity prices: Do biofuels matter? Int Econ 2018;155:48–60. [24] Chiu FP, Hsu CS, Ho A, Chen CC. Modeling the price relationships between crude oil, energy crops and biofuels. Energy 2016;109:845–57. [25] Natalenov V, Alam MJ, McKenzie AM, Van Huylenbroeck G. Is there comovement of agricultural commodities futures prices and crude oil? Energy Policy

31

ACCEPTED MANUSCRIPT

2011;39(9):4971–84. [26] Du X, McPhail L. Inside the black box: the price linkage and transmission between energy and agricultural markets. Energy J 2012;33 (2):171–94. [27] Serra T, Zilberman D. Biofuel-related price transmission literature: A review. Energy Econ 2013;37(1):141–51. [28] Myers RJ, Johnson SR, Helmar M, Baumes H. Long-run and short-run co-movements in energy prices and the prices of agricultural feedstocks for biofuel. Am J Agr Econ 2014;96(4):991–1008. [29] Rezitis AN. Empirical Analysis of Agricultural Commodity Prices, Crude Oil Prices and US Dollar Exchange Rates Using Panel Data Econometric Methods. Int J Energy Econ Policy. 2015;5(3):851–68. [30] Chang TH, Su HM. The substitutive effect of biofuels on fossil fuels in the lower and higher crude oil price periods. Energy 2010;35:2807–13. [31] Saghaian SH. The impact of the oil sector on commodity prices: correlation or causation? J Agr Appl Econ 2010;42(3):477–85. [32] Zhang ZB, Lohr L, Escalante C, Wetzstein M. Food versus fuel: what do prices tell us? Energy Policy 2010;38(1):445–51. [33] Vacha L, Janda K, Kristoufek L, Zilberman D. Time–frequency dynamics of biofuelfuel-food system. Energy Econ 2013;40(2):233–41. [34] Avalos F. Do oil prices drive food prices? The tale of a structural break. J Int Money Finan 2014;42(42):253–71. [35] Pala A. Structural Breaks, Cointegration, and Causality by VECM Analysis of Crude Oil and Food Price. Int J Energy Econ Policy 2013;3(3):238–46.

32

ACCEPTED MANUSCRIPT

[36] Apergis N, Eleftheriou S, Voliotis D. Asymmetric Spillover Effects Between Agricultural Commodity Prices and Biofuel Energy Prices. Int J Energy Econ Policy 2017;7(1):166–77. [37] Reboredo JC. Do food and oil prices co-move? Energy Policy 2012;49(10):456–67. [38] Gilbert CL. How to Understand High Food Prices. J Agric Econ 2010;61(2):398–425. [39] Qiu C, Colson G, Escalante C, Wetzstein M. Considering macroeconomic indicators in the food before fuel nexus. Energy Econ 2012;34(6):2021–8. [40] Toda HY, Phillips PCB. Vector Autoregressions and Causality. Econometrica 1993;61:1367–93. [41] Toda HY, Yamamoto T. Statistical Inference in Vector Autoregressions with Possibly Integrated Processes. J Econom 1995;66:225–50. [42] Shukur G, Mantalos P. Tests for Granger Causality in Integrated-Cointegrated VAR Systems. 1997. Working paper, Department of Statistics, University of Lund, Sweden. [43] Shukur G, Mantalos P. Size and Power of the RESET Test as Applied to Systems of Equations: A Bootstrap Approach. 1997. Working paper, Department of Statistics, University of Lund, Sweden. [44] Mantalos P. A Graphical Investigation of the Size and Power of the Granger-Causality Tests in Integrated-Cointegrated VAR Systems. Stud Nonlinear Dyn Econom 2000;4:17–33. [45] Shukur G, Mantalos P. A Simple Investigation of the Granger-Causality Test in Integrated-Cointegrated VAR Systems. J Appl Stat 2000;27:1021–31. [46] Balcilar M, Ozdemir ZA. The Export-Output Growth Nexus in Japan: A Bootstrap Rolling Window Approach. Empir Econ 2013;44:639–60.

33

ACCEPTED MANUSCRIPT

[47] Andrews DWK. Tests for parameter instability and structural change with unknown change point. Econometrica 1993;61:821–56. [48] Andrews DWK, Ploberger W. Optimal Tests When a Nuisance Parameter Is Present Only Under the Alternative. Econometrica 1994;62:1383–414. [49] Nyblom J. Testing for the Constancy of Parameters over Time. J Am Stat Assoc 1989;84:223–30. [50] Hansen BE. Tests for Parameter Instability in Regressions with I(1) Processes. J Bus Econ Stat 1992;10:321–36. [51] Pesaran MH, Timmermann A. Small Sample Properties of Forecasts from Autoregressive Models under Structural Breaks. J Econom 2005;129:183–217. [52] Cross J, Nguyen BH. The relationship between global oil price shocks and China's output: A time-varying analysis. Energy Econ 2017;62:79–91. [53] Chen W, Huang Z, Yi YP. Is there a structural change in the persistence of WTI-Brent oil price spreads in the post-2010 period? Econ Model 2015;50:64–71.

34

ACCEPTED MANUSCRIPT

Table 1 Full-sample Granger causality tests. H0: COP does not Granger cause

H0: ACP does not Granger

ACP

cause COP

Tests Statistics

p-values

Statistics

p-values

2.124

0.360

6.592**

0.040

2.885

0.310

5.889*

0.060

4.469

0.120

6.970**

0.020

0.637

0.740

4.460*

0.080

Bootstrap LR Test for maize Bootstrap LR Test for soybeans Bootstrap LR Test for tea Bootstrap LR Test for cocoa beans

Notes: We calculate p-values using 10,000 bootstrap repetitions. *, ** denote significance at 10% and 5%, respectively. Table 2 Parameter stability tests. ACP Equation Statistics

Bootstrap p-value

COP Equation Statistics

Bootstrap p-value

VAR(2) System Statistics

Bootstrap p-value

Maize Sup-F

15.272**

0.031

11.919

0.347

13.679*

0.083

Mean-F

2.537**

0.028

6.060

0.258

8.413*

0.069

Exp-F

2.797**

0.046

3.696

0.310

4.797

0.104

1.726**

0.032

Lc Soybeans Sup-F

30.942***

0.000

42.804***

0.000

28.129**

0.035

Mean-F

8.505*

0.065

18.187***

0. 000

18. 314**

0.012

Exp-F

11.340***

0.000

18.251***

0. 000

10.941**

0.030

2.945**

0.014

Lc Tea Sup-F

24.356**

0.022

10.203

0.815

26.601*

0.260

Mean-F

13.492**

0.018

5.031

0.777

16.368**

0.227

Exp-F

9.177**

0.016

3.127

0.777

10.236**

0.244

2.853**

0.168

Lc

35

ACCEPTED MANUSCRIPT

Cocoa beans Sup-F

13.389

0.233

45.346***

0.000

26.336*

0.061

Mean-F

4.507

0.525

18.424***

0. 000

14. 684*

0.072

Exp-F

4.050

0.242

19.002***

0. 000

9.950*

0.059

2.678**

0.032

Lc Notes: We calculate p-values using 10,000 bootstrap repetitions. *, **, *** denote significance at 10%, 5%, and 1%, respectively.

Hansen-Nyblom (Lc) parameter stability test for all parameters in the VAR jointly.

36

ACCEPTED MANUSCRIPT

(c) Bootstrap p-v alues of rolling test statistic testing the null that ACP does not Granger cause COP

1.0

1.0

0.8

0.8

Bootstrap p-values

Bootstrap p-values

(a) Bootstrap p-v alues of rolling test statistic testing the null that COP does not Granger cause ACP

0.6

0.4

0.2

0.0 1993

0.6

0.4

0.2

1996

1999

2002

2005

2008

2011

2014

0.0 1993

2017

1996

1999

2002

Tim e

1.2

2011

2014

2017

0.6 0.4

0.8

Sum of the rolling coefficients

Sum of the rolling coefficients

2008

(d) Bootstrap estimates of the sum of the rolling-window coef f icients f or the ef f ect of ACP on COP

(b) Bootstrap estimates of the sum of the rolling-window coef f icients f or the ef f ect of COP on ACP

0.4

0.0 -0.4

-0.8

-1.2 1993

2005

Tim e

0.2 0.0 -0.2 -0.4 -0.6 -0.8

1996

1999

2002

2005

2008

2011

2014

-1.0 1993

2017

Tim e

1996

1999

2002

2005

2008

2011

2014

2017

Tim e

sum of coefficients lower bound for sum of coefficients upper bound for sum of coefficients

sum of coefficients lower bound for sum of coefficients upper bound for sum of coefficients

Fig. 1. Rolling window estimation results for maize. Note: The y-axis of panel (a) and (c) refers to the bootstrap p-values; the y-axis of panel (b) and (d) refers to the sum of the rolling coefficients; the x-axis denotes time during the sample period. The x-axis and y-axis in the following Figs. 2-4 are defined in the same way as Fig. 1.

37

ACCEPTED MANUSCRIPT

(c) Bootstrap p-v alues of rolling test statistic testing the null that ACP does not Granger cause COP

1.0

1.0

0.8

0.8

Bootstrap p-values

Bootstrap p-values

(a) Bootstrap p-v alues of rolling test statistic testing the null that COP does not Granger cause ACP

0.6

0.4

0.2

0.0 1993

0.6

0.4

0.2

1996

1999

2002

2005

2008

2011

2014

0.0 1993

2017

1996

1999

2002

2005

2008

2011

2014

Time

Time

(b) Bootstrap estimates of the sum of the rolling-window coef f icients f or the ef f ect of COP on ACP

(d) Bootstrap estimates of the sum of the rolling-window coef f icients f or the ef f ect of ACP on COP

2.4

2017

.4

Sum of the rolling coefficients

Sum of the rolling coefficients

2.0 1.6 1.2 0.8 0.4 0.0 -0.4

.2

.0

-.2

-.4

-0.8 -1.2 1993

1996

1999

2002

2005

2008

2011

2014

-.6 1993

2017

Time

1996

1999

2002

2005

2008

2011

Time

sum of coefficients lower bound for sum of coefficients upper bound for sum of coefficients

sum of coefficients lower bound for sum of coefficients upper bound for sum of coefficients

Fig. 2. Rolling window estimation results for soybeans.

38

2014

2017

ACCEPTED MANUSCRIPT

(c) Bootstrap p-v alues of rolling test statistic testing the null that ACP does not Granger cause COP

1.0

1.0

0.8

0.8

Bootstrap p-values

Bootstrap p-values

(a) Bootstrap p-v alues of rolling test statistic testing the null that COP does not Granger cause ACP

0.6

0.4

0.2

0.0 1993

0.6

0.4

0.2

1996

1999

2002

2005

2008

2011

2014

0.0 1993

2017

1996

1999

2002

2005

2008

2011

2014

Tim e

Tim e

(b) Bootstrap estimates of the sum of the rolling-window coef f icients f or the ef f ect of COP on ACP

(d) Bootstrap estimates of the sum of the rolling-window coef f icients f or the ef f ect of ACP on COP

2.0

2017

1.5

Sum of the rolling coefficients

Sum of the rolling coefficients

1.5 1.0 0.5 0.0 -0.5 -1.0 -1.5 1993

1996

1999

2002

2005

2008

2011

2014

1.0

0.5

0.0

-0.5

-1.0 1993

2017

1996

1999

2002

Tim e

2005

2008

2011

Tim e

sum of coefficients lower bound for sum of coefficients upper bound for sum of coefficients

sum of coefficients lower bound for sum of coefficients upper bound for sum of coefficients

Fig. 3. Rolling window estimation results for tea.

39

2014

2017

ACCEPTED MANUSCRIPT

(c) Bootstrap p-v alues of rolling test statistic testing the null that ACP does not Granger cause COP

1.0

1.0

0.8

0.8

Bootstrap p-values

Bootstrap p-values

(a) Bootstrap p-v alues of rolling test statistic testing the null that COP does not Granger cause ACP

0.6

0.4

0.2

0.0 1993

1996

1999

2002

2005

2008

2011

2014

0.0 1993

2017

1996

1999

2002

2005

2008

2011

2014

Time

Time

(b) Bootstrap estimates of the sum of the rolling-window coef f icients f or the ef f ect of COP on ACP

(d) Bootstrap estimates of the sum of the rolling-window coef f icients f or the ef f ect of ACP on COP

2017

.6

Sum of the rolling coefficients

Sum of the rolling coefficients

0.4

0.2

1.0

0.5

0.0

-0.5

-1.0

-1.5 1993

0.6

1996

1999

2002

2005

2008

2011

2014

.4 .2 .0 -.2 -.4 -.6 1993

2017

Time

1996

1999

2002

2005

2008

2011

Time

sum of coefficients lower bound for sum of coefficients upper bound for sum of coefficients

sum of coefficients lower bound for sum of coefficients upper bound for sum of coefficients

Fig. 4. Rolling window estimation results for cocoa beans.

40

2014

2017

ACCEPTED MANUSCRIPT

Highlights 

Causalities between oil and agricultural prices in global market are investigated.



Price transmission is explored for commodities both used and not-used in biofuels.



Bidirectional positive causality between oil and agricultural prices is confirmed.



The system mandating global petroleum concerted action should be expanded.