Examining the determinants of global and local price passthrough in cereal markets: Evidence from DCC-GJR-GARCH and panel analyses

Guo, Jin; Tanaka, Tetsuji Article Examining the determinants of global and local price passthrough in cereal markets: Evidence from DCC-GJR- GARCH and panel analyses Agricultural and Food Economics Provided in Cooperation with: Italian Society of Agricultural Economics (SIDEA) Suggested Citation: Guo, Jin; Tanaka, Tetsuji (2020) : Examining the determinants of global and local price passthrough in cereal markets: Evidence from DCC-GJR-GARCH and panel analyses, Agricultural and Food Economics, ISSN 2193-7532, Springer, Heidelberg, Vol. 8, Iss. 1, pp. 1-22, https://doi.org/10.1186/s40100-020-00173-1 This Version is available at: https://hdl.handle.net/10419/240286 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ RESEARCH Open Access Examining the determinants of global and local price passthrough in cereal markets: evidence from DCC-GJR-GARCH and panel analyses Jin Guo * and Tetsuji Tanaka * Correspondence: kaku@econ. setsunan.ac.jp Department of Economics, Setsunan University, 17-8 Ikedanakamachi, Neyagawa, Osaka 572-0074, Japan Abstract Existing literature has not yet identified the common determinants of price volatility transmission in agricultural commodities from international to local markets and has rarely investigated the role of self-sufficiency measures in the context of national food security. We analyzed several factors to determine the degree of volatility transmission in wheat, rice and maize prices between world and domestic markets using GARCH models with dynamic conditional correlation specifications and panel feasible generalized least square models. Our findings indicate that a grain autarky system can reduce volatility passthroughs for three grain commodities. While the substitutive commodity consumption behaviour between maize and wheat buffers the volatility transmissions of both, rice does not function as a transmission-relieving element for the volatility implying that rice is not a substitute for wheat or maize consumption; grain consumption proves a more effective substitute than cereal selfsufficiency for insulating passthroughs from global markets. These findings may help the governments of developing nations to protect their domestic food markets from the uncertain movements of foreign markets and may thus improve food security. Keywords: Volatility transmission, Grain self-sufficiency, Food security JEL classification: Q18, Q17, Q02 Background Global food price volatilities have worsened food access for households in recent years, especially in low-income countries (Ivanic and Martin 2008), provoking societal and political instability in various regions (see Fig. 1) (Bellemare 2014). 1 For instance, the Prime Minister of Haiti, Jacques Edouard Alexis, was forced to resign because the price of rice in the nation spiked and remained high (Delva and Loney 2008). Although there is no consensus among researchers about the causes behind global food market ‘storms’, biofuel production, yield variability by weather conditions, export restrictions, © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 1 Bellemare (2014) econometrically reveals a relationship between food prices and social unrest, such as food riots. Agricultural and Food Economics Guo and Tanaka Agricultural and Food Economics (2020) 8:27 https://doi.org/10.1186/s40100-020-00173-1 high energy prices and financial speculation are widely perceived as the major driving factors (Abbott and Borot de Battisti 2011; Headey and Fan 2008; Gutierrez 2017; Lagi et al. 2011; Tanaka et al. 2012; Trostle 2008; von Braun 2008). Given the international food situation, impoverished nations must urgently implement countermeasures against food price transmissions from extraneous markets in order to calm their internal markets. Food prices were more volatile from 2007 to 2010 than they were from 2003 to 2006 (Minot 2011). Price volatility affects producers with risk-aversion preferences, making it more difficult for farmers to determine the optimal time at which to sell their commodities; this can lead to the misallocation of inputs and distort markets, resulting in limited agricultural investment and lower productivity. Consumers, acting as input procurement (including households and the buyers of food items), are also likely to suffer from food price volatilities, losing the ability to make optimal budget allocations. The recent protracted market volatilities of agricultural commodities in the regional markets of developing countries are largely attributable to external markets (Ceballos et al. 2017). Self-sufficiency measures have attracted the attention of policymakers in developing regions such as Egypt, Senegal, India, the Philippines, Qatar, Bolivia and Russia as a potent strategy for national food security (Clapp 2017). Governments seek to increase self-sufficiency for several reasons, such as concerns about food supply disruption due to war, poor harvests in foreign countries and export restrictions; to avoid politically vulnerable positions in international negotiations; to conserve the natural environment and to grapple with concessions in agricultural industries. Nevertheless, economists who espouse modern economic theories are generally opposed to costly protectionist policies on the grounds that market interventions lead to inefficient resource allocations. Here, it is helpful to note that Magrini et al. (2017) argue that no consensus yet exists on the relationship between agricultural incentives and food security improvement (degradation). The food market turbulence of the 1970s caused governments to lean toward protectionist policies. Between the 1980s and 2000s, global agricultural prices were relatively low, and many African nations became net importers of food as a Fig. 1 Food riots between 2007 and 2014 (shown in red). Source: authors’work based on the food riot dataset of the World Bank Guo and Tanaka Agricultural and Food Economics (2020) 8:27 Page 2 of 22 result of structural adjustment reforms advised by the International Monetary Fund or the World Bank which compelled governments to prioritize free-market policies. The present enthusiasm for food autarky policies re-emerged during the food crisis of 2007 −2008 and was complemented by the food sovereignty movement that began in the 1990s (Clapp 2017). The research has not yet identified which factors may influence volatility passthroughs between world and local markets. Most studies focus on the links between the domestic markets of developing countries (Abdulai 2000; Baulch 1997; Lutz et al. 2006; Moser et al. 2009; Myers 2013); only a relatively small number of studies on domestic market linkages examine price transmission from world to local markets, including volatility transmission (Ceballos et al. 2017; Conforti 2004; Guillaume and Kaminski 2019; Minot 2011; Hatzenbuelhler et al. 2017; Mundlak and Larson 1992). Many of these works use an error-correction method to examine the relationship between global and domestic prices for specific countries. However, these studies fail to identify the determinants of the extent of the spillover effects in a comprehensive manner (i.e. through a panel analysis). The qualitative research on food self-sufficiency is much more abundant than the quantitative research (Bishwajit 2014; Bishwajit et al. 2013; Clapp 2017; Warr 2011). While Tanaka and Hosoe (2011) and Tanaka (2018) quantify how rice and wheat selfsufficiency impact households in Japan and Egypt, respectively, they use a stochastic general equilibrium model, the parameters of which are often criticized for producing unreliable estimations (more specifically, this model generates a point estimation from a single-year social accounting matrix). Several studies have conducted quantitative analyses associated with self-sufficiency in food other than the CGE works explained above (Anderson and Tyres 1984; Yang and Tyers 1989), but these articles focus on assessing the impacts of a certain variable(s) on food self-sufficiency rather than the effects of food self-sufficiency on economies. Studies of the former type do not address the effectiveness of food autarky policy measures for national food security. The present research extends Guo and Tanaka’s(2019) work 2 on wheat markets by exploring the determinants of grain (i.e. wheat, rice, maize) price volatility transmission from international markets to local markets with a two-step experimental procedure, namely, GARCH models with a dynamic conditional correlation (DCC) specification and panel feasible generalized least square (FGLS) models. The analysis considers data from January 2006 to December 2013 for 16 developing countries in Africa, Asia and Latin America in order to analyze food policies that reduced price transmission from global markets. 3 We focus on the self-sufficiency rates (SSRs) of wheat, rice and maize to investigate the price transmission of the individual commodities and the consumption of substitutive goods (i.e. the substitutive consumption of rice and maize amid wheat price transmission). We hypothesize that consumers engage in substitution 2 Guo and Tanaka (2019) analyze wheat price volatility transmissions from global to local markets in 10 wheat-importing countries, identifying the potential determinants with a panel analysis. They reveal that the volatility correlations from international to local markets were strengthened around the period of the 2007– 08 food crisis and a higher self-sufficiency rate plays a role in alleviating volatility passthroughs from international markets. 3 As sensitivity tests, we use extensive data comprising 16, 26 and 26 countries for wheat, rice and maize, respectively, and find that our main results are generally robust against changes in country selection (see the Appendix for details). Guo and Tanaka Agricultural and Food Economics (2020) 8:27 Page 3 of 22 behaviour during their grain consumption and that substitutive cereal consumption buffers price volatility transmission from global markets. We thus include the consumption of other cereals as explanatory variables in the identification process. This study contributes to the literature in several ways. First, it makes a methodological contribution to international cereal volatility spillover research by applying the DCC framework to the volatility issue, which captures the time-variant correlated relationships between markets. By contrast, the conventional GARCH-BEKK method, as used by Ceballos et al. (2017), outputs a single coefficient value for each commodity, which would prevent us from regressing the correlation outcomes on potential factors due to the limited sample size. Second, this study is the first to conduct factor identification for the international volatility transmission of agricultural products within a rigorous econometric framework. Finally, despite the popularity and importance of food self-sufficiency policies, their effects on market steadiness have not been tested using econometric models. The remainder of this paper proceeds as follows. The ‘Methods’section describes the study’s data, sample statistics and methodology. The ‘Results and discussion’section outlines the empirical results. Finally, the ‘Conclusions’section offers concluding remarks and several policy implications stemming from the findings. Methods Data description In the first step, we used monthly data from the Global Information and Early Warning System (GIEWS), which provides monthly international food commodity prices in various countries to estimate the monthly conditional correlations between international and local markets for each cereal good. This dataset primarily covers developing markets at the city level. We selected a series of monthly local grain prices for individual countries in US dollars to deflate the domestic prices. Data on monthly local and international prices were collected for 16 countries from January 2006 to December 2013. The 16 countries selected differ between grains due to limited data availability. 4 The data provided useful information on the following countries: for wheat, we considered Afghanistan, Argentina, Brazil, Cameroon, Ethiopia, Georgia, India, Israel, Kazakhstan, Kyrgyzstan, Mauritania, Peru, South Africa, Tajikistan, Ukraine and Uruguay; for rice, we considered Argentina, Brazil, El Salvador, Nicaragua, Panama, Peru, Cameroon, Ethiopia, Mozambique, Nigeria, Rwanda, South Africa, Zambia, Uganda, Israel and Ukraine and for maize, we considered Bangladesh, Brazil, Cambodia, Cameroon, Colombia, Salvador Guatemala, Haiti, India, Mauritania, Mozambique, Niger, Panama, Peru, South Africa and Uruguay. 5 The international prices of wheat and maize are based on US (Gulf) no. 2 hard red winter wheat and US (Gulf) no. 2 yellow, respectively. International rice prices are based on Thailand 5% milled white rice. Global prices are quoted from the GIEWS as well. Our country sample includes net grain exporters, 4 Since yearly data is used in the panel analysis, the dimensions of the data set were chosen to include as many countries as possible to increase the sample size and degrees of freedom to yield more precise estimates. In the GIEWS, only 16 countries met the study’s requirements for available data on the prices of wheat from 2006 to 2013. Although more data was available for rice and maize, the sample of countries differed for each grain (the countries selected for rice and maize were adjusted to be consistent with wheat prices). 5 The Appendix details an identical estimation using a wider range of countries for rice and maize. Guo and Tanaka Agricultural and Food Economics (2020) 8:27 Page 4 of 22 such as Ukraine and Kazakhstan, with cereal self-sufficiency exceeding 100% (the selfsufficiency standard is 100%). We hypothesize that a country with high cereal selfsufficiency will tend to be resistant against violent fluctuations in international cereal prices. In addition, continuously compounded returns for each series are defined as ln(P t /P t−1 ) × 100, where P t is the monthly value of the global and local prices of wheat, maize and rice in a selected country. Tables 1,2and 3report the descriptive statistics of the returns on wheat prices (international and domestic markets), maize prices (international and domestic markets) and rice prices (international and domestic markets), respectively. These tables show that almost all mean returns are positive, suggesting an increase in grain prices during the study period. It is worth noting that the standard deviations of maize and rice prices reach levels higher than the standard deviation of wheat prices. This indicates that extreme changes tend to occur more frequently for maize and rice. The nonzero skewness and positive excess kurtosis of all the price returns exhibit a leptokurtic distribution (i.e. fat tails). Furthermore, the Jarque-Bera statistics reject normality at the 1% significance level for almost all price returns, indicating that most price returns depart from normal distribution. Before estimating the dynamic correlations, it was necessary to verify the stationarity of each price return used in the analysis. The augmented Dickey and Fuller (1979; ADF) and Phillips and Perron (1988; PP) unit root test (which has a null hypothesis of a unit root) were employed to investigate whether the series of price returns was stationary. The results of the unit root tests indicated that all the variables are not stationary in their levels and none of the variables had unit root processes in their first logdifferenced forms. 6 This suggests that all grain prices are stationary in their returns, guaranteeing that we can model volatility spillovers based on the GARCH with a DCC approach. 7 It is vital to note that we lag the domestic price of each developing country by one period (i.e. by one month) to capture the information flowing from the global grain market to the local grain market with a time lag. After estimating the monthly conditional correlations between international and local prices, 8 we converted them into yearly data to conduct panel data analyses to identify the determinants of volatility transmission. 9 Yearly consumption data for wheat, rice and maize and on the SSRs of individual grain products (i.e. the production and consumption of each nation) between 2006 and 2013 were retrieved from FAOSTAT. Table 4defines the variables used in the panel estimation. Econometric methodology Our analysis employed a two-step econometric methodology. In the ‘Dynamic conditional correlations’section, we use GARCH models within a DCC framework to define the linkages between the internal and external price volatilities of cereal markets in 6 For sake of brevity, we do not report the results of the unit root tests here. They are available upon request. 7 A number of researches have focused on the problem of testing for a unit root in the presence of volatility. For instance, Cavaliere et al. (2015) suggest that standard lag selection methods show a tendency to over-fit the lag order under heteroskedasticity, which results in significant power losses in the ADF tests. 8 It is vital to note that we lag the global price in the cereal market by one period (i.e. one month) to capture information flowing from global markets to local markets in developing countries with a time lag. 9 David and Amir (2017) use the same method to obtain yearly DCCs by taking the average of the monthly DCCs. Guo and Tanaka Agricultural and Food Economics (2020) 8:27 Page 5 of 22 Table 1 Summary statistics for wheat price returns Mean Median Maximum Minimum Std. dev. Skewness Kurtosis Jarque-Bera International wheat price 0.007 −0.006 0.239 −0.278 0.085 0.184 4.329 7.449 a Afghanistan 0.006 0.000 0.515 −0.229 0.088 1.924 14.602 585.233 b Argentina 0.019 0.000 0.627 −0.302 0.104 2.793 16.68 855.266 b Brazil 0.006 0.008 0.128 −0.241 0.059 −0.874 5.517 36.778 b Cameroon 0.003 0.000 0.216 −0.227 0.053 −0.162 9.043 143.457 b Ethiopia 0.005 0.002 0.338 −0.216 0.074 0.511 7.35 78.212 b Georgia 0.006 0.000 0.118 −0.168 0.046 −0.404 5.252 22.419 b India 0.002 0.000 0.161 −0.144 0.048 0.154 4.839 13.622 a Israel 0.008 0.008 0.109 −0.108 0.037 0.223 4.173 6.174 a Kazakhstan 0.005 0.000 0.182 −0.185 0.040 0.404 12.599 363.438 b Kyrgyzstan 0.006 0.000 0.405 −0.123 0.068 2.613 14.988 669.883 b Mauritania 0.004 0.000 0.139 −0.217 0.039 −0.788 16.351 707.839 b Peru 0.005 0.000 0.078 −0.035 0.017 0.904 5.902 45.785 b South Africa 0.007 0.002 0.178 −0.288 0.069 −0.793 6.224 50.578 b Tajikistan 0.007 0.000 0.281 −0.141 0.053 1.249 10.02 217.443 b Ukraine 0.006 0.009 0.152 −0.269 0.054 −1.38 10.969 278.556 b Uruguay 0.009 0.014 0.274 −0.227 0.067 0.151 7.984 97.636 b a and b indicate statistical significance at the 5% and 1% levels, respectively Table 2 Summary statistics for maize price returns Mean Median Maximum Minimum Std. dev. Skewness Kurtosis Jarque-Bera International maize price 0.007 0.004 0.210 −0.236 0.075 −0.164 4.176 5.841 a Argentina 0.008 0.004 0.277 −0.228 0.079 0.582 5.366 27.228 c Brazil 0.003 0.009 0.184 −0.244 0.071 −0.326 3.924 5.016 a Cameroon 0.01 0.016 0.314 −0.147 0.06 1.728 12.007 364.521 c Ethiopia 0.009 0.001 0.507 −0.311 0.121 0.519 5.803 34.998 c Israel 0.007 0.007 0.156 −0.17 0.051 −0.475 5.158 21.782 c Mozambique − 0.001 0.000 0.182 −0.375 0.094 −0.928 5.988 48.465 c Nicaragua 0.007 0.028 0.239 −0.554 0.147 −1.419 5.836 63.017 c Nigeria 0.004 0.001 0.372 −0.378 0.119 −0.515 4.756 16.23 c Panama 0.009 0.000 0.154 −0.192 0.057 −0.231 4.521 9.9 c Peru 0.009 0.007 0.123 −0.034 0.026 1.962 9.055 203.863 c Rwanda 0.004 0.011 0.434 −0.605 0.13 −1.04 8.364 129.644 c Salvador 0.004 0.000 0.214 −0.147 0.064 0.451 4.786 15.687 c South Africa 0.005 0.011 0.232 −0.238 0.075 −0.103 4.026 4.287 Uganda 0.005 0.01 0.535 −0.546 0.171 −0.361 4.233 8.000 b Ukraine 0.005 0.016 0.323 −0.521 0.114 −2.262 11.622 371.29 c Zambia − 0.003 0.013 0.145 −0.324 0.079 −1.376 5.565 55.448 c a , b and c indicate statistical significance at the 10%, 5% and 1% levels, respectively Guo and Tanaka Agricultural and Food Economics (2020) 8:27 Page 6 of 22 each developing country selected. Then, in the ‘Panel analysis with FGLS regression’ section, we explore which factors may curb these linkages by employing a panel analysis. Dynamic conditional correlations The econometric framework of our analysis can be formulated as follows: Table 3 Summary statistics for rice price returns Mean Median Maximum Minimum Std. dev. Skewness Kurtosis Jarque-Bera International price 0.005 0.000 0.412 −0.190 0.076 2.425 14.387 599.983 c Bangladesh 0.005 0.102 0.522 −0.605 0.333 −0.234 1.601 8.517 b Brazil 0.003 −0.004 0.553 −0.588 0.241 −0.437 3.378 3.556 Cambodia 0.005 0.022 0.711 −0.928 0.353 −0.479 3.087 3.626 Cameroon 0.005 0.021 0.441 −0.347 0.172 0.295 3.079 1.386 Colombia 0.006 0.069 0.576 −0.864 0.326 −1.268 4.296 31.747 c Guatemala 0.004 0.015 0.565 −0.507 0.249 −0.031 3.118 0.070 Haiti 0.007 0.070 0.805 −1.224 0.450 −1.008 4.357 23.121 c India 0.003 0.042 0.280 −0.545 0.197 −1.173 4.082 26.144 c Mauritania 0.002 −0.019 0.606 −0.425 0.224 0.133 2.624 0.832 Mozambique 0.006 0.016 0.465 −0.602 0.245 −0.698 3.601 9.050 b Nigeria 0.003 0.050 0.500 −0.466 0.232 −0.334 2.256 3.916 Panama 0.006 0.027 0.357 −0.452 0.169 −1.226 4.752 35.581 c Peru 0.001 0.024 0.362 −0.274 0.162 0.122 1.993 4.202 Salvador 0.004 −0.012 0.675 −0.411 0.277 0.61 2.877 5.886 a South Africa 0.005 −0.038 0.639 −0.515 0.256 0.284 3.044 1.272 Uruguay 0.006 0.019 0.788 −0.953 0.349 −0.591 3.403 6.104 b a , b and c indicate statistical significance at the 10%, 5% and 1% levels, respectively Table 4 Definitions of variables in panel analysis Variable Definition Source DCC i,tW Dynamic conditional correlation of wheat between international price and country i’s domestic price with one lag at time t. Estimated by author DCC i,tM Dynamic conditional correlation of maize between international price and country i’s domestic price with one lag at time t. Estimated by author DCC i,tR Dynamic conditional correlation of rice between international price and country i’s domestic price with one lag at time t. Estimated by author SSRW i;tSelf-sufficiency rate is of wheat is defined as Production /(Production +Import − Export) in country iat time t. FAOSTAT SSRM i;tSelf-sufficiency rate of maize is defined as Production /(Production +Import − Export) in country iat time t. FAOSTAT SSRR i;tSelf-sufficiency rate of rice is defined as Production /(Production +Import −Export) in country iat time t. FAOSTAT WC i,t Consumption of wheat is defined asln(Consumption i,t /Consumption i,t−1 ) × 100 in country iat time t. FAOSTAT MC i,t Consumption of maize is defined asln(Consumption i,t /Consumption i,t−1 ) × 100 in country iat time t. FAOSTAT RC i,t Consumption of rice is defined asln(Consumption i,t /Consumption i,t−1 ) × 100 in country iat time t. FAOSTAT Guo and Tanaka Agricultural and Food Economics (2020) 8:27 Page 7 of 22 rt¼EðrtjΩt−1Þþεt εtjΩt−1N0;Ht ðÞ Ht¼DtRtDt 8 < :ð1Þ where r t =(r 1, t ,r 2, t ) ′ is a 2 × 1 vector of returns, including the international price r 1, t and one of the developing countries’domestic prices, r 2, t .Ω t−1 is the time t−1 information set. ε t =(ε 1, t ,ε 2, t ) ′ is a 2 × 1 vector of the error term with conditional mean E(ε i,t |Ω i,t−1 ) = 0 and conditional variance Eðε2 i;tjΩi;t−1Þ¼hi;t;i¼1;2. ε t is assumed to follow a conditionally normal distribution. H t is a 2 × 2 conditional variance-covariance matrix. D t is the diagonal matrix containing the conditional standard deviations, in which the arrays are Dt¼diag½ffiffiffiffiffiffiffi h1;t p;ffiffiffiffiffiffiffi h2;t p. The time-varying conditional correlation matrix R t is calculated as Rt¼½diagðQtÞ−1 2Qt½diagðQtÞ−1 2, where Q t is the conditional correlation matrix of the standardized residuals. Moreover, the matrix H t can be computed using the standardized residuals zi;tðzi;t¼εi;t=ffiffiffiffiffiffi hi;t pÞ;i¼1;2. According to Glosten et al. (1993), the specification for the univariate GJR-GARCH (1,1) model is as follows: hi;t¼ωþαiε2 i;t−1þλiI− i;t−1ε2 i;t−1þβihi;t−1;i¼1;2ð2Þ where the indicator function I− i;tequals 1 if ε i,t −1 < 0 and 0 otherwise. The parameter λ i is designed to capture the asymmetric effect. For this specification, a positive value for λ i indicates that negative residuals (bad news) tend to increase the variance more than positive residuals (good news). In other words, bad news (ε i,t −1 < 0) increases volatility more than good news (ε i,t −1 > 0). The univariate GJR-GARCH model described above is applied to estimate the time variances for each price return. Moreover, the standardized residuals obtained from the GJR-GARCH model were used to estimate the conditional cross-correlation. Cappiello et al. (2006) indicate that the shortcoming of the original DCC model is that the correlation evolves according to a process with identical news impacts and smoothing parameters for all pairs of variables. They also confirm that, for high dimensional models, the assumption of the identical impact of shocks is too strong. In response, they propose using an asymmetric generalized DCC (AG-DCC) model to better capture any heterogeneities present in the data. We follow Cappiello et al. (2006) and employ the AG-DCC model to account for both the time-varying correlation between variables and the asymmetric response of correlation to positive and negative shocks. The dynamic correlation structure of the AGDCC model is expressed as Qt¼Z−φ0Zφ−ξ0Zξ−η0Pη  þφ0zt−1zt−10φþη0pt−1pt−10ηþξ0Qt−1ξð3Þ where matrix φevaluates the impacts of the past standardized shocks to current dynamic conditional correlations, matrix ξindicates how lagged correlations affect current correlations and matrix ηjustifies the presence of asymmetric responses to positive and negative shocks. Zrepresents the unconditional matrices of z t and Prepresents the unconditional matrices of pt¼I½zt<0⊙zt, where I½zt<0is an indicator function equal to 1 if z t < 0 and 0 otherwise and ⊙indicates a Hadamard product. Ding and Engle (2001) argue that a sufficient condition for Q t in Eq. 3is positive definite for all possible realizations where the intercept, Z−φ0Zφ−ξ0Zξ−η0Pη, is positive semi- Guo and Tanaka Agricultural and Food Economics (2020) 8:27 Page 8 of 22 Cameroon (0.208) and the lowest in Guatemala (−0.046). Additionally, some countries, such as Cameroon and Mozambique, display remarkable fluctuations during the 2008 global financial crisis. Identification of potential factors In this subsection, we estimate Eqs. 5,6and 7to determine which factors might influence the time-varying conditional correlations identified above. Table 11 summarizes Fig. 6 Plots of dynamic correlations between international and domestic maize prices Fig. 7 Plots of dynamic correlations between international and domestic rice prices Guo and Tanaka Agricultural and Food Economics (2020) 8:27 Page 15 of 22 the results of a panel regression based on the FGLS model. It provides several interesting empirical findings. First, the coefficients of SSR are negative and significant at the 1% level for all three grains. These findings confirm that SSR can be considered a significant factor in determining dynamic correlations. Furthermore, the negative coefficient of SRR on DCC implies that an increase in SSR reduces the correlation between international and domestic grain prices. In other words, the governments of developing countries can lessen the impacts of unexpected excess volatility from global to local markets by adopting practices that increase their grain self-sufficiency rates. However, it is important to note that this finding cannot be simply applied to countries with domestic yield variability greater than import supply variability: higher self-sufficiency means higher dependency on domestic production, which, accordingly, makes domestic production more influential to domestic price. Therefore, the outcome obtained in our estimation needs to be interpreted as that a self-sufficiency policy tends to placate local price volatility from the collective observation of the 16 countries focused, but is not necessarily applicable to any individual nation. As a matter of fact, Tanaka and Hosoe (2011) demonstrate that the abolition of import tax on rice to Japan stabilizes the domestic price of rice—because Japan’s rice productivity is more volatile than the international rice supply to Japan, cutting import taxes on rice increases the nation’s rice imports. Along these lines, we found that the coefficient of maize’s SSR has the largest negative value (−0.054), while the coefficient of wheat’s SSR is relatively small (− 0.006). These results suggest that maize’s self-sufficiency rate has the greatest impact on maize’s global and local price transmission in developing countries. Second, the coefficient of maize’s consumption is significant at the 5% level in the regression for wheat. The coefficient is approximately −0.05, which suggests that, on average, an increase in maize consumption will reduce the dynamic correlations between international and domestic wheat prices. It is also worth noting that the magnitude of maize consumption is larger than that of wheat’s SSR, indicating that an increase in maize Table 8 Summary statistics for the dynamic conditional correlations of wheat Mean Median Maximum Minimum Std. dev. Afghanistan 0.112 0.111 0.576 −0.488 0.164 Argentina −0.048 −0.022 0.178 −0.656 0.116 Brazil −0.196 −0.174 −0.040 −0.720 0.097 Cameroon −0.192 −0.193 0.220 −0.457 0.155 Ethiopia −0.099 −0.115 0.370 −0.663 0.201 Georgia 0.011 0.015 0.426 −0.326 0.125 India −0.003 0.025 0.498 −0.538 0.214 Israel −0.101 −0.094 0.321 −0.460 0.168 Kazakhstan −0.061 −0.059 0.533 −0.921 0.236 Kyrgyzstan −0.131 −0.096 0.206 −0.994 0.190 Mauritania 0.113 0.099 0.997 −0.873 0.276 Peru 0.074 0.074 0.432 −0.150 0.089 South Africa 0.014 0.004 1.000 −0.364 0.178 Tajikistan 0.025 0.018 0.693 −0.469 0.205 Ukraine −0.172 −0.131 0.070 −0.991 0.143 Uruguay −0.238 −0.223 −0.079 −0.777 0.112 Guo and Tanaka Agricultural and Food Economics (2020) 8:27 Page 16 of 22 consumption impacts wheat’s DCC more than an increase in wheat’s SSR. Third, and most interestingly, our results indicate that the coefficient of wheat’s consumption also negatively affects maize’s DCC at a statistical significance level of 5%. Moreover, the magnitude of the coefficient is −0.104, which is approximately twice as large as the coefficient of maize’s SSR. In other words, an increase in wheat consumption plays a greater role in buffering price volatility transmissions from global to local maize markets than an increase in maize’s SSR. These results reveal a substitutive effect between maize and wheat and imply that increasing wheat (maize) consumption could be used Table 9 Summary statistics for the dynamic conditional correlations of maize Mean Median Maximum Minimum Std. dev. Argentina 0.210 0.233 0.540 −0.216 0.134 Brazil 0.371 0.360 0.740 0.147 0.109 Cameroon −0.059 −0.065 0.336 −0.435 0.126 Ethiopia 0.161 0.162 0.388 −0.224 0.088 Israel 0.537 0.535 0.844 0.117 0.144 Mozambique 0.103 0.091 0.739 −0.253 0.149 Nicaragua 0.217 0.198 0.816 −0.339 0.190 Nigeria 0.249 0.235 0.968 −0.129 0.200 Panama 0.154 0.131 0.655 −0.028 0.115 Peru −0.035 −0.040 0.746 −0.525 0.194 Rwanda 0.069 0.070 0.789 −0.684 0.253 Salvador 0.202 0.238 0.591 −0.396 0.172 South Africa 0.226 0.221 0.929 −0.256 0.195 Uganda 0.051 0.044 0.442 −0.366 0.135 Ukraine 0.356 0.372 0.497 0.048 0.076 Zambia −0.100 −0.107 0.606 −0.873 0.198 Table 10 Summary statistics for the dynamic conditional correlations of rice Mean Median Maximum Minimum Std. dev. Bangladesh −0.032 −0.035 0.693 −0.743 0.349 Brazil 0.116 0.166 0.710 −0.646 0.260 Cambodia 0.040 0.085 0.791 −0.758 0.352 Cameroon 0.208 0.208 0.486 −0.016 0.100 Colombia 0.106 0.132 0.447 −0.353 0.153 Guatemala −0.046 −0.065 0.689 −0.749 0.266 Haiti 0.107 0.113 0.147 0.047 0.024 India 0.135 0.157 0.887 −0.596 0.281 Mauritania −0.006 0.020 0.988 −0.947 0.316 Mozambique 0.043 0.043 0.452 −0.795 0.151 Nigeria 0.118 0.103 0.979 −0.340 0.227 Panama 0.076 0.069 0.693 −0.643 0.204 Peru 0.125 0.128 0.999 −0.432 0.240 Salvador 0.086 0.069 0.449 −0.356 0.173 South Africa 0.141 0.170 0.693 −0.489 0.223 Uruguay 0.133 0.156 0.358 −0.178 0.118 Guo and Tanaka Agricultural and Food Economics (2020) 8:27 Page 17 of 22 as a strategy to buffer countries from excessive fluctuations in international maize (wheat) prices. Finally, contrary to the results for wheat and maize, the coefficients of the consumption of wheat and maize are insignificant in the regression model for rice. This may be due to a weak substitutive relationship between rice, wheat and maize. Conclusions This study sought to identify the factors determining the degree of price volatility transmission between the international and local markets of wheat, rice and maize in developing countries. In the first step, a GARCH model with AG-DCC and G-DCC specifications was developed to estimate the strength of the price volatility links between the global and regional cereal markets. In the second step, we regressed the correlated outcomes obtained in the first step on potential factors such as self-sufficiency in grain and the consumption of substitutive commodities. Our main findings were as Table 11 Estimation results of panel data analysis Dependent variable Independent variable DCC W DCC M DCC R SSR −0.006 b −0.054 b −0.013 b (−5.91) (−7.41) (−0.72) WC –−0.104 a 0.069 –(−2.58) (1.39) MC −0.05 a –−0.022 (−2.14) –(−0.72) RC 0.014 0.039 – (0.47) (0.55) – Constant −0.065 b 0.148 b 0.082 b (−11.06) (16.02) (9.43) Observations 128 128 128 a and b indicate statistical significance at the 5% and 1% levels, respectively. z-statistics are in parentheses Table 12 Estimation results of panel data analysis with full-country selection Dependent variable Independent variable DCC W DCC M DCC R SSR −0.006 b −0.021 b −0.002 (−5.91) (−6.14) (−1.13) WC −−0.129 a 0.029 –(−2.93) (0.576) MC −0.05 a –0.050 (−2.14) –(1.29) RC 0.014 −0.126 b – (0.47) (−3.33) – Constant −0.065 b 0.171 b 0.090 b (−11.06) (14.29) (4.48) Observations 128 208 208 a and b indicate statistical significance at the 5% and 1% levels, respectively. z-statistics in parentheses Guo and Tanaka Agricultural and Food Economics (2020) 8:27 Page 18 of 22 follows: (1) self-sufficiency policies for grain commodities can reduce volatility passthroughs from global markets; (2) the consumption of wheat and maize as substitutive goods can buffer the price volatility transmissions of maize and wheat, respectively; however, these substitutive effects many not occur in rice markets and (3) grain substitution impacts volatility transmission more than enhancing SSR. Several policy implications can be drawn from our experimental results. First, policymakers of food-deficit countries should increase import tariffs on grains and/or subsidize farming production to enhance their SSR if the cost of the policy matches the benefit. Because the expected beneficiaries are consumers and producers with riskaverse preferences, the policy implementation process must evaluate the aggregated benefit for these stakeholders against the cost of boosting food autarky rates. As the ‘Identification of potential factors’section makes clear, policymakers must be attentive to the relation between domestic price volatility and/or domestic yield volatility and import price volatility before enacting agricultural autarky policy—raising SSRs may make domestic prices more volatile if the domestic yield of a commodity is more volatile than the rate at which foreign exporting regions produce that commodity. Our results also underscore the importance of substitutive cereal goods, suggesting that consumers respond quickly to price variations. The deeper point here is that a high elasticity of substitution may help to prevent short-term market turmoil, a finding that encourages the regular consumption of foodstuff made from a variety of grains. Put differently, a more balanced consumption of cereals is likely to more effectively buffer volatility waves from international markets. Because our estimation concentrated on short-term volatility spillovers, our research informs best practices for assuaging unforeseen shocks from external markets; however, it does not directly advise how to mollify food poverty fundamentally caused by chronically high food prices. While our results confirm the ability of self-sufficiency to insulate domestic markets from international ones, the cost-effectiveness of such a directive remains vitally important to consider in policymaking. Imposing tariffs or subsidizing farming operations to boost food self-sufficiency can incur enormous costs. Normalizing balanced food consumption is a much less expensive way to bolster against stormy foreign market shocks. Japan provides a good example of how nationwide food preferences can change. While Japanese citizens consume a wide variety of internationally sourced meals, their pre-war culinary culture was based on traditional Japanese foods, that is, it was primarily based on rice and, to a lesser extent, wheat (such as bread and pasta). This culture began to change when the US donated wheat for Japanese school lunches in 1947. Furthermore, Bellemare’s(2014) verification of the association between food prices and social and political unrest implies that such disturbances may be prevented or alleviated by minimizing price conveyance. Although this type of societal burden appears difficult to measure, the overall benefits of prevention may be sizable. One limitation of this analysis is its simultaneous equation biases, given that local prices could also affect international prices. To deal with this problem, the causality the study establishes needs to be tested using, for instance, the cross-correlation function developed by Hong (2001). However, most of the countries we examined are foodimporting, low-income nations, and the possibility that local prices can alter global cereal prices is very small. Guo and Tanaka Agricultural and Food Economics (2020) 8:27 Page 19 of 22 Appendix Robustness test against country selection As a robustness check, Table 12 presents the estimation results of the panel regression with the full-country sample. In contrast to what is observed in Table 11, we extended the samples of maize and rice to 26 countries, with 208 observations for both grains. Overall, the coefficients behave similarly to those of the selected developing countries (in Table 11) in terms of statistical significance and signs, except for two coefficients. First, the coefficient of rice’s SSR is not significant, even at the 10% level. This indicates that the SSR of rice does not significantly impact price transmission between the global and local rice markets. Second, the coefficient of rice’s consumption on wheat’s DCC is significant at the 1% level. Furthermore, the negative coefficient of rice’s consumption suggests that an increase in the consumption of rice will diminish price volatility transmission from global grain markets to local wheat markets. Generally, our results are robust against the change in country selection. However, the changes in the results concerning rice reveal that rice has characteristics distinct from those of both wheat and maize. Acknowledgements The research of the second author is in part supported by a Grant-in-Aid from the Japan Society for the Promotion of Science (Grant Number (A) 18 K14533). Authors’contributions JG and TT designed the research. JG constructed the econometric model and edited the program code for analysis. JG also analyzed and interpreted the data regarding the common determinants of the price volatility transmission of agricultural commodities from international to local markets in developing countries. TT performed the background of the paper and discussed the policy implications of the empirical results. The authors equally contributed to this work. The author(s) read and approved the final manuscript. Authors’information Jin Guo is an Associate Professor of Economics at the Faculty of Economics, Setsunan University. He obtained his Ph.D. from Osaka Prefecture University. His main research focuses on applied time series analysis, empirical finance, data science and open macroeconomics. His recent publications appear in the Journal of Economics and Finance,Applied Economics,Global Finance Journal and Palgrave Communications. Tetsuji Tanaka is an Assistant Professor at the Department of Economics, Setsunan University, Japan. He holds a Ph.D. in Finance & Management from School of Oriental and African Studies, University of London. His work focuses on food security, international trade, environmental taxation and modelling. He has published several articles in international peer-reviewed journals such as the Food Policy,Journal of Agriculture Economics and Palgrave Communications. Availability of data and materials The datasets generated and analyzed in this study are available in the Global Information and Early Warning System repository: http://www.fao.org/giews/en/ and Food and Agriculture Organization Corporate Statistical Database (FAOSTAT), accessed at http://www.fao.org/faostat/en/#home. The data that support the findings of this study can be obtained from the corresponding author upon request. Competing interests The authors declare that they have no competing interests. Received: 8 March 2019 Revised: 3 December 2019 Accepted: 2 November 2020 References Abbott P, Borot de Battisti A (2011) Recent global food price shocks: causes, consequences and lessons for African governments and donors. J Afr Econ 20:112–162 Abdulai A (2000) Spatial price transmission and asymmetry in the Ghanaian maize market. 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