Статті в журналах: "Commodity movements"

1

Eberhardt, Markus, and Andrea Presbitero. "Commodity Price Movements and Banking Crises." IMF Working Papers 18, no. 153 (2018): 1. http://dx.doi.org/10.5089/9781484366776.001.

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2

Yin, Libo, and Liyan Han. "Co-movements in commodity prices: Global, sectoral and commodity-specific factors." Economics Letters 126 (January 2015): 96–100. http://dx.doi.org/10.1016/j.econlet.2014.11.027.

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3

Sumner, Daniel A. "Recent Commodity Price Movements in Historical Perspective." American Journal of Agricultural Economics 91, no. 5 (December 2009): 1250–56. http://dx.doi.org/10.1111/j.1467-8276.2009.01292.x.

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4

Ma, Yan-Ran, Qiang Ji, Fei Wu, and Jiaofeng Pan. "Financialization, idiosyncratic information and commodity co-movements." Energy Economics 94 (February 2021): 105083. http://dx.doi.org/10.1016/j.eneco.2020.105083.

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5

Gourinchas, Pierre-Olivier, M. Ayhan Kose, and Thomas Helbling. "Policy Responses to Commodity Price Movements—1." IMF Economic Review 60, no. 4 (December 2012): 465–69. http://dx.doi.org/10.1057/imfer.2012.23.

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6

Gourinchas, Pierre-Olivier, M. Ayhan Kose, and Thomas Helbling. "Policy Responses to Commodity Price Movements—2." IMF Economic Review 61, no. 1 (April 2013): 1–5. http://dx.doi.org/10.1057/imfer.2013.8.

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7

Charlot, Philippe, Olivier Darné, and Zakaria Moussa. "Commodity returns co-movements: Fundamentals or “style” effect?" Journal of International Money and Finance 68 (November 2016): 130–60. http://dx.doi.org/10.1016/j.jimonfin.2016.07.001.

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8

Azhar, Rialdi, Febryan Kusuma Wisnu, Fajrin Satria Dwi Kesuma, Widya Rizki Eka Putri, and Rian Andri Prasetya. "State-space Implementation in Forecasting Carbon and Gas Prices in Commodity Markets." International Journal of Energy Economics and Policy 12, no. 3 (May 18, 2022): 280–86. http://dx.doi.org/10.32479/ijeep.12894.

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Carbon is a waste that becomes a new economic commodity. It has been traded like stock prices in commodity markets. Owing to its new status, research on carbon price movements as a new economic commodity linked to gas has gained a considerable amount of interest. A deeper observation of the future of world gas prices can also be an indicator of the stability of this commodity. We use the state-space model as a statistical basis for modeling the daily movement of carbon values and gas prices. As a result, as we have calculated, the state-space model is able to predict and provide in-depth observational information on the relationship between carbon and gas. The economic implications show that carbon is not only good for the environment; its trading in the stock exchange also indicates that it can be a new stable and sustainable economic commodity.

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9

Herlina, Deswita, and Amalia Romadhona. "Price analysis between commodity groups of inflation in Banten province from 2008 to 2018." International Journal of Financial, Accounting, and Management 2, no. 4 (March 1, 2021): 235–71. http://dx.doi.org/10.35912/ijfam.v2i4.447.

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Purpose: This study aimed to see how each commodity group's movement pattern that forms inflation between one commodity group and another, and to see any major linkages between groups of inflation-forming commodities in Banten Province. Research methodology: The research is undergone by using the Vector Auto Regressive (VAR) model approach through testing of Impulse Response Function (IRF) and Variance Decomposition (VD). Results: The results show that the inflation rate in the goods/services commodity groups in Banten Province have a dynamic relationship between one another. The group itself dominates inflationary movements in all commodity groups. Monthly time series data is used for 2008-2018 time spans. Limitation: The linkages presented as the results are performed in commodity groups; thus, further research would inform more about inter-linkage between commodities. Contribution: The insight of knowing the pattern is beneficial for implication policy in regional inflation targeting especially in Banten Province. Keywords: Inflation, IRF, VAR, VD

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10

Cahyaningrum, Anisha Wirasti. "FORMATION OF COMPOSITE GLOBAL COMMODITY PRICES AS AN INFLATION INDICATOR FOR EAST JAVA." East Java Economic Journal 2, no. 2 (August 5, 2021): 210–17. http://dx.doi.org/10.53572/ejavec.v2i2.20.

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With the average contribution of imports to Gross Regional Domestic Product (GRDP) in the last five years reaching 19.1%, the dynamics of global commodity prices also influence the economic performance of East Java, including the movement of inflation. A composite indicator of global commodity prices is needed to find out the impact of changes in various global commodity prices on inflation in East Java. By adopting the Bank Indonesia methodology in forming a composite global price known as the Imported Inflation Price Index (IHIM) which has considered the method of forming a global composite price created by the IMF (IMF Commodity Price Index), the compilation of East Java global price composites also examines the accuracy of commodity selection and aspects of data availability. The selected global price composite for East Java is a composite of seven global commodities which include food (wheat, soybeans, corn and CPO) and non-food (iron, gold and oil). These are two aspects determining the relative weight, namely (I) the import portion of the total input based on the Input-Output table and (ii) the commodity weight of derivatives in the East Java Consumer Price Index (IHK) basket. Furthermore, with OLS regression, the composite of East Java global commodity prices affects the core-traded inflation movement in East Java. Thus, the composite of global commodity prices in East Java can be used as an indicator of East Java inflation projections, especially core-traded inflation. This study, in general, will also examine the effect of the exchange rate impact on the movement of core inflation, especially traded groups in East Java. Based on the regression results it is known that the impact of the exchange rate movement on core traded inflation in East Java is more significant than the effect of world commodity price movements.

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11

West, Kenneth D., and Ka-Fu Wong. "A factor model for co-movements of commodity prices." Journal of International Money and Finance 42 (April 2014): 289–309. http://dx.doi.org/10.1016/j.jimonfin.2013.08.016.

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12

Byrne, Joseph P., Giorgio Fazio, and Norbert Fiess. "Primary commodity prices: Co-movements, common factors and fundamentals." Journal of Development Economics 101 (March 2013): 16–26. http://dx.doi.org/10.1016/j.jdeveco.2012.09.002.

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13

Yuan, Xinyu, Jiechen Tang, Wing-Keung Wong, and Songsak Sriboonchitta. "Modeling Co-Movement among Different Agricultural Commodity Markets: A Copula-GARCH Approach." Sustainability 12, no. 1 (January 3, 2020): 393. http://dx.doi.org/10.3390/su12010393.

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The aim of this research is to explore the volatility contagion among different agricultural commodity markets. For this purpose, this research make use of the copula-GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model for the daily spot prices of six major agriculture grain commodities including corn, wheat, soybeans, soya oil, cotton, and oat over the period from 2000 to 2019. Our results provide evidence that significant contagion effects and risk transmissions exist among different agricultural grain commodity markets, suggesting that potential speculation effects on one agricultural market could be contagious for another agricultural market and result an increase in volatility in agricultural product markets. Second, agricultural commodities appears to co-move symmetrically. We also find substantial extreme co-movements among agricultural commodity markets. This indicates that agricultural commodity markets tend to crash (boom) together during extreme events. Moreover, after the food crisis, contagion effects and risk transmissions among different agricultural commodity markets increased substantially. Fourth, we find that the strongest contagion effects and risk transmissions are between corn and soybeans, and the weakest contagion effects and risk transmissions are between soya oil cotton and between cotton and oat. Last, we document that the co-movement varies over time. Our findings hold important implications for modeling the co-movement by the copula-GARCH approach.

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14

GODA, THOMAS, and JAN PRIEWE. "Determinants of real exchange rate movements in 15 emerging market economies." Brazilian Journal of Political Economy 40, no. 2 (June 2020): 214–37. http://dx.doi.org/10.1590/0101-31572020-3072.

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ABSTRACT Previous work has established that an appreciation of the real effective exchange rate (REER) contributes to premature deindustrialization, less productive investment and dependence on commodity booms and busts in emerging markets economies (EME). From the literature, it is less clear, however, what the most important drivers for the cyclical REER movements in EME are. The aim of this study is to provide empirical evidence about the determinants of the REER movements of 15 emerging markets during the last two decades, using statistical analysis and a dynamic panel fixed effects model approach. Our analysis shows that although “commodity” and “industrial” EME are heterogeneous, REER volatility tends to be higher among the former. EME that had more stable REER fared better than those that had a depreciating or appreciating trend (with the notable exception of China). As theoretically expected, commodity prices are an important structural driver of REER movements in “commodity EME”. Moreover, the results confirm the existence of the Harrod-Balassa-Samuelson effect, and show the importance of financial inflows. Further, the interventions of central banks were partially successful to avoid more substantial appreciations (depreciations). Finally, we find that lower country risk and, at least in some periods, growing broad money in OECD countries has led to REER appreciations in our sample countries.

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15

Rao, D. Tripati, and Saurabh Goyal. "Global Commodity and Oil Price Movements, Macroeconomic Performance and Challenges for an Emerging Economy: The Indian Experience." Global Business Review 19, no. 3 (February 14, 2018): 650–74. http://dx.doi.org/10.1177/0972150917713885.

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Commodity and oil price fluctuations have significant bearing on domestic macroeconomic performance and macroeconomic policymaking of an emerging economy. The article explores the impact of non-energy commodity and oil price fluctuations on output, inflation and real exchange rate (RER) in India; and commodity and oil constituting sizeable imports. The empirical analysis carried out through vector error correction model (VECM) for the post-liberalization period 1991–2014 clearly points out that commodity and oil price shocks have a significant impact on the variation in output and prices accounting for RER adjustment and the role of a developed financial market (private credit). The RER adjusts to commodity and oil price shocks, accounting for foreign exchange reserves and financial markets (private credit). The impulse response functions indicate that one standard deviation shock in commodity and oil price persists for three to eight quarters over domestic prices and output. While these results point to lessening of commodity and oil imports through a series of medium and long-term structural-cum-policy reform measures, in the immediate, they also lend a role of intervention by monetary authority (central bank) in pursuit of inflation targeting. Conjointly, pursuance of countercyclical fiscal policy to stabilize domestic output and prices in short run are called for.

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16

Krishnan, Venkatesh, and Kathleen L. Hancock. "Highway Freight Flow Assignment in Massachusetts Using Geographic Information Systems." Transportation Research Record: Journal of the Transportation Research Board 1625, no. 1 (January 1998): 156–64. http://dx.doi.org/10.3141/1625-20.

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Goods movement is an important aspect of the transportation system. Freight flow, complemented with the much-researched passenger movement, provides a way for understanding the complete vehicle flow scenario on the highways. Commodity movement prediction has not received much attention because of the lack of sufficient and easily accessible data sources. Most data sources give aggregated commodity movements and, because of the heterogeneity of freight, accurate predictions of truck flows have not been possible. A methodology for calculating the truck flows on the various highways in Massachusetts from interstate commodity flow data is presented. Freight tons originating and ending in Massachusetts have been converted to truck numbers by using a quantitative procedure and distributed to different areas in the state by using employment as an economic indicator variable. The truck flow is assigned to the important highways and validated against existing survey counts. On comparison, a large percentage of the roads show the estimated truck counts are within a tolerable error margin. The research also shows that statewide analyses need to be refined near urban areas because of a variety of complexities involved.

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17

Ogunmola, Omotoso Oluseye, Abiodun Elijah Obayelu, and Sakiru Oladele Akinbode. "Volatility and Co‑movement: an Analysis of Food Commodity Prices in Nigeria." Agricultura Tropica et Subtropica 50, no. 3 (September 26, 2017): 129–39. http://dx.doi.org/10.1515/ats-2017-0014.

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AbstractThis study explains volatility as a measure and interaction of the possible movement in a particular economic variable. Prices change rapidly in adjustment to market circumstances. Food prices hike experienced overyears has resulted in widespread menace which led to increase in food price volatility. However, volatility and co-movement had generally been lower for the past two decades than for the previous ones. Wide price movements over a short period of time connote high volatility, rendering the producers and consumers vulnerable. Excess volatility can be subjected to sector ineffectiveness and is commodity specific. Producers and processors are mostly concerned about increased price volatility, which greatly exposed them to unpredictable risks and uncertainty associated with price changes. This study examined the volatility and co-movement of food commodity prices in Nigeria using price series data on rice, maize, sorghum, cassava and yam for the period of 1966 to 2013. The data were analysed using Vector Autoregressive Model to forecast food price volatility and to examine the food commodity prices that Granger cause food price volatility in other food commodities. The GARCH regression model is used to estimate the magnitude of volatility which revealed that, food commodity prices exhibit high volatility and there is persistent increase in prices over the period of study. The Nigerian food commodity prices have experienced high fluctuations over the period; therefore, the study recommends proper storage facilities and infrastructure for the food distribution corporations in Nigeria.

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18

Trabelsi, Jamel, Mohamed Mehdi Jelassi, and Gaye Del Lo. "A Volatility Analysis of Agricultural Commodity and Crude Oil Global Markets." Applied Economics and Finance 4, no. 2 (January 23, 2017): 129. http://dx.doi.org/10.11114/aef.v4i2.2086.

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The purpose of this study is to provide insights on volatility features of major agricultural commodity global markets. In order to achieve this, we estimate the volatility in the global markets of crude oil and four main agricultural commodities, namely rice, wheat, cotton and coffee over the period 1980:2014. We also investigate the nexus between the volatilities in these global markets. More precisely, we first model the volatility of agricultural commodity and crude oil markets based on the GARCH methodology. Second, we assess the risk in these global markets by the Value-at-Risk technique. Finally, we evaluate the co-movements between returns in agricultural commodity and crude oil markets by the copula methodology. Our empirical findings reveal that, unlike in the financial market, upside shocks in the agricultural market tend to increase volatility more than downside shocks do. In addition to that, risk in global agricultural commodity markets turned out to be high and little evidence in favor of interdependence between these markets is found. Moreover, the co-movement between agricultural commodity market risk and oil prices is detected for recent years only and little evidence is found for the whole sample period.

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19

BUDACH, GABRIELE, SYLVIE ROY, and MONICA HELLER. "Community and commodity in French Ontario." Language in Society 32, no. 5 (November 2003): 603–27. http://dx.doi.org/10.1017/s0047404503325011.

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Linguistic minority movements have long adopted dominant discourses linking language and community. Analysis of two sites of discursive production in francophone Canada (literacy centers and a call center workplace in Ontario) shows that current socioeconomic changes challenge those discourses. Language and community are uncoupled and redefined as commodities with exchange value in the globalized market for services and information. “Community” serves mainly to legitimize struggles for privileged access to newly commodified linguistic and cultural resources.

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20

Arseneau, David M., and Sylvain Leduc. "Commodity Price Movements in a General Equilibrium Model of Storage." International Finance Discussion Paper 2012, no. 1054 (August 2012): 1–37. http://dx.doi.org/10.17016/ifdp.2012.1054.

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21

Cai, Xiao Jing, Zheng Fang, Youngho Chang, Shuairu Tian, and Shigeyuki Hamori. "Co-movements in commodity markets and implications in diversification benefits." Empirical Economics 58, no. 2 (September 6, 2018): 393–425. http://dx.doi.org/10.1007/s00181-018-1551-3.

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22

Arseneau, David M., and Sylvain Leduc. "Commodity Price Movements in a General Equilibrium Model of Storage." IMF Economic Review 61, no. 1 (April 2013): 199–224. http://dx.doi.org/10.1057/imfer.2013.9.

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23

Asborno, Magdalena I., Collin G. Burris, and Sarah Hernandez. "Truck Body-Type Classification using Single-Beam Lidar Sensors." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 1 (January 2019): 26–40. http://dx.doi.org/10.1177/0361198118821847.

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Understanding commodity flow through a region is key for estimating the demand for freight transportation facilities and services, forecasting energy consumption, analyzing safety risks, and addressing environmental concerns. Transportation planners and decision makers use commodity flow data to develop and implement long-term freight plans and manage infrastructure. State-of-the-practice commodity flow estimations based on regional socioeconomic data and periodic surveys have limited spatial and temporal coverage. Moreover, no existing methods tie vehicles to commodity movements at the link level. Although intrusive inductive loop detectors can identify the industry served (or commodity carried) by trucks based on the truck’s body type, intrusive sensor performance is limited by pavement quality. Unfortunately, poor pavement conditions are common in locations with high truck volumes. This paper investigates the use of a non-intrusive traffic sensor, Lidar, for high-resolution truck body-type classification. This paper develops a proof-of-concept Lidar sensor and a truck body-type classification model capable of classifying five-axle tractor-trailers into distinct body types: van and container, platform, low-profile trailer, tank, and hopper and end dump. These body-class groups link to commodity movements and provide insight into link-level commodity flows. Data for model development and validation were collected along a major interstate corridor and a low-speed local road. The classification model achieves an 81% true positive rate (TPR) with class-specific TPR as high as 94% and average volume accuracy of 87% for the primary test location. Overall, the proposed sensor represents an adequate proof of concept to evaluate the industry served by trucks on a network link.

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24

Živkov, Dejan, Biljana Stankov, Milijana Roganović, and Mirela Momčilović. "Dynamic correlation between selected cereals traded in commodity exchange market in AP Vojvodina." Ekonomika poljoprivrede 69, no. 2 (2022): 395–410. http://dx.doi.org/10.5937/ekopolj2202395z.

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This paper investigates the level of pairwise dynamic correlations between prices of four agricultural commodities - corn, wheat soybean and barley that are traded in Novi Sad commodity exchange market. We use DCC-GARCH model, which is specially designed for this type or research. The results of the estimated dynamic conditional correlations show that low and positive correlation exist between all the pairs of the selected agricultural commodities, where the highest correlation is recorded between wheat and barley (24%), corn-barley pair follows (20%), while all other dynamic correlations are below 20%. The results indicate that price movements of the selected agricultural cereals are independent, which means that price discovery of one agricultural commodity does not provide information about the price of another agricultural commodity. Therefore, our results strongly suggest that traders in this market do not rely on the price co-movements between particular agricultural assets when they plan their selling or buying strategies, but to analyze fundamental macroeconomic factors.

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25

Ndlovu, Xolani, Eric Schaling, and Paul Alagidede. "Modelling the rand and commodity prices: A Granger causality and cointegration analysis." South African Journal of Economic and Management Sciences 17, no. 5 (November 28, 2014): 673–90. http://dx.doi.org/10.4102/sajems.v17i5.334.

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This paper examines the ‘commodity currency’ hypothesis of the Rand, that is, the postulate that the currency moves in line with commodity prices, and analyses the associated causality using nominal data between 1996 and 2010. We address both the short run and long run relationship between commodity prices and exchange rates. We find that while the levels of the series of both assets are difference stationary, they are not cointegrated. Further, we find the two variables are negatively related, with strong and significant causality running from commodity prices to the exchange rate and not vice versa, implying exogeneity in the determination of commodity prices with respect to the nominal exchange rate. The strength of the relationship is significantly weaker than other OECD commodity currencies. We surmise that the relationship is dynamic over time owing to the portfolio-rebalance argument and the Commodity Terms of Trade (CTT) effect and, in the absence of an error correction mechanism, this disconnect may be prolonged. For commodity and currency market participants, this implies that while futures and forward commodity prices may be useful leading indicators of future currency movements, the price risk management strategies may need to be recalibrated over time.

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26

Lawuobahsumo, Kokulo K., Bernardina Algieri, Leonardo Iania, and Arturo Leccadito. "Exploring Dependence Relationships between Bitcoin and Commodity Returns: An Assessment Using the Gerber Cross-Correlation." Commodities 1, no. 1 (August 25, 2022): 34–49. http://dx.doi.org/10.3390/commodities1010004.

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We use a robust measure of non-linear dependence, the Gerber cross-correlation statistic, to study the cross-dependence between the returns on Bitcoin and a set of commodities, namely wheat, gold, platinum and crude oil WTI. The Gerber statistic enables us to obtain a more robust co-movement measure since it is neither affected by extremely large nor small movements that characterise financial time series; thus, it strips out noise from the data and allows us to capture effective co-movements between series when the movements are “substantial”. Focusing on the period 2014–2022, we construct the bootstrapped confidence intervals for the Gerber statistic and test the null that all the Gerber cross-correlations up to lag kmax are zero. Our results indicate a low degree of dependence between Bitcoin and commodities prices, both when we consider contemporaneous correlation and when we employ correlations between current Bitcoin and lagged (one day, one week, or one month) commodities returns. Further, the cross-correlation between Bitcoin and commodities’ returns, although scanty, shows an increasing trend during periods of economic, health and financial turbulence. This increased cross-correlation of returns during hectic market periods could be due to the contagion effect of some markets by others, which could also explain the strong dependence across volatilities we detected. Based on our results, Bitcoin cannot be considered the “new digital gold”.

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27

Rezitis, Anthony N., and Maria Sassi. "Commodity Food Prices: Review and Empirics." Economics Research International 2013 (March 17, 2013): 1–15. http://dx.doi.org/10.1155/2013/694507.

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The present paper provides a literature review of studies examining the potential causes and consequences of recent surges in food and agricultural commodity prices. Furthermore, this paper uses the structural trend methodology proposed by Koopman et al. (2009) to analyze movements in the IMF monthly commodity food price index for the period 1992(11)–2012(10) and to provide forecasts for the period 2012(11)–2014(12). The empirical results indicate that commodity food prices present seasonality and cyclicality with the longest periodicity of two years. The empirical findings identify certain structural breaks in commodity food price series as well as outliers. These structural breaks seem to capture the trend component of the price series well, while the outliers take account of temporal effects, that is, short-lived spikes. Finally, the presented forecasts show high and volatile commodity food prices.

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28

Matesanz, David, Benno Torgler, Germán Dabat, and Guillermo J. Ortega. "Co-movements in commodity prices: a note based on network analysis." Agricultural Economics 45, S1 (July 10, 2014): 13–21. http://dx.doi.org/10.1111/agec.12126.

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29

Al-Battaineh, Omar, and Isam A. Kaysi. "Genetically-optimized origin-destination estimation (GOODE) model: application to regional commodity movements in Ontario." Canadian Journal of Civil Engineering 34, no. 2 (February 1, 2007): 228–38. http://dx.doi.org/10.1139/l06-127.

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The problem of origin-destination (O-D) matrix estimation has attracted significant research attention in the past few decades. This paper proposes a novel approach to estimate a regional freight O-D matrix using different data sources. The genetically optimized origin-destination estimation (GOODE) model takes advantage of the genetic algorithm's (GA) global search procedure to find the O-D matrix that is associated with the minimum deviation between estimated and observed data values. The GOODE-commodity model, an extension of the GOODE model, estimates the freight O-D matrix by interfacing GOODE with a trip generation model based on input-output data. The GOODE model and its extension bring together national input-output data, truck survey data, a global searching method, and a GIS platform for data manipulation. This paper presents the GOODE model structure, a prototypical numerical example, a benchmarking exercise with an existing O-D estimation model, and a real-world application of the GOODE-commodity model for a case study of commodity movements in Ontario. Avenues for future research are also addressed.Key words: origin-destination (O-D) matrix estimation, truck transportation modelling, input-output, Ontario, genetic algorithm.

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30

Naifar, Nader. "Exploring the Dynamic Links between GCC Sukuk and Commodity Market Volatility." International Journal of Financial Studies 6, no. 3 (August 13, 2018): 72. http://dx.doi.org/10.3390/ijfs6030072.

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This study investigates the impact of commodity price volatility (including soft commodities, precious metals, industrial metals, and energy) on the dynamics of corporate sukuk returns. Using a sample of sukuk indices from Gulf Cooperation Council (GCC) countries, we study the dynamic conditional correlation using a multivariate generalized autoregressive conditional heteroskedasticity dynamic conditional correlation (GARCH-DCC) process. Empirical results show a time-varying negative correlation between GCC sukuk returns and commodity prices. In fact, a negative conditional correlation among assets of a given portfolio implies higher gain-to-risk ratios. An understanding of volatility and dynamic co-movements in financial and commodity markets is important for portfolio allocation and risk management practices.

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31

Niblett, Michael. "World-Literature, Commodity Frontiers, and Aesthetic Form." Via Atlântica, no. 40 (December 6, 2021): 116–77. http://dx.doi.org/10.11606/va.i40.173470.

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This article speaks of a literary comparativism provided by the environment-making dynamics of commodity frontiers. How is world-literature imbricated in these movements? How do texts mediate the logic of commodity frontiers and how might this mediation be differently inflected by the specific political ecologies of sugar, coffee, oil, or rubber? To approach literature from this angle clearly resonates with Patricia Yaeger’s call to attend to the energy resources that make texts possible (2011). By responding to this call through the optic of the commodity frontier, I seek to underscore the necessity of understanding those resources in terms of the systemic logic and structural relations of capitalism as a world-ecology.

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32

Sakai, Takanori, Kazuya Kawamura, and Tetsuro Hyodo. "Logistics Chain Modeling for Urban Freight." Transportation Research Record: Journal of the Transportation Research Board 2609, no. 1 (January 2017): 55–66. http://dx.doi.org/10.3141/2609-07.

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In existing freight models, the choice of locations for logistics facilities and the choice of logistics facilities for routing the shipments are often treated without distinction, although these two decisions are separate and affected by different factors. In this paper, models for choosing a logistics facility are developed that match truck trip ends with logistics facilities by using a large urban freight survey data from the Tokyo Metropolitan Area. The models can be used to estimate truck traffic flows associated with transshipments. The urban portion of the logistics chains was categorized into five types of movements, and 30 models were developed to analyze six commodity groups separately. The results indicated that the proposed approach could capture the mechanism of the selection of a logistics facility by movement and commodity type. The tests for the reproducibility of the models warrant the future use of the models for analysis of urban freight demand.

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33

de Boyrie, Maria E., and Ivelina Pavlova. "Equities and Commodities Comovements: Evidence from Emerging Markets." Global Economy Journal 18, no. 3 (April 26, 2018): 20170075. http://dx.doi.org/10.1515/gej-2017-0075.

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The financialization of commodities and their inclusion in financial portfolios as part of an investment strategy may result in higher correlations and volatility spillovers between commodity and equity markets. In this paper, we estimate the correlation between equity markets and commodities using the dynamic conditional correlation (DCC) model, while emphasizing the differences between emerging and developed markets co-movements with commodities. The results reveal that certain emerging markets, especially those in Asia, show a much lower level of co-movement with commodities than developed markets do, while Latin American equities exhibit a higher level of integration with commodities. Furthermore, it is found that both agricultural and precious metals commodities offer better diversification possibilities in the less developed markets. We also find that increases in the CBOE Volatility Index (VIX) are related to higher agriculture commodities-equities correlations, while commodity net index investment has limited explanatory power in our study.

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Welch, Clifford Andrew. "ESTRATÉGIAS DE RESISTÊNCIA DO MOVIMENTO CAMPONÊS BRASILEIRO EM FRENTE DAS NOVAS TÁTICAS DE CONTROLE DO AGRONEGÓCIO TRANSNACIONAL." REVISTA NERA, no. 6 (May 29, 2012): 35–45. http://dx.doi.org/10.47946/rnera.v0i6.1465.

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The article surveys strategies developed by Brazilian rural social movements to combat the hegemony of transnational agricultural corporations and agribusinesses utilizing biotechnologies such as transgenic seed to gain control of agricultural production and commodity markets.

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35

Waldron, David. "Witchcraft for Sale! Commodity vs. Community in the Neopagan Movement." Nova Religio 9, no. 1 (August 1, 2005): 32–48. http://dx.doi.org/10.1525/nr.2005.9.1.032.

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The growth of the Pagan and Witchcraft revivalist movements (Neopaganism) is well documented in the Anglophone world. However, Witchcraft movements are also closely linked to a vibrant set of subcultures and a multitude of representations in popular culture. In this context investigating the relationship between Witchcraft as a religious community and its representation in consumer culture and mass media is extremely significant. This article examines the ambiguous relationship between witch and Wiccan communities and the vast array of merchandising, popular culture and media representations that surround them. In creating the WIKID WITCH KIT I hope to take you on a magickal and exciting journey! Through ritual, music, song and spoken word I will help you unleash your inner magick and discover the wonderful and positively empowering world of Witchcraft. As part of this journey you will discover your WIKID magickal name, giving you access to our exclusive website and online coven. There you can meet up with other WIKID Witches to swap spells, stories, and ideas. And every full moon I will personally join you for an online gathering——which will be truly WIKID. WIKID Witch Kit features WIKID Magick Fizz/WIKID Magick Potions/WIKID Magick Fire/WIKID Magick Star/WIKID Magick Cord/WIKID Magick Audio CD.1

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Webster, Michael, and Rory C. Tarnow-Mordi. "Decomposing Multilateral Price Indexes into the Contributions of Individual Commodities." Journal of Official Statistics 35, no. 2 (June 1, 2019): 461–86. http://dx.doi.org/10.2478/jos-2019-0020.

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Abstract This article describes methods for decomposing price indexes into contributions from individual commodities, to help understand the influence of each commodity on aggregate price index movements. Previous authors have addressed the decomposition of bilateral price indexes, which aggregate changes in commodity prices from one time period to another. Our focus is the decomposition of multilateral price indexes, which aggregate commodity prices across more than two time periods or countries at once. Multilateral indexes have historically been used for spatial comparisons, and have recently received attention from statistical agencies looking to produce temporal price indexes from large and high frequency price data sets, such as scanner data. Methods for decomposing these indexes are of practical relevance. We present decompositions of three multilateral price indexes. We also review methods proposed by other researchers for extending multilateral indexes without revising previously published index levels, and show how to decompose the extended indexes they produce. Finally, we use a data set of seasonal prices and quantities to illustrate how these decomposition methods can be used to understand the influence of individual commodities on multilateral price index movements, and to shed light on the relationships between various multilateral and extension methods.

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Zhang, Yongmin, and Shusheng Ding. "Liquidity effects on price and return co-movements in commodity futures markets." International Review of Financial Analysis 76 (July 2021): 101796. http://dx.doi.org/10.1016/j.irfa.2021.101796.

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38

Krawiec, Monika. "Commodities Versus Stocks: Analysis of Their Performance from 2009 Through 2015." European Journal of Multidisciplinary Studies 3, no. 1 (December 1, 2016): 8. http://dx.doi.org/10.26417/ejms.v3i1.p8-20.

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Although over the last several years one could have witnessed unprecedented interest in commodity investments, the view of commodities from an investor’s perspective is of more recent date (with the exception of precious metals). There are several reasons for investing in commodities. First of all, they let investors gain equity-like or higher returns. Then, they can help to mitigate risk and improve portfolio diversification. They can also provide a possible hedge against unanticipated inflation. The growing popularity of commodity investing has been followed by a great number of new investment vehicles that make commodity investments available to a wider audience. Thus, investors based on their risk-return criteria and individual requirements may select from a broad range of commodity-linked financial instruments. One of possibilities is investing through a commodity index. This approach is especially attractive to investors that are familiar with investing in stock indexes. In theory, commodity indexes share a similar goal: to create a broad indicator of commodity price movements, though in practice portfolio weightings, construction, and calculation methodology vary significantly from one index to another. The most important of commodity indexes are: the Thomson Reuters/Core Commodity CRB Index, the S-P Goldman Sachs Commodity Index, the Bloomberg Commodity Index (former Dow-Jones AIG Commodity Index), and the Deutsche Bank Liquid Commodity Index. The present paper is aimed at assessing return and risk characteristics of these indexes and at providing a comparative analysis of their performance in relation to the most important equity indexes, such as S-P500, FTSE100, CAC40, DAX, WIG, BUX, IBovespa, Nikkei, Shanghai Composite (SSE), TSE300 (current S-P/TSX Composite Index), and AOI (All Ords). The empirical data covers daily quotations from January 5, 2009 to December 30, 2015. To verify whether the commodity indexes returns differ significantly from the returns of equity indexes, the nonparametric Mann-Whitney test is applied. The test has been chosen as returns of commodity indexes are not normally distributed.

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39

Firoozi, Fathali. "On the Second-Best Foreign Investment Policy and Pattern of Commodity Trade." American Economist 42, no. 1 (March 1998): 34–41. http://dx.doi.org/10.1177/056943459804200103.

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An agreement on free commodity trade often does not preclude countries from protecting their national interests through restrictive policies toward cross-border movements of production factors (e.g., capital and labor). A number of studies have suggested second-best international capital flows (welfare maximizing under free commodity trade) that officials of a country must encourage via various policy measures. However, an emerging literature indicates that policy toward foreign direct investment is being increasingly utilized as a new form of protectionism under free trade. Utilizing a generalized Heckscher-Ohlin model, this study characterizes the necessary adjustments to the suggested second-best foreign investment policies of a country when there is an extraneous protectionist objective regarding the pattern of trade in a commodity. An implication is that until all production factors can freely move internationally without policy impediments of a participating country, unrestricted commodity trade alone cannot achieve its full potential in removing protectionism and setting comparative advantage as the basis for trade. (JEL F21, F15)

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40

Radivojevic, Nikola, Almir Muhovic, Milica Joksimovic, and Miroslav Pimic. "Examining the Impact of Movements of the Commodity Price on the Value of the Baltic Dry Index during Covid19 Pandemic." Asian Journal of Economics and Empirical Research 8, no. 2 (September 22, 2021): 67–72. http://dx.doi.org/10.20448/journal.501.2021.82.67.72.

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The Baltic Dry Index (BDI) is one of the most well-known indices, as it is perceived as a leading indicator of economic activity. Reductions in the movement of people, commodities, and capital in the conditions of economic crises, such as the one in 2008 and 2009, as well as the current economic crisis generated by the COVID-19 pandemic, were affected by the reduction of economic activities. It is interesting to point out that the analysis of the basic trend of the BDI movements in the period before the economic crisis shows that the index fell to near record lows just before the derivatives and credit crisis hit stocks full force. This is a clear signal that the index can be used as a tool for stock market forecasting. The paper aims to examine whether the changes in these raw materials affect the changes in the value of BDI. For these purposes in the paper was use GMM and 2SLS estimator. The results show that different raw materials have a different impact on the value of the BDI, which indicates that based on individual movements value of raw materials which composes the BDI cannot forecast its movement.

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41

Wanliss, James. "Efficiency of Price Movements in Futures Markets." Indian Economic Journal 68, no. 2 (June 2020): 193–206. http://dx.doi.org/10.1177/0019466220966599.

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We study behaviour in the E-mini S&P (ES) commodity futures data market to test for violation of the efficient market hypothesis (EMH), and test for market inefficiency. We demonstrate that, on long timescales, a single scaling determines dynamics. ES returns behave in a more general manner than random walks. We find that deviations from the EMH, and the associated heavy-tailed distributions, are more common than expected, and price returns can be fitted with an alpha-stable Lévy distribution. Our results indicate that while the ES futures market operates close to the state predicted by the EMH, the observed transient deviations from this state fail to have a statistical origin consistent with a purely random geometric Brownian motion, and are better described by the fractal market hypothesis.

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42

BHANUMURTHY, N. R., PAMI DUA, and LOKENDRA KUMAWAT. "WEATHER SHOCKS AND AGRICULTURAL COMMODITY PRICES IN INDIA." Climate Change Economics 04, no. 03 (August 2013): 1350011. http://dx.doi.org/10.1142/s2010007813500115.

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We analyze the impact of weather shocks on price formation in spot and futures market for food in India where until the recent introduction of commodity futures markets in 2005, the transmission of these shocks to short-term (spot) price movements was unclear. Hitherto, the price discovery mechanism was weak and end price was expected to be different (mostly higher unless some product prices were administered) from the market-clearing price. In addition, this weak mechanism was expected to result in higher price volatility. The introduction of a futures market is expected to reduce risk, a major component in agricultural production as well as in price formation. Though the commodity futures market in India is nascent, we model transmission of weather shocks to futures and spot prices using monthly data. Based on cointegration analysis, our results suggest strong long-run co-movement between futures prices and spot prices for commodities traded in futures markets. Changes in rainfall affect both futures and spot prices with different lags. However, rainfall shocks generate larger responses from futures prices than from spot prices. Although there could be other factors that affect futures prices, after controlling for fuel prices, our results clearly show the transmission mechanism of weather shocks from futures to spot prices. We also explore the changes in responsiveness of prices of major agricultural commodities to rainfall with introduction of futures contracts to facilitate the pass-through of various types of shocks to agricultural commodity prices. Using smooth transition regression, we find that the bivariate relationships between rainfall and prices of rice, wheat and pulses show some nonlinearity with the structural change happening after the introduction of futures market. These relations are found to be much stronger in the post-structural change period that broadly coincides with the introduction of futures market.

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43

Often, Einar M., and Craig H. Wisen. "Disaggregated Commitment Of Traders Data And Prospective Price Effects." Journal of Applied Business Research (JABR) 29, no. 5 (August 28, 2013): 1381. http://dx.doi.org/10.19030/jabr.v29i5.8021.

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<p>The interplay between speculative levels in futures contracts and prospective price changes is an important issue for hedgers, speculators, consumers, and regulators. The Commodity Futures Trading Commission (CFTC) began the dissemination of disaggregated data in September 2009; which allows for additional analysis that was previously not possible. This study analyzes Disaggregated Commitments of Traders data and prospective price effects in underlying futures markets. The relationship between position changes and subsequent price movements is examined through a series of Granger causality tests in agricultural commodity, energy, and metal markets. Primary findings indicate large traders and hedgers have more influence in future price changes in some commodity markets. In addition, there is limited evidence that changes in the positions of swap dealers and producers can cause subsequent futures prices to change, although the effect is inconsistent across time.</p>

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44

Silhacek, D., C. Murphy, and R. T. Arbogast. "Behavior and movements of Indian meal moths (Plodia interpunctella Hübner) during commodity infestation." Journal of Stored Products Research 39, no. 2 (January 2003): 171–84. http://dx.doi.org/10.1016/s0022-474x(01)00051-0.

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45

Walsh, Bruce. "OPINION: The scoop in olympic style pulling movements— Is it a teachable commodity?" National Strength & Conditioning Association Journal 11, no. 2 (1989): 65. http://dx.doi.org/10.1519/0744-0049(1989)011<0065:tsiosp>2.3.co;2.

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46

JONES, RONALD W. "CO-MOVEMENTS IN RELATIVE COMMODITY PRICES AND INTERNATIONAL CAPITAL FLOWS: A SIMPLE MODEL." Economic Inquiry 27, no. 1 (January 1989): 131–41. http://dx.doi.org/10.1111/j.1465-7295.1989.tb01168.x.

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47

Oglend, Atle, and Tore Selland Kleppe. "How regular are directional movements in commodity and asset prices? A Wald test." Journal of Empirical Finance 38 (September 2016): 290–306. http://dx.doi.org/10.1016/j.jempfin.2016.07.001.

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48

Lucotte, Yannick. "Co-movements between crude oil and food prices: A post-commodity boom perspective." Economics Letters 147 (October 2016): 142–47. http://dx.doi.org/10.1016/j.econlet.2016.08.032.

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49

Algieri, Bernardina, Arturo Leccadito, and Pietro Toscano. "A Time-Varying Gerber Statistic: Application of a Novel Correlation Metric to Commodity Price Co-Movements." Forecasting 3, no. 2 (May 16, 2021): 339–54. http://dx.doi.org/10.3390/forecast3020022.

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This study investigates the daily co-movements in commodity prices over the period 2006–2020 using a novel approach based on a time-varying Gerber correlation. The statistic is computed considering a set of probabilities estimated via non-traditional models that give a time-varying structure to the measure. The results indicate that there are several co-movements across commodities, that these co-movements change over time, and that they are tendentially positive. Conditional auto-regressive multithreshold logit models show higher forecasting accuracy for agricultural returns, while dynamic conditional correlation models are more accurate for energy products and metals. The proposed models are shown to be superior in terms of forecasting power to the benchmark method which is based on estimating the Gerber correlation moving a rolling window.

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50

Kandil, Magda. "Domestic policies and external spillovers." International Journal of Development Issues 15, no. 3 (September 5, 2016): 254–93. http://dx.doi.org/10.1108/ijdi-06-2016-0031.

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Purpose Using data for a sample of advanced and developing countries, this paper aims to study the responses of monetary growth and the growth of government spending to external spillovers, namely, the growth of exports and imports, movement in the real effective exchange rate and the change in the oil price. The objective is to study movements in domestic policy variables in open economies that are vulnerable to trade and commodity price shocks. Design/methodology/approach The analysis evaluates correlations between the responses of the policy variables to external spillovers. Further, the analysis studies the effects of indicators of economic performance on domestic policy responses to various shifts across countries. Findings Higher variability of real and nominal growth increases the fiscal policy response to external spillovers with an aim to stem further variability. Monetary policies appear to be more responsive to trend price inflation with an aim to stem further inflationary pressures. Fiscal policy’s reaction to trend price inflation aims at striking a balance between countering potential inflationary pressures, as well as recessionary conditions attributed to the various spillovers. Originality/value Overall, the evidence points to the importance of trade and commodity price shifts to the design of domestic policies. Further indicators of economic performance differentiate the degree of policy responses to trade and commodity price developments with a goal to stem inflationary pressures and reduce aggregate uncertainty.

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