scholarly journals Permutation Entropy and Statistical Complexity Analysis of Brazilian Agricultural Commodities

Entropy ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. 1220 ◽  
Author(s):  
Fernando Henrique Antunes de Araujo ◽  
Lucian Bejan ◽  
Osvaldo A. Rosso ◽  
Tatijana Stosic
Keyword(s):  
Market Integration ◽  
Complexity Analysis ◽  
Commodity Prices ◽  
Food Prices ◽  
Permutation Entropy ◽  
Agricultural Commodities ◽  
Agricultural Commodity ◽  
Abrupt Increase ◽  
Statistical Complexity ◽  
Cattle Markets

Agricultural commodities are considered perhaps the most important commodities, as any abrupt increase in food prices has serious consequences on food security and welfare, especially in developing countries. In this work, we analyze predictability of Brazilian agricultural commodity prices during the period after 2007/2008 food crisis. We use information theory based method Complexity/Entropy causality plane (CECP) that was shown to be successful in the analysis of market efficiency and predictability. By estimating information quantifiers permutation entropy and statistical complexity, we associate to each commodity the position in CECP and compare their efficiency (lack of predictability) using the deviation from a random process. Coffee market shows highest efficiency (lowest predictability) while pork market shows lowest efficiency (highest predictability). By analyzing temporal evolution of commodities in the complexity–entropy causality plane, we observe that during the analyzed period (after 2007/2008 crisis) the efficiency of cotton, rice, and cattle markets increases, the soybeans market shows the decrease in efficiency until 2012, followed by the lower predictability and the increase of efficiency, while most commodities (8 out of total 12) exhibit relatively stable efficiency, indicating increased market integration in post-crisis period.

scholarly journals Determinants of prices increase of agricultural commodities in a global context

Management ◽  
2015 ◽  
Vol 19 (2) ◽  
pp. 152-167 ◽  
Author(s):  
Michał Borychowski ◽  
Andrzej Czyżewski
Keyword(s):  
Raw Materials ◽  
Commodity Prices ◽  
Food Prices ◽  
Biofuel Production ◽  
Agricultural Commodities ◽  
Agricultural Commodity ◽  
Factors Affecting ◽  
Global Context ◽  
Dollar Depreciation ◽  
The Individual

Summary The main objective of this article is to present the determinants of increase in agricultural commodity prices after 2006. The other specific aim is to show the factors affecting agricultural raw materials and food prices in the global context. This article is a review paper of the determinants of recent commodity and food prices spikes. However, it provides an outlook on these determinants that were the most important for the increases in the last decade. The last part of the article (conclusions) to some extent is a synthesis of considerations and includes the authors’ opinions concerning determinants and an attempt to identify which ones were the most important in the growth of agricultural commodity prices. These increases in agricultural commodity prices resulted from many factors and it is very difficult to separate the individual impact of each of them, because they occurred in parallel. However, it is possible to indicate several main reasons for these price increases, which are: adverse changes in supply-demand relations in agricultural markets, increases in oil prices (and increases of the volatility of those prices), development of biofuel production from agricultural commodities (the first generation biofuels), dollar depreciation, an increase in operations of a speculative nature on commodity markets and improper economic policy that created an environment for the growth of prices of agricultural products.

Prediction of Future Market Price for Agricultural Commodities

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
Sagar Pathane ◽  
Uttam Patil ◽  
Nandini Sidnal
Keyword(s):  
Mobile Application ◽  
Market Price ◽  
Commodity Prices ◽  
Future Price ◽  
Future Market ◽  
Graphical Interface ◽  
Agricultural Commodities ◽  
Agricultural Commodity ◽  
Web Based ◽  
Agriculture Products

The agricultural commodity prices have a volatile nature which may increase or decrease inconsistently causing an adverse effect on the economy. The work carried out here for predicting prices of agricultural commodities is useful for the farmers because of which they can sow appropriate crop depending on its future price. Agriculture products have seasonal rates, these rates are spread over the entire year. If these rates are known/alerted to the farmers in advance, then it will be promising on ROI (Return on Investments). It requires that the rates of the agricultural products updated into the dataset of each state and each crop, in this application five crops are considered. The predictions are done based on neural networks Neuroph framework in java platform and also the previous years data. The results are produced on mobile application using android. Web based interface is also provided for displaying processed commodity rates in graphical interface. Agricultural experts can follow these graphs and predict market rates which can be informed to the farmers. The results will be provided based on the location of the users of this application.

scholarly journals Modeling the Relationship between Crude Oil and Agricultural Commodity Prices

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1344 ◽  
Author(s):  
Duc Hong Vo ◽  
Tan Ngoc Vu ◽  
Anh The Vo ◽  
Michael McAleer
Keyword(s):  
Crude Oil ◽  
Global Financial Crisis ◽  
Aggregate Demand ◽  
Commodity Prices ◽  
Impulse Response Functions ◽  
Agricultural Commodities ◽  
Agricultural Commodity ◽  
Vector Autoregressive ◽  
Crude Oil Prices ◽  
Oil Markets

The food-energy nexus has attracted great attention from policymakers, practitioners, and academia since the food price crisis during the 2007–2008 Global Financial Crisis (GFC), and new policies that aim to increase ethanol production. This paper incorporates aggregate demand and alternative oil shocks to investigate the causal relationship between agricultural products and oil markets. For the period January 2000–July 2018, monthly spot prices of 15 commodities are examined, including Brent crude oil, biofuel-related agricultural commodities, and other agricultural commodities. The sample is divided into three sub-periods, namely: (i) January 2000–July 2006, (ii) August 2006–April 2013, and (iii) May 2013–July 2018. The structural vector autoregressive (SVAR) model, impulse response functions, and variance decomposition technique are used to examine how the shocks to agricultural markets contribute to the variance of crude oil prices. The empirical findings from the paper indicate that not every oil shock contributes the same to agricultural price fluctuations, and similarly for the effects of aggregate demand shocks on the agricultural market. These results show that the crude oil market plays a major role in explaining fluctuations in the prices and associated volatility of agricultural commodities.

scholarly journals Agricultural Policy: High Commodity and Input Prices

2009 ◽  
Vol 38 (1) ◽  
pp. 18-35 ◽  
Author(s):  
Andrew Schmitz ◽  
Hartley Furtan ◽  
Troy G. Schmitz
Keyword(s):  
Commodity Prices ◽  
The United States ◽  
Food Prices ◽  
Commodity Markets ◽  
The European Union ◽  
Agricultural Commodity ◽  
Commodity Trade ◽  
Country Of Origin Labeling ◽  
Input Prices ◽  
Meat Sector

Because of high commodity prices, beginning in 2006, subsidies to farmers in the United States, the European Union, and Canada have been reduced significantly. However, significant losses have been experienced by the red meat sector, along with escalating food prices. Because of rising input costs, the “farm boom” may not be as great as first thought. Ethanol made from corn and country-of-origin labeling cloud the U.S. policy scene. Higher commodity prices have caused some countries to lower tariff and non-tariff barriers, resulting in freer commodity trade worldwide. Policymakers should attempt to make these trade-barrier cuts permanent and should rethink current policy legislation to deal with the possibility of a collapse of world commodity markets. Agricultural commodity prices have dropped significantly since early 2008.

Financialization of Indian agricultural commodities: the case of index investments

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Manogna RL ◽  
Aswini Kumar Mishra
Keyword(s):  
Institutional Investors ◽  
Extreme Values ◽  
Commodity Prices ◽  
Equity Markets ◽  
Commodity Markets ◽  
Agricultural Products ◽  
Agricultural Commodities ◽  
Agricultural Commodity ◽  
Content Type ◽  
Index Value

PurposeThe phenomenon known as financialization of commodities, arising from the speculation in commodity derivatives market, has raised serious concerns in the recent past. This has prompted distortion in agricultural commodity prices driving them away from rational levels of supply and demand shocks. In the backdrop of financialized commodities leading to increase in price of agricultural products and their interaction with equity markets, the authors examine the investment of institutional investors in impacting the agricultural returns. The paper aims to focus on the financial mechanism that drives extreme values and the mean of agricultural returns.Design/methodology/approachThe authors employ the Threshold AutoRegressive Quantile (TQAR) methodology to find evidence of linkages between the Indian agricultural and equity markets from January 2010 to May 2020 consistent with the rise in inflows of institutional investors in agricultural markets.FindingsThe results reveal that the investors impact the agricultural commodity markets strongly when the composite commodity index value (COMDEX) is low. Additionally, in the lower extreme quantiles (0.25) of agricultural returns, the integration between the equity index and agricultural returns is found to be highly significant compared to insignificant values in the higher quantiles (0.75 and 0.95) in both the regimes. The results suggest that low values of agricultural commodities are more closely linked to equity indices when composite commodity index value is low. This implies that, at the lower quantiles of COMDEX return (bad day), the investors move to the stock market. In that way, the commodity index returns are seen to be as a strong channel for the financialization of Indian agricultural commodities and suggesting potential involvement of investors during those regime.Research limitations/implicationsRegulators need to anticipate the price fluctuations in spot and futures markets. Investors in commodity markets need to strengthen risk awareness to carry out portfolio strategies.Practical implicationsFrom policy perspective, it is of pivotal importance to enhance the understanding of the financialization of agricultural products. The findings provide reference measures to stabilize the commodity markets, alleviate price distortions and carry out further evidence of price discovery and risk management in Indian commodity markets.Originality/valueTo the best of the authors’ knowledge, this study is the first to highlight the potential influence of financial markets on the financialization of agricultural commodities in an emerging economy like India.

scholarly journals Does Oil Price Drive World Food Prices? Evidence from Linear and Nonlinear ARDL Modeling

Economies ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 12 ◽  
Author(s):  
Mourad Zmami ◽  
Ousama Ben-Salha
Keyword(s):  
Commodity Prices ◽  
Food Prices ◽  
Food Price ◽  
Oil Price ◽  
Agricultural Commodity ◽  
Distributed Lag ◽  
Nonlinear Ardl ◽  
Long Run ◽  
Linear And Nonlinear ◽  
The Impact

The macroeconomic outcomes of oil price fluctuations have been at the forefront of the debate among economists, financial analysts and policymakers over the last decades. Among others, the oil price–food price nexus has particularly received a great deal of attention. While an abundant body of literature has focused on the linear relationship between oil price and food price, little is known regarding the nonlinear interactions between them. The aim of this paper is to conduct aggregated and disaggregated analyses of the impact of the Brent and West Texas Intermediate (WTI) oil prices on international food prices between January 1990 and October 2017. The empirical investigation is based on the estimation of linear and nonlinear autoregressive distributed lag (ARDL) models. The findings confirm the presence of asymmetries since the overall food price is only affected by positive shocks on oil price in the long-run. While the dairy price index reacts to both positive and negative changes of oil price, the impact of oil price increases is found to be greater. Finally, the asymmetry is present for some other agricultural commodity prices in the short-run, since they respond only to oil price decreases. All in all, the study concludes that studies assuming the presence of a symmetric impact of oil price on food price might be flawed. The findings are important for the undertaking of future studies and the design of international and national policies in the fight against food insecurity.

Investigating anthropically induced effects in streamflow dynamics by using permutation entropy and statistical complexity analysis: A case study

2016 ◽  
Vol 540 ◽  
pp. 1136-1145 ◽  
Author(s):  
Tatijana Stosic ◽  
Luciano Telesca ◽  
Diego Vicente de Souza Ferreira ◽  
Borko Stosic
Keyword(s):  
Complexity Analysis ◽  
Permutation Entropy ◽  
Statistical Complexity

Book Reviews

2015 ◽  
Vol 53 (2) ◽  
pp. 377-378
Keyword(s):  
Social Protection ◽  
Commodity Prices ◽  
Price Volatility ◽  
Commodity Price ◽  
Food Prices ◽  
Food Price ◽  
Agricultural Commodity ◽  
Vector Autoregressive ◽  
Jean Paul ◽  
The University

Finn Tarp of UNU-WIDER and University of Copenhagen reviews “The Economics of Food Price Volatility”, by Jean-Paul Chavas, David Hummels, and Brian D. Wright. The Econlit abstract of this book begins: “Nine papers, plus nine comments, present and assess recent research on central issues related to recent food price volatility. Papers discuss influences of agricultural technology on the size and importance of food price variability; corn production shocks in 2012 and beyond─implications for harvest volatility; biofuels, binding constraints, and agricultural commodity price volatility; the evolving relationships between agricultural and energy commodity prices─a shifting-mean vector autoregressive analysis; the question of bubble troubles─rational storage, mean reversion, and runs in commodity prices; bubbles, food prices, and speculation─evidence from the Commodity Futures Trading Commission's daily large trader data files; food price volatility and domestic stabilization policies in developing countries; food price spikes, price insulation, and poverty; and trade insulation as social protection.” Chavas is Anderson-Bascom Professor of Agricultural and Applied Economics at the University of Wisconsin-Madison. Hummels is Professor of Economics in the Krannert School of Management at Purdue University. Wright is Professor of Agricultural and Resource Economics at the University of California at Berkeley.

scholarly journals Price Volatility for Selected Agricultural Commodities in Ethiopia: Evidence from GARCH Models

2021 ◽  
Vol 18 ◽  
pp. 1380-1388
Author(s):  
Tirngo Dinku ◽  
Worku Gardachw ◽  
Ngozi Adeleye
Keyword(s):  
Commodity Prices ◽  
Price Volatility ◽  
Bad News ◽  
Good News ◽  
Garch Models ◽  
Time Varying ◽  
Agricultural Commodities ◽  
Agricultural Commodity ◽  
Distributional Assumption ◽  
Egarch Model

This study models the volatility of returns for selected agricultural commodity prices in Ethiopia using the generalized autoregressive conditional heteroskedasticity (GARCH) approach. GARCH family models, specifically threshold GARCH and exponential GARCH were employed to analyze the time varying volatility of selected agricultural commodities prices from 2010 to 2021. The data analysis results revealed that, out of the GARCH specifications, the EGARCH model with the normal distributional assumption of residuals was a better fit model for the price volatility of “teff” and “red pepper” in which their return series reacted differently to the “good” and “bad” news. The study indicated the existence of a leverage effect, which implied that the “bad” news could have a larger effect on volatility than the “good” news of the same magnitude, and the asymmetric term was statistically significant.

scholarly journals The Optimization of Bayesian Extreme Value: Empirical Evidence for the Agricultural Commodities in the US

Economies ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 30
Author(s):  
Jittima Singvejsakul ◽  
Chukiat Chaiboonsri ◽  
Songsak Sriboonchitta
Keyword(s):  
Commodity Prices ◽  
The United States ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Agricultural Product ◽  
Agricultural Commodities ◽  
Agricultural Commodity ◽  
Optimal Point ◽  
Value Analysis ◽  
Optimal Interval

Bayesian extreme value analysis was used to forecast the optimal point in agricultural commodity futures prices in the United States for cocoa, coffee, corn, soybeans and wheat. Data were collected daily between 2000 and 2020. The estimation of extreme value can be empirically interpreted as representing crises or unusual time series trends, while the extreme optimal point is useful for investors and agriculturists to make decisions and better understand agricultural commodities future prices warning levels. Results from the Non-stationary Extreme Value Analysis (NEVA) software package using Bayesian inference and the Newton-optimal methods provided optimal interval values. These indicated extreme maximum points of future prices to inform investors and agriculturists to sell the contract and product before the commodity prices dropped to the next local minimum values. Thus, agriculturists can use this information as an advanced warming of alarming points of agricultural commodity prices to predict the efficient quantity of their agricultural product to sell, with better ways to manage this risk.

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