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.

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.

Global growth and market prospects may improve in 2016

2016 ◽  
Keyword(s):  
Commodity Prices ◽  
Equity Markets ◽  
Commodity Price ◽  
The United States ◽  
Real Economy ◽  
Content Type ◽  
Positive Effects ◽  
New Energy ◽  
Growth Prospects ◽  
Global Growth

Subject Correlation between oil prices, equity markets and global growth. Significance Weak global growth and volatile equity markets in early 2016 illustrate how the real economy and distressed investors are struggling with rapid changes in such key parameters as the new energy and commodity price regime. This is because the 'losers' have to react quickly, plunging economies into recession before the 'gainers' generate any positive effects. These asymmetries, along with disappointing data, are spooking stock markets into a broad-based sell-off. After a nearly 10% fall in global equities between end-December and mid-February wiped as much as 6-7 trillion dollars off wealth, markets have rallied, especially in the United States, where key indices have recouped losses to trade at levels last seen at end-2015. Impacts A recovery in global growth prospects could emerge by mid-2016, stabilising commodity prices and underpinning gains in equity markets. Distressed sales of assets should abate and have less influence on markets. Easing fears over China will help markets rebound after the panic attack in early 2016. The consumer benefits of low energy and food costs have disappointed, but there could be higher spending throughout 2016.

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.

Prospects for global agriculture in the second quarter

2015 ◽  
Keyword(s):  
Southern Hemisphere ◽  
Northern Hemisphere ◽  
Negative Impact ◽  
Commodity Prices ◽  
Weather Conditions ◽  
Agricultural Commodity ◽  
Content Type ◽  
Global Demand ◽  
Global Agriculture

Subject Prospects for global agriculture in the second quarter. Significance In the last quarter of 2014 and the first quarter of 2015, abundant harvests in the northern hemisphere and moderate increases in global demand tended to suppress agricultural commodity prices. In the three months to June, similar conditions are likely to persist, albeit with two main downside risks: poor weather conditions, especially in the southern hemisphere, and the continued negative impact of Russia's food embargo on major exporters.

scholarly journals Measuring Bilateral Market Power in International Markets of Vertically Differentiated Agricultural Commodities

2016 ◽  
Vol 14 (1) ◽  
Author(s):  
Koichi Yamaura ◽  
Tian Xia
Keyword(s):  
Market Power ◽  
Genetically Modified ◽  
International Markets ◽  
Commodity Markets ◽  
Agricultural Commodities ◽  
Empirical Estimation ◽  
Agricultural Commodity ◽  
Oligopoly Model ◽  
Soybean Trade

AbstractTwo features of international markets of agricultural commodities are bilateral market power of exporting and importing countries and the coexistence of non-genetically modified (non-GM) and genetically modified (GM) products. The two features were not taken into account in most extant studies on market power in international agricultural commodity markets. This research develops a bilateral oligopoly model with the interaction between non-GM and GM commodity and conducts an empirical estimation for U.S.–Japan soybean trade. The estimation results show that U.S. exporters and Japanese importers are almost equally sharing the dominance of market power. The analysis in this research provides new measures of market power and improves the understanding on world soybean markets.

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.

Price discovery and volatility spillover: an empirical evidence from spot and futures agricultural commodity markets in India

2020 ◽  
Vol 10 (4) ◽  
pp. 447-473 ◽  
Author(s):  
Manogna R L ◽  
Aswini Kumar Mishra
Keyword(s):  
Granger Causality ◽  
Futures Markets ◽  
Price Discovery ◽  
Spillover Effects ◽  
Futures Market ◽  
Commodity Markets ◽  
Volatility Spillover ◽  
Agricultural Commodity ◽  
Content Type ◽  
Spot Prices

PurposePrice discovery and spillover effect are prominent indicators in the commodity futures market to protect the interest of consumers, farmers and to hedge sharp price fluctuations. The purpose of this paper is to investigate empirically the price discovery and volatility spillover in Indian agriculture spot and futures commodity markets.Design/methodology/approachThis study uses Granger causality, vector error correction model (VECM) and exponential generalized autoregressive conditional heteroskedasticity (EGARCH) to examines the price discovery and spillover effects for nine most liquid agricultural commodities in spot and futures markets traded on National Commodity and Derivatives Exchange (NCDEX).FindingsThe VECM results show that price discovery exists in all the nine commodities with futures market leading the spot in case of six commodities, namely soybean seed, coriander, turmeric, castor seed, guar seed and chana. Whereas in case of three commodities (cotton seed, rape mustard seed and jeera), price discovery takes place in the spot market. The Granger causality tests indicate that futures markets have stronger ability to predict spot prices. Supporting these, the results from EGARCH volatility test reveal that there exist mutual spillover effects on futures and spot markets. Thus, it could be inferred that futures market is more efficient in price discovery of agricultural commodities in India.Research limitations/implicationsThese results can help the market participants to benefit by hedging out the uncertainty and the policymakers to design futures contracts to improve the efficiency of the agricultural commodity derivatives market.Practical implicationsThe findings provide fresh view on lead–lag relationship between future and spot prices using the latest data confirming that futures market indeed is dominant in price discovery.Originality/valueThere are very few studies that have explored the efficiency of the agricultural commodity spot and futures markets in India using both price discovery and volatility spillover in a detailed manner, especially at the individual agriculture commodity level.

Financial engineering in pricing agricultural derivatives based on demand and volatility

2016 ◽  
Vol 76 (1) ◽  
pp. 42-53 ◽  
Author(s):  
Hirbod Assa
Keyword(s):  
Financial Engineering ◽  
Commodity Prices ◽  
Demand Elasticity ◽  
Insurance Companies ◽  
Leverage Effect ◽  
Model Parameters ◽  
Market Demand ◽  
Agricultural Commodities ◽  
Cev Model ◽  
Content Type

Purpose – The purpose of this paper is twofold. First, the author proposes a financial engineering framework to model commodity prices based on market demand processes and demand functions. This framework explains the relation between demand, volatility and the leverage effect of commodities. It is also shown how the proposed framework can be used to price derivatives on commodity prices. Second, the author estimates the model parameters for agricultural commodities and discuss the implications of the results on derivative prices. In particular, the author see how leverage effect (or inverse leverage effect) is related to market demand. Design/methodology/approach – This paper uses a power demand function along with the Cox, Ingersoll and Ross mean-reverting process to find the price process of commodities. Then by using the Ito theorem the constant elastic volatility (CEV) model is derived for the market prices. The partial differential equation that the dynamics of derivative prices satisfy is found and, by the Feynman-Kac theorem, the market derivative prices are provided within a Monte-Carlo simulation framework. Finally, by using a maximum likelihood estimator, the parameters of the CEV model for the agricultural commodity prices are found. Findings – The results of this paper show that derivative prices on commodities are heavily affected by the elasticity of volatility and, consequently, by market demand elasticity. The empirical results show that different groups of agricultural commodities have different values of demand and volatility elasticity. Practical implications – The results of this paper can be used by practitioners to price derivatives on commodity prices and by insurance companies to better price insurance contracts. As in many countries agricultural insurances are subsidised by the government, the results of this paper are useful for setting more efficient policies. Originality/value – Approaches that use the methodology of financial engineering to model agricultural prices and compute the derivative prices are rather new within the literature and still need to be developed for further applications.

Price discovery and volatility spillovers in futures and spot commodity markets

2014 ◽  
Vol 11 (2) ◽  
pp. 211-226 ◽  
Author(s):  
Mantu Kumar Mahalik ◽  
Debashis Acharya ◽  
M. Suresh Babu
Keyword(s):  
Price Index ◽  
Price Discovery ◽  
Commodity Prices ◽  
Spot Market ◽  
Commodity Markets ◽  
Spot Price ◽  
Future Price ◽  
Reverse Causality ◽  
Content Type ◽  
Volatility Spillovers

Purpose – The purpose of this paper is to investigate empirically the price discovery and volatility spillovers in Indian spot-futures commodity markets. Design/methodology/approach – The study has used four futures and spot indices of Multi-Commodity Exchange, Mumbai. The study also employs vector error correction model (VECM) and bivariate exponential Garch model (EGARCH) to analyze the price discovery and volatility spillovers in Indian spot-futures commodity market. Findings – The VECM shows that agriculture future price index (LAGRIFP), energy future price index (LENERGYFP) and aggregate commodity index (LCOMDEXFP) effectively serve the price discovery function in the spot market implying that there is a flow of information from future to spot commodity markets but the reverse causality does not exist. There is no cointegrating relationship between metal future price index (LMETALFP) and metal spot price index (LMETALSP). Besides the bivariate EGARCH model indicates that although the innovations in one market can predict the volatility in another market, the volatility spillovers from future to the spot market are dominant in the case of LENERGY and LCOMDEX index while LAGRISP acts as a source of volatility toward the agri-futures market. Research limitations/implications – The results are aggregate in nature. Further study at disaggregated level will provide further insights on behavior of specific commodity prices and the price discovery process. Originality/value – The paper provides useful information about the evolution and structures of futures commodity trading in India, related literature and relevant methodology concerning the hypotheses.

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