Exogenous impacts on the links between energy and agricultural commodity markets

Han, Li; Zhou, Yimin; Yin, Libo

doi:10.1016/j.eneco.2015.02.021

Cited by 48 publications

(24 citation statements)

References 36 publications

(45 reference statements)

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“…The study found that the impact of oil-specific demand shocks on the many agricultural commodity prices was only significant after the food price crisis. Han et al (2015) also argued that the changes in crude oil price and agricultural commodity price relationship are most likely to be affected by the last financial crisis, of 2007-2008. Their analysis employed the multivariate normal mixture models to analyze the interactions of energy price and agricultural commodity prices.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling the Impact of Agricultural Shocks on Oil Price in the US: A New Approach

Hồ

et al. 2019

JRFM

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The current literature has generally considered prices of the agricultural commodity as an endogenous factor to crude oil price. As such, the role of the agricultural market in the energy sector has been largely ignored. We argue that the expansion of agricultural production may trigger a significant increase in oil price. In addition, the world has recently witnessed a growth in biofuel production, leading to an increase in the size of the agricultural sector. This study is conducted to examine the impact of different agricultural shocks on the oil and agricultural markets in the US for the period from 1986 to 2018. The study utilizes the Structural Vector Autoregressive (SVAR) model to estimate the relationship between the agricultural market and the crude oil market. Moreover, the variance decomposition is also used to quantify the contribution of agricultural demand shocks on oil price variations. Findings from this paper indicate that different agricultural shocks can have different effects on oil price and that corn use in ethanol plays an important role in the impact of corn demand shocks on oil price. We find evidence that the agricultural market can have an impact on oil prices through two main channels: indirect cost push effect and direct biofuel effect. Of these, the biofuel channel unexpectedly suggests that the expansion of bioethanol may in fact foster the dependency of the economy on fossil fuel use and prices.

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Section: Literature Reviewmentioning

confidence: 99%

Modeling the Impact of Agricultural Shocks on Oil Price in the US: A New Approach

Hồ

et al. 2019

JRFM

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“…Studies on risk spillover for the corn market are common. Compulsory policies to add corn‐based ethanol to gasoline, such as the Energy Policy Act (2005) and the Energy Independence and Security Act (2007) in the United States, have increased the interdependence between energy and agricultural markets (Han, Zhou, & Yin, 2015). As the Energy Independence and Security Act of 2007 determined the increase in biofuel production by 2022, the crude oil and corn markets will continue to expand, becoming increasingly interconnected (Wu, Guan, & Myers, 2011).…”

Section: Literature Surveymentioning

confidence: 99%

Conditional correlation and volatility between spot and futures markets for soybean and corn

et al. 2020

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This paper investigates the dynamics of volatility and conditional correlations between corn and soybean prices in the spot and futures markets. Faced with price and production risks, farmers must use all information available in their risk management process, both in their product's spot and futures markets, and in related products' markets, either domestic or foreign. Dynamic conditional correlation specifications with a bivariate GARCH model are employed, with data for Brazilian and U.S. markets in the period 2004-2017, during which several structural changes and extreme climate phenomena have occurred. We find the highest correlations between the corn and soybean spot markets, and evidence of spillovers in both products in the spot and futures markets. We also find that the financial crisis have significantly affected the relationship between the corn and soybean markets. We also find that in periods of increased conditional correlation between the soybean and corn markets, there is an increase in the optimal hedge ratio for risk management strategies involving corn futures contracts at B3 and Chicago Mercantile Exchange.

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“…Some other researchers constructed correlation networks over a sliding window, such as Djauhari and Gan [ 18 ], and Papana et al [ 19 ]. Although not strictly relevant to the issue of dynamics of stock networks, but still relevant to the analysis of dynamic correlations, we can also see some other methods on dependence analyses in the literature, such as the time-varying copula approach [ 20 ], bivariate EGARCH model [ 21 ], DSTCC-GARCH models [ 22 ], multivariate normal mixture models [ 23 ], detrended cross-correlation analysis (DCCA) [ 24 , 25 ], and detrended fluctuation analysis (DFA) [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Stock Net Entropy: Evidence from the Chinese Growth Enterprise Market

Han

Wan

et al. 2018

Entropy

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By introducing net entropy into a stock network, this paper focuses on investigating the impact of network entropy on market returns and trading in the Chinese Growth Enterprise Market (GEM). In this paper, indices of Wu structure entropy (WSE) and SD structure entropy (SDSE) are considered as indicators of network heterogeneity to present market diversification. A series of dynamic financial networks consisting of 1066 daily nets is constructed by applying the dynamic conditional correlation multivariate GARCH (DCC-MV-GARCH) model with a threshold adjustment. Then, we evaluate the quantitative relationships between network entropy indices and market trading-variables and their bilateral information spillover effects by applying the bivariate EGARCH model. There are two main findings in the paper. Firstly, the evidence significantly ensures that both market returns and trading volumes associate negatively with the network entropy indices, which indicates that stock heterogeneity, which is negative with the value of network entropy indices by definition, can help to improve market returns and increase market trading volumes. Secondly, results show significant information transmission between the indicators of network entropy and stock market trading variables.

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Exogenous impacts on the links between energy and agricultural commodity markets

Cited by 48 publications

References 36 publications

Modeling the Impact of Agricultural Shocks on Oil Price in the US: A New Approach

Modeling the Impact of Agricultural Shocks on Oil Price in the US: A New Approach

Conditional correlation and volatility between spot and futures markets for soybean and corn

Stock Net Entropy: Evidence from the Chinese Growth Enterprise Market

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