Correlations between biofuels and related commodities before and during the food crisis: A taxonomy perspective

By Ladislav Kristoufek, Karel Janda, David Zilberman. 2012

149 citations recorded by the source corpus.

In this paper, we analyze the relationships between the prices of biodiesel, ethanol and related fuels and agricultural commodities with a use of minimal spanning trees and hierarchical trees. To distinguish between short-term and medium-term effects, we construct these trees for different frequencies (weekly and monthly). We find that in short-term, both ethanol and biodiesel are very weakly connected with the other commodities. In medium-term, the biofuels network becomes more structured. The system splits into two well separated branches — a fuels part and a food part. Biodiesel tends to the fuels branch and ethanol to the food branch. When the periods before and after the food crisis of 2007/2008 are compared, the connections are much stronger for the post-crisis period. This is the first application of this methodology on the biofuel systems.

Subfields: Economics and Econometrics, Environmental Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/90a5729ede151d59423620ed6d3ba09adf9d1fee

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Citations in the corpus

How is Volatility in Commodity Markets Linked to Oil Price Shocks (2016, result): “Our findings are consistent with the view that the link between oil and agricultural commodity markets has been stronger since 2006 (see Kristoufek et al. [2012], Nazlioglu [2011], Nazlioglu et al. [2013] and Reboredo, 2011).”
Asymmetric risk spillovers between oil and agricultural commodities (2018, background): “Further, higher prices are forcing people in many economies to purchase food that is inappropriate in both quantity and quality (Kristoufek et al., 2012; Nazlioglu et al., 2013; Saucedo et al., https://doi.org/10.1016/j.enpol.2018.03.074 Received 12 August 2017; Received in revised form 5 February…”
Time-Frequency Dynamics of Biofuels-Fuels-Food System (2012, methodology): “Out of all 45 possible pairs of commodities in our dataset, we focused only on two biofuels branches – the ethanol (ethanol, corn, wheat, sugarcane, crude oil and the US gasoline) and biodiesel branch (biodiesel, soybeans, rapeseed oil, crude oil and German diesel) – and analyzed only the relevant c”
Volatility linkages between energy and agricultural commodity prices (2016, methodology): “(2011) by applying a semiparametric multivariate GARCH model proposed by Long et al. (2011), which is robust to potential misspeci cations of the error density and of the functional form of the conditional covariance matrix.”