Time-Frequency Dynamics of Biofuels-Fuels-Food System
By Lukáš Vácha, Karel Janda, Ladislav Kristoufek, David Zilberman. 2012
114 citations recorded by the source corpus.
For the first time, we apply the wavelet coherence methodology on biofuels (ethanol and biodiesel) and a wide range of related commodities (gasoline, diesel, crude oil, corn, wheat, soybeans, sugarcane and rapeseed oil). This way, we are able to investigate dynamics of correlations in time and across scales (frequencies) with a model-free approach. We show that correlations indeed vary in time and across frequencies. We find two highly correlated pairs which are strongly connected at low frequencies – ethanol with corn and biodiesel with German diesel – during almost the whole analyzed period (2003–2011). Structure of correlations remarkably changes during the food crisis — higher frequencies become important for both mentioned pairs. This implies that during stable periods, ethanol is correlated with corn and biodiesel is correlated with German diesel mainly at low frequencies so that they follow a common long-term trend. However, in the crisis periods, ethanol (biodiesel) is led by corn (German diesel) even at high frequencies (low scales), which implies that the biofuels prices react more rapidly to the changes in their producing factors.
Subfields: Analytical Chemistry, Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/19b00525b481893bebaa2ba7b18e009100f231df
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In Analytical Chemistry
In Economics and Econometrics
- Teresa Serra
- David Zilberman
- Subrata Kumar Mitra
- Debdatta Pal
- Franziska Schulz
- Brenda López Cabrera
- Walid Mensi
- Ladislav Kristoufek
In Finance
Citations in the corpus
- Biofuel-related price transmission literature: A review (2013, background): “Only Kristoufek et al. (2012a, 2012b) and Vacha et al. (2012) use biofuel prices.”
- Time-frequency contained co-movement of crude oil and world food prices: A wavelet-based analysis (2017, methodology): “Vacha et al. (2013) was the first to employ a continuous type of wavelet transformation, followed by Dedeoglu (2014) and Kristoufek et al. (2016), to explore price co-movement among crude oil, ethanol, biofuel, wheat, and agricultural feedstocks.”