TY - JOUR
T1 - Quantile VAR connectedness and price spillovers between soybean and energy
AU - Das, Narasingha
AU - Tanin, Tauhidul Islam
AU - Gangopadhyay, Partha
AU - Abbas, Qaiser
AU - Akadiri, Seyi Saint
AU - Janjua, Laeeq Razzak
PY - 2025/9
Y1 - 2025/9
N2 - We analyze the relationship between US energy (i.e., crude oil, diesel, gasoline, and ethanol) and soybean prices using monthly data from 2005 to 2024. Using a quantile VAR connectedness model, we examine the interplay between energy and food prices across different quantiles. Our findings show that 96 % of the volatility in the network of fossil fuel, biofuel, and food prices is driven by changes in this network. Crude oil and ethanol prices primarily transmit shocks that influence soybean prices, whereas soybean price changes impact ethanol prices, creating feedback mechanisms. Soybean and gasoline prices showed the most significant shocks in the nexus. Our study reveals the dynamic feedback between prices and their interaction within the network, contributing to the understanding of the consequences of biofuels as a clean energy source. Rigorous robustness tests using cross-quantilogram and wavelet quantile correlation validate our findings, offering insights for policymakers managing energy and food price fluctuations.
AB - We analyze the relationship between US energy (i.e., crude oil, diesel, gasoline, and ethanol) and soybean prices using monthly data from 2005 to 2024. Using a quantile VAR connectedness model, we examine the interplay between energy and food prices across different quantiles. Our findings show that 96 % of the volatility in the network of fossil fuel, biofuel, and food prices is driven by changes in this network. Crude oil and ethanol prices primarily transmit shocks that influence soybean prices, whereas soybean price changes impact ethanol prices, creating feedback mechanisms. Soybean and gasoline prices showed the most significant shocks in the nexus. Our study reveals the dynamic feedback between prices and their interaction within the network, contributing to the understanding of the consequences of biofuels as a clean energy source. Rigorous robustness tests using cross-quantilogram and wavelet quantile correlation validate our findings, offering insights for policymakers managing energy and food price fluctuations.
KW - Cross-Quantilogram (CQ)
KW - Energy Price-Soya Price Nexus
KW - Quantile VAR (QVAR) connectedness
KW - US economy
KW - Wavelet-based Quantile Correlation (WQC)
UR - http://www.scopus.com/inward/record.url?scp=105012305400&partnerID=8YFLogxK
UR - https://go.openathens.net/redirector/westernsydney.edu.au?url=https://doi.org/10.1016/j.eneco.2025.108774
U2 - 10.1016/j.eneco.2025.108774
DO - 10.1016/j.eneco.2025.108774
M3 - Article
AN - SCOPUS:105012305400
SN - 0140-9883
VL - 149
JO - Energy Economics
JF - Energy Economics
M1 - 108774
ER -
