Nonlinear Granger causality between grains and livestock

Abstract

Alekneviciene A., Stareviciute B., Alekneviciute E. (2018): Evaluation of the efficiency of European Union farms: a risk-adjusted return approach. Agric. Econ. -Czech, 64: 241-255. Abstract:The aim of this study was to assess the efficiency of EU member-state farms using a risk-adjusted return approach and to determine the impact of subsidies on the efficiency of EU farms. Farm efficiency was analysed by the member-state and by the type of farming and was based on the calculation of Sharpe and Treynor ratios. Systemic risk was expressed by standard deviation in order to estimate the share of systemic risk in the total risk. The change in Sharpe ratios was assessed to determine the impact of subsidies on EU farm efficiency. The results of the risk-adjusted return analysis reveal that farms in the EU-15 were more efficient than farms in the EU-12 in 2004-2013, possibly due to being more experienced in risk management. Nevertheless, the EU-15 did not undertake a bigger share of systemic risk when compared to the EU-12 farms. The impact of financial support on the efficiency of the EU-12 farms was also not stronger when compared to the EU-15 farms.

Price Dynamics in South African Agriculture: A Study of Cross-Commodity Spillovers between Grain and Livestock Markets

“…Additionally, impulse response function analysis suggests minimal impact of soybean market shocks on livestock prices. Our results are comparable to those of Fiszeder and Orzeszko [54], who similarly observed limited causal relationships involving soybean prices relative to the livestock market in the U.S. The isolation of soybean prices from livestock variables in our study may be attributed to unique factors in the South African livestock and grain markets.…”

“…Firstly, according to AFMA [2], soybeans constitute only about 15% of the total feed content in South Africa, suggesting its limited direct impact on overall livestock production. Similarly, Fiszeder and Orzeszko [54] attributed the lack of causal effects between livestock market and soybean price to the fact that cattle are not a huge consumer of soybeans, and therefore such a result is expected. The low usage of soybeans in livestock feeds is attributed to soybean's status as one of the most expensive protein sources in feed rations [55].…”

Dynamic nonlinear connectedness between the green bonds, clean energy, and stock price: the impact of the COVID-19 pandemic

“…Traditional methods cannot capture the dynamic change of the relationship. If the non-linear relationship between variables is ignored, some important characteristics among variables may be ignored (Piotr & Witold, 2018 ). Moreover, an important point that has been ignored in certain literature is the lack of considering different economic conditions.…”

Agricultural policy uncertainty and its impact on commodity markets

“…The nonlinear Granger causality test, which was first introduced by Baek and Brock (1992) and further developed by Hiemstra and Jones (1994), fits well in our context. It has been commonly applied in the literature to understand the causality relationships between, for example, oil and agricultural commodity prices (Nazlioglu, 2011), grain and livestock prices (Fiszeder & Orzeszko, 2018). The nonlinear Granger causality test is based on the following models, which generalize the linear Granger causality test: $$${x}_{1t}={{\rm{\Phi }}}_{1}({x}_{1t-1},\text{\unicode{x02026}},{x}_{1t-p})+{\varepsilon }_{1t},$$$ $$${x}_{1t}={{\rm{\Phi }}}_{2}({x}_{1t-1},\text{\unicode{x02026}},{x}_{1t-p},{x}_{2t-1},\text{\unicode{x02026}},{x}_{2t-p})+{\varepsilon }_{1t}^{^{\prime} },$$$ where $${{\rm{\Phi }}}_{1}$$ and $${{\rm{\Phi }}}_{2}$$ are flexible functions for predicting $${x}_{1t}$$ without and with the historical series of $${x}_{2t-j},j=1,\text{\unicode{x02026}},p$$, respectively.…”

“…The nonlinear Granger causality test, which was first introduced by Baek and Brock (1992) and further developed by Hiemstra and Jones (1994), fits well in our context. It has been commonly applied in the literature to understand the causality relationships between, for example, oil and agricultural commodity prices (Nazlioglu, 2011), grain and livestock prices (Fiszeder & Orzeszko, 2018). The nonlinear Granger causality test is based on the following models, which generalize the linear Granger causality test:…”