Time-varying correlation between agricultural commodity and energy price dynamics with Bayesian multivariate DCC-GARCH models

Shiferaw, Yegnanew A.

doi:10.1016/j.physa.2019.04.043

Cited by 42 publications

(28 citation statements)

References 38 publications

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“…Indeed, assuming that the agriculture sector is energy-intensive, an increase in the price of oil was followed by higher input costs, lower production, higher prices, and an uncertain effect on net farm income (). Assuming the strong relationship between the energy prices and the agriculture commodity prices (Shiferaw 2019), the increases in energy prices hurt the agricultural sector leading to a food price surge and more uncertainty.…”

Section: Estimates Of Commodity Price Returns Uncertaintymentioning

confidence: 99%

The Dynamic Spillover Effects of Macroeconomic and Financial Uncertainty on Commodity Markets Uncertainties

2021

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The present paper has two main objectives: first, to accurately estimate commodity price uncertainty; and second to analyze the uncertainty connectedness among commodity markets and the macroeconomic uncertainty, using the time-varying vector-autoregressive (TVP-VAR) model. We use eight main commodity markets, namely energy, fats and oils, beverages, grains, other foods, raw materials, industrial meals, and precious metals. The sample covers the period from January 1960 to June 2020. The estimated commodity price uncertainties are proven to be leading indicators of uncertainty rather than volatility in commodity markets. In addition, the time-varying connectedness analysis indicates that the macroeconomic uncertainty has persistent spillover effects on the commodity uncertainty, especially during the recent COVID-19 pandemic period. It has also found that the energy uncertainty shocks are the main drivers of connectedness among commodity markets, and that fats and oils uncertainty is the influence driver of uncertainty spillovers among agriculture commodities. The achieved results are of important significance to policymakers, firms, and investors to build accurate forecasts of commodity price uncertainties.

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Section: Estimates Of Commodity Price Returns Uncertaintymentioning

confidence: 99%

The Dynamic Spillover Effects of Macroeconomic and Financial Uncertainty on Commodity Markets Uncertainties

2021

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“…Yahya et al found the temporal and spectral dependence between crude oil and 10 major agricultural commodities [12]. Shiferaw [13] applies Bayesian multivariate DCC-GARCH (Dynamic Conditional Correlation GARCH) models to research the correlation between prices of agricultural commodities and energy, finding that agricultural commodity and energy markets exhibit strong co-movement. Nicola et al [14] use a multivariate GARCH model to investigate co-movement among energy, agricultural, and food commodity markets, finding high correlation among major energy, agricultural, and food commodity price returns.…”

Section: Introductionmentioning

confidence: 99%

Modeling Co-Movement among Different Agricultural Commodity Markets: A Copula-GARCH Approach

et al. 2020

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The aim of this research is to explore the volatility contagion among different agricultural commodity markets. For this purpose, this research make use of the copula-GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model for the daily spot prices of six major agriculture grain commodities including corn, wheat, soybeans, soya oil, cotton, and oat over the period from 2000 to 2019. Our results provide evidence that significant contagion effects and risk transmissions exist among different agricultural grain commodity markets, suggesting that potential speculation effects on one agricultural market could be contagious for another agricultural market and result an increase in volatility in agricultural product markets. Second, agricultural commodities appears to co-move symmetrically. We also find substantial extreme co-movements among agricultural commodity markets. This indicates that agricultural commodity markets tend to crash (boom) together during extreme events. Moreover, after the food crisis, contagion effects and risk transmissions among different agricultural commodity markets increased substantially. Fourth, we find that the strongest contagion effects and risk transmissions are between corn and soybeans, and the weakest contagion effects and risk transmissions are between soya oil cotton and between cotton and oat. Last, we document that the co-movement varies over time. Our findings hold important implications for modeling the co-movement by the copula-GARCH approach.

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“…On the one hand, the Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model is characterized as the best parametric estimation method. Many studies have used DCC-type models to investigate the nature of time-varying correlation and its drivers (see Tao and Green 2012;Lean and Teng 2013;Canh et al 2019;Shiferaw 2019;Ghosh et al 2020, among others). However, parametric or semi-parametric correlation estimations, the DCC-GARCH (see Missio and Watzka 2011;Aielli 2013), BEKK (named byYoshi Baba, Dennis Kraft and Ken Kroner) (Engle and Kelly 2012), and Markov-Switching Vector Autoregressive (VAR) models (see, e.g., Casarin et al 2018) are employed as heavily parameterized models primarily for prediction instead of estimation.…”

Section: Relevant Literaturementioning

confidence: 99%

Uncertainty Due to Infectious Diseases and Stock–Bond Correlation

2021

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We study the non-linear causal relation between uncertainty-due-to-infectious-diseases and stock–bond correlation. To this end, we use high-frequency 1-min data to compute daily realized measures of correlation and jumps, and then, we employ a nonlinear Granger causality test with the use of artificial neural networks so as to investigate the predictability of this type of uncertainty on realized stock–bond correlation and jumps. Our findings reveal that uncertainty-due-to-infectious-diseases has significant predictive value on the changes of the stock–bond relation.

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Time-varying correlation between agricultural commodity and energy price dynamics with Bayesian multivariate DCC-GARCH models

Cited by 42 publications

References 38 publications

The Dynamic Spillover Effects of Macroeconomic and Financial Uncertainty on Commodity Markets Uncertainties

The Dynamic Spillover Effects of Macroeconomic and Financial Uncertainty on Commodity Markets Uncertainties

Modeling Co-Movement among Different Agricultural Commodity Markets: A Copula-GARCH Approach

Uncertainty Due to Infectious Diseases and Stock–Bond Correlation

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