Short and long-term volatility transmission from oil to agricultural commodities – The robust quantile regression approach

Živkov, Dejan; Manić, Slavica; Đurašković, Jasmina

doi:10.1016/j.bir.2020.10.008

Cited by 15 publications

(6 citation statements)

References 39 publications

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“…Our work is in line with, and indeed significantly extends, the current literature. For example, other recent studies confirm that volatility spillover is transmitted between the energy and agricultural sectors (Tiwari et al, 2022), but such spillover effects vary over time (Bonato, 2019) and across different futures (Zivkov et al, 2020). In this regard, certain combinations in the same portfolio are better than others (Yang & Awokuse, 2003).…”

Section: Hedgingmentioning

confidence: 99%

Assessing the asymmetric volatility linkages of energy and agricultural commodity futures during low and high volatility regimes

Rezitis,

Andrikopoulos,

Daglis

2023

Journal of Futures Markets

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This study investigated the volatility linkages between energy and agricultural futures, including possible causes for these comovements, such as external macroeconomic and financial shocks during low and high volatility regimes. A combination of Markov‐switching regressions and quadrivariate VAR–DCC–GARCH and VAR–BEKK–GARCH modeling revealed that external shocks have an asymmetric effect on the relationship of these assets with higher cross‐correlations reported during high volatility regimes. This comovement effect outweighs the substitution effect between energy and agricultural products. Furthermore, the quadrivariate VAR–BEKK–GARCH model provides strong evidence of a bidirectional price volatility spillover between the agricultural and energy markets during periods of high volatility. Overall, the results suggest that energy futures can be effectively used for hedging in a portfolio comprising agricultural futures (and vice versa), while a combination of macroeconomic and financial index futures can serve as an effective hedging tool in investment portfolios comprising both energy and agricultural commodities.

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Section: Hedgingmentioning

confidence: 99%

Assessing the asymmetric volatility linkages of energy and agricultural commodity futures during low and high volatility regimes

Rezitis,

Andrikopoulos,

Daglis

2023

Journal of Futures Markets

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“…Because of this, the study's results can be compared to what other researchers have found. Most other studies employ the generalized GARCH approach and use one residual error and one generalized variability time offset, GARCH (1,1) [16,76,78]. This reduces the number of parameters, makes them easier to read, and improves the economic interpretation of the results.…”

Section: Month Selection Modelmentioning

confidence: 99%

“…Various methodological frameworks have been used to investigate the interplay between the financial markets, speculation in derivatives, and the agricultural commodities markets. This includes methods used for time-series analysis: the Johansen cointegration test [63], the Granger non-causality test [63,64], vector autoregression (VAR) models [65], nonparametric regression tests [65], the autoregressive distributed lag (ARDL) model [66], the generalized autoregressive conditional heteroskedasticity (GARCH) model [19,50], panel Granger non-causality analysis [22], the vector error correction model (VECM) [63,67], the dynamic conditional correlation (DCC) GARCH model [68,69], the stochastic volatility (SV) model [70,71], the standard heterogeneous autoregressive (HAR) model [72,73], the structural vector autoregressive (SVAR) model [1,34], the continuous Granger non-causality test [74], and quantile regression models [75,76].…”

Section: Introductionmentioning

confidence: 99%

Financial Speculation Impact on Agricultural and Other Commodity Return Volatility: Implications for Sustainable Development and Food Security

Staugaitis

Vaznonis

2022

Agriculture

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Global commodity markets, due to major health crises, political tension, sanctions, growing demand, and other global supply and demand factors, are currently particularly unstable. In addition to the macro-environmental factors that drive the prices, agricultural and other commodity markets are becoming more susceptible to the continuously-growing speculation on major commodity exchanges. Therefore, the purpose of this study is to analyze the influence of financial speculation on agricultural and other commodity prices and return volatility. In our study, we use daily returns on wheat, soybean, corn, and oats futures from the Chicago Mercantile Exchange as well as two additional commodities (crude oil and gold) to compare the extent of this effect. To measure this impact, we, besides traditional tools for time-series analysis, apply the threshold autoregressive conditional heteroskedasticity (TGARCH) technique. We also provide a model using dummy variables for the season to determine whether or not financial speculation’s impact on return volatility differs among seasons, as seasonality plays an important role in return dynamics for agriculture. Our study’s findings show that financial speculation, except for the oats market, either has no impact or makes the underlying futures returns less volatile. Therefore, we draw the conclusion that either there is no relationship between the rise in short-run speculation and the volatility of agricultural commodity prices, or the link is at best questionable. Research results provide important implications for the sustainable development of commodity markets, as passive legislation measurers can be seen as more effective ones compared to more strict active ones in order to maintain these markets liquid and capable of distributing price risks for agricultural producers and manufacturers in a challenging economic and geopolitical environment.

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“…Recognizing these theoretical linkages, several studies provide empirical evidence on the role of oil prices in agricultural commodity prices and accommodate possible pass-through, causality, spillovers, and volatility inherent in both markets (Chang et al, 2019 ; Dahl et al, 2019 ; Hau et al, 2020 ; Nazlioglu & Erdem, 2013 ; Shahzad et al, 2018 ; Yip et al, 2020 ; Zivkov et al, 2020 ). By examining the volatility pass-through from oil to wheat, corn, soybeans, and sugar markets, Nazlioglu and Erdem ( 2013 ) show no evidence of risk transmission between oil and agricultural prices in the pre-crisis period until the post-crisis period, with the exception of sugar.…”

Section: Introductionmentioning

confidence: 99%

“…They reveal spillovers between both industries. Zivkov et al ( 2020 ) investigate both permanent and transitory spillover effects from Brent oil to corn, wheat, soybean, and canola futures using the GARCH model. By embedding the volatility series in a quantile model, they show transitory effects from the oil market and portend a stronger impact on agricultural commodities relative to their permanent counterparts.…”

Section: Introductionmentioning

confidence: 99%

Revisiting the Oil and Food Prices Dynamics: A Time Varying Approach

et al. 2023

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Given the cyclicality of energy and food commodity prices influenced by global macroeconomic uncertainties, there is a need to provide appropriate measures for understanding the predictive relationship between energy and food commodities. This study revisits the dynamics of oil and food prices using Shi et al. (J Financ Econom 18:158–180, 2020) bootstrapped time-varying Granger causality method to identify and date-stamp causal changes in the predictive effects between oil and food markets, while considering homoscedasticity and heteroscedasticity assumptions. Our results reveal bidirectional and feedback influences between Brent oil and six food commodity prices: corn, rice, sugar, coffee, meat, and palm oil. These influences align with critical global events such as the mid-1990s Asian financial crisis, the early 2000s recession, the 2000s energy crisis, the 2014 oil price crisis, the GFC and food crisis of 2008, the 2020 oil-price war, and the COVID-19 pandemic. Additionally, we observed a causal effect running from wheat and soybean prices to Brent oil prices, highlighting the importance of the predictive power of food prices in the trajectory of oil prices during periods of global events. Longer episodes of Granger causality from food price to oil price were date-stamped across the algorithms. The study suggests that global economic events and crises can affect the relationship between prices in different markets, indicating that the ability to predict prices based on information from another market may change during times of economic and financial instability. The research has a number of practical implications.

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Short and long-term volatility transmission from oil to agricultural commodities – The robust quantile regression approach

Cited by 15 publications

References 39 publications

Assessing the asymmetric volatility linkages of energy and agricultural commodity futures during low and high volatility regimes

Assessing the asymmetric volatility linkages of energy and agricultural commodity futures during low and high volatility regimes

Financial Speculation Impact on Agricultural and Other Commodity Return Volatility: Implications for Sustainable Development and Food Security

Revisiting the Oil and Food Prices Dynamics: A Time Varying Approach

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