A Volatility Analysis of Agricultural Commodity and Crude Oil Global Markets

The purpose of this study is to provide insights on volatility features of major agricultural commodity global markets. In order to achieve this, we estimate the volatility in the global markets of crude oil and four main agricultural commodities, namely rice, wheat, cotton and coffee over the period 1980:2014. We also investigate the nexus between the volatilities in these global markets. More precisely, we first model the volatility of agricultural commodity and crude oil markets based on the GARCH methodology. Second, we assess the risk in these global markets by the Value-at-Risk technique. Finally, we evaluate the co-movements between returns in agricultural commodity and crude oil markets by the copula methodology. Our empirical findings reveal that, unlike in the financial market, upside shocks in the agricultural market tend to increase volatility more than downside shocks do. In addition to that, risk in global agricultural commodity markets turned out to be high and little evidence in favor of interdependence between these markets is found. Moreover, the co-movement between agricultural commodity market risk and oil prices is detected for recent years only and little evidence is found for the whole sample period.

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Pandemics and Oil Shocks

10.21203/rs.3.rs-58981/v1 ◽

2020 ◽

Author(s):

Mikidadu Mohammed ◽

Jose A. Barrales-Ruiz

Keyword(s):

Crude Oil ◽

Financial Market ◽

Oil Prices ◽

Oil Price ◽

Strong Impact ◽

Financial Investment ◽

Oil Markets ◽

Price Movements ◽

Market Shocks ◽

The Impact

Abstract At the onset of coronavirus in January 2020, crude oil price was around $51.63 per barrel. But the subsequent spread of the virus across countries all over the world adversely impacted the day-to-day functioning of major industries, corporations, and economies. This adverse impact was amplified by the lockdown measures by governments who were justifiably concerned about the potential devastating effect of the pandemic. As the outbreak intensified, so did oil prices plunge into historic lows (at some point, negative). Is the precipitous drop in oil prices due to the COVID-19 pandemic or are there potentially other factors at play? In this paper, we investigate this question using a pentavariate structural vector autoregression (SVAR) model. Specifically, we identify an exogenous oil price shock arising from the pandemic together with the traditional underlying supply, demand, and financial market shocks to global crude oil markets. We find that a pandemic shock causes a delayed adverse effect on oil prices. In addition, our findings lend support to the view that changes in financial market conditions that affect financial investment decisions also play a significant role in oil price movements. There is however no evidence of a strong impact emanating from the brief Russia-Saudi price war. We also compute the forecast error variance decomposition and find that the impact of a pandemic shock together with aggregate demand and financial market shocks are not trivial in the short run. Taken together, the findings underscore the fruitfulness of research aimed at better understanding the effects of a pandemic shock on oil price movements and highlight the need for policymakers and market stakeholders to explicitly consider global health conditions when analyzing the causes and consequences of oil price shocks.

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Modeling the Relationship between Crude Oil and Agricultural Commodity Prices

Energies ◽

10.3390/en12071344 ◽

2019 ◽

Vol 12 (7) ◽

pp. 1344 ◽

Cited By ~ 9

Author(s):

Duc Hong Vo ◽

Tan Ngoc Vu ◽

Anh The Vo ◽

Michael McAleer

Keyword(s):

Crude Oil ◽

Global Financial Crisis ◽

Aggregate Demand ◽

Commodity Prices ◽

Impulse Response Functions ◽

Agricultural Commodities ◽

Agricultural Commodity ◽

Vector Autoregressive ◽

Crude Oil Prices ◽

Oil Markets

The food-energy nexus has attracted great attention from policymakers, practitioners, and academia since the food price crisis during the 2007–2008 Global Financial Crisis (GFC), and new policies that aim to increase ethanol production. This paper incorporates aggregate demand and alternative oil shocks to investigate the causal relationship between agricultural products and oil markets. For the period January 2000–July 2018, monthly spot prices of 15 commodities are examined, including Brent crude oil, biofuel-related agricultural commodities, and other agricultural commodities. The sample is divided into three sub-periods, namely: (i) January 2000–July 2006, (ii) August 2006–April 2013, and (iii) May 2013–July 2018. The structural vector autoregressive (SVAR) model, impulse response functions, and variance decomposition technique are used to examine how the shocks to agricultural markets contribute to the variance of crude oil prices. The empirical findings from the paper indicate that not every oil shock contributes the same to agricultural price fluctuations, and similarly for the effects of aggregate demand shocks on the agricultural market. These results show that the crude oil market plays a major role in explaining fluctuations in the prices and associated volatility of agricultural commodities.

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Do oil prices and exchange rates account for agricultural commodity market spillovers? Evidence from the Diebold and Yilmaz Index

Agrekon ◽

10.1080/03031853.2019.1694046 ◽

2020 ◽

Vol 59 (3) ◽

pp. 366-385 ◽

Cited By ~ 1

Author(s):

Mehmet Balcilar ◽

Festus Victor Bekun

Keyword(s):

Exchange Rates ◽

Oil Prices ◽

Commodity Market ◽

Agricultural Commodity

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Dynamic Interrelationship and Volatility Spillover among Sustainability Stock Markets, Major European Conventional Indices, and International Crude Oil

Sustainability ◽

10.3390/su12093908 ◽

2020 ◽

Vol 12 (9) ◽

pp. 3908 ◽

Cited By ~ 1

Author(s):

Basel Maraqa ◽

Murad Bein

Keyword(s):

Crude Oil ◽

Stock Returns ◽

Global Financial Crisis ◽

Oil Prices ◽

Volatility Spillover ◽

Socially Responsible ◽

Crude Oil Prices ◽

Daily Data ◽

Oil Markets ◽

The Global Financial Crisis

This study examines the dynamic interrelationship and volatility spillover among stainability stock indices (SSIs), international crude oil prices and major stock returns of European oil-importing countries (UK, Germany, France, Italy, Switzerland and The Netherlands) and oil-exporting countries (Norway and Russia). We employ the DCC-MGARCH model and use daily data for the sample period from 28 September 2001 to 10 January 2020. We find that the dynamic interrelationship between SSIs, stock returns of European oil importing/exporting countries and oil markets is different. There is higher correlation between SSIs and oil-importing countries, while oil-exporting countries have higher correlation with the oil market. Notably, the correlation between oil and stock returns became higher during and after the global financial crisis. This study also reveals the existence of significant volatility spillover between sustainability stock returns, international oil prices and the major indices of oil importing/exporting countries. These results have important implications for investors who are seeking to hedge and diversify their assets and for socially responsible investors.

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The Long-Term Dynamics in Crude Oil Prices

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.260-261.846 ◽

2012 ◽

Vol 260-261 ◽

pp. 846-851

Author(s):

Bao Ming Qiao ◽

Si Zhang ◽

Hao Jin

Keyword(s):

Crude Oil ◽

Oil Prices ◽

Crude Oil Prices ◽

Oil Markets ◽

Increasing Trend ◽

Empirical Performance ◽

High Volatility ◽

Historical Data Analysis ◽

Jump Diffusion Model

This paper reviews a long-term crude oil markets and trend of dynamic prices during 1986-2011. Based on the hypothesis that crude oil prices dynamics reflect the activity of a competitive market, a jump diffusion model is investigated to examine the empirical performance in a time series. Historical data analysis shows that crude oil prices were characterized by high volatility, high intensity jumps, and strong upward drift, and were concomitant with underlying fundamentals of crude oil markets and world economy. Furthermore, the model forecast that crude oil prices will still have an increasing trend, stay in jump for the next couple of years.

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