Analysis of the impact of COVID-19 on the correlations between crude oil and agricultural futures

Wang, Jian; Shao, Wei; Kim, Junseok

doi:10.1016/j.chaos.2020.109896

Cited by 131 publications

(71 citation statements)

References 19 publications

(22 reference statements)

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“…Complementarily, the GARCH method can be used for electricity prices to investigate and model volatility with a lesser parameter number than ARCH. The GARCH method (1,1) presents that the error variance of a method in period t will relate to three terms, 22,29 namely: a medium-term or constant; shocks of innovations on the volatility, which is determined by the square of the waste ε 2 (t−1) of the period t − 1, represented by ARCH (outdated volatility information), and the volatility revision made in the last period σ 2 (t−1) , which is a GARCH indicator (past predicted variances). The GARCH (1,1) method can be expressed by:…”

Section: Methodsmentioning

confidence: 99%

Retracted: The impact of COVID‐19 on the electricity sector in Spain: An econometric approach based on prices

Norouzi

Rubens

Enevoldsen

et al. 2020

Intl J of Energy Research

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According to the World Health Organization (WHO), the COVID-19 pandemic has been the greatest challenge to humanity since World War II. Spain is one of the countries experiencing severe consequences from the COVID-19 pandemic outbreak. In such a situation, when the population is quarantined, hospitals are crowded, and industries are shutting down; energy supply becomes a critical and "hard to predict" service. In this way, the study of the impact of COVID-19 on the selling price of electricity in Spain is highlighted more comprehensively. Given the high level of uncertainty in global energy markets, there is a research gap that needs to be filled, that is, the creation of new decision support mechanisms that can reduce risks and ensure more significant forecasting for investments and energy management to deal with seemingly unprecedented effects experienced through the current pandemic. This study

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Section: Methodsmentioning

confidence: 99%

Retracted: The impact of COVID‐19 on the electricity sector in Spain: An econometric approach based on prices

Norouzi

Rubens

Enevoldsen

et al. 2020

Intl J of Energy Research

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“… Salisu et al (2020a) use a panel VAR model to show that both oil and stock markets may experience greater initial and prolonged impacts of own and cross shocks during the pandemic than the period before it. Wang et al (2020) examine the impact of COVID-19 on the cross-correlations between crude oil and agricultural futures markets. Aloui et al (2020) assess the impact of COVID-19 shocks on the energy futures markets, particularly on crude oil and natural gas S&P GS Indexes.…”

Section: Introductionmentioning

confidence: 99%

The COVID-19 global fear index and the predictability of commodity price returns

Salisu

Akanni

Raheem

2020

Journal of Behavioral and Experimental Finance

167

102

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In this paper, we subject the global fear index (GFI) for the COVID-19 pandemic to empirical scrutiny by examining its predictive power in the predictability of commodity price returns during the pandemic. One of the attractions to the index lies in its coverage as all the countries and by extension regions and territories in the world are considered in the construction of the index. Our results show evidence of a positive relationship between commodity price returns and the global fear index, confirming that commodity returns increase as COVID-19 related fear rises. By way of extension, we further establish that commodity market offers better safe-haven properties than the stock market given the negative association between GFI and the latter. Finally, the GFI series improves the forecast accuracy of the predictive model for commodity price returns and its forecast outcome outperforms the historical average (constant returns) model both for the in-sample and out-of-sample forecasts. Our results are robust to alternative measures of pandemics.

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“…Since the outbreak of the COVID-19 pandemic, various studies were conducted to investigate its effect on the economy, including its impact on the correlations between crude oil and agricultural futures [ 1 ], co-movement between COVID-19 and Bitcoin [ 2 ], the tourism industry [ 3 ], the covariance between temperature, COVID-19 and exchange rate in Wuhan [ 4 ], Italian manufacturing firms [ 5 ], B2B sales forces [ 6 ], efficiency of equity and cryptocurrency markets [ 7 ], airline employment [ 8 ], entrepreneurial uncertainty [ 9 ], consumer behavior [ 10 ], marketing innovations [ 11 ], corporate social responsibility and marketing [ 12 ], and randomness and mutual information between markets [ 13 ], to name few. In addition, other interesting studies focused on the forecasting of new cases [ 14 ] and knowledge sharing and collaboration for preparedness to fight the pandemics [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Randomness, Informational Entropy, and Volatility Interdependencies among the Major World Markets: The Role of the COVID-19 Pandemic

Lahmiri

Bekiros

2020

Entropy

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The main purpose of our paper is to evaluate the impact of the COVID-19 pandemic on randomness in volatility series of world major markets and to examine its effect on their interconnections. The data set includes equity (Bitcoin and Standard and Poor’s 500), precious metals (Gold and Silver), and energy markets (West Texas Instruments, Brent, and Gas). The generalized autoregressive conditional heteroskedasticity model is applied to the return series. The wavelet packet Shannon entropy is calculated from the estimated volatility series to assess randomness. Hierarchical clustering is employed to examine interconnections between volatilities. We found that (i) randomness in volatility of the S&P500 and in the volatility of precious metals were the most affected by the COVID-19 pandemic, while (ii) randomness in energy markets was less affected by the pandemic than equity and precious metal markets. Additionally, (iii) we showed an apparent emergence of three volatility clusters: precious metals (Gold and Silver), energy (Brent and Gas), and Bitcoin and WTI, and (iv) the S&P500 volatility represents a unique cluster, while (v) the S&P500 market volatility was not connected to the volatility of Bitcoin, energy, and precious metal markets before the pandemic. Moreover, (vi) the S&P500 market volatility became connected to volatility in energy markets and volatility in Bitcoin during the pandemic, and (vii) the volatility in precious metals is less connected to volatility in energy markets and to volatility in Bitcoin market during the pandemic. It is concluded that (i) investors may diversify their portfolios across single constituents of clusters, (ii) investing in energy markets during the pandemic period is appealing because of lower randomness in their respective volatilities, and that (iii) constructing a diversified portfolio would not be challenging as clustering structures are fairly stable across periods.

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Analysis of the impact of COVID-19 on the correlations between crude oil and agricultural futures

Cited by 131 publications

References 19 publications

Retracted: The impact of COVID‐19 on the electricity sector in Spain: An econometric approach based on prices

Retracted: The impact of COVID‐19 on the electricity sector in Spain: An econometric approach based on prices

The COVID-19 global fear index and the predictability of commodity price returns

Randomness, Informational Entropy, and Volatility Interdependencies among the Major World Markets: The Role of the COVID-19 Pandemic

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