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Based on the spillover index and an improved spillover asymmetric measure method, this paper studies the volatility spillover and its asymmetric effect between crude oil and agricultural commodity futures in pre- and post-outbreak of COVID-19. We find that the total volatility spillover is higher with pre-outbreak of COVID-19. In addition, the volatility spillover caused by China's crude oil is more prominent than international crude oil around the COVID-19, which highlights the necessity of risk control through the establishment of an energy financial market in China. Finally, although the asymmetric effect of volatility spillover has always existed, crude oil was less impacted by good news post-outbreak of COVID-19, indicating that the outbreak of COVID-19 makes assets dominated by commodity attributes more sensitive to bad news. These findings are beneficial for investors to establish a cross-sector risk hedging portfolio, and provide empirical evidence for policymakers to ensure energy and food security. [ABSTRACT FROM AUTHOR]

Forecasting return and profit is a primary challenge for financial practitioners and an even more critical issue when it comes to forecasting energy market returns. This research attempts to propose an effective method to predict the Brent Crude Oil return, which results in remarkable performance compared with the well-known models in the return prediction. The proposed hybrid model is based on long short-term memory (LSTM) and convolutional neural network (CNN) networks where the autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroscedasticity (GARCH) outputs are used as features, along with return lags, price, and macroeconomic variables to train the models, resulting in significant improvement in the model's performance. According to the obtained results, our proposed model performs better than other models, including artificial neural network (ANN), principal component analysis (PCA)-ANN, LSTM, and CNN. We show the efficiency of our proposed model by testing it with a simple trading strategy, indicating that the cumulative profit obtained from trading with the prediction results of the proposed 2D CNN-LSTM model is higher than those of the other models presented in this research. In the second part of this study, we consider the spread of COVID-19 and its impact on the financial markets to present a precise LSTM model that can reflect the impact of this disease on the Brent Crude Oil return. This paper uses the significance test and correlation measures to show the similarity between the series of Brent Crude Oil during the SARS and the COVID-19 pandemics, after which the data during the SARS period are used along with the data during COVID-19 to train the LSTM. The results demonstrate that the proposed LSTM model, tuned by the SARS data, can better predict the Brent Crude Oil return during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

To obtain the price return and price volatility spillovers between renewable energy stocks, technology stocks, oil futures and carbon allowances under different investment horizons, this paper employs a frequency-dependent method to study the dynamic connectedness between these assets in four frequency bands. The results show that, first, there is a strong spillover effect between these assets from a system-wide perspective, and it's mainly driven by short-term spillovers. Second, in the time domain, technology stocks have a more significant impact on renewable energy stocks compared to crude oil. However, through the study in the frequency domain, we find renewable energy stocks exhibit a more complex relationship with the other two assets at different time scales. Third, renewable energy stocks have significant spillover effect on carbon prices only in the short term. On longer time scales, other factors such as energy prices, climate and policy may have a greater impact on carbon allowance prices. Fourth, the spillover effect of the system is time-varying and frequency-varying. During the European debt crisis, the international oil price decline and the COVID-19 pandemic, the total spillover index of the system has experienced a substantial increase, mainly driven by medium, medium to long or long term spillovers. [ABSTRACT FROM AUTHOR]

Prices of oil and other commodities have fluctuated wildly since the outbreak of the COVID-19 pandemic. It is crucial to explore the causes of price fluctuations and understand the source and path of risk contagion to better mitigate systemic risk and maintain economic stability. The paper adopts the method of network topology to examine the path of risk contagion between China's and foreign commodities, focusing on the dynamic evolution and transmission mechanism of risk contagion during the pandemic. This research found that among China's commodities, energy, grain, and textiles are net recipients of risk contagion, while chemical products and metals are net risk exporters. Among international commodities, industries have positive risk spillover effects on metals and textiles. During the first phase of the pandemic, China's commodities were the main exporters of risk contagion. However, international industries and metals became the main risk exporters and exerted risk spillover on China's commodities in the second phase of the pandemic. Moreover, based on total volatility spillover index of commodities, the risk contagion among the commodities follows three paths: "interest rate → commodities → money supply", "China's economic expectation → commodities → foreign economic expectation", and "commodities → consumer confidence". [ABSTRACT FROM AUTHOR]

The challenges of the world economy and their societies, after the outbreak of the COVID-19 pandemic have led policy-makers to seek for effective solutions. This paper examines the oil price volatility response to the COVID-19 pandemic and stock market volatility using daily data. A general econometric panel model is applied to investigate the relationship between COVID-19 infection and death announcements with oil price volatility. The paper uses data from six geographical zones, Europe, Africa, Asia, North America, South America, and Oceania for the period 21 January 2020 until 13 May 2021 and the empirical findings show that COVID-19 deaths affected oil volatility significantly. This conclusion is confirmed by a second stage analysis applied separately for each geographical area. The only geographical area where the existence of correlation is not confirmed between the rate of increase in deaths and the volatility of the price of crude oil is Asia. The conclusions of this study clearly suggest that COVID-19 is a new risk component on top of economic and market uncertainty that affects oil prices and volatility. Overall, our results are useful for policy-makers, especially in the case of a new wave of infection and deaths in the future. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, uncertainty has increased in many areas of both business supply and demand, notably oil demand and pricing have become even more unpredictable than before. Thus, for companies that buy large quantities of oil, effective oil price risk management is crucial for business success. Nevertheless, businesses' risk appetite, specifically willingness to accept more risk to achieve desired business benefits, varies significantly. The aim of this paper is to deepen the analysis of the effectiveness of employing artificial neural networks (ANNs) in hedging against oil price changes by searching for buy signals for European WTI (West Texas Intermediate) crude oil call options, while taking into account the level of risk appetite. The number of generated buy signals decreases with increasing risk appetite, and thus the amount of capital necessary to buy options decreases. However, the results show that fewer buy signals do not necessarily translate into lower returns generated by networks in a given class. Thus, higher levels of return on the purchase of call options may be obtained. The conducted analyses clearly proved that ANNs can be a useful tool in the process of managing WTI crude oil price change risk. Using the analyzed network parameters, up to 29.9% of the theoretical maximum possible profit from buying options every day was obtained in the test set. Furthermore, all proposed networks generated some profit for the test set. The values of all indicators used in the analyses confirm that the ANNs can be effective regardless of the level of risk appetite, so in this respect they may be described as a universal decision support tool. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is having a strong influence in all areas of society, like wealth, economy, travel, lifestyle habits, and, amongst many others, financial and energy markets. The influence in standard energies, like crude oil, and renewable energies markets has been twofold: from one side, the predictability of volatility has strongly decreased; secondly, the linkages of the price time series have been modified. In this paper, by using DCC-GARCH and Price Leadership Share methodology, we can investigate the changes in the influences between standard energies and renewable energies markets by analyzing one-minute time series of West Texas Intermediate crude oil futures contract (WTI), the Brent crude oil futures contract (BRENT), the STOXX Europe 600 oil & gas index (SXEV), and the European renewable energy index (ERIX). Our results confirm volatility spillover between the time series. However, when assessing the accuracy of the predictability of the DCC-GARCH model, the results show that the model fails its prediction in the period of higher instability. Besides, we found that price leadership has been strongly influenced by the virus spreading stages. These results have been obtained by dividing the period between September 2019 and January 2021 into 6 subperiods according to the pandemic stages. [ABSTRACT FROM AUTHOR]