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Our paper studies the impact of the COVID-19 epidemic on commodity pricing premiums in the Chinese commodity futures market. After summarizing the explanatory power of documented benchmark pricing factors, we apply the difference-in-difference regression for our event study. We document a substantial impact of the COVID-19 pandemic on increasing the commodity basis premium by at least 30%. Basis-momentum premium, especially for agriculture futures, also increases during the epidemic. The results are robust and validated by sub-sample regressions. The influence of COVID-19 on the commodity market is more prevailing than the trade war.
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The volatility of international crude oil and gold markets has affected stock markets through several economic channels, and the impact tends to be more evident with the appearance of emergencies. However, the volatility linkages between commodities and Chinese sector stocks in the presence of emergencies are understudied. To examine the asymmetric relationship and time-varying connectedness between commodities and Chinese sector stocks, this paper first employs GJR-GARCH to capture the realized volatility of international oil, gold, and Chinese sector stocks. Secondly, we decompose the realized volatility of international oil and gold into bad and good volatility and then employ the TVP-VAR-DY approach to obtain the connectedness index. The final result shows asymmetric volatility spillover among oil, gold, and Chinese sector stocks. During the COVID-19 outbreak, the gold good volatility transmission is intenser than bad volatility. Thirdly, the analysis is also carried out under different subperiods. They include three international events: the global financial crisis and the European debt crisis, the oil crisis, and COVID-19. The result reveals heterogeneity exists in the impact of international oil and gold on the Chinese sector stocks under different emergencies. These findings are of great significance for policymakers to improve the sector management under the impact of different emergencies and for investors to design diversified portfolios according to the commodity-sector risk spillover effects. © 2023 Elsevier Ltd
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This paper aims to investigate the dynamic connectedness and the cross-quantile dependence structure between carbon emission trading and commodity markets in China. We employ both the Baruník and Křehlík (2018) connectedness method and the Baruník and Kley (2019) cross-quantile dependence method to provide time-frequency-quantile evidence. In addition, we use a daily dataset from September 2, 2013, to September 30, 2022, to gauge the macroeconomic effects of the COVID-19 pandemic. We find that Petrochemical is the biggest contributor and recipient in the carbon-commodities system, and the results show that carbon markets are more influenced by other commodity markets than the reverse. Furthermore, the total connectedness is stronger in the short term but can increase over the long term, especially during the onset of COVID-19. The dynamic pair-wise results show that the carbon market can impact other commodity markets, but the effects are diverse and varied. The quantile-varying dependence between the carbon market and commodities is detected, and the cross-quantile dependence gradually strengthens as the trading days increase. This paper concludes with fruitful policy implications for resource decision-makers. © 2023 Elsevier Ltd
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This paper presents a unique time-varying parameter vector autoregression (TVP-VAR) based extended joint connectedness approach to quantify the connectedness and transmission mechanism of shocks of nine commodities futures returns (namely;Gold and Silver from the category of precious metals;Copper, Lead, Zinc, Nickel and Aluminium from the category of base or industry metals;Natural Gas and Brent Crude Oil from energy sector) obtained from Multi Commodity Exchange of India Limited (MCX) from January 1, 2018 to December 31, 2021. This paper employs Balcilar et al. (2021)'s TVP-VAR extended joint connectedness approach, which combines the TVP-VAR connectedness approach of Antonakakis et al. (2020) with the joint spillover approach of Lastrapes and Wiesen (2021), to investigate the dynamic connectedness among the select commodity futures of interest. Our findings show that system-wide dynamic connectedness varies over time and is driven by economic events. The pandemic shocks appear to have an impact on system-wide dynamic connectedness, which peaks during the COVID-19 pandemic. Crude oil and zinc are the primary net shock transmitters, whereas gold and silver are the primary net shock receivers. We also discovered that the role of aluminum in shock transmitters and shock receivers changed during the course of the investigation. Pairwise connectivity, on the other hand, shows that Zinc, Copper, Nickel, and Crude oil are the key drivers of gold price changes, explaining the network's high degree of interconnectivity. During the study period, it was also discovered that silver has a significant influence on gold. Furthermore, in comparison to natural gas, gold's spillover activity is still relatively modest (on a scale), indicating that gold is less sensitive to market innovations. © 2023 Elsevier Ltd
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The contribution of commodity risks to the systemic risk is assessed in this paper through a novel approach that relies on the stochastic property of concordance ordering of CoVaR. Considering the period that spans from 2005 to 2022 and the VIX as the proxy for the stability of the financial system, we build the stochastic ordering of systemic risk for 35 commodities belonging to four sectors: Agriculture, Energy, Industrial Metals, and Precious Metals. The estimates of the ΔCoVaR signal that contagion effects from commodity markets to the financial system have been stronger during the years 2017–2019. Backtests validate CoVaR as a more resilient risk measure than the VaR, especially during periods of market turmoils. The stochastic ordering of CoVaR shows that severe losses (downside risk) in commodity markets tend to exacerbate systemic financial distress more than gains (upside risk). Commodity risks arising from WTI and EUA are threatening triggers for systemic risk. In contrast, the financial system is less vulnerable to a broader range of scenarios arising from fluctuations in Gold prices. As top contributors to the systemic risk, among the sectors we find Energy and Precious Metals with respect to upside risk and downside risk. The Covid-19 crisis has deeply amplified the systemic influence arising from the downside risk of WTI, Gasoline, and Natural Gas UK and has confirmed the safe-haven role of Gold. © 2022 Elsevier B.V.
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This paper investigates the dynamic volatility spillover among energy commodities and financial markets in pre-and mid-COVID-19 periods by utilizing a novel TVP-VAR frequency connectedness approach and the QMLE-based realized volatility data. Our findings indicate that the volatility spillover is mainly driven by long-term components and prominently time-varying with a remarkable but short-lived surge during the COVID-19 outbreak. We further spot that WTI and NGS are prevailingly transmitting and being exposed to the system volatility simultaneously, especially during the global pandemic, suggesting the energy commodity market becoming more integrated with, more influential and meanwhile vulnerable to global financial markets.
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Jumps in commodity prices can make asset risk management challenging. This study explores the influence feature of the COVID-19 epidemic on China's commodity price jumps, using 5-min intraday high-frequency futures data of three China's commodity markets (energy, chemical, and metal) from January 23, 2020 to June 10, 2022. We find that firstly the information spillover from the COVID-19 spread situation to China's energy price jumps is relatively weak, and the COVID-19 epidemic shows the most substantial jump information spillover pattern to China's chemical price. The information spillover pattern is time-varying across the COVID-19 spread situation phase. Secondly, there are co-movement patterns between China's commodity price and China/global COVID-19 confirmed cases. This co-movement feature mainly occurs at the medium- or long-run time scales, and varies across commodities. Thirdly, the demand elasticity for China's commodities and its dependence on imports and exports are the main factors influencing the sensitivity of its price jumps to the COVID-19 outbreak.
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The study aims to examine the existence of a correlation between the stock prices of the energy sector, commodities prices of the energy sector, and market indices. The study uses an empirical approach to develop various VAR (Vector Autoregression) with Variance Decomposition Models for each company under the energy sector indexed in NIFTY50 by considering daily prices for 3 years. For a comparative study, the data have been divided into two parts. The first part is considered pre-COVID era, i.e., from July 1, 2018, to December 31, 2019, and the second part is considered post-COVID era, i.e., from January 1, 2020, to June 30, 2021. While observing the estimates of VAR of different companies, it can be said that crude oil is significant in most of the models during pre-COVID whereas, during post COVID, lag term of crude oil and NIFTYENGERGY are significant. On the other hand, while observing the estimates of variance decomposition in all the VAR models, the first lag term of the particular company’s share price is strongly endogenous. In comparison, the other independent variable, i.e., lag term of the price of crude oil and natural gas, values of NIFTY50 and NIFTY ENERGY are strongly exogenous to the stock prices of the energy sector. © 2022, Econjournals. All rights reserved.
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The novel coronavirus (2019-nCoV) originated in China has now covered around 213 countries globally. It has posed health calamities which have threatened the world with the emergence. Owing to the number of confirmed cases still rising every day, it has now become a phase of an international health emergency. Sudden outbreak of coronavirus disease 2019 (COVID-19) has brought global declines in the commodity process. This has majorly affected the demand as well as supply of the commodities. The oil market has been severely affected due to the outrageous collapse in the demand majorly due to travel restrictions which has also caused the steepest decline in oil prices. The prices of both precious and industrial metals have also fallen, although the price drop is less than that of oil prices. The agriculture industry is one of the least affected so far by this pandemic due to its indirect relation with economic activities. However, the ultimate impact of COVID-19 pandemic will greatly depend on the severity and duration of its outspread, but it is expected to have long-lasting implications.
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The objective of the paper was to evaluate the mutual relationship of copper and zinc prices between 2011 and 2021 and to predict their future prices until the year 2030. For this purpose, the following methods were used: regression of neural structures in TIBCO's Statistica software, version 13.0, time-series smoothing by means of multilayer perceptron network, graphical representation, Pearson correlation coefficient, and logical judgment. According to the prediction, the copper price will decrease slightly compared to the preceding years. In 2026, it is expected to stabilize at USD 610/t until the year 2030. Zinc price is expected to increase slightly until the end of the year 2030 when the resulting predicted price is USD 410/t. Pearson correlation coefficient of copper and zinc achieves the approximate value of 0.65. The results thus confirm the fact that these commodities are not perfect complements. First, the mutual relationship of the two commodities indicates that the price of zinc is pushed up mainly by the copper price. On the contrary, the copper price is pushed down by the price of zinc. There is a price convergence between the two commodities. The future development of copper and zinc prices is not subject to unpredictable events, such as a political situation or the aforementioned COVID-19 pandemic. Such a long-term prediction might thus not provide an objective result.
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As a consequence of the COVID-19 pandemic outbreak, most commodities experienced significant price drops, which were expected to continue well into 2020. As a result, the Markov switching model is used to study the influence of policy uncertainty and the COVID-19 pandemic on commodity prices in the USA. Commodity markets are stimulated by economic policy uncertainty, according to results from a two-state Markov switching model. In both high and low regimes, economic policy uncertainty (EPU) influences the commodity market, according to the study's findings. However, in the high regime, EPU has a greater influence on the energy and metal sectors. EPU has different influences on commodity markets in high- and low-volatility regimes, according to this study. There is a wide range of correlations between COVID-19 outcomes and EPU and how the prices of natural gas, oil, corn, silver, soybean, copper, gold, and steel respond to these tremors, in both high- and low-volatility tenure. Oil and natural gas, on the other hand, are unaffected by shifts in COVID-19 death rates under either regime. Results show that in both high- and low-volatility regimes, the demand and supply for most commodities are responsive to historical prices.
Subject(s)
COVID-19 , COVID-19/epidemiology , Humans , Natural Gas , Pandemics , UncertaintyABSTRACT
The paper focuses on investigating the time-varying influence of geopolitical risks (GPR) and trade policy uncertainty (TPU) on commodity prices by using time-varying parameter vector autoregressive model with stochastic volatility (TVP-VAR-SV). We find that (i) TPU and GPR have significant time-varying effects on the aggregate (classified) commodity market, and the former is a short-term effect before 2006, and it becomes a medium-to-long-term effect after 2006, while the latter is mainly a short-term effect;(ii) TPU shock has a significant positive and time-varying impact on GPR, and the relatively long-term impact is more obvious before 2017, while the short-term impact will dominate after 2017. Additionally, the GPR shock has a short-term negative impact on TPU, and a medium- and long-term positive impact except for the period from 2002 to 2006;(iii) the impact of geopolitical threats (GPT) and geopolitical acts (GPA) on aggregate commodity market have positive and negative alternating shock effects with time variability and a significant short periods impact;(iv) there is a certain degree of heterogeneity in the response of different commodity prices, and the response of individual commodities is related to specific external shocks, such as the COVID-19 pandemic.
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This paper provides a fresh perspective to explore the network correlations among commodity, exchange rate, and categorical economic policy uncertainties (EPU) in China. We try to contribute to the literature by examining the spillover mechanism with a relatively novel connectedness network using the monthly data over the period between June 2006 and January 2021. Our results suggest that prior to the recession, China's commodity price is subject to greater spillovers from the exchange rate than recessions. The domestic commodity prices are more sensitive to monetary policy uncertainty and fiscal policy uncertainty. The occurrence of COVID-19 revises the dominance in the system from monetary policy uncertainty and fiscal policy uncertainty to trade policy uncertainty. © 2022 Elsevier Inc.
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This paper aims to build an incentive to mobilize the financial resources needed to accelerate the transition to a climate resilient economy. To this end, we examine the dependence structure using copulas theory and then the risk transmissions between green financial products and the energy commodity market index. This methodology provides opportunities to investors in green finance to protect their portfolios against downside or upside risk by taking long or long position. In our empirical study for the period July 2014 to September 2020, marginal equities show a long memory in the volatility process captured by FIGARCH model, justifying by the various crisis, the last of which is the ongoing COVID-19 pandemic. Using VaRs and CoVaRs measures, we find that green instruments (mainly the green bonds) are significantly affected by substantial price spillovers from energy commodity market during critical periods. Many obstacles to set up investments’ opportunities are discussed.
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This paper investigates the switching effect of COVID-19 pandemic and economic policy uncertainty on commodity prices. We employ Markov regime-switching dynamic model to explore price regime dynamics of eight widely traded commodities namely oil, natural gas, corn, soybeans, silver, gold, copper, and steel. We fit two Markov switching regimes to allow parameters to respond to both low and high volatilities. The empirical evidence shows oil, natural gas, corn, soybean, silver, gold, copper, and steel returns adjust to shocks in COVID-19 outcomes and economic policy uncertainty at varying degrees--in both low volatility and high volatility regimes. In contrast, oil and natural gas do not respond to changes in COVID-19 deaths in both regimes. The findings show most commodities are responsive to historical price in terms of demand and supply in both volatility regimes. Our findings further show a high probability that commodity prices will remain in low volatility regime than in high volatility regime--owing to COVID-19-attributed market uncertainties. These findings are useful to both investors and policymakers--as precious metals and agricultural commodities show less negative response to exogenous variables. Thus, investors and portfolio managers could use precious metals, viz. Gold for short-term cover against systematic risks in the market during the period of global pandemic.
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The COVID-19 global pandemic has disrupted business-as-usual, hence, affecting sustained economic development across countries. However, it appears economic uncertainty following COVID-19 containment measures favor market signals of cryptocurrencies. Here, this study empirically and structurally investigates the implication of COVID-19 health outcomes on market prices of Bitcoin, Bitcoin Cash, Ethereum, and Litecoin. Evidence from the novel Romano-Wolf multiple hypotheses reveal COVID-19 shocks spur Litecoin by 3.20-3.84%, Bitcoin by 2.71-3.27%, Ethereum by 1.43-1.75%, and Bitcoin Cash by 1.34-1.62%.
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Over the past few years, the application of deep learning models to finance has received much attention from investors and researchers. Our work continues this trend, presenting an application of a Deep learning model, long-term short-term memory (LSTM), for the forecasting of commodity prices. The obtained results predict with great accuracy the prices of commodities including crude oil price (98.2 price(88.2 on the variability of the commodity prices. This involved checking at the correlation and the causality with the Ganger Causality method. Our results reveal that the coronavirus impacts the recent variability of commodity prices through the number of confirmed cases and the total number of deaths. We then investigate a hybrid ARIMA-Wavelet model to forecast the coronavirus spread. This analyses is interesting as a consequence of the strong causal relationship between the coronavirus(number of confirmed cases) and the commodity prices, the prediction of the evolution of COVID-19 can be useful to anticipate the future direction of the commodity prices.