key: cord-0954535-kmfqq3fd
authors: Guo, Shanwen; Wang, Qibin; Hordofa, Tolassa Temesgen; Kaur, Miss Prabjot; Bahetta, Soufiyan; Maneengam, Apichit
title: Does COVID-19 pandemic cause natural resources commodity prices volatility? Empirical evidence from China
date: 2022-04-13
journal: Resour Policy
DOI: 10.1016/j.resourpol.2022.102721
sha: 1eee9bd533e6e1f6d16cc0b4789669238dc17d8c
doc_id: 954535
cord_uid: kmfqq3fd

COVID-19 pandemic caused havoc around the globe in both economic and non-economic sectors. This paper, unlike previous studies, evaluates the role of COVID-19 on the volatility in natural resources. The volatility of natural resources commodity prices has been the center of discussion, especially during the pandemic. Unlike previous studies, this study aims to evaluate the role of the pandemic, i.e., Covid-19 and its possible impact on volatility in natural resources commodity prices for China. China has been the center of this epidemic disease and is considered one of the major economies affected by the Covid-19; therefore, it is better to conduct this study for China. This study uses data from January 2020 till September 2021 to capture the peak time of Covid-19. Moreover, this study employs the novel wavelet power spectrum and wavelet coherence approach to better capture volatility within commodity prices volatility and Covid-19 and evaluate the association between both variables. The empirical results reveal that only natural resources commodity prices are volatile and only short. While Covid-19 positive cases and Covid-19 deaths are not vulnerable during the study period. Moreover, the wavelet coherence conforms that both Covid-19 positive cases and Covid-19 deaths significantly cause volatility in natural resources commodity prices. Although, volatility is found at different periods; still, volatility is observed only in the short-run. The study also provides relevant policy implications to ensure a relevant and timely solution for the existing issue. Moreover, future research guidelines and the study's limitations are also provided.

In the shape of COVID-19, the world has experienced another major shock to the global economy 24 after the global financial crisis. The world has changed dramatically during the previous three 25 decades as a result of many economic and non-economic events or crises. Specifically, the Gulf 26 War (1990s), Asian financial crisis (1997), oil price spike (2004), global financial crisis (2007), 27

European sovereign debt crisis (2010-2012), oil supply glut (2015) (2016) , and the recent Covid-19 28 pandemic outbreak are among the events attributed for global and regional economic issues (Lyu 29 J o u r n a l P r e -p r o o f economic development and create uncertainty in natural resources and their prices (Sun and Wang, 31 2021; Guan et al., 2021) . The Covid-19 pandemic causes severe illness and death, making the 32 people fear this novel disease led economies to recession. In China, this novel pandemic is 33 identified at first, which reached out to most nations. The said economy is affected the most in 34 employment, services, production, tourism, and others. However, the primary sources of economic 35 growth of Chinese economy are considered as the industrial or production sector. Which is 36 severely affected as a result of the lock-down environment in the country. This postponement in 37 the industrial sector reduces production and consequently leads to a decline in the natural resources 38 demand. As a result, the overall supply and demand chain of natural resources like crude oil, coal, 39 natural resources are disturbed. Hence, the prices of such natural resources are not stable in this 40 pandemic period. Therefore, it is important to analyze the association of natural resources volatility 41 and the Covid-19 pandemic in the region affected the most in the world. 42

Since the last three decades, scholars and policy-makers have been involved in a contradictory 43 debate, where the earlier claimed that natural resources abundance weakens economic growth 44 (Gelb, 1988; Sachs and Warner, 2001 ). However, the latter studies oppose these claims by 45 revealing the conditional positive impact of natural resources on economic growth by enhancing 46 human capital and institutional quality product diversification (Rahim et al., 2021; Joya, 2015; 47 Epo and Nochi Faha, 2020). However, whether a blessing or a curse, natural resources have long 48 been discussed. Currently, volatility in natural resources attracted the attention of policy-makers 49 and the academic world. 50

The Covid-19 epidemic caused damage to the global economy, which also caused fear in financial 51 markets throughout the world (Li et al., 2021) . Since the recent outbreak, most economic sectors 52 have been closed down due to the locked-down environment in most parts of the world. Likewise, 53 in China, the spread of the Covid-19 pandemic postponed production, manufacturing, and the 54 pharmaceutical industry, which causes uncertainty in the global supply chain and causes a severe 55 shortage of life saving drugs (Gupta et al., 2020). This postponement in economic and industrial 56 sectors reduces the demand for natural resources such as oil, which dramatically reduces the prices 57 of raw materials and natural resources in China and the rest of the world. Additionally, the Covid-58

Since the last three decades, there is a growing literature regarding the influence of natural 120 resources and various economic and non-economic factors and indicators. However, after the 121 emergence of Covid-19 pandemic, the scholars focused more on volatility in natural resources 122 commodity prices. Specifically, Ma et al. (2021) investigated the pre and posit-Covid-19 pandemic 123 periods in case of China by using the wavelet power spectrum, wavelet coherence, and the 124 frequency domain causality tests. The estimated results asserted that natural resources commodity 125 prices are more volatile in Covid-19 pandemic period. Also, the results reveal bidirectional causal 126 association between natural resources commodity price volatility and economic performance. 127

Regarding global economic performance and natural resources commodity price volatility, Sun 128 and Wang (2021) report that prior to Covid-19 pandemic, the gold market was inefficient due to downward trends, 171 while the inefficiency of gold market was reported during the Covid-19 pandemic. Besides, the oil 172 market is found inefficient by following upward trends before the pandemic and downward trends 173 during the Covid-19 pandemicwhich is evidence of the volatility in natural resources commodity economies. Employing panel regression in the daily data, the study unveils that six-month-ahead 203 volatility indices fell when first or re-imposed lockdowns were announced but did not fall 204 considerably once the lockdowns were eased. For three-month-ahead predicted volatility, such 205 patterns are weaker, and for one-month-ahead expected volatility, they are almost non-existent. 206

To summarize the literature, this study observed from the given literature that natural resources 207 volatility and pandemics are greatly associated. To be more specific, the literature appeals that 208 the demand for natural resources across the globe has slowed down, which causes volatility in 210 natural resources. However, studies have provided a safe haven for future investment instead of 211 natural resources like oil. Such safe havens are gold, and the bitcoins. Moreover, the literature also 212 demonstrates that the pandemic has created fear amongst the industrialists. While this fear of 213 sickness and death leads drop the stock market participation and performance. Nonetheless for this fear in the general public. Specifically, the fear of Covid-19 sickness, and the death due to 236 this novel disease. Keeping in mind these two factors, the industrial and production sectors 237 postponed production, severely affecting the demand and supply chain of goods and services. Also, 238 temporary closing of the industrial and production sector reduces demand for natural resources, 239 which disturbs imports and exports of natural resources such as oil. Therefore, reducing the 240 demand for natural resources and particularly crude oil, its prices tend to reduce. Also, the trade 241 war between Russia and Saudi Arabia regarding oil exports also affects the demand and supply 242 chain of natural resources. Hence, it is important to analyze whether Covid-19 pandemic is leading 243 in this perspective. In order to discover the results, daily data for the said variables have been 244 obtained from various sources, particularly for China, covering the period from January 2020 to 245 September 2021. Regarding the data sources, the oil prices data is obtained from West Texas 246 Intermediate (WTI) 1 , while data for Covid-19 active cases and deaths due to Covid-19 positive is 247 extracted from World Health Organization (WHO) 2 . 248

The current study used a wavelet technique to measure the correlation of time series such as 250 

This study used the wavelet coherence approach after evaluating the wavelet power spectrum. 312

Despite the commonalities and contrasts between wavelet coherence and other existing 313 methodologies, this method is unique in that it allows for the detection of correlations between two 314 if the 2 ( , ) value is approaching to zero, this designates that there is no correlation between the 329 two time series. However, if 2 ( , ) value is approaching to one, this reveals that there is a strong 330 correlation between the two time series. Colors ranging from blue to yellow-red might be used to 331 distinguish the connection in a wavelet coherence. The colour blue denotes a lack of or weak 332 connection, but the colour yellow-red denotes a high association between two time variables p(t) 333

and q(t). 

The graphical depiction of wavelet power spectrum in Figures-1, -2 , and -3, depicts the zone of 384 influence, which also specifies an edge. However, the wavelet power spectrum produces negligible 385 findings below that edge and cannot be interpreted. Furthermore, Monte Carlo simulation was used 386 to achieve such substantial estimationsdemonstrated by the black contour. The black contour 387 represents the empirical findings' at 5% significance level. In addition, the colors of the wavelet 388 power spectrum graph reflect vulnerabilities, with blue (colder) indicating low or no vulnerabilities 389 and red (hot) indicating larger vulnerabilities in time series variables (Kirikkaleli, 2020) . 390

Regarding the graphical display of wavelet power spectrum for oil prices (Figure-1) , only one 391 significant region is found that indicates the vulnerable oil prices. Specifically, the vulnerable 392 period is between February and May (2020). The scale is found lower, indicating that the frequency 393 in natural resources commodity prices, yet these regions are insignificant. The vulnerability in 395 natural resources during these months is likely due to the lockdown environment in China (Gupta 396 et al., 2020) . Specifically, the lockdown due to Covid-19 pandemic leads to reduced production, 397 trading, and other economic activities, which significantly reduces oil demand and causes 398 fluctuations in oil prices among other natural resources. Besides, the recent conflict of Russia and 399

Saudi Arabia leads to a substantial supply of oil at a lower price for market capturing strategy 400 Focusing on such advantages, the current study used the wavelet coherence approach to investigate 428 the short-and long-term causal links between Covid-19 positive cases and deaths and natural 429 resource commodity price volatility in China across the time period under study. The wavelet 430 coherence graph, like the wavelet power spectrum, uses colors to identify the causal effect, with 431 blue (colder) color denoting weaker or no inter-relationship and red (hot) color denoting high inter-432 relationship between the variables under investigation (Kirikkaleli, 2020) . Furthermore, the black 433 cone-shaped curve denotes a significant zone, and the contour denotes a 5% significant level. and left-down designate that there is a bidirectional causal association between the said variables. 480

Specifically, the Covid-19 death increases fear in general public (Li et al., 2021) . This fear reduces 481 the supply of labor force that offsets production and other economic activities. As a result, demand 482 for natural resources has fallen down, creating volatility in natural resources commodity prices. 483 

Nonetheless, the wavelet coherence demonstrates bidirectional causal association between OP -493 CP, and OP-CPD. However, to validate this association of OP and explanatory variables like CP 494 and CPD, this study uses Quantile-on-Quantile (QQ) regression. The prime advantage of the said 495 approach is that it tackles the irregularity or non-normality issue of data. Figure-6 provides the 496 empirical estimates of QQ regression for OP and CPD. As per the study of Xu et al. (2021) , the 497 darker blue color represents the lowest value of coefficient, and the darker red color indicates the 498 higher coefficient value. While the darker red color indicates higher value of coefficient. Current 499 in the earlier quantile (0 -0.2) of OP, the influence of CP is observed positive, while in the middle 501 quantiles (0.3 -0.6) it is negative but with a lower coefficient value. However, in the higher 502 quantiles (0.8 -1) of OP, the influence of CP is found negative with a relatively higher value. This 503 demonstrates that with the increase of Covid-19 pandemic active cases, the oil prices become more 504 unstable and volatility increases. Hence, this study's findings are consistent with the existing study 505 of Sun and Wang (2021) and Ma et al. (2021) , that validates the enhancement of volatility in 506 natural resources during the pandemic period. 507 In recent times, the major issue considered responsible for economic recession and postponement 527 of industrial production is the Covid-19 pandemic. Also, China remained the first country to 528 experience the worst effect of Covid-19 in the shape of industrial postponement. Due to such 529 shock, unemployment rises in the country and similar for the income levels of the investors, 530

industrialists, and households. Consequently, demand for natural resources diminishes while the 531 supply is also surging due to the Russia and Saudi Arabia conflict of capturing higher market 532 proportion by lowering the prices of oil. Simultaneously, the spread of Covid-19 pandemic 533 increases, which causes fear in the general public and leads to the temporary closing of the 534 industries. Keeping this in mind, current study analyzes whether there is any association between 535 the Covid-19 active cases, Covid-19 deaths, and oil prices in case of China. The estimated results 536 of wavelet power spectrum reveals that the oil prices are volatile in the month of May. This 537 indicates that the oil prices are unstable during the Covid-19 pandemic peak period instead of 538 and Covid-19 active cases, and similar for the oil prices and Covid-19 deaths. This demonstrates 540 that enhancement in the Covid-19 active cases and death leads the government of China to 541 implement strict policies regarding the health of the general public. In this sense, China found the 542 lockdown a more suitable policy of public health instead of economic sustenance, However, the 543 drawback of this policy is that the industrial production slowed down, which further reduces 544 natural and energy resources demand such as crude oil in the global market. Also, the conflict 545 between Saudi Arabia and Russia further fuels the higher supply chain for economic benefits. Due 546 to this uncertain situation in China caused by Covid-19 pandemic, the oil prices are unstable and 547 volatility persists. Hence, to recover from natural resources volatility, Chinese government must 548 take substantial and appropriate initiatives. 549

The recent trend of natural resources commodity price volatility and Covid-19 pandemic nexus 552 motivate scholars to add more to this critical issue. Current study aims to analyze whether there is 553 volatility in natural resources commodity prices and Covid-19? If yes, what is the causal nexus of 554 these variables? In this regard, this study investigated China by using daily data from January 2020 555 to September 2021. In order to determine volatility, there are various methods available in the 556 literature. However, the wavelet power spectrum is considered the most efficient technique since 557 it considers both the time and frequency domain simultaneously. Besides, the causal nexus could 558 be better analyzed with the novel wavelet coherence specifications by underlining the 559 characteristics mentioned above. Therefore, the current study employed these approaches, which 560 provides short-run and long-run estimates. The empirical findings reveal that oil prices are 561 vulnerable during at only one point. Where it is observed that this vulnerability in oil prices is 562 during the Covid-19 pandemic peak period. Specifically, in the said period, the Covid-19 pandemic 563 was reported as highest in the selected span, which led the country's government to stop industrial 564 production and other economic activities for public health. Also, the causal association between 565 the variables validates the bidirectional causal association between oil prices and Covid-19 active 566 cases, and the oil prices and Covid-19 deaths. Since the emergence of this novel pandemic, China 567 has faced a severe shock in the shape of the lockdown, which triggered the postponement of 568 J o u r n a l P r e -p r o o f down and even reach the negative. Nonetheless, the fear of Covid-19 illness and death further leads 570 to closing other economic activities such as trading inside or across the borders. The supply and 571 demand chain is highly disturbed. Hence, the primary reason for fluctuations in natural resource 572 commodity prices is Covid-19 pandemic in the shape of active cases increase and surge in Covid-573 19 patients deaths. These results are found robust by QQ regression approach. 574

Based on the empirical findings, this study suggests that immediate actions are required to stabilize 576 natural resources volatility in China. Specifically, there are two appropriate policies that this study 577 suggests: firstly, the price ceiling or price freezing policy could be adopted to regulate the prices 578 of natural resources like crude oil. Nonetheless, the price ceiling or freezing would help the natural 579 resources market stabilize in various crisis conditions regardless of the demand-supply chain 580 disturbance. Secondly, natural resources hedging could be an appropriate policy in these crisis 581 times. The hedging of natural resources will benefit the economy in the short-run and the longer 582 run and tackle the issue of natural resources volatility. Moreover, the Covid-19 is observed as the 583 primary reason for this conflict of natural resources volatility. In this regard, innovative policies 584 in the health sector are required to recover from this novel pandemic for economic and natural 585 resources markets recovery. 586

Although this study provides substantial findings, it is still limited from various perspectives. 588

Specifically, this study utilized only oil prices to represent natural resources commodity prices 589 volatility. However, future researchers could extend this study by investigating other natural 590 resources commodity prices such as coal price, gold price, forest prices, metallic natural resources 591 prices, etc. Also, this study provides empirical results only for China as it is the first country to 592 experience a novel Covid-19 pandemic crisis. However, researchers in the future shall investigate 593 developed, emerging, and developing economies. Moreover, both the pre and post Covid-19 594

which indicates that natural resources and oil prices

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