On the relation between Pandemic Disease Outbreak News and Crude oil, Gold, Gold mining, Silver and Energy Markets

Abstract

Fear of the disease outbreak news (DONs) has shocked commodity markets and raised the likelihood of economic uncertainty and recession globally. This article examines the unprecedented overreaction of investors sentiments in the commodities such as Crude oil, Gold, Gold Mining, Silver, and the Energy sector. The deadly effects of DONs-COVID-19 in the commodities market have been the worst in history; it appeared the first time higher than the common stock's volatility. Covid-19 induced economic uncertainty has impacted severely through all commodities except the safe-haven Gold (GVZ). Importantly, ETF Options based Implied Volatility Index of Crude (OVX), Silver (VXSLV), and Energy (VXXLE) stocks have crossed the peak level what it was prevailing during the global financial crisis 2008. The unparalleled upsurge of the implied volatility index across all commodities indicates higher demand for the hedge funds to protects the commodity portfolio. ETF options on the commodity act as the best hedge against market uncertainty. Overburden on the put option results in an increased risk premium, henceforth higher expected volatility. ETF options truly measure the investor's fear of predominant in the commodity market.

Dr. Imlak Shaikh

Imlak Shaikh is an assistant professor of accounting and finance at MDI Gurgaon. Previously, he served as an assistant professor at the Birla Institute of Technology & Science, BITS Pilani. Shaikh has also taught at IIM Rohtak as an adjunct faculty in FPM program. Shaikh received his post-graduation degree in commerce from the Veer Narmad South Gujarat University Surat; his Ph. D from Indian Institute of Technology Bombay (IIT-Bombay). Shaikh has been awarded with ‘Ph.D Theses Excellence Award’ from IIT-Bombay. He has recently published in Applied Economics, North American Journal of Economics and Finance, Engineering Economics, Journal of Business Economics and Management, Economic Change and Restructuring, Journal of Economic Studies and so on.

Google Scholar: https://scholar.google.co.in/scholar?hl=en&as_sdt=0%2C5&q=imlak+shaikh&oq=

Research Gate:https://www.researchgate.net/profile/Imlak_Shaikh.

1. Introduction

Commodity markets have perceived severe fear than the common stocks globally, along with worries of rising cases of infectious pandemic disease outbreak Covid-19. Consequently, it has raised more uncertainty for the natural resources such as crude oil, gold, and silver markets. Disease outbreak of novel coronavirus has increased the global health crisis, decreased consumption, and investment in the commodities. Unlike the disaster that occurred in the worldwide equity market, the commodity has shown more pronounced effects of Covid-19. Energy commodities have been harshly affected due to the contagious spread of the virus from Wuhan, China, and other parts of the world. The deadly effects of Covid-19 resulted in a decrease of West Texas Intermediate (WTI) crude oil price by 50%, except for bullion and other precious metal commodities that are likely to fall further in the near future (US Energy Information Administration EIA, 2020; and Word Gold Council WCG, 2020). Hence, the study aims to measure the investors' sentiment, trading in the commodities market. United States of America is the major supplier of various commodities like WTI crude, Silver, and precious metals; amid Covid-19, demand for commodities has slumped mostly in the first quarter of 2020. The commodities market investor has become more fearful and anxious about the future prices of commodities; hence, they trade into futures and options written on the commodities. Chicago Board Options Exchange (CBOE) has introduced options on the Exchange Traded Fund (ETF) available and imitating underlying commodities' prices. VIX is the implied volatility index, a registered trademark of CBOE, and an estimate of the near term (30-days) stock market volatility.1 The measure of investors' behaviour expressed in percentage term is based on the option prices traded in real-time. To measure the investor's fear and nervousness in the commodity market, CBOE has licensed the VIX methodology to apply on the most active six exchange-traded funds (ETF) specific to the commodities market.2 Why investors prefer options over the other hedge funds? —the reason is that options create options during the market ambiguity. It mitigates risks in a dwindling market and produces income in a flat market. Hence, options act like mini insurance policies, and with varied options strategy, investors can avail such protection against further falling market amid Covid-19.

The historic decline in the demand for Crude oil happened due to slower economic activity, international travel ban, lockdown, and geopolitics of crude oil among Saudi Arabia and Russia and OPEC's role in Crude's supply. The oil prices (Brent and WTI) in the first quarter of 2020 first time went under $20, and further US Shale producers were struggling to turn profitable (Ramkumar and Hodari, 2020). More recently, the market has experienced backwardation and contagion effects. Formerly, the commodities market (SPXGSCI) outperformed the equity market (SPX500), but still, commodities are facing contagion and backwardation (Wallace, 2020). The WTI's price to be delivered in May 2020 stood negative (-$5.33) a barrel on April 21, 2020; the clearing price also reaches -$37.63, which indicates that sellers must reward buyers for taking barrels off their hands.3 Unlike the equity and Crude oil market, Gold prices remain less volatile following the tail events. During the crisis and uncertainty period, Gold serves as the best hedge, a safe-haven with high liquidity and risk-adjusted best returns. WCG (2020) reports that Gold has performed much healthier as a tail hedge over the past 2 to 3 decades. There is a general wisdom that precious metals, such as Gold, deliver a reliable, effective hedge over the market uncertainties. In particular, Gold is a better performer during the risk-on environments while poor in risk-off environments. WCG (2020) further analyses that Gold and Silver are the real assets, unlike the financial assets and linked with the current inflationary environment and yield better returns during the crisis period —the plausible reason may be no credit risk and counterparty failure.

On the other hand, Silver inclines to be much more associated with the economy's outlook than Gold, predominantly to the downside-market. The recent extraordinary rise of volatility (GVZ) in the -Gold market has been obsessed with massive closings across all assets and Covid-19 induced demand in the emerging markets. Gold's future performance depends on the contagious effects of Covid-19, which may sleep into another global recession and existing business ecosystem –travel prohibitions, the shutdown of many industries, and equity market volatility. Investors' choice as a safe-haven, Gold has proved that it's a good source of liquidity and collateral for portfolio rebalancing. Gold, a precious metal, has been an attraction of the worlds' major central banks to have excessive buying in the last two years. Gold is preferred over other asset classes —low opportunity cost, high return during an economic expansion, and resilient to risk and uncertainty.

The infectious disease outbreak event is one of the disappointing news for the investing community, past pandemic disease outbreak (e.g., 1918–20 influenza, HIV/AIDS, SARS, Ebola, Swine Flu) has not much impacted financial market as Covid-19. Markets are efficient (e.g., Fama et al., 1969; Malkiel and Fama, 1970; Fama, 1991), and asset prices digest the arrival of new information, for example, disease outbreak news (DONs). The informational efficiency of options (e.g., Christensen and Prabhala 1998; Corrado and Miller 2005 and Li and Yang, 2009; Shaikh, 2018, 2019) hold good for the observed traded call-put-options prices, and it contains market-relevant information. Options' implied volatility appears as the best estimate of the ex-post volatility. Therefore, our study attempts to uncover the effects of Covid-19 on the options trading in the commodities market reflected in terms of the implied volatility index (e.g., OVX), a fearful and anxious investor in the commodities market hedge the portfolio investment via buying put options in order to meet the future uncertainty of drawdown of real assets.

Some of the prominent works (e.g., Laxminarayan and Malani, 2006; Suhrcke et al., 2011; Sands et al., 2016) present the theoretical and conceptual framework to show the consequences of infectious diseases outbreaks across the global economies. The authors show that pandemic disease outbreaks significantly influence investment decisions, risk-taking behaviour, and economic activity; furthermore, authors argue that the financial crisis does have long-run effects on infectious diseases. Another strand of studies (e.g., Lucey and Dowling, 2005; Baker and Wurgler, 2007; Cen and Yang, 2013) deals with the investors' sentiment, e.g., investors' feelings, cognitive biases and overconfidence, and investors behaviour following the global financial crisis and internet bubble and burst. Recent scholarly efforts (e.g., Kamstra et al., 2003; Kaplanski and Levy, 2012; Cen and Yang; 2013) find that mood swings, investors' anxiety, public holidays, and sunshine affect the various asset classes. Moreover, another aspect of studies (e.g., Yuen and Lee, 2003; Kaplanski and Levy, 2010; Donadelli et al., 2017) elucidate that unanticipated and tail events influence investor sentiment; subsequently it affects the adverse behaviour, distress for trading, lower inclination to join in a risky investment proposal. Besides, disease outbreaks events show an encouraging effect on pharmaceutical stocks. Hence, our study extends the previous works of investors' unprecedented overreaction in the commodities market and disease outbreak news (DONs).

Some studies (e.g., Chen et al., 2007; Chen et al., 2009; and Wang et al., 2013) examine the effects of disease outbreaks in Taiwan's stock market in relation to Enterovirus 71, Dengue Fever, SARS, and H1N1 global pandemics. Authors find that SARS-induced uncertainty causes the stock to fell by 29%, further tourism and hospitality shares were affected undesirably, while the pharmaceutical stocks were on the rise. Findings advocate that investors consider operative efficiency throughout the pandemic period of the above discussed infectious diseases and plan investment accordingly. More remarkably, Pendell and Cho (2013) reveal the effects of foot-and-mouth-disease (FMD) on the Korean agribusiness stocks and find an asymmetric stock market reaction. And also, report that disease outbreak effects are more steady than instantaneous; firms in small size account for considerable stock volatility than the other industry. More recently, Donadelli et al. (2017) examine the effects of disease-related news (DRNs) on the pharma stocks in the US setting concerning the WHO media coverage. They find that DRNs shows heartening and substantial sentimental effects. Unlike studies of Donadelli et al. (2017), our study employs investor sentiment related to commodities markets to expose the impact of disease-outbreak-news (DONs) in Crude oil, Gold, Silver, and Gold mining industries. Unlike the commodities market most recent work of Ichev and Marinč (2018) shows the effects of the Ebola outbreak event on the stock prices with geographic proximity information and finds a robust impact on the stocks located in West Africa and U.S.-based operations.

Further, they advocate that Ebola has augmented the risk of perceived implied volatility. Likewise, Kowalewski and Spiewanowski (2020) examine the mining stock's performance in relation to potash mine disaster, and they find that man-made and natural catastrophe events affect the greenfield firms' stock. On the eve of the emergency, there is a decline of about 1.15 percent in a firm's value.

Baker et al., 2020a, Baker et al., 2020b employ a news-related quantifiable framework to understand the recent Covid-19 outbreak in the US equity market. The textual investigation shows that recent equity market volatility has exceeded the realized return volatility level of 1987, a global financial crisis in 2008–09, and the period of prodigious depression 1929–30. The authors have presented excellent work on infectious disease outbreaks such as Covid-19 consequences, and Covid-19 encouraged uncertainty. Recently, Al-Awadhi et al. (2020) also examine the effects of Covid-19 for the Chines equity market and show that growth in contagious disease outbreaks impacts the Chines equity market. Hence, our motivation for the work is to demonstrate the commodities market's behaviour on the pandemic disease outbreak and Covid-19 induced uncertainty and its impact on the market participants in the commodities trading.

Section 1 discusses the introduction, theoretical background, and literature evidence, and Section 2 explains the data description and a preliminary assessment. Section 3 builds an empirical model and hypothesis. Section 4 presents results and discussion. Section 5 further extends the robustness check of the results, and Section 6 ends with a conclusion and practical implications.

2. Data sources and preliminary analysis

Commodities' markets reaction to the pandemic disease outbreak Covid-19 has been studied concerning investors' sentiment. Global commodities such as Crude oil, Gold, and Silver are traded in a large volume based on variants of futures, options, and exchange-traded funds. There have been many ETFs available across various asset classes; Gold and Silver are precious metals and remain the main attraction for portfolio planning investors. The study aims to measure investors' behaviour expressed in terms of volatility index across various commodities traded globally. Hence, in our empirical analysis, we consider daily closing prices of the implied volatility index, i.e., OVX-Oil, GVZ-Gold, Silver-VXSLV, and Energy-VXXLE and Mining as well (for more detail, see Appendix A). Our study is concerned about the effects of Covid-19 on the commodities market. We consider the daily growth of Covid-19 in the USA and China; these two countries share a large part of world trade, investments, and economic development. Covid-19 was declared an infectious pandemic disease and health emergency globally by the WHO on January 30, 2020. Our source for daily commodities prices is Bloomberg terminal, and Covid-19 data are available from the European Centre for Disease Prevention and Control. We set our estimation process for the sample period from April 2018 to April 2020 (Ichev and Marinč 2018), in which Disease Outbreak News (DONs, Covid-19) event associated from December 31, 2019, to April 30, 2020. We also consider some uncertainty and control variables such as economic policy uncertainty (EPU), equity market uncertainty (EMU), SPXVIX and US T-bill, SPGSCI, WTI, and Brent prices.

Table 1 summarizes descriptive statistics in relation to our sample period across various asset classes of commodities. First, we look at the Crude and the Energy sector. The mean level of the United States Oil Fund (USO) appears to be 95.29 with an adverse annualized return of minuses 4.91% and an average level of market volatility of 42.10%. In line with the USO, the Energy sector also exhibited a similar pattern. Seeing the maximum record of the USO(XLE) prices, it seems 128.64(78.91) with a peaked level of market volatility OVX(VXXLE) 325.15% (130.61%). More impressive when we compare with the equity market, SPX yield 0.21% annualized returns with a maximum VIX level of 82.29%. This tail event materialized first time in history after GFC. One of the crucial observations from the summary statistic is that the Gold market has remained less affected due to such pandemic disease outbreak news. In a comparison of the various asset classes, Gold and Gold miner stock has reported annualized positive returns with 0.70% and 1.13%, respectively. The extreme level of investors fear and panic in the commodity —Gold, it appears 49.97% (GVZ) and Gold miner 118.75% (VXGDX). It signifies that gold mining companies face the demand side; due to a sudden slump in the demand for Gold from China and globally, Gold miners unable to encompass the overhead associated with the mining operations. Overall, a Gold market participant feels nervous about their Gold investment, but again it seems Gold investment acts as safe-haven. Another most actively traded global commodity, Silver, also exhibited unique variation in price (14.93) and yield (−0.30%). The maximum level of investors' concerns about Silver market volatility measured 100.66%, which is the highest historically. Now looking at the commodities market instability, Crude oil is seen with Std. Dev. = 34.70, and the second-highest found for the Energy sector 17.99, Gold, and Silver appear to be less volatile, among other commodities.

Table 1.

Summary statistics of commodities' market volatility and underlying ETF.

	Crude oil			Energy sector			Equity market
	USOUS	RUSOUS	OVX	XLEUS	RXLEUS	VXXLE	SPX	RSPX	VIX
Mean	95.2872	−4.9179a	42.0953	62.7259	−1.6811a	26.4971	2866.6870	0.3159a	18.6357
Maximum	128.6400	0.1542	325.1500	78.9100	0.1487	130.6100	3386.1500	0.0897	82.6900
Minimum	17.0400	−0.2919	23.3100	23.5700	−0.2249	13.4100	2237.4000	−0.1277	10.8500
Std. Dev.	19.8470	0.0337	34.6999	11.2156	0.0239	17.9864	203.9297	0.0155	10.7230
Observations	542	542	542	542	542	542	542	542	542
	Gold			Gold miner			Silver
	GLDUS	RGDLUS	GVZ	GDXUS	RGDXUS	VXGDX	SLVUS	RSLVUS	VXSLV
Mean	129.8819	0.6699a	13.6740	23.8604	1.1334a	30.4600	14.9349	−0.2953a	22.7801
Maximum	163.3400	0.0474	48.9800	34.0300	0.1686	118.7500	18.3400	0.0766	100.6600
Minimum	111.1000	−0.0407	8.8800	17.5700	−0.2591	15.4000	11.2100	−0.1318	14.8900
Std. Dev.	13.1497	0.0084	5.8415	3.7418	0.0264	13.2569	1.2022	0.0145	10.4878
Observations	542	542	542	542	542	542	542	542	542

^a-indicate an annualized return in percentage.

Table 2 exhibits summary statistics about the uncertainty index and growth indicators of Covid-19. The mean level of macroeconomic outlook (EPU) and equity market uncertainty (EMU) appeared respectively 125.04 and 86.72, but the maximum level reported highest for the equity market (944.58) with EPU (738.05). Moreover, we also consider a daily index of EMV tracked for the pandemic disease outbreak (IDsMV). The average reading was found to be 3.81 with a maximum level (68.54) of media coverage in real-time; zero indicates no news reporting of infectious disease relevant to the financial market. The next panel of the table shows the pandemic development during the period December 30, 2019, to April 30, 2020, in terms of new confirmed cases of Covid-19 and new daily deaths. The average daily new cases in the US (8215.55) exceed China (727.81), along with the mean daily death in the US (507.27) and China (41.55). The maximum level of reporting of daily Covid-19 infections appears 37,289 for the US and 15,141 in China; it seems the rate is double in the US, and it's also true for the number of mortality cases. Fig. 1 exhibits the temporal occurrence of contagious disease outbreaks in the US and China. Further, it shows the infectious disease (IDs) market volatility (MV) tracker appears with several spikes starting from December 30, 2019 and ends the first quarter of 2020. This phenomenon has been documented through regression modelling in the next section.

Table 2.

Summary statistics of policy uncertainty and Covid-19 cases.

	Uncertainty trackers			Covid-19 daily growth
	EMU	EPU	IDs	NCUS	NCCH	NDUS	NDCH
Mean	86.72	125.04	3.81	8215.55	727.81	507.27	41.55
Maximum	944.58	738.05	68.54	37289.00	15141.00	4928.00	1290.00
Minimum	5.32	10.92	0.00	0.00	0.00	0.00	0.00
Std. Dev.	115.61	115.11	11.24	12224.10	1863.76	939.79	141.54
Observations	543	543	543	88	88	88	88

[EMU = Equity Market Uncertainty; EPU = Economic Policy Uncertainty; IDs = Infectious Diseases Tracker; NCUS = Daily New confirmed Covdi-19 Cases in the USA; NCCH = Daily New confirmed Covdi-19 Cases in China ].

Fig. 1.

Growth of the Pandemic disease outbreak Covid-19 and Infectious Diseases (IDs) Market Volatility (MV) tracker [2020].

Table 3 present the correlation matrix between Covid-19 induced uncertainty and the commodities market. By conventions, it seems that greater uncertainty lowers the market performance measured in terms of benchmark stock and commodities indices. Hence, uncertainty and asset prices are adversely associated, and it is quite apparent from Table 3, the degree of association appears negative and statistically significant. Further, we can see that the precious metal Gold and Gold miners stock exhibited a positive association during the market uncertainty. It also confirms that Gold acts as a safe-haven during crisis and market boom as well. Table 4 demonstrates the correlation between the most popular commodities' volatility index and uncertainty indicators. By conventions, the higher the uncertainty greater is the market volatility. It is clearly visible that most of the volatility index yields a positive and statistically significant association with uncertainty variables. Fig. 2 is the plot of commodity benchmark indices with volatility indexes against the various uncertainty indicators. Fig. 3 displays the time series plot of the commodity market volatility with Infectious Diseases (IDs) market volatility tracker (MV) calculated by Baker et al., 2019a, Baker et al., 2019b, Baker et al., 2019c. One can see that IDsMV was proliferating post-February 2020 and reached its peak level during April 2020.

Table 3.

Correlation matrix: Underlying ETFs and Uncertainty indicators.

	USO	XLE	GLD	GDX	SLV	SPX	SPGSCI	WTI	Brent
EPU	−0.805 a	−0.749 a	0.516 a	0.292 a	−0.218 a	−0.273 a	−0.802	−0.793 a	−0.789 a
p-value	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
EMU	−0.716 a	−0.746 a	0.465 a	0.225 a	−0.174 a	−0.258 a	−0.739	−0.698 a	−0.730 a
p-value	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
VIX	−0.775 a	−0.797 a	0.421 a	0.112 b	−0.317 a	−0.443 a	−0.789	−0.761 a	−0.783 a
p-value	0.000	0.000	0.000	0.009	0.000	0.000	0.000	0.000	0.000
IDs	−0.768 a	−0.797 a	0.534 a	0.242 a	−0.221 a	−0.230 a	−0.784	−0.755 a	−0.782 a
p-value	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
Covid-19	−0.749 a	−0.464 a	0.764 a	0.508 a	−0.416 a	−0.329 a	−0.673	−0.721 a	−0.665 a
p-value	0.000	0.000	0.000	0.000	0.000	0.002	0.000	0.000	0.000

Significant at ^a1%, ^b5%, ^c10% level.

Table 4.

Correlation matrix: Volatility Indexes and Uncertainty indicators.

	OVX	VXXLE	GVZ	VXGDX	VXSLV
EPU	0.872 a	0.814 a	0.811 a	0.818 a	0.806 a
p-value	0.000	0.000	0.000	0.000	0.000
EMU	0.704 a	0.804 a	0.766 a	0.771 a	0.725 a
p-value	0.000	0.000	0.000	0.000	0.000
VIX	0.824 a	0.979 a	0.898 a	0.920 a	0.859 a
p-value	0.000	0.000	0.000	0.000	0.000
IDs	0.845 a	0.931 a	0.900 a	0.888 a	0.853 a
p-value	0.000	0.000	0.000	0.000	0.000
Covid-19	0.665 a	0.310 a	0.345 a	0.359 a	0.360 a
p-value	0.000	0.003	0.001	0.001	0.001

Significant at ^a1%, ^b5%, ^c10% level.

Fig. 2.

Covid-19 induced Uncertainty and commodities market.

Fig. 3.

Commodities' major volatility indexes and IDsMV tracker during pandemic development.

3. Methodology and hypothesis framework

Fear of the infectious disease Covid-19 has shocked the worlds' commodity markets and raised great concern over economic uncertainty and recession globally. This empirical work examines investors' unprecedented overreaction in the commodities market across the Crude oil, Gold, Gold Mining, Silver, and Energy sector in the USA. There are many prominent studies (e.g., Chen et al., 2007; Chen et al., 2009; and Wang et al., 2013) dig into the likely impact of infectious diseases on the hospitality industry, tourism stock, pharma and biotech stocks, equity markets even in the sports business, and find shocking asymmetric effects. To uncover the impact of Covid-19 on the commodities market following regression specification structured in terms of ex –ante volatility index,

$$VOLIND{X}_t^{Commodities}={\alpha }_0+{\alpha }_1{R}_t^{Underlying}+{\alpha }_{2}COVID\_U{S}_{t-1}+{\alpha }_{3}COVID\_C{H}_{t-1}+{\alpha }_4{U}_{t-1}+{\alpha }_5{X}_{t-1}+{\in }_t$$ (1)

$$VOLIND{X}_t^{Commodities}={\beta }_0+{\beta }_1{R}_t^{Underlying}+D_t\left\{{\delta }_0+{\delta }_{1}COVID\_U{S}_{t-1}\right\}+{\beta }_{2}COVID\_C{H}_{t-1}+{\beta }_3{U}_{t-1}+{\beta }_4{X}_{t-1}+\in {\text{'}}_t$$ (2)

$$VOLIND{X}_t^{Commodities}={\gamma }_0+{\gamma }_1{R}_t^{Underlying}+D_t{U}_{t-1}\left\{{\lambda }_0+{\lambda }_{1}COVID\_U{S}_{t-1}\right\}+{\gamma }_{2}COVID\_C{H}_{t-1}+{\gamma }_3{X}_{t-1}+{\in }^{\text{'}}{\text{'}}_t$$ (3)

where $VOLIND{X}_t^{Commodities}$ = represent the daily log-transformed change in the implied volatility index of various commodities classes, e.g., OVX, GVZ, VXSLV, VXGDX, and VXXLE. ${\alpha }_0$ = intercept coefficient, it measures investors' behaviour during the uncertainty of the information and uncontained nature of economic outlook; it should appear positive and significant. $R_t^{Underlying}$ = returns calculated corresponding to each underlying asset on which options are written, the associated coefficient should appear negative and statistically significant, by underlying convention assets and commodities' volatility are inversely related. $COVID\_U{S}_{t-1}$ = number of confirmed cases of Covid-19 in the US, it's expressed as an index and one-period lag, the index is calculated by taking a log of (1+ new confirmed cases) (e.g., Eckstein and Tsiddon, 2004; Persitz, 2006), similarly it's calculated for other indicators of Covid-19. $COVID\_C{H}_{t-1}$ = number of confirmed cases in China, the main epicenter of the virus outbreak is the Wuhan city of China, encompassing Covid-19 because investors regard the news related to the pandemic, and China is one of the strategic trading partners of the US. $U_t$ = is the vector of variables indicating outlook of macroeconomy of the US, its economic policy uncertainty (EPU), equity market policy uncertainty (EMU), SPXVIX, here we take the log of the EPU and other uncertainty variables with one-period lag. The coefficient on the $U_{t-1}$ should increase investors' fear and panic in the commodities market; hence it should be positive and statistically significant. $X_{t-1}$ = is the vector of other control variables that influence the trading into the commodities market, e.g., S&P GSCI, WTI Crude oil, Brent Crude oil, US T-bill. $D_t$ = is the dummy variable created to measure the DONs period, and to know what was the response of various commodity market-specific during the infectious disease outbreak, e.g., it assumes 1 for January 2020 otherwise zero. Here, ${\in }_t$, $\in {\text{'}}_t,$and ${\in }^{\prime }{\text{'}}_t$ is the classical white noise process and meets the requirement of OLS estimation.

eq. (1) uncovers the effects of the growth of the Covid-19 outbreak on the various classes of commodities measured in terms of implied volatility index. eq. (2) is an augmented regression model expressed in terms of interaction dummy variables; this specification measures the likely impact of Covid-19 during the DONs period, i.e., through January, February, March, and April 2020. Further, eq. (3) modified from eq. (2) to analyze effects of Covid-19 induced uncertainty in the commodities markets. In particular, our empirical hypothesis is that ‘there is an adverse impact of Covid-19 induced uncertainty in the commodities markets'.

4. Results and discussions

Table 5 shows the Crude oil market's regression outcome amid Covid-19 —expressed in terms of investors' sentiment index OVX. The oil volatility index is calculated in real-time based on the USO ETF options. OVX is the most important measure of oil price volatility. It shows investors' fear and worries specific to trade in the variety of Crude oil. The global Crude oil prices traded between $20 to $150 from 1999 to 2016. Still, recently Crude was hovering between $40-$60 and further in near-term futures contract traded below zero, the reason lack of liquidity, high transaction costs, and constrained storage. For the first time in history during 2007–2008, Crude traded more than $150, but recent pandemic drawdown the Crude at its low level. The Crude demand is subject to oil and petroleum products and the geological and geopolitical conditions among the major oil producer countries. Crude price volatility depends on future demand and supply of the Crude, and hardly short-run dynamics affect crude demand and supply, but the first time in history due to pandemic outbreak, Crude trade low in the near term. The recent disease outbreak news has triggered Crude price fluctuation among the top five Crude traded globally. Due to Covid-19 economic activity and the consumption of petroleum products has decayed significantly. Consequently, there has been a decline of about 24% in Crude refining activity in the first quarter of 2020.

Table 5.

Crude oil market volatility and Covid-19.

Panel A: OVX and Covid-19

[1]

[2]

[3]

[4]

[5]

[6]

Regressor

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Intercept

3.7865

42.59a

3.7361

44.48a

3.6473

6.98a

2.9642

12.14a

5.2528

11.84a

5.4182

9.48a

RUSO

−5.1250

−1.96b

−4.8507

−1.93c

−5.5874

−2.08b

−5.2872

−1.96b

−4.6737

−2.05b

−3.7554

−1.71c

COVIDUS

0.1966

3.22a

−0.0024

−0.07

COVIDCH

3.35E-05

6.38a

2.45E-05

4.99a

EPU

0.0339

0.30

−0.2029

−1.93c

EMU

0.2290

3.51a

0.0775

1.41

USTBILL

−1.1213

−7.00 a

−0.9611

−6.17a

SPREAD

0.1762

5.19 a

0.1788

5.45a

Panel B: OVX and DONs period

[1]

[2]

[3]

[4]

[5]

[6]

Regressor

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Intercept

3.7893

42.46a

3.79

42.21a

39.4218

14.98a

36.6937

17.60a

3.7884

42.50a

5.1707

11.94

RUSO

−4.9638

−1.92c

−5.05

−1.92c

−3.0567

−1.80c

−2.4381

−3.77a

−4.8574

−1.88c

−4.3517

−1.93

COVIDUS*DJAN20

0.9484

6.01a

COVIDUS*DFEB20

0.14

3.22a

COVIDUS*DMAR20

0.8364

4.99a

COVIDUS*DAPR20

0.4121

7.16a

COVIDUS*DQ120

0.1677

2.48b

0.1390

3.11

USTBILL

−1.0954

−6.96

SPREAD

0.1777

5.25

Panel C: OVX and economic policy uncertainty

[1]

[2]

[3]

[4]

[5]

[6]

Regressor

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Intercept

3.7904

42.40a

3.7896

42.26a

39.5393

14.90a

36.6162

17.53a

3.7877

42.47a

4.6542

12.43a

RUSO

−5.5607

−2.05b

−5.1689

−1.97c

−3.1180

−1.85c

−2.5085

−3.55a

−5.1335

−1.96c

−4.1071

−1.84c

COVIDUS*DJAN20*EPU

0.2941

10.48a

COVIDUS*DFEB20*EPU

0.0345

4.40a

COVIDUS*DMAR20*EPU

0.0153

5.47a

COVIDUS*DAPR20*EPU

0.0672

7.07a

COVIDUS*DQ120*EPU

0.0378

3.48a

0.0238

3.08a

USTBILL

−2.5641

−6.83a

SPREAD

0.1864

5.48a

Panel D: OVX and equity market uncertainty

[1]

[2]

[3]

[4]

[5]

[6]

Regressor

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Estimate

t-stat

Intercept

3.7920

42.32a

3.7906

42.17a

39.5001

14.93a

36.5781

17.57a

3.7888

42.37a

4.6600

12.40a

RUSO

−5.6837

−2.07b

−5.1839

−1.97b

−3.0917

−1.82c

−2.6037

−3.68a

−5.1668

−1.97b

−4.1304

−1.85c

COVIDUS*DJAN20*EMU

0.1966

9.41a

COVIDUS*DFEB20*EMU

0.0277

5.14a

COVIDUS*DMAR20*EMU

0.0164

5.47a

COVIDUS*DAPR20*EMU

0.0750

6.88a

COVIDUS*DQ120*EMU

0.0305

3.76a

0.0191

3.28a

USTBILL

−2.5689

−6.84a

SPREAD

0.1863

5.48a

The table shows the regression results of eqs. (1), (2), (3) concerning Covid-19 induced uncertainty in the Crude oil market, regression estimates are consisting of autocorrelation and heteroskedasticity (HAC- Newey-West), Significant at ^a1%, ^b5%, ^c10% level].

EIA (US Energy Information Administration) (2020) presents some insights into Crude oil prices' major driver. Geopolitical and economic events have played the main role in driving crude prices globally. For example, Crude traded between $20-$76 from 1970 to 1985 following US spare capacity, Arab Oil-price Embargo, the Iranian revolution, Iran-Iraq War, and Saudis abandon. And from 1985 to 2015, Crude appeared around $20-$125 with many geopolitical events like the Asian crisis, 9/11 terrorist attacks, GFC, and more recently, Crude hovered from $40-$68 after 2015 due to the OPEC production quota. For the first time in history, Covid-19 induced uncertainty has disrupted the Crude's entire supply chain, and global Crude has traded at its lower level during pandemic development. Further, the demand for petroleum products, slower economic growth, non-OPEC production, and Saudi Arabia oil production remained the main determinant of the global crude prices.

Table 5 exhibits investors' sentiment in the crude oil market amid the Covid-19 outbreak and uncertainty induced by pandemic development. First, looking at the slope of the COVIDUS, it appears positive and statistically significant at a 1% level. The coefficient linked to COVIDCH was also found to be positive and significant. It implies that the growth of the Covid-19 cases in the US and China does impact future oil market volatility. In order to hedge oil market uncertainty, investors, buy options, and put-options act as insurance against a large fall of oil prices. An excess demand for put-option leads to a rise of premiums. Higher the premium resulting implied volatility of OVX will be higher, and one can see the maximum level of OVX found to be about 325%. The unpreceded rise of the OVX signifies the overreaction of the market participants concerning the commodities prices. Now referring to the slope of uncertainty factors associated with the macroeconomy and equity market, the respective estimates for EPU and EMU found to be positive. Still, only equity market uncertainty contributes to a significant rise in the level of OVX. The future level of oil market volatility is directly associated with the underlying's performance, i.e., prices of USO fund and short-term interest rates. One can see that yield associated with USO and USTBILL appears to be negative and statistically significant. It indicates that volatility and returns are negatively associated; a negative return or yield causes a rise in the volatility; this phenomenon is further supported by the volatility feedback and leverage effects hypothesis. In our regression specification, we also add one of the control variables, ‘spread,’ it's a difference between two benchmarks of the Crude oil traded globally, i.e., WTI and Brent. We can see that across all panels of Table 5, it appears positive and statistically significant, indicate that the higher the degree of spread, the higher the level of future OVX. Panel B depicts the investors' fear and panic during the disease outbreak news (DONs) period; we set dummy variables for the first four calendar months and jointly for the first quarter of 2020. One can see that the spread of Covid-19 infection in the US through January–April 2020 has impacted the Crude oil market adversely. The significant positive estimate indicates that there has been an unparalleled rise in oil price volatility during the first quarter of 2020. Panel C and D shows the augmented regression results of the Covid-19 induced EPU and EMU. We can see that all estimates appear positive and statistically significant. It implies that the macroeconomy and equity market's contagious pandemic-related uncertainty does explain the sentiment in the commodities market (e.g., (Baker et al., 2019a, Baker et al., 2019b, Baker et al., 2019c; Baker et al., 2020b; Shaikh, 2020). It is also due to the volatility feedback effects; the decline in the stock price eventually results in a rise in market volatility.

Table 6 shows the behaviour of the investors' sentiment who trade into the Energy sector; XLE trails a market-cap-weighted index of US Energy firms in the SPX500. XLE provides absolutely liquid exposure to a basket of US energy firms. CBOE writes options on the XLE fund in order to provide Energy sector insurance to investors trading in the Energy market. VXXLE is the overall indicator of investor fear and nervousness in the energy sector; on March 16, 2020, it appeared with the highest about 169% level, indicating extreme concern of the market participant about the energy stocks. Panel A clearly shows that the energy sector has an impact due to Covid-19 growth in the US and China. The EPU and EMU also explain the investors' sentiment adversely due to slow economic growth and equity market fall. The energy stocks' volatility depends on the ETF fund's performance, short-term interest rates, and overall performance of the commodities market. Hence, one can see that coefficients related to XLE, USTBILL, and SPGSCI returns were negative and statistically significant. Panel B reports the Covid-19 induced pandemic outbreak effects on the investors' sentiment during the first quarter of 2020; it's evident that the first quarter and second exhibit an increased level of investors' anxiety in the energy sector. Panel C also shows the effects of the economic outlook and federal policy uncertainty amid Covid-19 on the energy stocks, and it uncovers some unpleasant consequences for the energy investors. Similarly, equity market-specific uncertainty (EMU) also has relevance with the energy sector volatility, and it seems that energy market volatility and equity market (RSPX) returns are negatively associated.

Table 6.

Energy sector market volatility and Covid-19.

Panel A: VXXLE and Covid-19
	[1]		[2]		[3]		[4]		[5]		[6]
Regressor	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat
Intercept	3.0960	106.69a	3.0428	155.14a	1.2384	3.74a	2.3101	17.30a	3.8777	19.93a	2.1421	9.88a
RXLE	−3.6139	−2.67a	−1.0674	−1.67	−2.4786	−2.16b	−1.5628	−1.23	−1.7598	−1.41	−1.5956	−2.31b
COVIDUS	4.84E-05	10.75a									0.0000	1.47
COVIDCH			1.36E-05	7.98a							0.0000	3.56a
EPU					0.4190	5.58a					0.0751	1.76c
EMU							0.2222	5.46a			0.0925	5.88a
USTBILL									−0.3154	−4.18a	0.0946	2.81a
RSPGSCI									−3.5380	−2.72a	−2.7237	−3.19a
Panel B: VXXLE and DONs period
	[1]		[2]		[3]		[4]		[5]		[6]
Regressor	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat
Intercept	3.1612	74.54a	3.1552	74.03a	3.1441	83.29a	3.1200	87.76a	3.1441	83.29a	3.7966	20.15a
RXLE	−2.0770	−1.11	−1.9291	−1.00	−2.4743	−1.48	−2.9753	−1.79c	−2.4740	−1.48	−2.0640	−1.81c
COVIDUS*DJAN20	−0.0333	−2.47a
COVIDUS*DFEB20			0.0063	1.82c
COVIDUS*DMAR20					1.71E-05	3.69a
COVIDUS*DAPR20							1.62E-06	5.65a
COVIDUS*DQ120									1.71E-05	3.69a	1.14E-05	2.90a
USTBILL											−0.2844	−3.88a
RSPGSCI											−3.2458	−2.32b
Panel C: VXXLE and economic policy uncertainty
	[1]		[2]		[3]		[4]		[5]		[6]
Regressor	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat
Intercept	3.1611	74.56a	3.1552	74.07a	3.1438	83.44a	3.1201	87.77a	3.1438	83.44a	3.7944	20.14a
RXLE	−2.0795	−1.11	−1.9284	−1.00	−2.4755	−1.48	−2.9799	−1.79c	−2.4753	−1.48	−2.0662	−1.82c
COVIDUS*DJAN20*EPU	−0.0075	−2.25b
COVIDUS*DFEB20*EPU			0.0014	1.92c
COVIDUS*DMAR20*EPU					2.84E-06	3.85a
COVIDUS*DAPR20*EPU							2.59E-07	5.65a
COVIDUS*DQ120*EPU									2.84E-06	3.85a	1.89E-06	3.00a
USTBILL											−0.2835	−3.87a
RSPGSCI											−3.2479	−2.31b
Panel D: VXXLE and equity market uncertainty
	[1]		[2]		[3]		[4]		[5]		[6]
Regressor	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat
Intercept	3.1610	74.58a	3.1552	74.24a	3.1430	83.82a	3.1203	87.82a	3.0430	63.32a	3.7990	20.01a
RXLE	−2.0765	−1.10	−1.9199	−1.00	−2.4642	−1.48	−3.0157	−1.82c	−2.2639	−2.38a	−2.4823	−1.85c
COVIDUS*DJAN20*EMU	−0.0066	−2.61a
COVIDUS*DFEB20*EMU			0.0013	2.35b
COVIDUS*DMAR20*EMU					3.12E-06	4.03a
COVIDUS*DAPR20*EMU							2.86E-07	5.19a
COVIDUS*DQ120*EMU									3.12E-06	5.03a	2.30E-06	3.32a
USTBILL											−0.2846	−3.84a
RSPX											−3.4111	−2.28c

The table shows the regression results of eqs. (1), (2), (3) concerning Covid-19 induced uncertainty in the Energy sector, regression estimates are consisting of autocorrelation and heteroskedasticity (HAC- Newey-West), Significant at ^a1%, ^b5%, ^c10% level].

Table 7 displays the investors' behaviour amid Covid-19, gauged in terms of the Gold volatility index (GVZ). GLD is an ETF fund that characterizes SPDR Gold Trust, which trades into gold bullion. Hence, GLD replicates the spot gold price after adjusting for the fund expenses. CBOE write option on the GLD ETF, gold traders, and investors buy GLD options to hedge Gold related uncertainty. By inverting observed option prices, one can estimate implied volatility termed as GVZ in real-time. For the first time in history, GVZ measured the investors' nervousness about 54% on the March 18, 2020, peak of the day during the outbreak of contagious diseases. WCG (2020) reports that hedging tail events are often a negotiation among the exciting protection and cost. Gold returns tend to outperform during the great recession, sovereign debt crises I&II, a pullback of 2018, but first time in history, Gold yield negative returns in the short-run amid the Covid-19 crisis. Gold has been a precious metal and a store of value under-represented in the commodity indices. Gold acts as a safe-haven and can optimize the portfolio. Hiller et al. (2006) analyze the relationship between the equity market and precious metals investment like Gold, Platinum, and Silver. The authors report a low degree of association between precious metals and stock returns. Moreover, the authors argue that precious metals can act as the best hedging commodities against market turmoil. Our regression result shows that gold market volatility and equity market returns (Panel D of Table 7, RSPX, −0.776, with t-stat = −1.42) exhibit a low negative relationship and statistically not significant. Further, Conover et al. (2009) experiment with the investment in precious metals that enhances the portfolio hedge and improved returns. The authors argue that moving for a 5%–25% allocation of the investment in precious metals results in a noticeable portfolio performance improvement. Further they deliberate that ETFs and ETNs in the commodities market provide enhanced distribution of equity investment. GVZ gold volatility index is based on the GLD ETF funds and represents the investors' worries about the future uncertainty in gold trading. Panel A of Table 7 clearly exhibits that Cvoid-19 induced uncertainty impacted the investors' sentiment. Further, one can see returns of SPGSCI, a broad commodities index, do not explain gold volatility significantly. Albeit, the short-term interest rate entirely responsible for the increased level of volatility in the gold market. Panel B uncovers that February, March, and April 2020 remain more volatile for the gold prices amid of Covid-19 outbreak in China and the USA. There has been a decrease of around 40% in demand for bullion. It is for the emerging markets, including China and India (WCG, 2020), during the first quarter of 2020. Panel C and D clearly show that economic uncertainty and equity market uncertainty matter for the precious metals' investment, but at the same time, other commodities and equity market returns do not explain the performance of the gold market significantly.

Table 7.

Gold market volatility and Covid-19.

Panel A: GVZ and Covid-19
	[1]		[2]		[3]		[4]		[5]		[6]
Regressor	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat
Intercept	2.5152	102.51a	2.4724	140.66a	1.1712	5.10a	1.9759	19.81a	3.0388	60.52a	2.4576	16.61a
RGLD	1.2251	0.38	1.9787	1.49	0.7146	0.31	1.5124	0.63	1.8491	1.30	1.4584	1.32
COVIDUS	3.31E-05	10.69a									−2.68E-07	−0.05
COVIDCH			9.80E-06	8.47a							4.03E-06	2.28b
EPU					0.3025	5.77a					0.0584	2.02b
EMU							0.1521	4.99a			0.0162	1.41
USTBILL									−0.6306	−11.81a	−0.3462	−5.59a
RSPGSCI									−0.8586	−1.38	−0.7479	−1.40
Panel B: GVZ and DONs period
	[1]		[2]		[3]		[4]		[5]		[6]
Regressor	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat
Intercept	2.5573	80.91a	2.5558	80.96a	2.5435	89.49a	2.5247	92.60a	2.5435	89.49a	3.0070	66.00a
RGLD	2.8274	0.80	2.9497	0.83	3.0692	0.92	1.3231	0.38	3.0697	0.92	1.9895	1.40
COVIDUS*DJAN20	−0.0315	−1.50
COVIDUS*DFEB20			0.0201	3.00a
COVIDUS*DMAR20					8.07E-05	4.52a
COVIDUS*DAPR20							2.95E-05	17.62a
COVIDUS*DQ120									8.07E-05	4.52a	3.21E-05	2.61a
USTBILL											−0.5961	−12.34a
RSPGSCI											−0.8043	−1.29
Panel C: GVZ and economic policy uncertainty
	[1]		[2]		[3]		[4]		[5]		[6]
Regressor	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat
Intercept	2.5573	80.89a	2.5558	80.98a	2.5435	89.47a	2.5247	92.60a	2.5435	89.47a	3.0068	65.99a
RGLD	2.8253	0.80	2.9406	0.83	2.9805	0.90	1.3062	0.37	2.9808	0.90	1.9552	1.38
COVIDUS*DJAN20*EPU	−0.0110	−1.60a
COVIDUS*DFEB20*EPU			0.0040	3.06a
COVIDUS*DMAR20*EPU					1.30E-05	4.70a
COVIDUS*DAPR20*EPU							4.71E-06	18.32a
COVIDUS*DQ120*EPU									1.30E-05	4.70a	5.19E-06	2.69a
USTBILL											−0.5959	−12.33a
RSPGSCI											−0.8061	−1.292
Panel D: GVZ and equity market uncertainty
	[1]		[2]		[3]		[4]		[5]		[6]
Regressor	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat
Intercept	2.5572	80.94a	2.5559	81.01a	2.5431	89.82a	2.5247	92.62a	1.4431	69.82a	3.0080	65.32a
RGLD	2.8310	0.80	2.9351	0.83	2.8206	0.86	1.2866	0.37	3.8661	0.76	1.7101	1.19
COVIDUS*DJAN20*EMU	−0.0057	−1.43
COVIDUS*DFEB20*EMU			0.0034	3.16a
COVIDUS*DMAR20*EMU					1.39E-05	4.76a
COVIDUS*DAPR20*EMU							5.25E-06	18.61a
COVIDUS*DQ120*EMU									2.23E-05	5.56a	5.87E-06	2.84a
USTBILL											−0.5966	−12.22a
RSPX											−0.7776	−1.42

The table shows the regression results of eqs. (1), (2), (3) concerning Covid-19 induced uncertainty in the Gold market, regression estimates are consisting of autocorrelation and heteroskedasticity (HAC- Newey-West), Significant at ^a1%, ^b5%, ^c10% level].

Table 8 discusses investors' sentiment within the outbreak of pandemic Covid-19 in the Gold miners' companies traded in the GDX ETF funds. Gold Miners ETF pursues to imitate the inclusive performance of firms tangled with the gold mining business as strictly as possible. CBOE allowed to introduced options on the GDX fund to provide market safeguarded against the gold mining business's uncertainty. VXGDX is the GDX options based real-time gold miner's volatility index that expresses investors' anxiety and fear in the near term. Foster (2020), a portfolio manager, deliberate on the gold mining stocks' investment that helps in risk-off-positioning, panic increased market momentum, and liquidity for meeting margin calls. Gold has performed as a hedge against both the turmoil and the inflation that might ultimately come from Federal policies and buying and selling treasuries and mortgage-backed securities. The unprecedented rise of the premium in the options market leads to an increase of the Gold mining volatility VXGDX; Gold mining has come across a fringe effect so far from the pandemic. Still, for the first time in history, the investors' panic peak was found to be about 190% on March 19, 2020. Panel A of Table 8 exhibits that pandemic Covid-19 growth in the US and China impact the gold mining business. Nonetheless, one can see that uncertainty of the Fed's policy actions during the pandemic period does not explain investors' sentiment trading in the gold market. But equity market uncertainty significantly impacts the future level of the gold miner stock volatility. The investors' response about short-term interest rates was adverse and significant, while gold miners' stock volatility remains unresponsive to the other commodities' trading (RSPGSCI). Panel B depicts the effects of Covid-19 growth during the first quarter on the gold miners' stocks, and it's apparent that there has been a pronounced effect of pandemic outbreak on the gold mining stock volatility. Panel C and D also exhibits that the economy's economic outlook in the short run and equity market uncertainty matter for the gold miners. One of the important observations is that gold miners' stock and equity market returns have weak associations; gold mining stocks are independent of equity market performance. Naeem et al. (2020) report no interdependence between BRICS equity markets and commodities like Gold and oil with a short horizon. Considering the global financial crisis, but asymmetric interdependence exists in the long run. Similarly, Morema and Bonga-Bonga (2020) examine volatility spillover between commodity and South African equity markets and find significant spillover among Oil(Gold) and stock markets. Hence, empirical evidence presented here provides important insight into portfolio optimization; a risk-averse investor can minimize and avoid the equity markets' uncertainty by investing in gold miners' stocks.

Table 8.

Gold miners market volatility and Covid-19.

Panel A: VXGDX and Covid-19
	[1]		[2]		[3]		[4]		[5]		[6]
Regressor	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat
Intercept	3.7570	38.25a	3.7278	40.32a	4.5098	6.69a	3.0228	10.04a	4.9692	12.61a	5.7245	7.72a
RGDX	0.2023	0.07	0.7871	0.28	1.1663	0.39	0.4272	0.15	0.6377	0.24	1.1985	0.48
COVIDUS	1.61E-01	6.53a									−1.93E-02	−0.81
COVIDCH			4.25E-04	2.82a							2.73E-05	4.25a
EPU					−0.1636	−1.10					−0.4428	−3.08a
EMU							0.2068	2.49a			0.1649	2.36b
USTBILL									−1.3944	−3.14a	−0.7510	−1.87c
RSPGSCI									−3.3582	−0.76	−1.2449	−0.31
Panel B: VXGDX and DONs period
	[1]		[2]		[3]		[4]		[5]		[6]
Regressor	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat
Intercept	3.7728	37.59a	3.7672	38.13a	3.7709	37.46a	29.0448	24.77a	3.7570	38.25a	4.8249	12.19a
RGDX	1.3222	0.44	1.5742	0.53	−0.3425	−0.11	−10.7167	−0.26	0.2023	0.07	0.2737	0.10
COVIDUS*DJAN20	0.8320	4.98a
COVIDUS*DFEB20			0.1840	2.98a
COVIDUS*DMAR20					0.1365	15.01a
COVIDUS*DAPR20							0.0013	19.15a
COVIDUS*DQ120									0.1610	6.53a	0.1031	3.98a
USTBILL											−1.2423	−2.78a
RSPGSCI											−3.5672	−0.82
Panel C: VXGDX and economic policy uncertainty
	[1]		[2]		[3]		[4]		[5]		[6]
Regressor	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat
Intercept	3.7720	37.60a	3.7678	38.09a	3.7709	37.46a	29.0452	24.76a	3.7581	38.18a	4.8315	12.18a
RGDX	1.3376	0.45	1.5228	0.52	−0.3487	−0.11	−12.1687	−0.29	0.1379	0.05	0.2348	0.09
COVIDUS*DJAN20*EPU	0.2704	5.30a
COVIDUS*DFEB20*EPU			0.0363	3.04
COVIDUS*DMAR20*EPU					0.0274	14.90a
COVIDUS*DAPR20*EPU							0.0002	18.66a
COVIDUS*DQ120*EPU									0.0317	7.00a	0.0202	4.07a
USTBILL											−1.2491	−2.79a
RSPGSCI											−3.6066	−0.83
Panel D: VXGDX and equity market uncertainty
	[1]		[2]		[3]		[4]		[5]		[6]
Regressor	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat
Intercept	3.7739	37.58a	3.7691	37.98a	3.7708	37.46a	29.0488	24.76a	3.7589	38.10a	4.8422	12.18a
RGDX	1.2836	0.43	1.4476	0.49	−0.3321	−0.11	−12.5544	−0.30	0.2267	0.08	0.3728	0.14
COVIDUS*DJAN20*EMU	0.1699	6.08a
COVIDUS*DFEB20*EMU			0.0288	3.37a
COVIDUS*DMAR20*EMU					0.0250	14.94a
COVIDUS*DAPR20*EMU							0.0002	16.70a
COVIDUS*DQ120*EMU									0.0274	7.48a	0.0166	3.87a
USTBILL											−1.2569	−2.80a
RSPX											−5.9294	−1.12

The table shows the regression results of eqs. (1), (2), (3) concerning Covid-19 induced uncertainty in the Gold miners' stock, regression estimates are consisting of autocorrelation and heteroskedasticity (HAC- Newey-West), Significant at ^a1%, ^b5%, ^c10% level].

Table 9 displays the measurement of investors' fear and panic in one of the precious metal Silver during pandemic development. SLV is an iShares Silver Trust that tracks the Silver spot price, after adjusting for the expenses and liabilities, utilizing silver bullion held in London. SLV has remained the main attraction for bullion investment because it provides exposure to meet daily fluctuation in the price of Silver, cost-effective, and convenient, and acts as the best hedge against inflation. CBOE introduces SLV ETF options to provide protections to the market player to meet bullion market uncertainty in the short-run. VXSLV is the real-time Silver market volatility index measures the future market volatility of trading into Silver. Khazzaka (2020) reports the ‘Fear—COVID-19′on the bullion market, stock market crash, and the global credit crunch lead to investors and fund managers to settle their market holdings, together with bullion, in order to encounter margin calls and uphold their positions. The author deliberate there is a fear of stagflation in the market in the long run amid Covid-19 and the Fed's recent actions' ambiguity. There has been a first time in history a peak of investors fears in the Silver market found to be about 114% on March 18, 2020. Besides, Sappor et al. (2020) and Rodwell (2020) explain the impacts of COVID-19 on Metals Prices and Volatility and further report that Silver prices reach the lowest in 11 years due to lack of inter-industry demand and negative yields on the government bonds. To combat such market uncertainty bullion traders, buy put-options. The higher the demand, the higher the premium will be and which results in higher implied volatility. Recent pandemic development holds a severe impact on the future production of precious metals and mining operations. It is expected that the mining budget will be cut by about 29% this fiscal, which will have a substantial impact on the mining activity. Panel A of Table 9 also reports the result in line with previous commodities market behaviour. The Covid-19 pandemic development in the US and China has disrupted the investors' apprehension in bullion trading. The slower economic activity and drawdown of the equity market adversely impacted investors' sentiment. Further, the decline in the short-term bond yield has increased Silver market volatility. The association of the Silver market volatility between other commodities and the equity market remains low responsive. Hence one can say that precious metals like Silver act as an archetypal safe-haven asset. Panel B also signifies that due to decreased consumption of precious metals in other industries, the first quarter of 2020 has raised investors' anxiety. Panel C and D also exhibits that policy uncertainty and equity market uncertainty affect the bullion market and metals mining.

Table 9.

Silver market volatility and Covid-19.

Panel A: VXSLV and Covid-19
	[1]		[2]		[3]		[4]		[5]		[6]
Regressor	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat
Intercept	3.0199	131.31a	2.9822	168.55a	1.6899	6.54a	2.5029	23.51a	3.5414	61.12a	2.9873	15.97a
RSLV	−1.2959	−0.47	0.6226	0.47	−1.1121	−0.59	−0.4169	−0.20	−0.0020	−0.13	−0.0497	−0.04
COVIDUS	3.52E-05	11.12a									5.13E-06	0.77
COVIDCH			9.60E-06	6.69a							3.36E-06	1.57
EPU					0.3000	5.11a					0.0505	1.37
EMU							0.1470	4.54a			0.0157	1.52
USTBILL									−0.6230	−9.98a	−0.3265	−4.26a
RSPGSCI									−0.2761	−0.34	−0.3440	−0.52
Panel B: VXSLV and DONs period
	[1]		[2]		[3]		[4]		[5]		[6]
Regressor	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat
Intercept	3.0656	96.48a	3.0648	96.25a	3.0510	109.97a	3.0301	113.70a	3.0510	109.97a	3.5043	69.35a
RSLV	−0.4919	−0.16	−0.4229	−0.14	−0.8112	−0.30	−1.0691	−0.36	−0.8108	−0.30	−0.2001	−0.14
COVIDUS*DJAN20	−0.0499	−1.34
COVIDUS*DFEB20			0.0070	1.32
COVIDUS*DMAR20					8.49E-05	3.92a
COVIDUS*DAPR20							3.14E-05	20.37a
COVIDUS*DQ120									8.49E-05	3.92a	3.77E-05	2.61a
USTBILL											−0.5825	−10.63a
RSPGSCI											−0.1858	−0.23
Panel C: VXSLV and economic policy uncertainty
	[1]		[2]		[3]		[4]		[5]		[6]
Regressor	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat
Intercept	3.0657	96.45a	3.0648	96.27a	3.0509	110.04a	3.0301	113.69a	3.0509	110.04a	3.5038	69.45a
RSLV	−0.4940	−0.16	−0.4249	−0.14	−0.8413	−0.31	−1.0698	−0.36	−0.8410	−0.31	−0.2146	−0.15
COVIDUS*DJAN20*EPU	−0.0169	−1.50
COVIDUS*DFEB20*EPU			0.0014	1.33
COVIDUS*DMAR20*EPU					1.37E-05	4.07a
COVIDUS*DAPR20*EPU							5.01E-06	20.96a
COVIDUS*DQ120*EPU									1.37E-05	4.07a	6.13E-06	2.71a
USTBILL											−0.5820	−10.64a
RSPGSCI											−0.1887	−0.23
Panel D: VXSLV and equity market uncertainty
	[1]		[2]		[3]		[4]		[5]		[6]
Regressor	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat
Intercept	3.0655	96.51a	3.0649	96.33a	3.0504	110.59a	3.0300	113.72a	3.0504	110.59a	3.5024	68.93a
RSLV	−0.4872	−0.16	−0.4256	−0.14	−0.8819	−0.33	−1.0915	−0.37	−0.8815	−0.33	−0.2649	−0.18
COVIDUS*DJAN20*EMU	−0.0096	−1.18
COVIDUS*DFEB20*EMU			0.0012	1.38
COVIDUS*DMAR20*EMU					1.48E-05	4.12a
COVIDUS*DAPR20*EMU							5.58E-06	23.12a
COVIDUS*DQ120*EMU									1.48E-05	4.12a	6.76E-06	2.70a
USTBILL											−0.5804	−10.56a
RSPX											−0.1595	−0.22

The table shows the regression results of eqs. (1), (2), (3) concerning Covid-19 induced uncertainty in the Silver market, regression estimates are consisting of autocorrelation and heteroskedasticity (HAC- Newey-West), Significant at ^a1%, ^b5%, ^c10% level].

5. Robustness check

In this section, we present the robustness check of the results reported in the previous sections. Baker et al. (2019a,b) deliberate on the stock market volatility and jumps based on the equity market volatility tracker (EMV). The tracker is based on the newspaper-text keyword counts related to the uncertainty. Further, they extend the same mechanism to track pandemic disease outbreaks in their EMV tracker and have a separate reading named ‘Daily Infectious Disease Equity Market Volatility Tracker (IDsMV)'.4 Our study employs the IDsMV tracker to uncover the effects of such news related to pandemic diseases on the commodities markets gauged in terms of implied volatility index. We set our specification as follows,

$$VOLIND{X}_t^{Commodities}={\alpha }_0+{\alpha }_1{R}_t^{Underlying}+{\alpha }_{2}IDs\_M{V}_{t-1}+{\alpha }_3{U}_{t-1}+{\alpha }_4{X}_{t-1}+u_t$$ (4)

$$VOLIND{X}_t^{Commodities}={\alpha }_0+{\alpha }_1{R}_t^{Underlying}+D_t\left\{{\mu }_0+{\mu }_{2}IDs\_M{V}_{t-1}\right\}+{\alpha }_4{U}_{t-1}+{\alpha }_4{X}_{t-1}+u{\text{'}}_t$$ (5)

In our regression specification $IDs\_M{V}_{t-1}$ is one period lag with log-transformation of $(1+IDs\_M{V}_t)$. Our empirical hypothesis is that ‘media coverage of pandemic disease outbreak (e.g., Covid-19) does impact the investor sentiment in the commodities market'.

Table 10 shows the relationship between commodities market volatility and infectious disease outbreak news. The options market is efficient, and the premium investors pay to avail hedge against the market uncertainty reflects the arrival of new information in the market on a real-time basis. Hence options' implied volatility gauges the investors' fear and estimates the future market volatility. Our study employs five major commodities' market volatility indexes that reflect the investors' anxiety and panic due to uncertainty of the future economic and political event, natural pandemic event as well. Panel A of Table 10 explains that the pandemic news outbreak has disrupted the investors' sentiment. The positive slopes across all classes of the commodities show that recent pandemic growth has impacted the commodities market with major sell-off to meet their margin requirement and rebalancing the portfolio. One of the interesting outcomes observed that short-term interest rates affect the future level of commodities market volatility adversely. The spread of WTI and Brent raises the market participant's concern in trading and investing in the Crude oil market. Also, Panel B explains that market volatility was approaching a normal level during January, but once WHO announced Covid-19 as an international emergency in February, and suddenly market overreacted and adjusted the future uncertainty, which is reflected in terms of investors' sentiment index (VIX). The first quarter of 2020 has been the period drawdown of the equity and commodities market, yet the market cannot adjust to the uncontained future effects of the Covid-19 outbreak.

Table 10.

Robustness Check: On the relationship between commodities markets and infectious diseases news.

Panel A: Commodities market volatility and Infectious Disease Market Volatility
	Crude oil		Energy		Gold		Gold miner		Silver
Regressor	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat
Intercept	1.5642	41.86 a	1.2539	33.09 a	1.2130	34.26 a	1.5173	43.09 a	1.4233	35.72 a
Return	−0.5317	−2.15 b	−0.5709	−2.44 b	0.8573	1.84 c	0.1183	0.79	0.2170	0.45
ID_MV	0.2840	7.27 a	0.3358	10.33 a	0.1664	6.36 a	0.1786	5.80 a	0.1695	4.95 a
USTBILL	−0.0637	−4.03 a	0.0136	0.90	−0.0636	−5.08 a	−0.0456	−3.43 a	−0.0593	−4.14 a
Spread/RSPGSCI	0.0093	2.83 a	−0.6982	−2.27 b	−0.2169	−1.03	−0.1661	−0.72	0.0255	0.09
Panel B: Commodities market volatility and IDsMV during DONs period
	Crude oil		Energy		Gold		Gold miner		Silver
Regressor	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat	Estimate	t-stat
Intercept	1.5853	45.76 a	1.2792	35.21 a	1.2286	35.31 a	1.5384	47.47 a	1.4398	35.97 a
Return	−0.4535	−2.65 a	−0.6148	−3.72 a	0.7242	1.77 c	0.0273	0.19	0.1348	0.37
ID_MV*DJAN20	−0.0268	−0.71	−0.0647	−1.44	−0.0327	−0.96	−0.0536	−1.94 c	−0.0329	−0.87
ID_MV*DFEB20	0.0637	3.62 a	0.1262	2.67 a	0.0270	0.59	−0.0130	−0.32	0.0068	0.26
ID_MV*DMAR20	0.3026	6.74 a	0.3782	12.68 a	0.2021	7.20 a	0.2123	5.64 a	0.1936	3.96 a
ID_MV*DAPR20	0.3751	13.38 a	0.3768	17.36 a	0.1771	9.12 a	0.1970	11.17 a	0.1944	9.28 a
USTBILL	−0.0494	−3.06 a	0.0181	1.17	−0.0624	−4.89 a	−0.0457	−3.50 a	−0.0582	−3.90 a
Spread/RSPGSCI	0.0058	1.92 c	−0.6832	−2.93 a	−0.1675	−1.05	−0.1611	−0.79	0.0184	0.08

The table shows the regression results of eqs. (4), (5) concerning Infectious Disease Market Volatility uncertainty in the commodities markets, regression estimates are consisting of autocorrelation and heteroskedasticity (HAC- Newey-West), Significant at ^a1%, ^b5%, ^c10% level].

6. Conclusion and policy implications

This article presented the commodities market's investors' sentiment in terms of the implied volatility index of various commodity specifics. We demonstrated the pandemic development of Covid-19 and its effects on the Crude oil, Energy sector, Gold, and Silver markets. Results are more pronounced during March 2020, tranquil Covid-19 induced uncertainty is disorderly to explain the bullion and precious metals market. In the short run, the Covid-19 disease outbreak event has increased to strong apprehensions about the impending commodity investment and consumption. Yet, the effects of the virus remain uncontained and vague for many industries and markets.

Overall, empirical results uncover that the Covid-19 cases in the US and China impact the ex -ante market volatility of the commodities market. In order to hedge market uncertainty, investors buy options and put-options act as insurance against a large fall of commodities prices. The unprecedented rise of the OVX signifies the overreaction of the market participants concerning the commodities prices. The future level of commodities' market volatility is directly connected with the underlying's performance (ETF fund) and short-term interest rates; empirical outcome shows that yield associated with the fund and USTBILL appears negative and statistically significant. In our regression specification, we also add one of the control variables, ‘spread,’ it's a difference between two benchmarks of the crude oil traded globally, i.e., WTI and Brent, higher the degree of spread higher is the level of OVX.

One can see that the range of Covid-19 infection in the US through January–April 2020 has impacted the furthermost of the commodities market adversely. The significant positive estimate indicates that there has been a remarkable upsurge of volatility during the first quarter of 2020. Moreover, contagious pandemic related uncertainty of the macroeconomy and equity market does explain the commodities market's sentiment.

Empirical findings presented here yield some policy implications: (i) in order to hedge against the tail events like Covid-19, investors should invest in the volatility product, for example, VIX futures and other put options linked with the underlying index. (ii) Volatility trading —for example, Futures and Options on VIX and F&O related strategies provide the strongest levered and best protection during the drawdown events. (iii) The study's most important findings reveal that crude oil and energy markets exhibit an extreme volatility level; it indicates investors do have limited access to other hedge instruments to protect market adversity except for options. Hence there is a need for more derivatives instruments and volatility products to meet risk aversion in the energy market. Our current study is limited to the number of Covid-19 infection cases and commodity markets. For example, Oxford or other countries' first vaccination news has a specific impact on stock and commodity markets in the west and emerging markets. Exploring the effects of vaccination news on the equity and commodity market can add more to market efficiency literature.

CRediT authorship contribution statement

Imlak Shaikh: Conceptualization, Methodology, Validation, Investigation, Formal analysis, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A.

Commodities index and allied volatility index (Commodity-ETFs linked Options).

Commodity /industry	Underlying ETF/Futures			Volatility index	Description
Crude oil	United States Oil Fund, LP (Ticker - USO)			OVX	Oil ETF Volatility Index
Energy sector	XLE Energy Select Sector SPDR Fund			VXXLE	Energy Sector ETF Volatility Index, US energy firms (SPX500)
Gold	The SPDR Gold Trust			GVZ	Gold ETF Volatility Index
Silver	iShares Silver Trust (SLV)			VXSLV	Silver ETF Volatility Index
Gold miners	GDX ETF			VXGDX	Gold Miners ETF Volatility Index
Commodity-based index:
S&P GSCI		SPXGSCI
WTI crude oil		WTI Futures
Brent crude oil		Brent Futures
Uncertainty and other variables:
Economic policy uncertainty			EPU
Equity market uncertainty			EMU
VIX index			VIX
Infectious Diseases EMV index			IDsMV
SPX index			SPX500
US treasury rates			USTBILL