A comparative analysis of the financialization of commodities during COVID-19 and the global financial crisis using a quantile regression approach.

The paper aims at studying the financialization of commodities during coronavirus pandemic, thereafter referred as COVID19 and comparing the same with global financial crisis, thereafter referred as GFC. The connectedness among energy commodities particularly after 2020 was found strong, the effect is medium in case of metals and least in case of agriculture commodities. The findings proved that the financialization of commodities during COVID 19 was much strong as compared to GFC. An investigation of comparative analysis of financialization in developed countries and developing countries is also made, which indicates that connectedness is strong in developed countries as compared to developing countries. The findings reveal the effects were less significant from 2010 to 2019. Gold has significant effect with stock market during COVID 19 and GFC period, marking it a safe haven asset during crisis. Overall, the findings cast doubt on the hedging properties of energy commodities. The finding also indicates the COVID 19 had a deeper impact as compared to GFC.

Introduction

Many studies have highlighted the pompous increase in the trading of commodities since the last decade has resulted in the sprouting of the capital flow in the commodity market, which is commonly referred as financialization of commodity market futures. Basak and Pavlova (2016), also found that the prices of various commodity futures had surged with the induction of financialization. Investment in the non-energy commodity futures has led to a concurrent increase demand and supply of crude oil (Tang and Xiong, 2012). This resulted in an increase in co-movement within the commodity market (Tang and Xiong, 2012). These studies indicate that the pricing behaviour of commodities have drastically changed since early 2000s with accelerating growth in commodity. The hedging and diversification properties of the commodities have attracted the attention of large investors (Tang and Xiong, 2012). A demand shock is generated when investors move his fund from one type of asset to the other particularly under financial distress, say from equity to commodity (Adams & Glück,2015). A hike in the commodity prices like oil can be decline in prices of a stock (Mensi et al., 2017). Eventually, it led to development of strong correlation between equity and commodities (Bianchi et al., 2020) and equity commodities and commodity futures (Basak & Pavlova, 2016), the presence of hedging funds in equity and commodity futures can be the reason behind this correlation (Buyuksahin and Robe,2014). Commodities being considered as a store of value are a good hedger against inflation and crisis (Mensi et al., 2017). Liu et al., 2020, Bouri et al. (2020), Boako and Alagidede (2016), Chevallier and Ielpo (2013), Boako et al. (2020), Ankrim and Hensel (1993) and Bodie and Rosansky (1980) found the corelation between stock and commodity returns to be negative. This correlation between equity and commodity market is evidence of an increase in the financialization of commodities (Bianchi et al., 2020). According to Wahal and Yavuz (2013), investment pattern and demand shocks are the main driving factors behind this correlation.

With the collapse of Lehman brothers during the GFC 2008 caused clash in stock prices, investors lost confidence on financial market and drove towards commodities (Caballero et al., 2008). This has resulted in the bubble in the oil prices (Masters,2008). Consequently, the prices of various class of commodities consisting of agriculture, metals and energy has surged in 2007–08 (Cheng and Xiong, 2014). Adams and Glück (2015) and Wahal and Yavuz (2013), have explained the equity spillovers to the commodity market during crisis. Onset of GFC caused bidirectional volatility spillover between oil and stock prices (Mensi et al., 2017). Zhang and Broadstock (2020), Zhang (2017), Cheng, and Xiong(2014), Silvennoinen and Thorp (2013) and Basak and Pavlova (2016) urged that financialization in commodity market has increased tremendously with the onset of GFC. Since then, there is a prompt increase in the financialization (Cheng and Xiong, 2014; Zhang, 2017) and interconnectedness in the commodity market (Zhang and Broadstock, 2020). Recently, the outbreak of coronavirus (COVID 19) has caused uncertainty in the financial world. It has triggered the unrivalled series of stock market clashes. During first week of March, the market value of the Standard & Poor (S&P) 500 and MSCI World Index had plunged to 30% and 17.5% respectively, while stock markets in Spain, China, UK, Hong Kong and US dropped by 25.1%,12.1%,21.4%,14.7%and 14.9% (Shehzad et al., 2020). This collapsing of the world financial market was identical to that of global financial crisis, 2008 (Adam ,2020). The impact of COVID 19 was gigantic than any other financial crisis like global financial crisis, SARS outbreak or Spanish flu. Various researchers have explained the impingement of COVID 19 outbreak on the global stock market (Ngwakwe, 2020; Fernandez-Perez et al., 2021; Al-Awadhi et al., 2020; Mazur et al., 2020; Zhang et al., 2020). As a result, investors moved away from equity market towards safe heaven assets. There was a sharp increase in investment in commodities particularly metals (Akhtaruzzaman et al., 2020a, Akhtaruzzaman et al., 2020b and Bouri et al., 2020).

A growing body of literature have examined the co movement between commodities and stock market during COVID 19 (Sharif et al., 2020; Vo and Hung, 2021; Amar et al., 2020). However, the academic literature has so far does not present robust models to compare the co movement between stock market and commodities during COVID 19 with the co movement between both the markets during GFC. Sharif et al. (2020) and Amar et al. (2020), used wavelet approach to study the spillover of pandemic risk on stock market and oil prices. However, till date no one has compared the financialization of commodities during COVID 19 pandemic and global financialization crisis. The paper has also differentiated the financialization of commodities during the times of financial crisis with non-crisis period. In this context, the aim of this paper is to examine co movement between stock market and commodities using a quantile regression approach similar to Mensi et al. (2014). Mensi et al. (2014), had adopted QR to examine the connectedness between BRICS and commodity market. Das et al. (2018), used QR approach to understand the dependence between financial stress and stock, crude oil and gold. Bhatia et al. (2018) used this method to examine the connectedness among the prices of metals. The QR approach explains the impingement of the crisis on stock market and its spill over on commodities. This approach has been adopted to evaluate the similarities and differences between COVID 19 and GFC in terms of financialization. The results pinnacle the safe heaven properties of gold under extreme market. Alternatively, investors can add energy commodities in their portfolio in order to hedge the risk of equity during COVID 19, however, the findings challenge the diversification properties of energy commodities. The findings suggest that the financialization of commodities intensifies during the period of crisis as compared to non-crisis. Further, the effects are more significant during COVID 19 as compared to GFC period. The potency of financialization can be seen more in case of energy commodities as compared to metals and agriculture commodities, which is similar to the findings of Bianchi et al. (2020). This may be due to the fact that equity and energy market respond similarly to a particular shock and therefore, there interaction substantially increased with the passage of time (Zhang and Li, 2016). Further, the financialization of agriculture commodities is weak as they are illiquid and unattractive for capital flows (Ding et al., 2021). The study also explores the connectedness among commodities and stock market in developed countries and developing countries.

Literature review

First, it deserves to mention that a significant part of relative literature has explored the financialization of commodities during COVID 19 (see for instance, Sharif et al., 2020; Salisu et al., 2020; Amar et al., 2020; Adekoya & Oliyide,2021, Le et al., 2020) and the benefits of using commodities in portfolio (Gagnon et al., 2020; Ji et al.,2020).

Sharif et al. (2020), studied the interdependency between oil commodities and stock market during COVID 19 pandemic. The wavelet coherence showed a strong dependency between oil prices and US stocks. The results indicated that COVID 19 had a negative impact on oil prices, US stocks and EPU. Similarly, Salisu et al. (2020), fear of infection can cause a hike in commodity returns due to plunge in the prices of equity. Hence, the study confirmed the existence of a positive relationship between stocks and commodity prices. In the similar way Gagnon et al. (2020), found the ways commodities can aid in improvement of portfolio performance. However, for a proper assessment of portfolio risk, the selection of an appropriate stock index is also important. Thereafter Sifat et al. (2021), found the safe heaven properties of metals especially during crisis. Supporting this Ji et al. (2020), urged the precious metals and agriculture commodities to be safe heaven assets during the crisis period. Umar et al. (2021), highlighted the diversification benefits of commodities. Among commodities, the co-movement among non-precious metals and stocks was found to be the weakest, indicating the hedging properties of nonmetals. As against it, Ma et al. (2021), revealed the connected among energy commodities to be the strongest. Thereafter, Amar et al. (2020), investigated corelation between equity and commodity market during COVID 19 pandemic. The results showed there was a significant impact of COVID 19 on financial market and a strong co movement between financial market and commodities. Similarly, Adekoya & Oliyide (2021), explored the risk spill over among various commodities and financial market during COVID 19 using causality in quantiles. The overall risk spillover was found high during initial four and half months from January to April. The results proved a significant risk spillover between commodities and financial market during COVID19. Le et al. (2020), investigated the dependency between various financial assets like stocks, commodities and currencies during COVID 19. Daily data for 14 stock indices, 23 currencies and 11 commodities consisting of 5 agriculture, 4 energy and 2 metals was collected for the period starting from 1st January 2019 to April 30, 2020. Data was analysed using quantiles and tailed dependency network. The results of tailed dependency indicate a strapping connectedness among commodities and equity against any other class of financial assets during COVID 19 pandemic.

Another significant strand of literature emphasizes on the relationship stock returns and commodities during GFC period. For instance, Silvennoinen and Thorp (2013), studied the correlation between stocks, commodities and GFC crisis. Data for the period starting from May 1990 to July 2009 was collected from Bloomberg and London Metal Exchange (LME). Overall, there were 24 commodities including metal, agriculture and energy. Data was analysed using DSTCC GARCH model. The results indicated the correlation between stocks and commodities was high during the crisis. To support this claim, Liu et al. (2013), studied the impact of oil on other financial markets during GFC. Long run relationship among OVX and other variables was found weak. Similarly, Mensi et al. (2014) explained the impact of oil, gold, policy and other global factors on stock market in BRICS (Brazil, Russia, India, China and South Africa). Data was analysed using quantile regression. The results revealed a significant increase in dependence among commodities and BRICS stock market since onset of GFC. Thereafter, Zhang and Broadstock, 2020, explored the connectedness among various commodities during GFC period. The study also highlighted the co movement among various class of commodities. The test results showed that there was 50% rise in connectedness among commodities after 2008 and effect were long lasting. Recently, Zhang et al. (2020), explained the risk contagion in commodity market during GFC period. VAR was applied to examine the degree of connectedness. The results found the food prices to be least connected with stocks.

Both of the above strands of literature have extensively explored the interdependence of commodities and stock market during crisis and how people can manage their risk portfolio during uncertainty. Understanding the connectedness between commodity market and equity in the times of crisis will be a relevant concern for portfolio managers, researchers, investors and policymakers. This paper differs from these previous studies by comparing the dependence between interdependency between commodities and stock market during COVID 19 with GFC period plus none of them has captured the timeseries data between 2008 and 2021. The study has included a wide range of commodities as compared to previous literature. A comparative analysis of financialization in developed countries and developing countries is also explored.

Empirical model

Coefficient of correlation helps to examine the degree of statistical relation between dependent and independent variables. However, the major limitation of this method is that it only ponders the linear association between dependent and independent variables, with no consideration given to the difference between the connectedness in small and large price movements.

Quantile regression approach overcomes the problem of classical regression model by involving a set of regression curves at different quantiles. As per Koenker and Pin (2005), QR provides more accurate and explicit relation between the variables in comparison to the classical regression model. QR model is used to explore the structure, degree of connectedness among stock returns and commodities (Baur, 2013; Lee and Li, 2012). QR provides a precise image on the average connectedness at lower and upper tail dependence, referred as corpula function.

Let x be a dependent variable on y at the αth quantile, which can be written as follows:where is the distribution function of x as given y, and the interconnectedness x and y at αth quantile is determined by QR coefficient β(α), α ∈ [0, 1].

The coefficients β(α) for a given τ are estimated by minimizing the weighted absolute deviations between x and y:where 1, is the constant indicator function. Buchinsky (1995) has proposed the pair bootstrapping procedure for obtaining the standard errors for the estimated coefficients.

In order to investigate the impact of stock returns and commodities on the quantile function during COVID 19 pandemic, we have used a QR model specified as follows:

The dummy variable M takes the value of 1 when the observation is from crisis period. The sample is divided into three sub samples. M1 takes value 1 for the observation between July 2008 and December 2009, and 0 otherwise. M2 takes value 1 for the observation between January 2009 and February 2020, and 0 otherwise. M3 tnk,j98tf akes value 1 for the observation between March 2020 and May 2021, and 0 otherwise This sub-sample analysis tests the hypothesis that the financialization of commodities during COVID 19 pandemic and the GFC period. The parameters λ(α) and θk(α) capture the marginal effect during crisis period for each quantile α.

Thus, the QR model in Eq. (3) allows one to examine: (a)the characteristics of dependency structure that exists in commodity market: (b) how a particular regressor influences the dependence; and finally, (c) the impact COVID 19 crisis on the dependence structure and the compare it with dependence structure during global financial crisis (d) A comparative analysis of financialization in developed countries with developing countries.

Data

The paper explores the dependence structure among stock returns using the daily data of MSCI, MSCI Emerging Index and twenty-three commodities comprising of four energy commodities, nine metals and ten agriculture commodities excluding livestock. The paper aims at selecting most of the commodities listed on Investing.com, trying to represent a complete picture of the commodity market. Twenty-three commodities comprise of Western Texas Intermediate (WTI) crude oil, heating oil, natural gas, gold, silver, lead, copper, wheat, corn, coffee, Brent oil (brent), London cocoa, London sugar, lumber, nickel, oats, orange juice, platinum, palladium, tin, zinc, US soyabean and US cotton out of which nine are similar of Bianchi et al. (2020), three energy commodities are similar of Zhang et al. (2017), seven commodities are identical to Adams and Glück (2015); Hung el at (2021) and Ding et al. (2021) and four are same as studied by Boako and Alagidede (2016). The data was collected for the period between July 7, 2008 and May 5, 2021. The 3074 observations have been divided into three time period i.e., the global financial crisis 2008–09, 2010–2020 and COVID 19 pandemic 2020. MSCI index includes 23 developed countries namely Canada, USA, Austria, Belgium, Denmark, Finland, France, Germany, Ireland, Israel, Italy, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, Australia, Hong Kong, Japan, New Zealand and Singapore. While MSCI emerging index comprises of 23 developing countries namely Brazil, Chile, Colombia, Mexico, Peru, Czech Republic, Egypt, Greece, Hungary, Kuwait, Poland Qatar, Russia, Saudi Arabia, South Africa, Turkey, United Arab Emirates, China, India, Indonesia, Korea, Malaysia, Philippines, Taiwan and Thailand.

Legend: The data sample of daily data span the period between July 07, 2008 to May 6, 2021. This sample is sub-classified in three parts i.e., 2008–2009 (global financial crisis), 2010–2019 (non-crisis period) and 2020–2021 (Covid- 19 pandemic). The reason behind this time period solely rests on the idea that the paper aims to compare financialization of commodities during two financial crisis (namely GFC and COVID- 19) with that to non-crisis period. The first sub sample covers the GFC period from 2008 to 2009, during which there was a rapid spike in the prices and trading of commodities, some touching historic high. The second sub sample covers a longer period from 2010 to 2019, thereafter referred as non-crisis period. The non-crisis period (2010–2019) is comprised of many turbulent events, such as Crimea annexation (2014), BREXIT (2016) and oil shale revolution (2012). The last sub-sample covers the covid-19 pandemic from 2020 to 2021. This period witnessed a sudden plunge in the prices of global stock market and most of the commodities due to restrictions on travelling and lockdown measures.

Empirical results and discussion

As an illustration, Fig. 1 shows the dynamics of logarithmic returns of stocks and commodity market. We see that the returns of all commodities and stocks are volatile after the 2020 when the novel coronavirus outbroke. The copper, gold, coffee, wheat, cocoa, nickel and zinc display return display a similar volatility pattern.

Fig. 1

Dynamics of logarithmic returns in commodity market.

Table 1 summarizes the descriptive statistics of daily returns on the stocks and commodities under consideration. Standard deviation is a measure of volatility. The descriptive statistics show that the tin has the highest standard deviation, while cotton is the least volatile. The average returns are highest for MSCI emerging index. Positive values for skewness are common for all commodities expect coffee and wheat. All returns show excess kurtosis. Therefore, the Jarque– Bera, Dickey and Fuller (1979) and Phillips and Perron (1988) augmented Dickey and Fuller (1979) and Phillips and Perron (1988) test for normality strongly rejects the normality of the unconditional distributions for all the series.

Table 1

Description of data set.

Authentic name	Abbreviation	Source of data collection	Number of observations
Western Texas Intermediate (WTI) crude oil	WTI	Investing.com	3074
Heating oil	Hto	Investing.com	3074
Natural gas	Ng	Investing.com	3074
Brent oil	Bo	Investing.com	3074
Platinum	Plt	Investing.com	3074
Gold	Gold	Investing.com	3074
Silver	Silver	Investing.com	3074
Copper	Copper	Investing.com	3074
Lead	Lead	Investing.com	3074
Tin	Tin	Investing.com	3074
Zinc	Zinc	Investing.com	3074
Nickel	Nickel	Investing.com	3074
Palladium	Pdm	Investing.com	3074
Corn	Corn	Investing.com	3074
Wheat	Wheat	Investing.com	3074
Coffee	Coffee	Investing.com	3074
London cocoa	Cocoa	Investing.com	3074
London Sugar	Sugar	Investing.com	3074
Lumber	Lumber	Investing.com	3074
Oats	Oats	Investing.com	3074
Orange juice	OJ	Investing.com	3074
US soyabean	Sb	Investing.com	3074
US Cotton	Cotton	Investing.com	3074
MSCI index	MSCI	Investing.com	3074
MSCI emerging market index	MSCI EM	Investing.com	3074

Quantile regressions for developed countries

Table 2 report the estimated results of the quantile regressions in developed countries. A quantile analysis reveals the effects between commodities and stock markets across lower, middle and upper quantiles to be significant. The effects are mostly positive at the upper quantile of the return distribution implying that a significant increase in volatility cause a drop in returns on commodity futures. We have also reported the standard errors denoted by δ and constant term is denoted by α. Fig. 2 , shows the graphical results for MSCI and commodities for all the quantiles during GFC. While Fig. 3 , unfolds the graphical results for MSCI and commodities for all the quantiles during COVID19.

Table 2

Descriptive statistics.

	Mean	Maximum	Minimum	Std Dev	Skewness	Kurtosis	Jarque Bera	ADF	PP	KPSS
Coffee	−0.004	23.55	−19.1	3.15472	−0.11977	7.110216	2171.174	−55.92	−55.92	0.060294
copper	−3.004	0.319	−0.23	0.04783	0.241273	6.749945	1830.943	−58.68	−58.59	0.296854
Corn	−0.018	165.25	−60.12	9.621	1.916399	37.4931	154271.8	−54.84	−54.84	0.364364
Gold	−0.294	140.4	−156.1	16.3278	0.243372	14.35788	16553.29	−57.34	−57.52	0.092638
Hto	0.2279	74.42	−47.92	6.6835	1.039534	16.36088	23418.21	−58.99	−59.12	0.483765
Silver	−0.014	71.92	−34.43	5.38902	1.825929	24.07477	58595.81	−56.98	−56.99	0.08032
Lead	−0.143	348	−204	39.5548	0.266034	8.869944	4449.531	−54.71	−54.79	0.02367
Ng	0.0095	3.255	−0.961	0.1403	5.903974	119.3118	1750627	−10.41	−61.59	1.62527
WTI	0.2439	88.895	−47.724	5.34998	2.854634	51.59825	306680.9	−7.753	−60.63	0.86682
Wheat	−0.085	89	−89.38	11.0504	−0.21444	9.267943	5055.583	−55.73	−55.73	0.08185
MSCI	−0.146	37	−41.76	3.46961	0.006887	35.72982	137208.2	−16.69	−67.28	1.14864
MSCI Emerging index	40.63	57.96	18.26	5.8297	−0.641	4.7121	585.8138	−2.35	−2.321	1.0554
VIX	19.873	82.69	9.14	9.9215	2.3945	10.522	10181.46	−5.043	−5.135	1.3854
Bo	−0.024	8.8	−12.85	1.5661	−0.6886	9.2959	5316.5	−58.74	−58.63	0.1921
Cocoa	−0.011	203	−268	30.159	−0.0617	7.9589	3149.59	−52.87	−52.87	0.0431
Cotton	0.0057	0.01	17.97	−11.26	1.628	−0.0123	17734.48	−46.52	−46.86	0.0555
Lumber	0.4513	108.5	−308	11.592	−7.944	204.92	5251	−15.55	−49.79	0.6251
Nickel	−0.94	3488.5	−2733	346.7	0.0503	10.83	7849	−55.65	−55.65	0.0922
Oats	−0.009	79.75	−97.75	7.123	−1.25	26.62	7224	−52.13	−52.03	0.1273
Oj	−0.007	18.8	−24.65	2.9695	−0.2784	8.034	3283	−50.64	−50.5	0.0431
Pdm	0.8213	460.7	−405.8	25.744	−0.3087	74.07	6467	−16.79	−50.36	0.5951
Plt	−0.246	88.8	−167.4	19.17	−0.803	8.165	3745	−52.36	−52.37	0.1705
Sb	0.1206	274	−311	18.62	−1.041	46.53	2431	−57.82	−57.83	0.1671
Sugar	0.0252	44.7	−93.3	8.994	−1.087	15.09	1931	−55.83	−55.83	0.0791
Tin	2.241	2515	−2856	325.8	−0.7054	12.11	1088	−53.23	−53.24	0.01791
Zinc	0.3631	216.7	−290.7	38.85	−0.1441	5.605	879.7	−57.76	−57.76	0.0369

Fig. 2

Coefficient plot of quantile regression in case of developed countries during GFC period.

Fig. 3

Coefficient plot of quantile regression in case of developed countries during COVID 19 crisis.

Note: β represents the full sample period. β takes the value of 1 when the observation is from the period between January 2008 and December 2009, and 0 otherwise. β takes the value of 1 for observations between January 2010 and February 2020, and 0 otherwise. β takes values of 1 for daily observations from the period between March 2020 and May 2021, and 0 otherwise. The significance is determined based on bootstrapped standard errors. ω denotes the effects of volatility index (VIX) on commodities, it contains values of observation between 2008 and 2021.

Fuller effect

The complete sample of period 2008 to 2021 without any crisis effect is depicted by β. Gold (6.70004) displays the highest average coefficient for all the quantiles and lowest for tin (−8.7500). The curve of stock market with WTI, lumber and nickel is U-shaped and inverse U-shaped dependency structure for corn, brent, orange juice, cotton and heating oil. The dependency is asymmetric from upper tail for lead and from lower tails for energy commodities. In other words, traders of energy commodities appear to behave differently during bullish market. The correlation among stock market and metals commodities is found strong. For energy commodities namely WTI, natural gas and heating oil, the effect is positive and significant for the lower quantiles, while the effects are negative for heating oil, brent and natural gas. Except heating oil, the effects are significant for energy commodities at upper quantiles. The returns for precious metals namely silver, platinum, palladium and gold are negative and significant at lower tail and upper tail, thereby indicating a presence of tail dependency structure. Tail dependency means that the extreme price movements of commodities have significant impact on the stock prices. In case of lead, the effects are positive and insignificant at lower quantiles, while in upper quantiles they are negative and significant. For copper, the effects are negative except the first quantile and significant at lower quantiles, while in upper quantiles the effects are insignificant. In case of lumber, nickel and zinc the values are negative at lower and upper tails. The effects are significant at both the tails for nickel and tin, while for lumber they are insignificant at both the tails. In case of zinc, the effects are significant at upper tail and lower tail is independent. The values for tin are positive at lower tail and upper tail. The returns for corn, coffee wheat, cocoa, sugar and cotton are mostly negative, implying a weak correlation with stock market. The effects are significant at lower and upper tail for corn, coffee, wheat and cotton. However, in case of cocoa and sugar, the effects are insignificant at both tails. In case of oats and soyabean, the values are negative at lower quantiles and positive at upper quantiles. For orange juice, the values are positive at lower quantiles and negative at upper quantiles. The effects are significant at lower quantiles and insignificant at upper quantiles for soyabean and orange juice. In case of oats, the effects are insignificant at lower quantiles and significant at upper quantiles.

GFC period: 2008–2009

GFC period a strong connectedness between commodities and stock returns (Tang and Xiong, 2012). Backtrack of Lehman has resulted in a crash in stock prices, which subsequently influenced investors to shift towards commodities. This eventually created a new spillover among stocks and commodities (Adams and Glück, 2015). A positive value indicates a rise in connectedness among equity and commodity market. The effect of stock returns on commodity market is mostly positive and significant for all quantiles. The results showed that the energy commodities were weakly correlated with stock returns, which support the findings of Bianchi et al. (2020). Metals have a significant effect with stock returns during GFC period, as the plunge in stock prices had increased the demand of precious metals particularly gold. The curve of stock market and gold, coffee, brent, nickel, cotton and platinum is U-shaped and dependency structure is inverse U-shaped for heating oil and wheat. Copper (5.69863) has highest coefficient value across the quantiles. Lead shows an insignificant impact at lower and upper tail, the values are negative at lower tail and positive at upper tail. In case of WTI and natural gas the effects are mostly positive and insignificant for the lower and upper quantiles, there by showing a symmetric relationship. For heating oil, the values are negative at lower and upper tail. The effects are insignificant at lower quantiles and significant at upper quantiles. In case of brent oil, the effects are positive and significant from lower to upper quantiles. For silver, the effect is positive except the first quantile and significant at lower tail, while upper tail is independent. In case of gold the effects are significant from lower to mid quantiles, while upper tail remains independent. The values are negative except first and second quantiles. This exhibits the safe heaven properties of gold in bullish markets. The effects are insignificant at lower and upper tail for case of copper, lumber, tin, zinc and platinum. The values are negative at lower and upper tail for tin and platinum, while they are positive at both the tails for zinc. In case of copper the values are negative at lower tail and positive at upper tail. While for lumber, the values are positive at lower tail and negative at upper tail. The values are mostly negative for coffee, corn and oats, while they are positive for cocoa. In case of cotton and soyabean, the values are positive at lower tail and negative at upper tail. While for orange juice and sugar, the values are negative at lower tail and positive at upper tail The effects are significant from lower to upper quantiles in case of wheat, orange juice and soyabean and for coffee and cotton the effect is significant for lower tail, while upper tail is independent. The lower tail is significant and upper tail is independent for oats and sugar. While for corn and cocoa, the effects are insignificant at both the ends. Agriculture commodities namely coffee, corn and wheat are less connected with stock returns. There is an absence of heterogenous effects during extreme market conditions (Table 5).

Table 4

Estimated results for developing economies.

	q05	q10	q25	q50	q75	q90	q95
Lead
β	0.00685	7.00040	0.00679	0.00241	0.00072	0.00241	−0.0013
β1	0.00081	0.00479	−0.00218	−0.00497	0.00486	0.00310	0.00611
β2	0.00183	0.00416	0.00493	0.00548	0.00488	0.00499	−0.0033
β3	−0.02303	0.00197	−0.00434	0.00303	0.01056	0.01226	−0.0137
ω	0.00268	9.67006	−0.00023	−0.00165	0.00290	0.00259	−0.0037
WTI
β	−0.27230	−0.23800	−0.03515	0.16066	7.51002	0.16066	0.19958
β1	−0.01588	−0.10718	−0.1297	−0.23570	−0.30901	−0.24157	−0.3335
β2	−0.04516	−0.00675	0.26183	0.10593	0.06696	−0.29297	−0.4021
β3	1.36798	1.17881	0.41908	−0.29916	1.60874	−0.61189	−0.5204
ω	−0.06724	−9.46005	−0.03439	0.06780	−0.02470	0.00431	0.36149
Hto
β	0.00556	0.01346	−0.02605	−0.10866	−0.02268	−0.10866	−0.0694
β1	−0.05006	−0.08153	−0.11014	−0.06533	−0.06374	−0.07345	−0.0908
β2	−0.09788	−0.12165	−0.02533	0.00746	0.06582	−0.27786	−0.2925
β3	−0.01108	−0.02734	−0.04647	−0.03624	−0.03633	−0.06796	−0.0565
ω	0.06062	−0.00543	0.02557	−0.01390	0.06754	0.15992	0.06846
Ng
β	−5.14560	−6.78643	−2.71851	−1.90720	−0.65840	−1.90720	−3.9821
β1	−9.11511	−5.87165	−1.63869	4.07295	3.98071	1.87033	14.688
β2	11.5356	7.70099	4.24273	−1.45855	0.16203	3.63636	7.82451
β3	−17.1198	−7.31520	−11.2126	65.3385	−146.636	−80.8573	−70.252
ω	5.38067	5.11140	6.58553	5.93207	3.45460	7.23561	7.57961
Silver
β	−0.00773	−0.03053	0.02419	0.02426	−0.04926	0.02426	−0.0128
β1	−0.03248	0.12829	0.26803	0.28359	0.57657	0.10025	0.07951
β2	−0.03436	−0.03230	−0.29209	0.11919	0.04350	0.09967	0.16109
β3	0.07455	−0.10407	−0.08739	−0.03498	−0.01831	−0.10661	−0.1146
ω	−0.12346	−0.06062	−0.02999	−0.02988	0.00771	0.02284	0.1244
Copper
β	1.32799	2.17319	−0.39114	−4.93900	−3.83234	−4.93900	−5.1211
β1	8.33485	16.6290	9.91849	−4.09766	7.11601	−4.50237	6.33453
β2	−10.1794	0.80537	14.2341	20.8508	13.0100	17.777	15.07
β3	−10.5088	−11.167	−18.3981	−10.4577	−23.1514	−9.53558	−9.0773
ω	6.99487	2.67532	−1.04817	−5.23856	−2.48976	6.92172	−3.921
Gold
β	0.01476	0.01936	2.6003	0.00239	0.01665	0.00385	0.01295
β1	0.00185	−0.01824	−0.09344	−0.10953	−0.10478	0.00399	−0.0074
β2	−0.01038	0.00089	0.02438	−0.06286	−0.05440	−0.10661	−0.0626
β3	−0.00642	0.04933	0.04762	0.03970	0.05986	0.04762	0.05857
ω	0.02434	1.1002	−0.00657	−0.00588	−0.01629	−0.00365	0.00102
Corn
β	0.00162	0.00924	−0.01274	−0.01079	−0.02222	−0.01799	−0.046
β1	0.01873	0.08672	0.079124	0.03451	0.01573	0.06837	0.07219
β2	0.06312	−0.02883	0.03552	0.07648	0.12347	0.03296	0.01759
β3	−0.38300	−0.39996	−0.28784	−0.22712	−0.03595	−0.00695	−0.0202
ω	−0.00783	−0.01716	−0.01964	0.00200	−0.00496	0.08421	0.04963
Coffee
β	0.20662	0.11060	−0.00487	0.00127	0.00697	−0.00062	−0.0026
β1	−0.09089	−0.03165	0.00422	−0.21371	−0.22468	−0.05693	−0.1421
β2	0.18968	−0.01240	−0.10713	−0.05672	0.09830	0.10444	−0.1638
β3	−0.01199	0.12885	−0.23582	−0.07224	0.17545	0.11619	0.03045
ω	−0.00366	−0.00651	−0.0028	0.0245	0.00133	−0.04413	−0.4513
Wheat
β	0.03273	0.04667	0.01064	0.00466	−0.00441	−0.02181	−0.03
β1	−0.01982	−0.01094	0.02300	0.02155	0.00440	0.03577	0.03977
β2	0.03989	0.01270	−0.03004	−0.00424	−0.01533	−0.06265	−0.0641
β3	−0.10737	−0.08880	−0.01515	−0.02619	−0.01399	0.05487	0.05206
ω	−0.00769	−0.00835	−0.00745	−0.01172	−0.03987	−0.04990	0.00308
Bo
β	−0.75434	−0.44927	−0.13406	0.00317	0.26267	0.21437	0.34417
β1	0.24949	−0.26151	−0.32641	−1.77088	−1.70761	−0.31212	−0.25534
β2	0.107007	0.62295	0.40428	0.32253	−0.64739	−0.59447	−0.1468
β3	0.75336	0.68772	−0.34181	0.32533	−0.04025	0.18326	0.19748
ω	0.15829	−0.02808	−0.02856	−0.04551	0.07824	0.31387	0.25723
cocoa
β	−0.01202	−0.01046	−0.00534	0.00070	0.00130	0.00174	−0.00135
β1	−0.00167	−0.00506	−0.01612	−0.00897	0.01081	0.00347	0.00778
β2	0.00444	0.00363	0.01446	0.01345	0.01610	0.02004	−0.00184
β3	−0.00467	−0.00252	0.01543	0.00780	0.02034	0.00780	0.00311
ω	0.00228	0.00097	0.00238	−0.00223	−0.00407	0.00683	0.039667
sugar
β	−0.01181	−0.04733	−0.01306	−0.00517	−0.00115	−0.01550	−0.02604
β1	0.12937	0.07677	0.11088	0.12790	0.04666	0.06884	0.05611
β2	−0.05173	−0.02847	0.04552	0.00409	0.12452	0.03073	0.07896
β3	0.06727	0.05457	−0.05240	−0.04380	−0.05414	−0.03719	−0.02005
ω	0.03858	0.02958	0.03084	0.02721	0.03302	0.06642	0.03315
lumber
β	0.16109	0.11146	0.08593	0.04568	0.02710	0.04880	0.07642
β1	0.03564	−0.02881	−0.00440	−0.13013	−0.00785	0.13650	0.14479
β2	0.06626	0.11916	−0.08138	−0.05339	−0.13306	0.22038	0.23798
β3	−0.36497	−0.25746	−0.15032	−0.04290	−0.58637	0.13796	0.19502
ω	0.00869	0.00205	0.03697	0.02422	−0.01405	−0.06839	−0.20879
nickel
β	−0.00235	−0.00106	0.00020	0.00002	−0.00045	−0.00070	−0.00158
β1	−0.00023	−0.00081	−0.00069	0.00119	−0.00015	0.00052	0.00081
β2	0.00031	0.00120	0.00447	0.00260	0.00368	0.00201	0.00050
β3	0.00285	0.00028	−0.00231	−0.00448	−0.00432	−0.00017	−0.00083
ω	−0.00027	−0.00013	0.00030	−0.00011	−0.00044	−0.00038	−0.00059
oats
β	0.13265	0.08425	0.00070	−0.00367	−0.00367	−0.03175	−0.04758
β1	0.01753	0.09471	0.17701	0.09508	0.02309	0.03359	0.01252
β2	0.03810	−0.11349	−0.18637	0.01191	−0.07484	−0.02405	−0.04421
β3	−0.19345	−0.12897	−0.01387	0.07957	0.08932	0.04694	0.04312
ω	−0.02396	−0.00997	−0.00619	−0.00827	0.02664	0.02586	0.00202
oj
β	−0.00955	−0.06587	−0.03777	−0.03242	−0.03242	0.01476	−0.0405
β1	0.03063	0.21218	0.27989	−0.16279	0.19837	0.01650	0.16782
β2	−0.07714	−0.18327	0.14800	0.15064	0.38994	0.56237	0.69257
β3	−0.28733	−0.23318	−0.45938	−0.31819	−0.39147	−0.36653	−0.25283
ω	0.02678	0.02012	0.01857	0.01213	0.00225	0.21795	−0.0291
tin
β	0.00094	0.00070	0.00018	0.00001	0.00004	0.00054	0.00224
β1	−0.00015	0.00101	0.00018	−0.00017	−0.00104	−0.00033	−0.00162
β2	−0.00063	−0.00123	−0.00233	−0.00166	−0.00524	−0.00347	−0.00107
β3	0.00183	0.00371	0.00595	0.00610	0.00570	0.00090	0.00052
ω	0.00034	0.00047	0.00042	0.00056	−0.00004	0.00033	0.00088
sb
β	−0.02418	−0.00079	0.00483	0.00125	0.00125	0.01530	0.01583
β1	−0.01349	−0.01084	−0.02477	0.00523	−0.00406	−0.01986	−0.02513
β2	0.01873	0.00445	0.00442	−0.01857	−0.06342	0.00470	0.02412
β3	0.06966	0.06703	0.14068	0.08740	−0.02140	−0.04123	−0.0416
ω	0.01240	0.00652	0.01096	0.01503	0.00268	0.02756	0.04932
zinc
β	−0.00190	−0.00061	−0.00647	−0.00114	−0.00114	−0.00239	−0.00215
β1	−0.00898	0.00832	0.01445	0.01105	0.02351	0.01002	0.00782
β2	0.00394	0.00355	−0.02863	−0.00016	0.01943	0.03184	0.00670
β3	−0.01496	0.00061	0.03103	−0.00298	−0.02512	−0.00686	−0.00304
ω	−0.00148	−0.00500	−0.00584	−0.00404	−0.00082	0.01013	0.01082
cotton
β	0.00667	−0.03141	−0.00368	0.07625	0.07625	−0.06416	−0.16103
β1	−0.19556	0.03129	−0.00928	−0.36229	0.47867	0.36729	0.12874
β2	0.27841	−0.30229	−0.31148	−0.64982	−0.23541	−0.08687	−0.46773
β3	0.47609	−0.18754	−0.13455	0.61549	0.20227	0.43908	0.39039
ω	0.08637	0.12817	0.00558	−0.02682	−0.02188	−0.33731	0.12827
plt
β	−0.02068	−0.00498	−0.00458	−0.00716	−0.00716	0.00526	0.00584
β1	−0.01324	−0.01279	−0.05716	−0.07136	−0.04444	−0.03490	−0.01156
β2	−0.01835	−0.00166	0.02862	0.02996	0.00580	−0.03881	−0.03004
β3	0.03393	0.01235	−0.02500	0.02907	0.04656	0.02832	0.03283
ω	−0.00012	0.00194	−0.00885	0.00236	0.00733	0.03328	0.04412
pdm
β	0.01407	0.00469	0.01193	0.00798	0.00693	0.00798	0.01172
β1	0.07511	0.10337	0.09259	0.06314	0.11603	0.02006	−0.00851
β2	0.00960	0.00128	0.02059	−0.00166	−0.00701	−0.00625	−0.01077
β3	−0.09065	−0.05948	−0.0384	−0.01822	0.00202	0.00125	−0.00558
ω	−0.00684	−0.00358	−0.0080	−0.01745	−0.02379	−0.04969	−0.05289
α	0.0401	0.0219	0.0107	0.0066	0.0076	0.0216	0.0427
δ	29.9326	33.5651	37.9933	41.1851	43.8309	46.892	48.947

Non crisis period (2010–2019)

The non-crisis period (2010–2019) is comprised of many turbulent events, such as BREXIT (2016) and oil shale revolution (2014). Stupendous extraction of shale oil and gas, has resulted in drastic reduction of the prices of energy commodities. Thereafter, withdrawal of United Kingdom from European Union in 2016, has caused a shortage in supplies and a hike in agriculture commodity prices (Hubbard et al., 2018). As a result of spillover transmission, there is a price fluctuation in stock and commodity market. Gold is positively correlated with stocks. The curve of stock market and lead, natural gas, coffee, wheat, brent oil, sugar, lumber, tin, zinc and cotton is U-shaped, while the curve of equity and heating oil, oats and palladium is inverse U shaped. As far as agriculture commodities are concerned, they can be used to hedge the risk of equity under normal market conditions. Additionally, in bearish agriculture market, the supply of agriculture generally exceeds the demand, and excess production is mainly digested by exports. Hence, the stock market has no significant impact on the returns of agriculture commodities in bullish market conditions. During the period 2010–2019, copper (2.8801) has the highest value of coefficient across the quantiles. The overall effect is positive for most of the commodities. The effects for heating oil and brent are significant for lower and upper tails. The values are negative at both tails for heating oil, while they are positive for brent oil. The values are mostly positive and effects are significant at lower and upper tails for WTI crude oil. In case of natural gas, the values are negative at both tails and effects are significant at upper tail, while lower tail is independent. For lead, nickel and copper, the effects are significant at upper tail, while lower tail is independent. The values are negative at lower quantiles and positive at upper quantiles for cooper, negative at both quantiles for nickel and positive at both the quantiles for lumber. The returns for silver are negative and insignificant at lower and upper quantiles. In case of gold, tin and platinum, the effects are positive across all quantiles and significant at lower and upper tail. The values are mostly negative and effects are significant at lower and upper tail for palladium and zinc. For lumber, the effects are significant at upper tail and lower tail is independent, while the values are positive at both the tails. The returns for corn, coffee, soyabean and orange juice are mostly positive at lower and upper tail, implying a strong connectedness with the stock market. In case of corn, the effect is significant at lower tail and upper tail is independent. While for coffee, soyabean and orange juice, the effects are significant at upper tail and lower tail is independent. The values for wheat are negative across all the quantiles and the effect is significant at lower tail, while upper tail is independent. The values are positive and significant at lower and upper tail for cotton. In case of cocoa, the effects are insignificant at lower tail and significant at upper tail, while the values are negative at lower tail and positive at upper tail.

COVID 19 pandemic: 2020–2021

COVID 19 witnessed a huge drop in commodities and stock returns, following the lockdown of economic activities and restrictions on travel. Hence, energy commodities especially crude oil was hit by the pandemic and there was huge fluctuation in their prices, (Ding et al., 2021). Which eventually led to volatility spillover in entire financial market due to their interconnectedness (Guo et al., 2021). This might be due to the fact that energy commodities play a crucial role in transportation and economic activities and therefore, they are an indispensable part of financial market (Zhang et al., 2017). Energy commodities are strongly connected with equities during pandemic. Most of the commodities were negatively correlated with stock returns, which is indicative that commodities can be used to hedge the risk of equities. Most of the commodities have insignificant effects both lower as well as upper tail. As far as agriculture commodities are concerned, the spillover effect is weak as compared to metals and energy commodities. The curve of stock market and gold is U-shaped and dependency structure is inverse U-shaped for lead, silver, coffee, cocoa, lumber, oats, soyabean, zinc, platinum and palladium. During the COVID 19 crisis, copper (20.4842) has the highest value for coefficient across the quantile and natural gas (−468.61) has the lowest value. Results for lead are positive and significant for lower quantiles and insignificant for upper quantiles. While the values for all energy commodities other than heating oil namely WTI, natural gas and brent are negative across all quantiles, thereby implying a reserve relation with equity market and insignificant for lower and upper tail showing an asymmetric tailed independency among variables. The results for heating oil are positive at lower quantiles and negative at upper quantiles and effect is significant at lower tail, while upper tail is independent. In case of silver, the results are positive at both the tails and the effect is significant at upper tail, while lower tail is independent. In case of copper, the effects are insignificant at lower and upper tails and the values are mostly negative. The results for gold are negative across all the quantiles and the effects are significant at lower and upper tails. The effect is significant at lower and insignificant at upper tails for nickel, lumber, tin and platinum. The values are positive at lower and upper tail for lumber and nickel, while they are negative at both the tail for platinum. For tin and zinc, the values are positive at lower tail and negative at upper tail. The effects are insignificant at lower and upper tail. In case of palladium, the values are positive and effects are insignificant at lower and upper tail. Coffee, cocoa, orange juice, soyabean and cotton are negatively correlated with equity at lower and upper tails. The effects are significant at lower and upper tails for orange juice and soyabean. In case of coffee, the effects are significant at upper tail and lower tail remains independent, while in case of cocoa and cotton, the effects are significant at lower tail and upper tail is independent. The values are positive at lower and upper tail for wheat and oats. The effect is significant at lower tail and insignificant at upper tail for wheat. This may be particularly due to fact that wheat attracts more liquidity than any other agriculture commodity (Ding et al., 2021). The effects are significant at lower and upper tail for oats and corn. The values for corn are negative at lower tail and positive at upper tail. In case of sugar the values are positive at lower tail and negative at upper tail, while the effects are insignificant at both the tails. Table 4 gives description on Wald test which conforms the heterogeneity across quantiles.

Dependency during COVID19 pandemic versus GFC period

The effects of most of the commodities are negative during COVID 19 indicating that commodities can be used to hedge the risk of equity market as prices are moving in an opposite direction. Connectedness among energy commodities is much stronger during COVID 19 as compared to GFC period. The effects of most of the commodities are insignificant at lower and upper tail during COVID 19. Energy commodities except heating oil are negatively correlated with stock returns during COVID 19, while during GFC period (2008–2010), they are positively correlated with equities except heating oil. The effect of energy commodities is insignificant at both the tails during COVID 19 as compared to non-crisis period. The effect of stock market is insignificant at upper and lower tail for most of the metals and agriculture commodities. There is a significant effect at the left tail of the distribution collate to right tail for metals and agriculture other than silver and coffee during COVID 19 as compared to non-crisis period (2010–2019), implying that price movements have impact on stock returns during bearish market. Soyabean, oats, orange juice and oats exhibit a dependency structure at both the tails. The curve of stock market is inverse U shaped with gold. Most of the commodities are positively correlated with stock returns during non-crisis period. There is a significant effect at the left tail of the distribution collate to right tail. In non-crisis period, coffee, wheat, sugar, lumber, zinc and energy commodities except heating oil pronounce a U-shaped marginal effect, whereas heating oil, oats, nickel and platinum exhibit an inverse U-shaped marginal effect. As against GFC period, where the curve of stock market and gold, coffee, nickel, brent, cotton and platinum is U-shaped and inverse U-shaped dependency structure for heating oil. During GFC period metals expect silver have more significant effect at left tail as compared to right tail, which indicates that stock market has more significant impact on the returns of metals in bearish market conditions. Heating oil and coffee and wheat have more significant effect at left tail as compared to right tail. While in case of GFC period, the values of most of the commodities are positive as against COVID 19 pandemic. All metals expect gold and palladium are positively correlated with equity market. Agriculture commodities except cocoa, oats and orange juice are negatively correlated with equities. Effects are more significant at the left tail of distribution as compared to right tail, which is similar to COVID 19.

Hence, it can be concluded that in general, the interdependency between commodities and stock market is strong during COVID 19 pandemic as compared to GFC period. Further, during the times of financial crisis the effects of equity on commodities are insignificant at lower and upper tail as compared to non-crisis period. Additionally, the effects are significant at left tail of distribution during the times of financial crisis as against non-crisis period.

Quantile regressions for developing countries

Table 3 report the estimated results of the quantile regressions in developing countries. A quantile analysis reveals the effects between commodities and stock markets across lower, middle and upper quantiles to be significant. We have also reported the standard errors denoted by δ and constant term is denoted by α. Fig. 4 , shows the graphical results for MSCI emerging index and commodities for all the quantiles during GFC. While Fig. 5 , unfolds the graphical results for MSCI emerging index and commodities for all the quantiles during COVID19 (see ).

Table 3

Estimated results for developed economies.

	q05	q10	q25	q50	q75	q90	q95
Lead
β	0.00163	4.00130	0.00066	−0.00007	−0.00036	−0.00118	0.00217
β1	−0.00908	−0.00367	−0.00170	−0.00072	6.0003	0.00116	−0.0008
β2	0.02378	0.00110	−0.00187	−0.00030	−0.00107	−0.00485	−0.0289
β3	0.11305	0.01107	−0.01465	0.00726	0.00498	0.07244	0.00355
ω	0.00268	9.0040	−0.00023	−0.00165	0.0029	0.00259	−0.0037
WTI
β	0.06453	0.00193	−0.01431	−0.01145	−1.39002	0.00423	0.03203
β1	0.05202	−0.00487	0.03699	0.01377	0.03971	−0.01276	0.08584
β2	0.40491	0.05333	0.02148	0.01346	−0.00023	0.04130	0.06791
β3	−3.70451	−3.93717	−1.24428	−0.55134	−1.53229	−0.54098	−0.7807
ω	−0.06724	−0.00009	−0.03439	0.06780	−0.02470	0.00431	0.36149
Hto
β	−0.03407	−0.04298	−0.00852	0.00179	−0.00794	−0.03817	0.00092
β1	−0.08706	−0.03957	−0.00804	0.00703	−0.00172	−0.03815	0.07720
β2	−0.01522	−0.18124	−0.16839	−0.05213	−0.10694	−0.20770	−0.1754
β3	−0.00682	0.00201	0.00283	0.00467	−0.00228	−0.01015	−0.0087
ω	0.06062	−0.00543	0.02557	−0.01390	0.06754	0.15992	0.06846
Ng
β	0.49306	1.26315	0.32679	0.09133	−0.02192	−1.65446	−4.0179
β1	−0.66644	−3.01706	−0.42955	0.18987	0.67212	2.70767	1.7851
β2	−1.29610	−1.95884	−0.03303	−0.32854	0.40952	0.09627	−0.2639
β3	−123.57	−468.61	−208.64	−182.51	−141.99	−188.63	101.34
ω	5.3806	5.1114	6.5855	5.9320	3.45460	7.2356	7.5796
Silver
β	−0.10414	−0.04058	−0.00744	0.00166	−0.00173	0.02252	0.04719
β1	−0.06097	0.02488	0.04172	0.05158	0.03539	0.06551	0.04358
β2	0.13383	−0.04138	−0.02308	−0.04038	−0.00347	−0.09268	−0.4463
β3	0.45626	−0.02859	0.06578	0.03748	0.24639	0.32218	0.42304
ω	−0.12346	−0.06062	−0.02999	−0.02988	0.00771	0.02284	0.1244
Copper
β	3.42527	−0.25938	−2.56405	−2.36644	−2.41118	−2.1699	−7.1002
β1	−0.23483	−1.06647	1.92217	0.92044	3.89247	5.69863	5.03426
β2	−43.358	−13.9095	2.59555	2.88001	3.06551	−8.59948	4.94068
β3	−21.4	11.040	4.47134	−6.14024	−2.18637	−22.5673	20.4842
ω	6.99487	2.6753	−1.04817	−5.23856	−2.48976	6.92172	−3.921
Gold
β	0.02391	0.00165	6.70004	−0.00268	−0.00218	−0.01370	−0.0167
β1	0.01063	0.00807	−0.01174	−0.01859	−0.02560	−0.02260	−0.0041
β2	0.00998	0.03536	0.04199	0.03531	0.04782	0.07030	0.13047
β3	−0.17521	−0.05051	−0.03188	−0.0429	−0.08918	−0.07851	−0.5936
ω	0.02434	1.09002	−0.00657	−0.00588	−0.01629	−0.00365	0.00102
Corn
β	−0.02845	−0.02352	−0.00308	0.00017	−0.00258	−0.00378	−0.007
β1	−0.01077	0.02091	−0.00753	−0.00903	−0.00286	−0.0093	0.02269
β2	0.18150	0.05606	−0.0006	0.01851	0.00042	0.07055	0.0076
β3	−0.46080	−0.23394	−0.09296	−0.02065	0.029654	0.01955	−0.0616
ω	−0.00783	−0.01716	−0.01964	0.00200	−0.00496	0.08421	0.04963
Coffee
β	−0.03885	−0.02941	−0.0006	−0.00574	0.00768	0.01685	0.03541
β1	0.08606	−0.00335	−0.0301	−0.02227	−0.00962	−0.03541	0.05037
β2	0.01066	0.21648	0.04753	0.03611	0.06906	0.16946	0.53095
β3	−0.64534	−0.34177	−0.35742	−0.09598	−0.19794	−0.46792	−0.6036
ω	−0.00366	−0.00651	−0.0028	0.0245	0.00133	−0.04413	−0.4513
Wheat
β	0.00945	−0.00758	−0.00026	0.00018	−0.00160	−0.00618	0.01072
β1	−0.06676	−0.00331	−0.00048	0.00141	0.00535	0.00146	−0.0217
β2	−0.11813	−0.07595	−0.01854	−0.00417	−0.00349	−0.02623	−0.042
β3	0.35853	0.17713	0.15932	0.05049	0.10205	−0.00251	0.07358
ω	−0.00769	−0.008351	−0.00745	−0.01172	−0.03987	−0.04990	0.00308
Bo
β	−0.34505	−0.3288	−0.10987	−0.05596	−0.08769	−0.25549	−0.30443
β1	0.45648	0.22189	0.09092	−0.01984	0.16353	0.27473	0.57991
β2	1.73443	1.84569	1.17395	0.46472	0.75899	0.98294	0.78451
β3	−0.6823	−1.85057	−1.66108	−1.28577	−1.06135	−1.07026	−0.35452
ω	−0.15829	−0.02808	−0.02856	−0.04551	0.07824	0.31387	0.25723
cocoa
β	−0.00018	0.00107	−0.00053	0.00022	0.00001	−0.00274	−0.00768
β1	0.01468	0.00567	0.00129	0.00067	−0.00076	0.00253	0.01126
β2	0.016334	−0.00060	−0.00255	−0.00011	0.00232	0.01735	0.03898
β3	−0.07723	−0.05468	−0.01923	−0.00742	−0.01819	−0.04603	−0.02212
ω	0.00228	0.00097	0.00238	−0.00223	−0.00407	0.00683	0.03966
sugar
β	0.01944	−0.00048	−0.00032	−0.00087	0.00220	−0.01168	−0.00236
β1	−0.01769	−0.02337	−0.00173	−0.00306	0.00393	−0.00947	0.00986
β2	−0.07356	−0.1902	−0.04050	−0.00380	−0.00327	0.02501	0.02709
β3	−0.04387	0.09847	0.06246	0.07442	−0.02511	−0.00739	0.44116
ω	0.03858	0.02958	0.03084	0.02721	0.03302	0.06642	0.03315
lumber
β	−0.02724	−0.03524	−0.01915	−0.00553	−0.00330	−0.00707	0.00588
β1	0.09864	0.03239	0.00669	−0.01133	−0.00373	0.00472	−0.08875
β2	0.01989	0.18744	0.17853	0.06591	0.11329	0.20308	0.21161
β3	−0.04388	1.20163	0.29159	−0.06686	0.21113	−0.02507	0.43696
ω	0.00869	0.00205	0.03697	0.02422	−0.01405	−0.06839	−0.20879
nickel
β	−0.00089	−0.00035	0.00003	−0.00002	−0.00003	−0.00025	−0.00132
β1	−0.00096	0.00024	0.00003	−0.00001	−0.00011	0.00079	0.00084
β2	−0.00079	−0.00204	−0.00076	0.00014	0.00039	−0.00004	−0.00272
β3	0.00036	0.00054	0.00281	0.00359	0.00041	0.00548	0.00514
ω	−0.00027	−0.00013	0.00030	−0.00011	−0.00044	−0.00038	−0.00059
oats
β	0.00089	−0.00167	−0.00157	−0.00125	−0.00034	0.01457	0.03025
β1	−0.06184	−0.02250	−0.02122	−0.04669	−0.04523	−0.05523	−0.05503
β2	−0.08480	−0.03847	0.02120	0.03510	0.00154	−0.13803	−0.22915
β3	0.30781	0.27897	0.10460	0.08498	−0.05176	0.25848	0.42268
ω	−0.02396	−0.00997	−0.00619	−0.00827	0.02664	0.02586	0.00202
oj
β	0.03265	0.00331	−0.00063	0.00052	−0.00927	−0.00969	0.00222
β1	−0.08927	−0.04716	−0.03923	−0.00606	0.03747	0.04504	0.15114
β2	−0.04167	0.18133	0.08192	0.04754	0.08988	0.25204	0.35140
β3	−0.30063	−0.10924	0.06275	−0.35046	−0.43970	−0.31307	0.44783
ω	0.02678	0.02012	0.01857	0.01213	0.00225	0.21795	−0.0291
tin
β	0.00021	0.00008	−0.00011	−0.00006	−8.7500	0.00025	0.00062
β1	−0.00070	−0.00027	−0.00004	−0.00023	−0.00006	−0.00119	−0.00207
β2	0.00104	0.00176	0.00029	0.00009	0.00001	0.00056	0.00363
β3	−0.00253	0.00054	0.00142	0.00079	−0.00132	−0.00703	−0.0101
ω	0.00034	0.00047	0.00042	0.00056	−0.00004	0.00033	0.00088
Sb
β	−0.01675	−0.00914	−0.00077	0.00015	0.00075	0.00195	−0.00577
β1	0.02388	0.01611	0.00135	0.00171	0.00010	−0.00329	−0.01096
β2	0.04190	0.01861	0.00929	0.00039	0.00279	0.00482	−0.00544
β3	−0.05044	−0.10927	0.00474	−0.00007	−0.00363	−0.06089	−0.13616
ω	0.01240	0.00652	0.01096	0.01503	0.00268	0.02756	0.04932
zinc
β	−0.00040	−0.00034	−0.00170	−0.00147	−0.00208	−0.00342	−0.0004
β1	0.01250	0.00600	0.00521	0.00369	0.00587	0.00358	0.00905
β2	−0.03200	−0.01424	0.00402	−0.00298	−0.00263	0.00598	0.02919
β3	0.00578	0.03131	0.00214	−0.00115	0.00029	−0.03418	−0.02392
ω	−0.00148	−0.00500	−0.00584	−0.00404	−0.00082	0.01013	0.01082
cotton
β	−0.25993	−0.10555	−0.00143	−0.00767	−0.01367	−0.06773	−0.09147
β1	0.24475	0.07472	0.01785	−0.00788	−0.03900	−0.02111	0.24458
β2	0.36059	0.50536	0.26576	0.06065	0.16492	0.53114	0.91295
β3	−0.50616	−0.64958	−0.96678	0.00363	−0.76000	−0.96334	0.13745
ω	0.08637	0.12817	0.00558	−0.02682	−0.02188	−0.33731	0.12827
plt
β	0.00490	0.00474	−0.00037	−0.00337	−0.00393	−0.00808	−0.00262
β1	0.00503	−0.00040	−0.00494	−0.00390	−0.01391	−0.00150	0.02214
β2	0.04114	0.03287	0.01508	0.01604	0.03406	0.04631	0.09009
β3	−0.07284	−0.06242	−0.03867	−0.00092	−0.06687	−0.06604	−0.13944
ω	−0.00012	0.00194	−0.00885	0.00236	0.00733	0.03328	0.04412
pdm
β	−0.00651	−0.01453	−0.01211	−0.00594	−0.00719	−0.00508	−0.00747
β1	0.01603	0.01771	0.01765	0.01861	0.01727	0.02939	0.04252
β2	−0.04424	−0.02940	−0.01359	0.00113	−0.01005	−0.02776	−0.02934
β3	0.01986	0.03699	0.01624	−0.00740	0.02586	0.03964	0.01702
ω	−0.00684	−0.00358	−0.00808	−0.01745	−0.02379	−0.04969	−0.05289
α	0.0412	0.0347	0.015	0.0095	0.0136	0.0243	0.0427
δ	−4.0775	−2.1213	−0.69815	−0.08757	0.49087	1.56198	3.802271

Fig. 4

Coefficient plot of quantile regression in case of developing countries during GFC period.

Fig. 5

Coefficient plot of quantile regression in case of developing countries during COVID 19.

Note: β represents the full sample period. β takes the value of 1 when the observation is from the period between January 2008 and December 2009, and 0 otherwise. β takes the value of 1 for observations between January 2010 and February 2020, and 0 otherwise. β takes values of 1 for daily observations from the period between March 2020 and May 2021, and 0 otherwise. The columns labelled Q 0.05 to Q 0.95 report quantile regression estimates for the corresponding quantiles obtained with Eq. (3). The significance is determined based on bootstrapped standard errors. The parameters in the variance equation are not shown for the sake of clarity but are available upon request. ω denotes the effects of volatility index (VIX) on commodities, it contains values of observation between 2008 and 2021.

Emerging economies are cardinal producers of commodities (Adams and Glück, 2015). In fact for countries, they are a major source of export (De Boyrie et al., 2018). For instance, the growth of Chinese economy has substantial impact on the increasing commodity prices (De Boyrie et al., 2018). The complete sample of period 2008 to 2021 without any crisis effect is depicted by β. Copper (19.74) has the highest values for average coefficient for all the quantiles and natural gas (−4.712) has the lowest values. The curve of stock market and tin, platinum and palladium is U-shaped and dependency structure is inverse U shaped for silver, cotton and sugar. The structure of dependency is more asymmetrically centred towards the right tail for heating oil. The correlation among stock market and energy commodities is found weak. This may be due to the fact that energy market in these countries is not fully developed. For lead, the returns are negative at lower tail and positive at upper tail, while the effects are insignificant for lower and upper tail, implying a tailed independency in lower and upper quantiles. In case of WTI and heating oil, the returns are mostly positive and insignificant for lower quantile and upper tails. For brent, the values are negative at lower tail and positive at upper tail, while the effects are significant at lower tail and upper tail is independent. In case of natural gas, the values are negative across all the quantiles and effects are significant at upper tail, while lower tail is independent. The returns are positive across all the quantiles for gold, lumber, tin and palladium. The effects are significant at upper tail and lower tail is independent for platinum and palladium. For copper, lumber, nickel, tin and zinc, the effects are significant at lower and upper tail, thereby indicating a strong connectedness with stock market. The values for platinum and silver are negative at lower tail and positive at upper tail. The effects are significant at lower tail and upper tail is independent for silver and gold. For copper the values are positive at lower quantiles and negative at upper quantiles. The values are negative at lower and upper tail for zinc and nickel. Returns for corn, coffee, oats and wheat are positive at lower quantiles and negative at upper quantiles. The effects are significant at lower tail and insignificant at upper tail for coffee and cocoa. For wheat, sugar and cotton, the effects are insignificant at lower tail and significant at upper tail. The values for sugar and cotton are negative at lower and upper tail. The effects for oats and soyabean are significant at lower and upper tail. For soyabean and cocoa, the values are negative at lower quantiles and positive at upper quantiles. The effects for corn and orange juice are insignificant at lower and upper tail.

A positive value indicates a rise in connectedness among equity and commodity market. The effect of stock returns on the commodity market is mostly positive and significant for all quantiles. The values for energy commodities other than heating oil were negative and effects were more significant at left tail as compared to right tail, which is an indicative that energy commodities in developing countries can hedge the risk of stock market in bearish market conditions as the integration of stock market and energy commodities has increased since the onset of GFC (Zhang et al., 2017). The curve of stock market and lead, lumber, gold, cotton, orange juice and platinum is U-shaped and the dependency structure is inverse U-shaped for oats, soyabean and nickel. Copper (16.6290) has the highest value of coefficient across the quantiles. In case of lead, the effects are mostly positive and insignificant at both upper and lower tail. The values for energy commodities other than natural gas are negative at lower and upper tail. The effects are significant at lower tail and insignificant at upper tail for WTI and heating oil. For natural gas, the effects are significant at lower tail and insignificant at upper tail, while the values are negative at lower quantiles and positive at upper quantiles. In case of brent, the effects are significant at lower and upper tail. In case of silver, zinc, palladium, tin and copper, the values are mostly positive at lower and upper quantiles, which is an indicative that metals can be used to hedge the risk of equity during GFC in developing countries particularly India and China where precious metals are consumed in form of jewellery (Mensi et al.,2014), the plunge in stock prices had increased the demand of precious metals particularly gold. The effects are significant at lower tail, while upper tail is independent for silver, gold and zinc. While for lumber, nickel, platinum and palladium, the effects are significant at upper tail and lower tail is independent. The values are negative at lower tail and positive at upper tail for lumber and nickel. Gold and platinum are negatively correlated with equity at lower and upper tail. In case of copper and tin, the effects are insignificant at lower and upper tail. A different picture emerges for agricultural commodities. For corn, sugar and orange juice, the values are mostly positive at lower and upper quantiles. The effects are significant at upper tail and lower tail is independent for corn and cotton. While for oats, coffee and orange juice, the effects are significant at lower tail and upper tail is independent. The values for coffee and soyabean are mostly negative at lower and upper quantiles. In case of soyabean, cocoa and sugar, the effects are insignificant at lower and upper tail. The values of cotton, oats, wheat and cocoa are negative at lower tail and positive at upper tail. In case of wheat, the effect is significant at lower and upper tail. There is an absence of heterogenous effects during extreme market conditions (Table 4).

The non-crisis period (2010–2019) is comprised of many turbulent events, such as Crimea annexation (2014). Annexation of Crimea by Russian federation in 2014 has caused a hike in commodity prices. The curve of stock market and natural gas, gold lumber and soyabean is U-shaped and the dependency structure is inverse U-shaped for lead, WTI crude oil, heating oil, copper, corn and platinum. Precious metals and agriculture commodities other than corn are negatively correlated with stock market, Dependency structure of metals has witnessed a change since most of developing countries have marked a shift from industrial based economies to service-based economies (Bianchi et al., 2020). During the period 2010–2019, the average coefficients across quantiles are highest for copper (20.8508). The effect for lead is positive at lower and upper tail. The effects are significant at lower tail, while upper tail is independent. In case of energy commodities other than natural gas, effects are significant at upper tail, while lower tail is independent. The values are positive at lower tail and negative at upper tail for brent, while for WTI and heating oil they are negative both the tails. The values for natural gas are positive and significant at lower and upper tail. The effects for silver, lumber, nickel, tin and palladium are significant for lower and upper tail. The values are negative at lower quantiles and positive at upper quantiles for silver and palladium. For lumber and nickel, the values are positive at lower and upper tail. While in case of gold, the values are positive at lower quantiles and negative at upper quantiles. The effects are significant at lower tail and upper tail is independent for gold, copper and zinc. For copper and zinc, the values are positive at lower and upper tail. In case of platinum, the effects are insignificant at lower and upper tail. The values are negative at lower and upper tail for platinum and tin. The returns for corn, soyabean, orange juice and cocoa are mostly positive at lower and upper quantiles. The effects are significant at upper tail, while lower tail is independent for corn, soyabean and orange juice. For cocoa, oats, sugar and cotton, the effects are significant at lower tail and insignificant at upper tail. The values are negative at lower and upper tail for oats and cotton. In case of sugar and coffee, the values are negative at lower quantiles and positive at upper quantiles. The effects are insignificant at lower and upper tail for coffee. In case of wheat, the values are positive at lower tail and negative at upper tail, while the effects are significant at both the tails.

COVID 19 pandemic has resulted in drastic drop in prices of stocks and commodities, particularly energy commodities (Jia et al., 2021) and their interaction with financial market is more prominent during financial crisis (Zhang, 2017). Silver, lead, coffee, brent oil, cocoa, oats, soyabean, zinc, platinum and palladium pronounce a U-shaped marginal effect. While the curve of stock market and gold is inverse U-shaped. The COVID 19 resulted in fluctuation in prices of agriculture commodities due to restriction in transportation and production, thereby led to food insecurities (Hung, 2021). During the COVID 19 crisis, the average coefficients across quantiles are highest for natural gas (65.3385). Returns for all energy commodities other than heating oil are negative at lower and upper tail, while the effects are insignificant at both the tails, showing an asymmetric tailed independency among variables. The values for heating oil are positive at lower tail and negative at upper tail, while the effects are significant at lower tail and insignificant for upper tail. Results for lead, silver, lumber, nickel and palladium are positive at lower and upper tail. The effects are significant at lower tail and upper tail is independent for lead, lumber, nickel, tin and platinum. The values for platinum, copper and gold are negative across all the quantiles. For gold and copper, the effects are significant at lower and upper tail. Whereas, in case of palladium, zinc and copper the effects are insignificant at both the tails. The values for zinc, copper and tin are positive at lower tail and negative at upper tail. For corn, the values are negative at lower quantiles and positive for upper quantiles. The effects are significant at lower and upper tail for corn, oats, orange juice and soyabean. The values are positive at lower and upper quantiles for oats and wheat. Whereas, for coffee, cocoa, orange juice, soyabean and cotton, the values are negative at lower and upper tail. The effects for cotton, cocoa and wheat are significant the lower tail and upper tail is independent. For coffee, the effects are insignificant at lower tail and significant at upper tail. In case of sugar the values are positive at lower tail and negative at upper tail, while the effects are insignificant at both the tails. Table 5 gives description on Wald test which conforms the heterogeneity across quantiles.

Energy commodities expect heating oil are negatively connected with stock returns during GFC period, whereas energy commodities except brent are negatively connected with equities at upper tail during COVID 19. The effects of non-precious metals other than tin are significant at lower and upper tail during COVID 19. While during non-crisis period (2010–2019), non-precious metals other than zinc have significant effect with equities at lower and upper tail. The effects are more significant at left tail as compared to right tail during COVID 19, which is similar to non-crisis period (2010–2019). Gold is positively correlated to stock market during COVID 19, while it is negatively correlated to equity during GFC. During GFC also, the effects are more significant at left tail as against the right tail. Metals other than tin, gold, lead and platinum are negatively correlated with stock market. Stock market have significant effects in non-precious metals other than tin and lead at lower and upper tail during COVID 19. WTI, brent, cocoa, orange juice, palladium and copper show a tailed dependency in lower and upper quantiles. In short, most of the commodities showed tail dependency during COVID 19. Gold, corn and coffee exhibit independency structure at lower tail and upper tail. In non-crisis period, natural gas, gold, lumber and soyabean pronounces a U-shaped marginal effect and Lead, WTI, heating oil, copper, platinum and corn exhibits an inverse U-shaped marginal effect. While during COVID 19 pandemic cocoa and tin, pronounce a U-shaped marginal effect and natural gas, coffee, brent and cotton exhibits an inverse U-shaped marginal effect. As against GFC period, where the curve of stock returns and lead, gold, lumber, orange juice, cotton and platinum is U shaped and dependency structure is inverse U-shaped for soyabean, oats, nickel and coffee. During GFC period metals expect silver are negatively correlated and have more significant effect with stock returns, as compared to non-crisis period. In case of GFC period, the values of most of the commodities are negative, which is similar COVID 19. During GFC period, agriculture commodities other than coffee, cocoa and soyabean are positively correlated with equities. While precious metals other than silver and palladium are negatively correlated with the stock market. However, most of the commodities have more significant effect at left tail as compared to right tail, depicting that price movement in stock market impacts commodities return under normal market conditions. Non-precious metals other than lumber and nickel, reveals an independency structure at right tail. Lead, cocoa, sugar and tin, shows independency structure at both the ends. While brent and wheat exhibits a tail dependency structure at lower and upper tail.

Hence, it can be concluded that in general, the interdependency between commodities and stock market was strong during COVID 19 pandemic as compared to GFC period. The relationship between gold and stock return is negative during, while it is positive during COVID 19 pandemic. Most of the metals were negatively corelated with equity during GFC and COVID 19. Further, during COVID 19, equities have significant effect on most of the commodities (particularly non precious metals) at lower and upper tail. An inverse relation among equities and commodities during COVID 19 indicates that commodities can hedge the risk of stock returns.

Dependency structure in developed countries versus dependency structure in developing countries

The values of most of the commodities are negative for MSCI index and MSCI emerging index during GFC and COVID 19. Gold is positively correlated to MSCI emerging market, while it is negatively correlated to MSCI during COVID 19. Energy commodities during COVID 19 pandemic have more significant effect with stock returns in developed countries as compared to developing countries. The reason behind it could be that the energy markets in developing countries are not fully develop. Most of the energy commodities are negatively correlated with MSCI emerging index during GFC and COVID 19. During GFC period, agriculture commodities are mostly negatively correlated with MSCI index, whereas they are positively correlated with MSCI emerging index. Most of the agriculture commodities are negatively correlated with MSCI index as well as MSCI emerging index during COVID 19. Further, during COVID 19, MSCI emerging index have significant effect on most of the commodities (particularly non precious metals) at lower and upper tail. Specifically, MSCI emerging index have significant effect on non-precious metals during GFC and COVID 19. While in case of MSCI index, the effects are insignificant at both the tails during COVID 19. In general, the connectedness between commodities and stock market is weak in developing countries as compared to developed countries during COVID 19 pandemic and GFC period.

Impact of volatility on commodities

Volatility index (VIX) is used to measure the fluctuation in the prices of stocks and commodities (Fernandes et al., 2014). In other words, it shows sensitivity of a stock or commodity. A significant effect between VIX and commodities implies that changes in market sentiment has a limited impact on the commodity prices and a negative relationship between them conveys that a change in market sentiment has caused the commodities prices to move downwards. VIX has prominent impact on the price movement of oil and metals (Sari et al., 2011). the average coefficients across quantiles are highest for lead (9.67006). Results for lead are mostly positive and significant for upper tail, while lower tail is independent. The effects for WTI crude oil and natural gas are significant for upper quantiles, while lower tail is independent. The values are positive at lower and upper quantiles for heating oil and natural gas, while for WTI crude oil and brent oil, the values are negative at lower tail and positive at upper tail. The effects are insignificant at lower and upper tail for heating oil, showing an asymmetric tailed independency. In case of brent oil, the effects significant for upper tail and insignificant for lower tail. Results for heating oil are negative effects are significant from lower tail, while upper tail is independent. Results for silver, zinc, and platinum are negative at lower quantiles and positive at upper quantiles. The effects are significant at upper tail and lower tail is independent for silver, tin, lumber, platinum and palladium. The values for tin are positive at lower and upper tail, while they are negative at both the tails palladium and nickel. For copper, the effects are significant at lower tail and insignificant at upper tail. The values are positive at lower tail and negative at upper tail for copper, gold and lumber. In case of gold and nickel, the effects are insignificant at lower and upper tail, while they are significant at both the tails for zinc. These findings are similar to findings of Bilgin et al. (2018). In case of coffee, the values are mostly negative and the effect is significant at upper tail, while lower tail is independent. The values are negative at lower quantiles and positive at upper quantiles for corn and oats. The effects are significant at upper tail and lower tail is insignificant for oats, soyabean and cocoa. The values are mostly positive across all quantiles for soyabean, orange juice, sugar and cocoa. The effects are insignificant at both the tails for corn, wheat, sugar, oats and cotton. The values for wheat are negative across all the quantiles, other than the last quantile. In case of cotton, the values are positive at lower tail and negative at upper tail. The effects for orange juice are significant at upper tail and lower tail is independent.

Conclusion

The paper aims at studying the impact of financialization of commodities during COVID 19 and comparing the same with GFC period. Quantile regression approach was used to study the effects of commodity markets' financialization since the onset of financial crisis and extricate the structure connectedness among commodities and stock returns. The empirical evidence for the daily data from July 7, 2008 and May 5, 2021 reveals asymmetric dependence structure between the commodities and the global stock market similar to Mensi et al. (2014). The effects during COVID19 are relatively more significant than GFC period. During COVID 19, equity has insignificant effect on most of the commodities at lower and upper tail. This effect is more prominent in developed countries. However, Stock returns have significant effect on non-precious metals in developing countries. Most of the commodities were positively correlated with stock returns during non-crisis period. Energy commodities are mostly negatively correlated with stock return in developed and developing countries during GFC and COVID 19. However, they are strongly connected with stock market particularly in developed countries, which questions the hedging properties of energy commodities. The main reason behind it is that market variables have a considerable impact on the prices of energy commodities particularly crude oil as they are inevitable part of global economy (Zhang, 2017). Hence, this market attracts more capital flow than metals and agriculture commodities. Investors are advised to study the connectedness of financial market prior to investment, particularly during extreme market conditions. Gold exhibited its safe haven property during COVID 19 and GFC period as it is weakly connected with stock returns, which is similar to the findings of Lahiani et al. (2021) and Ding et al. (2021). Stock returns are highly connected with energy commodities (WTI crude oil, Heating oil, brent oil and Natural gas) which is consistent with the findings of Bianchi et al. (2020), moderately with the metals (lead, gold, silver and copper) and magnitude is lowest for the agriculture commodities (corn, coffee and wheat). The reason behind it could be that most of the agriculture commodities like corn, coffee are illiquid and unattractive to capital fund (Ding et al., 2021). Connectedness among commodities and stock market is stronger in developed countries as compared to developing countries during COVID19 and GFC period, which is similar to the findings of (de Boyrie and Pavlova, 2018). This reason behind it could be that the market contagion is developing economies is low as compared to developed economies. The outcomes of the study can be beneficial for stakeholders and policymaker to frame decision related to portfolio allocations. It will also aid in facilitating optimization of portfolio risk. Finally, this study calls for future research. The researchers can evaluate the co-movement of stocks and each sub-sector of the commodity market. Further, researchers can also investigate the impingement of Russia-Ukraine war on the financialization of commodities.

Author statement

The author confirms sole responsibility for the following: study conception and design, data collection, analysis and interpretation of results, and manuscript preparation.

I, the undersigned declare that this manuscript is original, has not been published before and is not currently being considered for publication elsewhere.

I confirm that there is no other person or persons who satisfied the criteria for authorship but is not listed and I am the sole author of this manuscript.

In this manuscript, Aarzoo Sharma has developed the theoretical formalism, framed the methodology, performed the analytic calculations and contributed to the final version of the manuscript.

Table 5

Heterogeneity tests (Wald tests) for equality of slopes for developed and developing countries.

	Developed countries								Developing countries
	COVID 19 pandemic				GFC Period				COVID 19 pandemic				GFC Period
	Q 0.05	p	Q .95	p	Q0.05	p	Q .95	p	Q.05	p	Q.95	p	Q.05	p	Q.95	p
Lead	−1.5	−0.1	−0.1	−0	1.25	0.02	0.11	0	0.61	0	0.64	0.02	0.16	0	−0.66	−0
WTI	1.76	3.73	0.57	0.75	−0.3	−0.1	−0.6	−0.2	−2	−1.6	0.29	0.6	0.06	0.03	0.06	0.03
Hto	−0.2	−0	−0.3	−0	−0.9	−0.1	0.74	0.08	−0.1	−0	−1.32	−0.1	−0.4	−0.1	−0.81	−0.1
Ng	0.41	124	−0.3	−102	0.09	1.1	−0.4	−3.7	0.08	12	0.45	69	0.86	19.2	−1.84	−26
Silver	−1.5	−0.5	−1.2	−0.4	0.69	0.1	−0	−0	0.88	−0	1.05	0.13	0.17	0.05	−0.23	−0.1
Copper	0.48	18.6	−0.5	−22	0.14	2.25	−0.5	−9.9	0.22	8.4	0.85	8.84	−0.9	−15	−0.71	−14
Gold	1.99	0.19	0.93	0.09	−0.4	−0	−0.9	−0	0.15	0	−1.69	−0.1	0.24	0.01	0.32	0.02
Corn	3.26	0.46	0.33	0.06	0.19	0.01	−1.1	−0	1.99	0.4	0.36	0.02	−0.2	−0	−1.97	−0.1
Coffee	0.82	0.63	1.47	0.64	−0.9	−0.2	−0.7	−0.1	0.85	0.1	−0.04	−0	0.35	0.09	0.41	0.09
Wheat	−2.2	−0.4	−0.5	−0.1	2.39	0.13	0.65	0.04	1.2	0.2	−1.28	−0.1	0.63	0.03	−1.45	−0.1
Bo	0.65	0.58	0.04	0.06	−2	−0.9	−2.3	−1.2	−1.4	−1.5	0.988	−0	−0.7	−0.4	0.5	0.55
Cocoa	1.94	0.08	0.49	0.02	−1.4	−0	−1.3	−0	−0.1	−0	−0.17	−0	−0.2	−0	−0.79	−0
Sugar	0.19	0.05	−2	−0.4	0.36	0.03	−0.5	−0	−0.9	−0.2	0.29	0.02	−2	−0.2	−1	−0.1
Lumber	−1.3	−1.1	−0.9	−0.5	−1.3	−0.1	0.79	0.09	1.74	0.5	−0.27	−0.2	−0.1	−0	−0.46	−0.1
Nickel	−0.1	−0	−0.5	−0	0.16	0	−0.8	−0	−0.6	−0	0.43	0	−0	−0	−0.94	−0
Oats	−1.4	−0.3	−1.6	−0.4	1.32	0.09	0.99	0.06	1.87	0.3	−0.49	−0.1	0.23	0.03	−0.61	−0.1
Oj	0.48	0.34	−0.7	−0.4	0.8	0.15	−1.3	−0.2	0.52	0.2	0.8	0.29	−0	−0	−0.91	−0.2
Tin	0.73	0	2.15	0.01	0.53	0	1.72	0	0.03	0	−0.32	−0	0.14	0	1.47	0
Sb	0.53	0.05	1.28	0.14	−1.5	−0	0.63	0.02	−0.9	−0.1	−0.93	−0.1	0.29	0.01	1.3	0.05
Cotton	0.31	0.38	−0.2	−0.2	−1.6	−0.5	−1.7	−0.5	−0.6	−0.8	0.21	0	0.5	0.01	−0.54	0.02
Zinc	−0.1	−0	0.4	0.02	−1.2	−0	−1	−0	−0	−0	−1.55	−0.4	0.76	0.42	−0.67	−0.2
Plt	0.88	0.07	1.36	0.14	−0.4	−0	−1.2	−0.1	0.36	−0.1	−0.85	−0	0.52	0.02	0.31	0.02
Pdm	−1.3	−0	−0.5	−0	−0.6	−0	−1	−0	3.65	0.2	0.24	0.01	−0.8	−0.1	0.18	0.01