The interdependence of gold, US dollar and stock market in the context of COVID-19 pandemic: an insight into analysis in Asia and Europe

Figures & data

Table 1. Variable description and source of data

Signs	Descriptions	Research	Source
SMR	Stock market return$SM{R}_{i,t}=\frac{Inde{x}_{i,t}-Inde{x}_{i,t-1}}{Inde{x}_{i,t-1}}$in which $Inde{x}_{i,t}$ is the stock price index of country i at the end of day t.	Xu et al. (2021), Okorie and Lin (2021)	investing.com
ER	Exchange rate return$E{R}_{i,t}=\frac{Clos{e}_{i,t}-Clos{e}_{i,t-1}}{Clos{e}_{i,t-1}}$in which $Clos{e}_{i,t}$ is the closing exchange rate of country i at the end of day t	Sui and Sun (2016)	investing.com
GP	The spot gold price in the local currency of the studied country, taken the form of logarithm	Yousef and Shehadeh (2020)	bullion-rates.com
TC	Total COVID-19 confirmed cases, taken the form of logarithm	Yousef and Shehadeh (2020), Yilmazkuday (2021)	Our world in data
NC	COVID-19 daily new cases, taken the form of logarithm	Yousef and Shehadeh (2020), Li et al. (2021)	Our world in data
TD	Total COVID-19 deaths, taken the form of logarithm	Li et al. (2021)	Our world in data
ND	Daily new COVID-19 deaths, taken the form of logarithm	Li et al. (2021)	Our world in data
DGTCC	Daily growth in total confirmed cases	Al Awadhi et al. (2020)	Our world in data
DGTDC	Daily growth in total cases of death	Al Awadhi et al. (2020)	Our world in data

Table 2. Descriptive statistics of variables in the model

Variables	SMR	GP	ER
Asia
Mean	0.077372	11.04706	0.007941
Median	0.0000	10.84194	0.0000
Maximum	10.69	17.67977	11.3259
Minimum	−13.34	6.342861	−13.23771
Std. Dev.	1.222573	2.926997	0.686717
Skewness	−0.671498	0.632381	−0.85821
Kurtosis	15.78278	2.615281	101.5765
Jarque-Bera	67,760.85	716.8211	3,986,932
Probability	0.0000	0.0000	0.0000
Sum	761.65	108,747.3	78.16763
Sum Sq. Dev.	14,712.18	84,328.07	4641.77
Observations	9844	9844	9844
Europe
Mean	0.090619	8.615159	−0.004077
Median	0.03	7.398027	−0.01
Maximum	12.84693	13.30646	7.83
Minimum	−16.92	7.099846	−4.17
Std. Dev.	1.343968	1.766883	0.510472
Skewness	−1.03556	1.186855	0.899321
Kurtosis	20.97884	3.181078	14.68474
Jarque-Bera	186,909.4	3234.132	79,761.03
Probability	0.0000	0.0000	0.0000
Sum	1241.119	117,993.2	−55.84
Sum Sq. Dev.	24,736.59	42,754.07	3568.668
Observations	13,696	13,696	13,696

Table 3. ADF unit root test results

Variables	ASIA	EUROPE
	I(0)	I(0)	I(1)
DGTCC	1873.37***	824.496***
DGTDC	1579.31***	1585.57***
ER	2285.92***	4932.57***
GP	111.161***	167.067***
NC	116.223***	76.6710	1412.94***
ND	96.42***	133.604***
SMR	2271.80***	2716.39***
TC	222.17***	82.6870	530.142***
TD	191.214***	73.7726	1406.03***

*** means significant at 1%.

Table 4. Testing optimal lag selection of PVAR model for Asia

Lag	LogL	LR	FPE	AIC	SC	HQ
0	−48,482.58	NA	4.097491	9.924006	9.939448	9.929239
1	5000.965	106,857.7	7.26e-05	−1.017077	−0.995018	−1.009602
2	5089.402	176.6403	7.14e-05	−1.033330	−1.004654	−1.023612
3	5159.737	140.4395*	7.05e-05*	−1.045880*	−1.010585*	−1.033919*

* indicates lag order selected by the criterion.

Table 5. Testing optimal lag selection of PVAR model for Europe

Lag	LogL	LR	FPE	AIC	SC	HQ
0	−58,882.36	NA	1.15973	8.661818	8.671768	8.665135
1	12,210.34	142,091.3	3.35E-05	−1.791667	−1.776742	−1.786692
2	12,573.91	726.4911	3.18E-05	−1.84381	−1.823909	−1.837175
3	12,802.83	457.3427*	3.07e-05*	−1.876151*	−1.851276*	−1.867858*

* indicates lag order selected by the criterion.

Figure 1. Inverse roots of AR characteristic polynomial.

Table 6. Results of Granger causality test

ASIA
Dependent variable: SMR
Excluded	Chi-sq	df	Prob.
GP	117.2259	3	0.0000	***
ER	10.40549	3	0.0154
All	127.7411	6	0.0000	***
Dependent variable: GP
Excluded	Chi-sq	df	Prob.
SMR	33.13595	3	0.0000	***
ER	1.96985	3	0.5787
All	37.71832	6	0.0000	***
Dependent variable: ER
Excluded	Chi-sq	df	Prob.
SMR	45.52646	3	0.0000	***
GP	2.020356	3	0.5682
All	47.17166	6	0.0000	***
Europe
Dependent variable: SMR
Excluded	Chi-sq	df	Prob.
GP	186.2729	3	0.0000	***
ER	99.79151	3	0.0000	***
All	298.8185	6	0.0000	***
Dependent variable: GP
Excluded	Chi-sq	df	Prob.
SMR	421.6999	3	0.0000	***
GP	208.5038	3	0.0000	***
All	646.4861	6	0.0000	***
Dependent variable: ER
Excluded	Chi-sq	df	Prob.
SMR	605.3949	3	0.0000	***
ER	191.3867	3	0.0000	***
All	805.0595	6	0.0000	***

Figure 2. Impulse-response function in Asia.

Figure 3. Impulse-response function in Europe.

Table 7. Variance decomposition analysis

Variance decomposition of SMR
Asia					Europe
Period	S.E.	SMR	GP	ER	S.E.	SMR	GP	ER
1	1.180649	100	0	0	1.20252	100	0	0
2	1.186262	99.14064	0.831191	0.02817	1.210551	99.07625	0.722982	0.20077
3	1.190838	98.84172	1.087092	0.071188	1.216999	98.59851	1.14917	0.25232
4	1.191075	98.80671	1.090879	0.102415	1.223011	98.01658	1.14428	0.83914
5	1.191094	98.80348	1.093964	0.102559	1.223038	98.01224	1.147614	0.840148
10	1.191096	98.8033	1.093988	0.102708	1.223474	97.96652	1.151686	0.881796
15	1.191096	98.8033	1.09399	0.102708	1.223474	97.9665	1.151693	0.881812
20	1.191096	98.8033	1.093991	0.102708	1.223474	97.9665	1.151693	0.881812
Variance decomposition of GP
		Asia				Europe
Period	S.E.	SMR	GP	ER	S.E.	SMR	GP	ER
1	1.180649	0.178475	99.82153	0	1.20252	0.185465	99.81453	0
2	1.186262	0.471516	99.52487	0.003613	1.210551	1.673672	98.32537	0.000959
3	1.190838	0.494586	99.48919	0.016229	1.216999	1.429795	98.43171	0.138491
4	1.191075	0.682499	99.29557	0.021935	1.223011	1.651732	97.56375	0.784516
5	1.191094	0.783388	99.19179	0.024821	1.223038	1.82493	97.05768	1.117392
10	1.191096	1.015368	98.95708	0.027554	1.223474	2.505779	95.73856	1.755663
15	1.191096	1.093493	98.87807	0.028441	1.223474	2.715489	95.35414	1.930367
20	1.191096	1.132624	98.83849	0.028885	1.223474	2.821896	95.15837	2.019735
Variance decomposition of ER
		Asia				Europe
Period	S.E.	SMR	GP	ER	S.E.	SMR	GP	ER
1	1.180649	3.0007	0.641919	96.35738	1.20252	2.203729	1.177368	96.6189
2	1.186262	2.988942	0.671677	96.33938	1.210551	5.398318	2.148458	92.45322
3	1.190838	3.26512	0.669743	96.06514	1.216999	5.642602	2.412523	91.94488
4	1.191075	3.477223	0.678468	95.84431	1.223011	6.251431	2.471015	91.27755
5	1.191094	3.480835	0.678785	95.84038	1.223038	6.269579	2.478266	91.25215
10	1.191096	3.484807	0.678987	95.83621	1.223474	6.286983	2.479072	91.23395
15	1.191096	3.484808	0.678987	95.83621	1.223474	6.287008	2.479073	91.23392
20	1.191096	3.484808	0.678987	95.8362	1.223474	6.287008	2.479073	91.23392

Figure 4. Ordering of Asian countries’ PVAR: GP, SMR, ER.

Figure 5. Ordering of Asian countries’ PVAR: ER, GP, SMR.

Figure 6. Ordering of European countries’ PVAR: GP, SMR, ER.

Figure 7. Ordering of European countries’ PVAR: ER, GP, SMR.

Table A1. Review of selected studies on the relationship between stock market, gold price and exchange rate

Authors	Method	Variables	Countries	Main results
Kim (2003)	VECM	Stocks, exchange rates.	USA	Negative relationship.
Ibrahim and Aziz (2003)	VAR	Stocks, exchange rates.	Malaysia	Negative relationship.
Tsai (2012)	Quantile regression	Stocks, exchange rates.	Singapore, Thailand, Malaysia, the Philippines, South Korea, and Taiwan	Negative relationship.
Srinivasan & Karthigai, 2014)	ARDL Granger’s causality test	Stocks, gold prices and exchange rates.	India	Gold prices and stock prices tend to have a long-term relationship with exchange rates.
Zhao (2010)	VAR and GARCH	Stocks, exchange rates.	China	No relationship
Suriani et al. (2015)	Granger’s causality test	Stocks, exchange rates.	Pakistan	No relationship
Jain and Biswal (2016)	DCC-GARCH	Stocks, gold prices and exchange rates.	India	Falling gold price causes value of Rupee and stock price index to fall.
Lou and Luo (2018)	Granger’s causality test	Stocks, exchange rates.	G7 countries	No significant relationship
Arfaoui and Ben (2017)	A Simultaneous Equation Model	Stocks, gold prices and exchange rates.	Global	Gold price and stock market are related. USD exchange rate and stock market have an negative relationship.
Akbar et al. (2019)	BVAR	Stocks, gold prices and exchange rates.	Pakistan	Stock price and Rupee price move negatively with gold price.
Ali et al. (2020)	Quantile regression	Stocks, gold prices and exchange rates.	Pakistan	Exchange rate and gold price have impact on stock market.
Alshammari et al. (2020)	Wavelet Coherence	Stocks, gold prices and exchange rates.	Kuwait	Positive relationship between stock market and exchange rate. Gold price has a negative short-term relationship with the stock market.
Ashraf (2020)	Panel pooled OLS	Stock market return, COVID-19 confirmed cases and confirmed deaths.	64 countries in the world.	Negative relationship between stocks and COVID-19.
Al Awadhi et al. (2020)	Panel regression	Stock market return, COVID-19.	China	Negative relationship.
Liu et al. (2021)	GARCH-S	Stock market return, COVID-19.	China	Negative impact but insignificant.
Okorie and Lin (2021)	DMCA and DCCA	Stock market return, COVID-19.	32 countries with the highest number of cases.	Relationship in the short term.
Syahri and Robiyanto (2020)	GARCH	Stocks, gold prices and exchange rates.	Indonesia	Positive dynamic correlation between stock price index and gold. Negative dynamic correlation between stock price index and exchange rate.
Samadi et al. (2021)	Wavelet Coherence	Stocks, exchange rates.	Iran	The relationship between the gold price and the exchange rate changes during the impact of the pandemic.
Kumar and Robiyanto (2021)	GARCG	Stocks, gold prices and exchange rates.	China, India	The price of gold has a significant influence on exchange rates and stocks.
Chaudhry et al. (2021)	GARCH	Stocks, gold prices and exchange rates, COVID-19.	Pakistan	No relationship

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