COVID-19 pandemic, economic indicators and sectoral returns: evidence from US and China

Figures & data

Figure 1. Daily cases reported in the China. The data source is Worldometer.

Figure 2. Daily cases reported in US. The data source is Worldometer.

Table 1. Variables, definition and data sources.

Variables	definition	Data sources	Reference
COVID-19	The number of daily active confirmed COVID-19 cases (daily growth in reported cases) in both countries	Worldometer	Al-Awadhi et al. (2020)
Stock market index	The stock market index returns of each country are used as a proxy of stock market returns. Stock market index data includes NYSE American Composite Index and Shanghai SE Composite Index.	Thomson Reuter DataStream	Pan and Mishra (2018)
Exchange rate	The exchange rate data is calculated as a percentage change in the exchange rate home/USD daily.	Thomson Reuter DataStream	Haddad, Lim, Pancaro, and Saborowski (2013).
CDS spread	$\Delta \text{ Credit Default Swap }\left(\mathrm{CDS}\right)$ spread is captured by taking the first difference of the CDS premium at the end of each day.	Thomson Reuter DataStream	Qureshi et al. (2019, 2020a); Caballero et al. (2019)
T-bill	$\Delta$ T-Bill is calculated using the Treasury bill rate taken at the end of each day.	Thomson Reuter DataStream	Qureshi et al. (2019, 2020a)
Sectoral price indices	The selected distinct sectoral indices include natural resources, auto and parts, industrial, energy, food and beverage, transportation, health, care and nursing, and information technology.	Thomson Reuter DataStream	Qureshi et al. (2020b)

The table presents the variables, definition and sources.

Table 2. Descriptive statistics.

Variables	Mean	Standard deviation	Skewness	Kurtosis	Minimum	Maximum	JB P-value
US
COVID-19	0.086	0.401	2.611	25.870	−1.791	2.99	[0.000]**
Macroeconomic variables
Stock returns	−0.002	0.032	−1.449	5.526	−0.136	0.092	[0.000]**
Exchange rate	0.001	0.002	0.582	1.441	−0.006	0.008	[0.009]**
CDS	0.004	0.103	1.006	18.359	−0.547	0.626	[0.000]**
T-bills returns	−0.027	0.300	−2.754	28.575	−2.251	1.386	[0.000]**
Sectors
Natural resources	−0.000	0.041	−0.647	1.688	−0.159	0.102	[0.004]**
Auto and parts	0.001	0.045	−0.845	3.045	−0.174	0.146	[0.000]**
Industrial sector	−0.002	0.031	−0.094	2.009	−0.108	0.110	[0.000]**
Energy sector	−0.004	0.049	−0.956	4.264	−0.231	0.149	[0.000]**
Transportation sector	−0.004	0.051	−0.899	3.429	−0.197	0.119	[0.000]**
Health and nursing	−0.000	0.026	−0.321	2.776	−0.108	0.073	[0.000]**
Food and beverages	−0.001	0.026	−0.417	3.092	−0.106	0.078	[0.000]**
Information technology	0.000	0.033	−0.602	4.227	−0.146	0.107	[0.000]**
Observations = 116
CHINA
COVID-19	0.006	0.836	−0.044	2.124	−2.944	2.398	[0.000]**
Macroeconomic variables
Stock returns	0.003	0.015	−1.05	7.62	−0.080	0.056	[0.000]**
Exchange rate	0.001	0.003	0.978	4.145	−0.007	0.014	[0.000]**
CDS	0.017	0.193	0.243	0.168	−0.409	0.556	[0.440]
China Repo/T-bills returns	0.015	0.193	1.405	4.970	−0.380	1.024	[0.000]**
Sectors
Natural resources	−0.003	0.015	−1.048	6.01	−0.084	0.040	[0.000]**
Auto and parts	−0.000	0.019	−1.110	3.943	−0.092	0.047	[0.000]**
Industrial sector	−0.001	0.0156	−1.309	8.870	−0.093	0.056	[0.000]**
Energy sector	−0.003	0.012	−1.446	8.207	−0.076	0.028	[0.000]**
Transportation sector	−0.002	0.017	−0.886	3.798	−0.091	0.035	[0.000]**
Health and nursing	0.002	0.016	−0.135	−0.338	−0.037	0.037	[0.685]
Food and beverages	0.002	0.017	−0.436	0.899	−0.058	0.038	[0.045]*
Information technology	0.004	0.028	−0.802	1.863	−0.105	0.063	[0.001]**
Observations = 128

Source: Author's own estimations.

Figure 3. The US: continuous wavelet transformation of variables.

Source: Author's own estimations.

Figure 4. China: continuous wavelet transformation of variables.

Source: Author's own estimations.

Figure 5. The US: wavelet coherence between variables.

Source: Author's own estimations.

Figure 6. China: wavelet coherence between variables.

Source: Author's own estimations.

Table 3. The US: DCC-GARCH model results.

Panel A: Exchange rate and sectoral returns						Panel B: Stock market and sectoral returns
Variables	Coefficient	P-value	Variables	Coefficient	P-value	Variables	Coefficient	P-value	Variables	Coefficient	P-value
${\rho }_{Ex-\mathit{Natural}\mathrm{ }}$	0.015	0.866	${\rho }_{Ex-\mathrm{Transportation}}$	0.059	0.358	${\rho }_{SR-\mathit{Natural}\mathrm{ }}$	0.824	0.000***	${\rho }_{SR-\mathrm{Transportation}}$	0.804	0.000***
${\mathit{DCC}}_{\alpha }$	0.002	0.767	${\mathit{DCC}}_{\alpha }$	0.006	0.000***	${\mathit{DCC}}_{\alpha }$	0.091	0.138	${\mathit{DCC}}_{\alpha }$	0.182	0.401
${\mathit{DCC}}_{\beta }$	0.821	0.000***	${\mathit{DCC}}_{\beta }$	0.854	0.000***	${\mathit{DCC}}_{\beta }$	0.909	0.000***	${\mathit{DCC}}_{\beta }$	0.172	0.179
${\rho }_{Ex-\mathit{auto}}$	0.136	0.423	${\rho }_{Ex-\mathit{Health}}$	0.091	0.303	${\rho }_{SR-\mathit{auto}}$	0.733	0.000***	${\rho }_{SR-\mathit{Health}}$	0.673	0.000***
${\mathit{DCC}}_{\alpha }$	0.035	0.316	${\mathit{DCC}}_{\alpha }$	0.001	0.000***	${\mathit{DCC}}_{\alpha }$	0.168	0.071*	${\mathit{DCC}}_{\alpha }$	0.117	0.024**
${\mathit{DCC}}_{\beta }$	0.870	0.000**	${\mathit{DCC}}_{\beta }$	0.867	0.001***	${\mathit{DCC}}_{\beta }$	0.382	0.120	${\mathit{DCC}}_{\beta }$	0.693	0.021**
${\rho }_{Ex-\mathit{industrial}}$	0.040	0.708	${\rho }_{Ex-\mathit{Food}\mathrm{ }\&\mathrm{ }\mathit{Beverages}}$	0.020	0.795	${\rho }_{SR-\mathit{industrial}}$	0.660	0.000***	${\rho }_{SR-\mathit{Food}\mathrm{ }\&\mathrm{ }\mathit{Beverages}}$	0.714	0.000***
${\mathit{DCC}}_{\alpha }$	0.001	0.432	${\mathit{DCC}}_{\alpha }$	0.024	1.000	${\mathit{DCC}}_{\alpha }$	0.009	1.000	${\mathit{DCC}}_{\alpha }$	0.037	0.388
${\mathit{DCC}}_{\beta }$	0.826	0.003***	${\mathit{DCC}}_{\beta }$	0.875	0.003***	${\mathit{DCC}}_{\beta }$	0.799	0.006***	${\mathit{DCC}}_{\beta }$	0.931	0.000***
${\rho }_{Ex-\mathit{Energy}}$	0.023	0.758	${\rho }_{Ex-IT}$	0.084	0.268	${\rho }_{SR-\mathit{Energy}}$	0.935	0.000***	${\rho }_{SR-IT}$	0.699	0.000***
${\mathit{DCC}}_{\alpha }$	0.000	0.000***	${\mathit{DCC}}_{\alpha }$	0.001	0.877	${\mathit{DCC}}_{\alpha }$	0.017	0.586	${\mathit{DCC}}_{\alpha }$	0.001	1.0000
${\mathit{DCC}}_{\beta }$	0.823	0.000***	${\mathit{DCC}}_{\beta }$	0.844944	0.000***	${\mathit{DCC}}_{\beta }$	0.877	0.000***	${\mathit{DCC}}_{\beta }$	0.824	0.366

Note: *, **, *** denotes statistical significance at the 10%, 5% and 1% level, respectively.

Source: Author's own estimations.

Table 4. China: DCC-GARCH model results.

Panel A: Exchange rate and sectoral returns						Panel B: Stock market and sectoral returns
Variables	Coefficient	P-value	Variables	Coefficient	P-value	Variables	Coefficient	P-value	Variables	Coefficient	P-value
${\rho }_{Ex-\mathit{Natural}\mathrm{ }}$	−0.390	0.008***	${\rho }_{Ex-\mathrm{Transportation}}$	−0.518	0.000***	${\rho }_{SR-\mathit{Natural}\mathrm{ }}$	0.408	0.026**	${\rho }_{SR-\mathrm{Transportation}}$	0.524	0.004***
${\mathit{DCC}}_{\alpha }$	0.172	0.090***	${\mathit{DCC}}_{\alpha }$	0.143	0.054*	${\mathit{DCC}}_{\alpha }$	0.074	0.051*	${\mathit{DCC}}_{\alpha }$	0.279	0.001***
${\mathit{DCC}}_{\beta }$	0.595	0.000***	${\mathit{DCC}}_{\beta }$	0.658	0.000***	${\mathit{DCC}}_{\beta }$	0.890	0.000***	${\mathit{DCC}}_{\beta }$	0.669	0.000***
${\rho }_{Ex-\mathit{auto}}$	−0.468	0.000***	${\rho }_{Ex-\mathit{Health}}$	−0.337	0.008***	${\rho }_{SR-\mathit{auto}}$	0.743	0.000***	${\rho }_{SR-\mathit{Health}}$	0.482	0.004***
${\mathit{DCC}}_{\alpha }$	0.138	0.021**	${\mathit{DCC}}_{\alpha }$	0.111	0.361	${\mathit{DCC}}_{\alpha }$	0.188	0.001***	${\mathit{DCC}}_{\alpha }$	0.058	0.007***
${\mathit{DCC}}_{\beta }$	0.741	0.000***	${\mathit{DCC}}_{\beta }$	0.590	0.143	${\mathit{DCC}}_{\beta }$	0.780	0.000***	${\mathit{DCC}}_{\beta }$	0.925	0.000***
${\rho }_{Ex-\mathit{industrial}}$	−0.505	0.000***	${\rho }_{Ex-\mathit{Food}\mathrm{ }\&\mathrm{ }\mathit{Beverages}}$	−0.379	0.009***	${\rho }_{SR-\mathit{industrial}}$	0.608	0.000***	${\rho }_{SR-\mathit{Food}\mathrm{ }\&\mathrm{ }\mathit{Beverages}}$	0.557	0.000***
${\mathit{DCC}}_{\alpha }$	0.122	0.862	${\mathit{DCC}}_{\alpha }$	0.168	0.021**	${\mathit{DCC}}_{\alpha }$	0.314	0.000***	${\mathit{DCC}}_{\alpha }$	0.096	0.225
${\mathit{DCC}}_{\beta }$	0.686	0.000***	${\mathit{DCC}}_{\beta }$	0.689	0.000***	${\mathit{DCC}}_{\beta }$	0.660	0.000***	${\mathit{DCC}}_{\beta }$	0.857	0.000***
${\rho }_{Ex-\mathit{Energy}}$	−0.557	0.000***	${\rho }_{Ex-IT}$	−0.322	0.016**	${\rho }_{SR-\mathit{Energy}}$	0.587	0.000***	${\rho }_{SR-IT}$	0.609	0.101
${\mathit{DCC}}_{\alpha }$	0.247	0.010**	${\mathit{DCC}}_{\alpha }$	0.115	0.225	${\mathit{DCC}}_{\alpha }$	0.278	0.000***	${\mathit{DCC}}_{\alpha }$	0.0835	0.003***
${\mathit{DCC}}_{\beta }$	0.553	0.000***	${\mathit{DCC}}_{\beta }$	0.717	0.000***	${\mathit{DCC}}_{\beta }$	0.660	0.000***	${\mathit{DCC}}_{\beta }$	0.917	0.000***

Note: *, **, *** denote statistical significance at the 10%, 5% and 1% level, respectively.

Source: Author's own estimations.

Al-Awadhi, A. M., Alsaifi, K., Al-Awadhi, A., & Alhammadi, S. (2020). Death and contagious infectious diseases: Impact of the covid-19 virus on stock market returns. Journal of Behavioral and Experimental Finance, 27, 100326. https://doi.org/10.1016/j.jbef.2020.100326

 PubMed Web of Science ®Google Scholar

Pan, L., & Mishra, V. (2018). Stock market development and economic growth: Empirical evidence from China. Economic Modelling, 68, 661–673. https://doi.org/10.1016/j.econmod.2017.07.005

 Web of Science ®Google Scholar

Haddad, M., Lim, J. J., Pancaro, C., & Saborowski, C. (2013). Trade openness reduces growth volatility when countries are well diversified. Canadian Journal of Economics/Revue Canadienne D'économique, 46(2), 765–790. https://doi.org/10.1111/caje.12031

 Web of Science ®Google Scholar

Qureshi, F., Khan, H. H., Rehman, I. U., Ghafoor, A., & Qureshi, S. (2019). Mutual fund flows and investors' expectations in BRICS economies: Implications for international diversification. Economic Systems, 43(1), 130–150. https://doi.org/10.1016/j.ecosys.2018.09.003

 Web of Science ®Google Scholar

Qureshi, F., Qureshi, S., Shah, S. S., Rehman, I. U., & Shahzad, F. (2020a). Further evidence on co-movement between equity funds and business cycle variables in BRICS: A wavelet analysis. Borsa Istanbul Review, 20, S93–S102. https://doi.org/10.1016/j.bir.2020.10.011

 Web of Science ®Google Scholar

Caballero, J., Fernández, A., & Park, J. (2019). On corporate borrowing, credit spreads and economic activity in emerging economies: An empirical investigation. Journal of International Economics, 118, 160–178. https://doi.org/10.1016/j.jinteco.2018.11.010

 Web of Science ®Google Scholar

Qureshi, S., Qureshi, F., Soomro, A. B., Chandio, F. H., Shah, S. S., & Rehman, I. U. (2020b). Exchange rate risk and sectoral returns: A wavelet-based MRA-EDCC GARCH analysis. Communications in Statistics-Theory and Methods, 1–29. https://doi.org/10.1080/03610926.2020.1772304

 Web of Science ®Google Scholar