A DEMAND-ORIENTED INDUSTRY-SPECIFIC VOLATILITY SPILLOVER NETWORK ANALYSIS OF CHINA’S STOCK MARKET AROUND THE OUTBREAK OF COVID-19

Fu Qiao1+--- Yan Yan2

1School of Economics and Management, University of Chinese Academy of Sciences, Beijing, China.
2Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, Beijing, China.

ABSTRACT

Using a carefully selected industry classification standard, we divided 102 industry security indices in China’s stock market into four demand-oriented sector groups and identified demand-oriented industry-specific volatility spillover networks. The demand-oriented concept is a new way in which to reconstruct the structure of the networks. Analyzing networks from a demand-oriented perspective can improve the understanding of the change in economic demand, especially when the macroeconomy is dramatically influenced by exogenous shocks, such as those due to the outbreak of COVID-19. At the beginning of the outbreak, spillover effects from industry indices of sectors meeting the investment demand to those meeting the consumption demands rose significantly in China's stock market. However, these spillover effects declined after the outbreak containment in China appeared to be effective. In addition, some service sectors, including utility, transportation and information services, have played increasingly important roles in the networks of industry-specific volatility spillovers since the COVID-19 outbreak. The efforts to contain the outbreak, led by the Chinese government, have been successful and work resumption has been organized with high efficiency. First, the risk of investment demand has therefore been controlled and eliminated relatively quickly. Second, the intensive use of non-pharmaceutical interventions (NPIs) has led to supply restrictions in services in China, which will still be a potential threat to economic recovery in the next stage.

Keywords:Volatility spillover network, BEKK-GARCH, Industry classification, China’s stock market, Demand, COVID-19.

JEL Classification: C58; G10.

ARTICLE HISTORY: Received:8 October 2020, Revised:26 October 2020, Accepted:6 November 2020, Published:23 November 2020

Contribution/Originality: This is one of very few studies that has investigated the volatility spillovers in the industry-specific networks of China’s stock market during the COVID-19 pandemic. The paper's primary contribution is finding the critical role that the service sectors play in the industry-specific network after the COVID-19 outbreak was contained.

1. INTRODUCTION

Frequently in the stock market, fluctuations in stock prices initially occur in companies belonging to one sector and gradually spread to other sectors. China’s stock market has become the second largest in the world. Up to January 2020, 3780 companies from a variety of sectors listed their shares on China’s stock market, which had a total value of more than 60.38 billion RMBs (approximately equal to 8.65 trillion US dollars). Thus, it is important for both investors and policymakers around the world to understand the complex linkage effects shown by fluctuations in the stock prices (or yields) of companies in different sectors in China’s stock market.

One of the typical measurements of the linkage characteristics between different variables is the spillover effect, which can be measured by using the generalized autoregressive conditional heteroskedastic (GARCH) family of models (such as the BEKK-GARCH and DCC-GARCH models) or the variance decomposition model under the vector autoregression (VAR) framework (Diebold & Yilmaz, 2012; Jiang, Jiang, Nie, & Mo, 2019; Singh, Kumar, & Pandey, 2010).

Studies on industry-specific volatility spillover networks have highlighted the measurement of the linkage level. These studies were initially motivated by finding the arbitrage opportunities between upstream and downstream industry sectors in the supply chain. In addition, studies, such as those by Yarovaya, Brzeszczyński, and Lau (2016) and Yin, Liu, and Jin (2020), further found that volatility spillovers also exist between industry sectors without a direct input–output relationship. However, the existing literature does not answer the question of how industry-specific volatility spillover networks reflect economic demand and its changes. We believe that there are two reasons for this. First, it is more difficult to provide a proper explanation for the findings in analyses on industry-specific volatility spillover networks than those done across countries or regions. Part of the detected spillovers in the networks might match economic theory, such as the spillovers between the energy and finance sectors, or those between the transportation and consumption sectors (Gonzalez-Navarro & Quintana-Domeque, 2016; Singh, Nishant, & Kumar, 2018). However, the rest of the spillovers might not be properly explained. Second, some scholars have criticized the arbitrariness when selecting industry classification standards. Mateus, Chinthalapati, & Mateus (2017) pointed out that the industry classification standard should be cautiously selected depending on the research targets. When necessary, self-built industry indices should also be used for pursuing more meaningful numerical results and theoretical implications.

Some early studies on this topic showed that exogenous shocks to the macroeconomy of a country do not lead to fluctuations in the prices of all securities in the country at the same time (Campbell, Lettau, Malkiel, & Xu, 2001; Ewing, Forbes, & Payne, 2003; Wang, 2010). Inspired by these studies, we further considered how the demand structure influences industry-specific spillover networks. The demands of a country mainly comprise consumption, investment and export. One of the critical factors of the profit and asset price of companies is whether or not their goods or services successfully meet a part of the demand (Acemoglu & Guerrieri, 2008). The influence of exogenous shocks on economic demand should, therefore, be reflected in the structural change in the industry-specific volatility spillover networks.

To highlight the economic demand structure, we chose the industry classification standard constructed by SWS Research Co., Ltd., which is the largest securities research institute in Mainland China . Using the SWS standard, we identified the GARCH-BEKK-based demand-oriented industry-specific volatility spillover networks of China’s stock market. Each node in the networks represents a level 2 industry securities index in the SWS industry list. We chose the minute-per-minute return data between January 2 and March 20, 2020 for 102 SWS industry indices as the sample. In this period, the exogenous shock of the outbreak of COVID-19 dramatically changed economic demand in China.

Recent studies have reported the influence of COVID-19 on both the macroeconomies and financial markets of different countries or regions. Some of them focused on the impact of the disease on the financial market in a single country, or the overall impact on global financial markets (Gupta & Chatterjee, 2020; Lewis, 2020; Procacci, Phelan, & Aste, 2020). Furthermore, according to Huang et al. (2020), industry-specific networks were identified based on macroeconomic data rather than data from financial markets. These studies provided us with a good incentive to design further research to illustrate how industry-specific volatility spillover networks can reflect change in economic demand.

Our study extends the literature and contributes the following:

(1) From the perspective of demand, we developed a new idea for reconstructing the structure of the industry-specific spillover network. By reorganizing the industry securities indices into demand-oriented sector groups, a better linkage between the theories of macroeconomics and the industry-specific network analysis of the financial market can therefore be obtained.
(2) We provided an early report of the structural change in the industry-specific volatility spillover networks of China’s financial market around the outbreak of COVID-19. We further analyzed how the changes in this network reflected the changes in economic demand as a result of the disease.
(3) A list of new economic implications was found from the numerical results. First, during the entire study period, there were stable spillovers from the capital goods sector group to the consumption goods sector group. The spillovers from the capital goods and equipment manufacturing sector groups, which represent the demand for investment, to other sector groups rose significantly at the beginning of the COVID-19 outbreak. However, these spillovers declined approximately one month later. Second, the level of spillovers from the unclassified services sector group was continuously rising during the whole study period. This rising trend reflected that the intensive use of non-pharmaceutical interventions (NPIs) (Lai et al., 2020) in China caused supply restrictions to services and, therefore, had an overall impact on all types of demand.

The next section introduces the data selection and preprocessing strategies used, section 3 discusses the methodology, section 4 presents the empirical study of the demand-oriented industry-specific volatility spillover network analysis based on the SWS industry classification standard, section 5 presents a further discussion, and section 6 concludes the study.

2. DATA

The study period was from January 2 to March 20, 2020. Considering the size of the spread and the progress in containing COVID-19 both inside and outside China, we divided the study period into three subperiods. Period 1 is between January 2 and January 23, 2020; period 2 is between February 3 and February 28, 2020, and period 3 is between March 2 and March 20, 2020. Periods 1, 2 and 3 have 16, 20 and 15 trading days, respectively.

We chose the SWS standard as the industry classification standard. According to this standard, securities in China’s stock market are divided into 28 sectors, which are further divided into 104 industry groups. As shown in Table 1, the SWS standard integrated the sectors into four demand-oriented sector groups.

Table 1. Official categories of sector groups and sectors according to SWS industry classification standard.
Sector Group
(Abbreviation)
Sector
The last four digits of the relative industry group (evel-2 category) indices codes
Consumption goods (Cg) Agriculture, forestry, husbandry and fishery
1011, 1012, 1013, 1014
1015, 1016, 1017, 1018
Household appliances
1111, 1112
Food and beverage
1123, 1124
Apparel and textiles
1131, 1132
Light manufacturing
1141, 1142, 1143
Biochemical and pharmaceuticals
1151, 1152, 1153, 1154, 1155, 1156
Leisure Services
1211, 1212, 1213, 1214
Commercial trade
1202, 1203, 1204, 1205
Capital goods (Kg) Mining
1021, 1022, 1023, 1024
Chemicals
1032, 1033, 1034, 1035, 1036, 1037
Non-ferrous metal
1051, 1053, 1054, 1055
Construction and decoration
1711, 1712, 1713
Building materials
1721, 1722, 1723, 1724, 1725
Ferrous metal
1041
Equipment manufacturing (Ke) Machinery
1072, 1073, 1074, 1075, 1076
Electronic components
1081, 1082, 1083, 1084, 1085
Electrical equipment
1731, 1732, 1733, 1734
Motor
1092, 1093, 1094, 1881
Defense and military industry
1741, 1742, 1743, 1744
Information facilities
1222, 1223
Unclassified services (Us) Utilities
1161, 1162, 1163, 1164
Transportation
1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178
Real estate
1181, 1182
Bank
1192
Non-bank financial services
1191, 1193, 1194
Information services
1222, 1223
Media
1751, 1752, 1761

Source: Wind Financial Database.
Notes 1: The conglomerates sector consists of listed companies with diversified businesses in which no single business is dominant. Although as a sector vertex in the sector-specific spillover network, the SWS conglomerates sector index belongs to none of the sector groups. 2: The codes of industry group indices (level 2 categories of industry classification system) consist of six digits in which the first two digits are “80”.

As a supplement of Table 1, we listed the names and the codes of all industry group indices according to the SWS standard in the Appendix A. In addition, because the level 3 categories (industry) were not mentioned in this paper, we will refer to the industry group securities index as the “industry securities index” in the following sections.

 The comprehensive descriptive statistics of the log return series of all industry securities indices in different periods can be found in the Appendix B.

3. METHODOLOGY

3.1. BEKK-GARCH-Based Volatility Spillover Network

To test volatility spillovers between multiple variables, a set of bivariate BEKK-GARCH models is required. After all testing has been completed, we can identify the BEKK-GARCH-based volatility spillover networks.

3.2. Node Importance Ranking Indicators

3.2.1. Connectivity and Relative Influence

When dividing the nodes into groups, additional indicators were required to assess the importance of the groups according to Billio, Getmansky, Lo, and Pelizzon (2012). The outward spillover effect from the nodes in one group to those in other groups can be defined as “total out to other” (TOTO). Similarly, the inward spillover to one group from other groups can be defined as “total in from other” (TIFO). The TOTO and TIFO are shown in Equation 11 and Equation 12:

3.2.2. Weighted K-Shell Decomposition

In addition to the number of neighbors, the location of a node in the network is also critical for the assessment of its importance. Kitsak et al. (2010), therefore, proposed k-shell decomposition to evaluate the locational importance of the nodes. K-shell decomposition is the method that reshapes the networks into a layered structure according to their connectivity patterns.

Vanilla k-shell decomposition fails to consider the intensity of connections, thus it cannot rank the nodes for weighted networks. Garas, Schweitzer, and Havlin (2012) extended vanilla k-shell decomposition to weighted k-shell decomposition. The alternative measure for node degree is shown in Equation 14.

3.2.3. Betweenness Centrality

3.3. Earth Mover’s Distance (EMD)

None of the indicators introduced in Section 3.2 highlights measuring the distributional change in the spillover intensity. Therefore, we introduce the EMD to consider the intensity distribution change in spillovers across different groups. The EMD is a cross-bin distance that is defined as the minimal cost that must be paid to transform one histogram into another (Rubner, Tomasi, & Guibas, 2000). An intensity distribution of spillovers can be represented by countable clusters. Each cluster is represented by its mean and by the fraction of the distribution that belongs to that cluster. We refer to such a representation as the signature of the distribution. Then, the distributional change in spillover intensity between periods 1 and 2 can be formalized and solved as a transportation problem.

4. DEMAND-ORIENTED INDUSTRY-SPECIFIC VOLATILITY SPILLOVER NETWORK ANALYSIS

4.1 Network before the COVID-19 Outbreak

Table 2. Medians for indicators of the nodes by sector group in period 1 (before the COVID-19 outbreak).
Sector group
O
TOTO
I
TIFO
Ri
WBC
Ke
4.50
3.54
5.67
4.48
-0.09
17.5
Cg
4.54
2.56
5.21
3.84
-0.15
21
Kg
6.71
5.39
5.50
3.69
0.09
39
Us
4.55
3.81
4.61
3.77
-0.05
16

Table 3 reports the intergroup spillovers of the network in period 1. We can find the significant asymmetry in the spillover between the Kg and Cg groups. Both the gross and net spillovers from Kg to Cg are the highest among all gross and net intergroup spillovers, which are 49.02 and 16.22. In addition, the net spillover from Kg to Ke is 14.8. Relatively, there is only slight asymmetry in the rest of spillovers. The net spillover from Cg to Ke is much weaker than that from Kg to Cg. Thus, integrating the Kg and Ke groups as a whole sector group meeting the investment demand has a net spillover to the Cg group.

In period 1, the Kg group is a main spillover contributor from all perspectives. The outward spillovers from Kg to other groups account for 23.2% of total spillovers in the network. In contrast, the Cg group is the main receiver of spillovers, which receives 22.3% of the total spillovers. The Ke group also receives 20.3% of the total spillovers, which is only slightly lower than that of the Cg group. In addition, the other three groups have net spillovers to the Ke group.

Table 3. Cross-sector group analysis of the volatility spillovers in period 1 (before the COVID-19 outbreak).
From/to
Intensity of spillovers
No. of direct spillover paths
Ke
Cg
Kg
Us
Total
Ke
Cg
Kg
Us
Total
Ke
30.16
36.73
26.05
21.04
537.55
223
307
207
222
4308
Cg
40.64
43.24
32.80
30.79
295
374
268
268
Kg
40.81
49.02
37.67
34.62
284
372
247
245
Us
27.74
34.50
27.19
24.54
217
292
202
198

4.2. Network at the Beginning of the COVID-19 Outbreak

Table 4. Medians for indicators of the nodes by sector group in period 2 (at the beginning of the COVID-19 outbreak).
Sector Group
O
TOTO
I
TIFO
Ri
WBC
Ke
5.19
3.96
6.74
4.71
-0.13
18
Cg
3.61
2.14
5.58
4.65
-0.31
8
Kg
8.81
6.39
5.58
3.38
0.20
33
Us
6.15
4.46
5.48
4.37
-0.06
27

Table 5 reports the intergroup spillovers of the network in period 2. We can see that the total spillover intensity and the number of spillover paths in period 2 are significantly higher than those in period 1. This means that the outbreak of COVID-19 intensified the overall spillovers in China’s stock market. The gross and net outward spillovers from Kg to Cg are 61.37 and 36.32, respectively, which account for a higher proportion of total spillovers than those in period 1. Specifically, the proportion of the gross spillover from Kg to Cg increased from 9.1% to 9.5% of the total spillovers of the network. The proportion of the net spillover increased even more rapidly from 3.0% to 5.6%. In addition, the Ke and Kg groups, as an integral whole, still have a net spillover effect on the Cg group.

In period 2, the outward spillovers from Kg to other groups account for 24.0% of the total spillovers of the network. The Cg group received 24.3% of the total spillovers. In addition, the other three groups have net outward spillovers to the Cg group, including the Ke group. Therefore, Kg and Cg can be viewed as the major contributor and receiver, respectively, of the spillovers in period 2.

Table 5. Cross-sector group analysis of the volatility spillovers in period 2 (at the beginning of the COVID-19 outbreak).
From/to
Intensity of spillovers
No. of spillover paths
Ke
Cg
Kg
Us
Total
Ke
Cg
Kg
Us
Total
Ke
42.48
46.01
31.08
36.65
646.31
277
348
230
234
4537
Cg
36.04
35.98
25.05
28.79
267
328
261
252
Kg
47.08
61.37
48.84
46.91
301
403
273
275
Us
38.09
49.51
33.26
39.17
214
313
222
245

4.3. Network after Preliminary Containment of Covid-19

Table 6. Medians for indicators of the nodes by sector group in period 3 (after COVID-19 is preliminarily contained).
Sector group
O
TOTO
I
TIFO
Ri
WBC
Ke
4.70
3.76
5.42
4.18
-0.12
25
Cg
3.95
2.52
5.46
3.82
-0.18
9
Kg
5.19
4.22
5.58
4.10
0.05
27
Us
5.37
4.33
5.76
3.96
-0.01
28

Table 7 reports the intergroup spillovers of the network in period 3. Both the total spillover intensity and the number of spillover paths in period 3 are less than those in period 2. The most significant difference between the intergroup spillovers in period 3 and those in periods 1 and 2 is the occurrence of the net outward spillover from the Us group to the Kg group. The Us group, therefore, has the net outward spillovers to other three groups. In addition, the Ke and Kg groups, as an integral whole, still have the net spillover to the Cg group.

In period 3, the outward spillovers from the Us group account for 20.4% of the total spillovers of the network. The inward spillovers received by the Cg group accounted for 21.9% of the total spillovers. Therefore, the Us and Cg groups were the major contributor and receiver, respectively, of the spillovers in period 3.

Table 7. Cross-sector group analysis of the volatility spillovers in period 3 (after COVID-19 is preliminarily contained).
From/to
Intensity of spillovers
No. of spillover paths
Ke
Cg
Kg
Us
Total
Ke
Cg
Kg
Us
Total
Ke
30.36
34.33
28.36
27.42
566.29
209
283
220
229
4254
Cg
39.40
47.55
35.03
32.77
278
373
261
283
Kg
35.40
41.61
33.25
32.19
250
326
232
253
Us
32.77
48.30
34.61
32.95
211
305
206
220

In conclusion, there are both stable patterns and significant changes in the demand-oriented industry-specific volatility spillover networks of China’s stock market during the study period. First, viewing the Kg and Ke sector groups as an integral whole, they maintained significant net spillovers to the Cg group. Such spillovers were relatively stronger in period 2 than in the other two periods. Second, the Kg group always had a net outward spillover to the Ke group. Furthermore, the importance of the Us group increased and finally became the major contributor of the spillovers in period 3. Figure 2 depicts the simplified spillover paths of networks in different periods by sector group.

Figure 1. Demand-oriented industry-specific volatility spillover networks in different periods (net effect by sector group).
Notes: The size of each node represents the total spillover effects from this sector group to others. The width and direction of each arrow represent the strength and direction of net spillover effect between the relevant pairs of sector groups, respectively. The black arrows in each subfigure represent the major paths in each period obtained through the maximum spanning tree method.

According to Figure 1, the spillover paths from Kg to Cg and Ke were stable in all periods. The outbreak of COVID-19 led to an increasing rise in the importance of the spillover paths from the Us group to other groups. In particular, the path from Us to Cg became one of the major paths of the spillover networks of China's stock market after the outbreak.

The findings of this section have inspiring economic implications. First, some studies, such as Justiniano, Primiceri, and Tambalotti (2010), proved that fluctuations in investment demand caused by exogenous factors are the main cause of fluctuations in China’s economic demand. Our numerical findings further show that the structural change in volatility spillover networks of China’s stock market can reflect the critical role that investment demand plays in the fluctuation of China's economic demand since the outbreak of COVID-19. On the one hand, the Kg and Ke groups, as an integral whole, are the stable spillover contributors to the Cg group in the networks. On the other hand, the outward spillover effect from the Kg and Ke groups to the Cg group rapidly rose at the beginning of the COVID-19 outbreak. After the outbreak was preliminarily contained, these spillovers significantly fell. One of the main types of damage caused by COVID-19 was the nationwide closure and idling of plants in all trades, which has undisputedly had an enormous impact on investment demand in China. The increased uncertainty of investment demand led to the fluctuation in stock prices of securities in the industry sectors, which supplies goods or services to meet the investment demand. Therefore, regarding the change in spillovers from the Ke and Kg sector groups to the Cg group provides empirical evidence, from the perspective of the financial market, for the economic theories proposed in literature by Greenwood, Hercowitz, and Krusell (2000).

Second, the increasingly rising importance of the Us sector group in spillover networks reveals the occurrence of supply restrictions on the service industry caused by the implementation of NPIs. To contain COVID-19, the Chinese government implemented immediate NPIs nationwide. The majority of businesses in the service sector were forced to shut down, and a large percentage of transportation services in China had to idle, despite the enormous freight and passenger traffic demands. The uncertainty of COVID-19 transformed into the uncertainty of the operational environment of the companies in the service sector and, consequently, their asset prices. According to Xu and Zhang (2020), service supply restrictions will lead to an imbalance between supply and demand and will negatively affect economic growth. As a result, companies in the service sector contribute more volatility spillovers to those in other sectors. When overseas market demand is strong, a country is still able to achieve high economic growth under the condition of service supply restrictions. However, once the overseas market demand becomes insufficient, service supply restrictions will seriously damage the economy. As introduced in section 2.1, COVID-19 began to spread outside China in period 3. As a global pandemic, COVID-19 will surely lead to insufficient overseas demand for Chinese products. As a result, the importance of the Us group in the networks in period 3 is even higher than in period 2. In addition, according to Herrendorf and Fang (2019), to compare the period in which developed countries were at a similar stage of development as China is currently, there is severe supply restriction on most service industries currently in China. Service supply restriction is an overall problem rather than a structural problem in the Chinese economy. The outbreak of COVID-19 was only an exogenous shock that intensified the problem. Therefore, we believe that our findings are still representative, although not all service industry sectors are classified as members in the Us group according to the SWS standard.

5. FURTHER DISCUSSIONS

We further discussed the demand-oriented industry-specific volatility spillover networks of China's stock market from three aspects. First, we calculated the earth mover’s distance (EMD) of the distributions of the spillover intensity of both inter- and intra-sector groups in different periods. Second, defining the major spillover paths as those with the top 20% highest intensity among a set of paths, we discussed the major spillover paths between different sector groups and their changes in different periods. Third, from various perspectives, we selected the systemically important nodes of the networks in different periods.

5.1. EMDs Between Spillover Intensity Distributions in Different Periods

Figure 2 depicts the intensity distributions of the spillovers between sector groups. Intensity distributions of the spillovers, both intergroup and intragroup, are right-skewed. Few spillover paths have high intensity. Most of the subfigures show that the intensity distributions of spillovers in period 1 are similar to their counterparts in period 3. The intensity distributions in period 2, in contrast, are significantly different from those in periods 1 and 3. This reveals that in period 2, the industry-specific volatility spillover network of China’s stock market has significant structural changes. As an exception, the intensity distribution of the intragroup spillover paths of the Us group, and of intergroup spillover paths between the Us and Cg groups in period 2, are more similar to their counterparts in period 3, rather than to those in period 1. This exception is also consistent with the findings in section 4 and proves that COVID-19 had a more long-lasting impact on the service sector than on other sectors.

Figure 2. Empirical probability density functions of spillover intensity in different periods.
Notes: From subfigure (a) to subfigure (f), to distinguish the spillover paths in one direction to another, we processed the data further following the rule called “sector group B=>sector group A: negative/positive: sector group A=>sector group B”. Following this rule, when drawing the PDFs, we took the original value of the intensity of the spillover paths from sector group A to sector group B. Otherwise, we took the opposite number of the intensity of the spillover paths from sector group B to sector group A.

Table 8 shows the EMDs between spillover intensity distributions in different periods. Between periods 1 and 2, most of the high EMDs were connected with the distribution changes in spillovers between the Us group and other groups. Specifically, the EMD of the change in the intensity distribution of spillovers from Ke to Us is 6.34%, and those from the Us group to the Ke and Cg groups are 5.11% and 4.38%, respectively. Between periods 2 and 3, most of the high EMDs were connected with the distribution changes in spillovers between the Kg group and other groups. Specifically, according to the EMD, the intensity distribution of the intragroup spillovers of the Kg group changed by 4.43%. The EMD of the change in the intensity distribution of spillovers from Kg to Us and those from Cg to Kg are 4.31% and 3.71%, respectively.

Table 8. EMDs between the intensity distributions of spillovers in different periods (%).
From\to
Period 1 vs. Period 2
Period 2 vs. Period 3
Ke
Cg
Kg
Us
Ke
Cg
Kg
Us
Ke
2.25
1.24
1.21
6.34
0.95
1.17
0.92
4.05
Cg
1.09
1.39
2.43
1.37
1.56
1.70
3.71
1.01
Kg
1.47
2.08
2.73
3.00
2.05
2.41
4.43
4.31
Us
5.11
4.38
2.37
3.70
2.53
1.39
1.90
1.40

The analysis in this section is a meaningful supplement for the analysis based on the sector influence indicator in section 4. From period 1 to period 2, the Us group is the sector group of which the spillover effect strength distribution had the most significant change. It revealed that, at the beginning of the outbreak of COVID-19, the nationwide implementation of NPIs is reflected immediately in the distributional characteristics of the spillover networks of China’s stock market. The significant distributional change in the spillover strength concerning the Kg group from period 2 to period 3 also shows that the risk for investment demand destruction has been controlled to some extent. This is mainly due to the successful containment of COVID-19 and the resumption of work that is strongly supported by both the central and local governments of China. This means that the influence of the pandemic on the investment demand fell rapidly after the pandemic was contained in China, while the influence on service sectors was long-lasting.

5.2. Systematically Important Nodes in the Spillover Network

We selected the systematically important nodes in the volatility spillover networks in different periods from various perspectives.

Figure 3. Major spillover effect contributors and receivers in different periods (classified by sector group).
Figure 4. The centrality and k-shell decomposition structure of the nodes in different periods.

According to Figure 3 (a), nodes in the commerce and trading sector (1202 and 1203), the construction sector (1721, 1723 and 1724), the utility sector (1161) and the transportation services sector (1175 and 1178) were the main contributors of the spillovers in all periods. Compared to period 1, a larger number of nodes in the Ke and Us groups were the main spillover contributors of the network in period 2. Compared to period 2, a larger number of nodes in the Cg and Us groups were the main spillover contributors of the network in period 3. According to Figure 3 (b), a list of nodes in the Cg group (1014, 1017, 1111, 1112, 1212, 1123, 1141, 1143 and 1156) and in the Us group (1171, 1176, 1123, 1223 and 1752) were the main spillover receivers of the network during the whole study period. After period 2, a larger number of nodes in the Kg (1712, 1037 and 1053) and Ke (1731, 1732, 1733, 1101 and 1084) groups were identified as the main spillover contributors of the network.

According to Figure 4, compared to the network in period 1, in period 2, a larger number of nodes in the Kg and Ke groups had a relatively higher betweenness centrality and k-shell level. However, in the network in period 3, a larger number of nodes in the Us group became the center of the networks. In conclusion, the analysis in section 5 further validates the main result in section 4. The spillovers from the Kg and Ke sector groups, as an integral whole, rose in period 2 and fell in period 3; the change in the spillovers from the Cg group is in contrast. Regarding the spillovers from sectors meeting the consumption demand of other sectors in China's stock market, our findings are consistent with those in the literature of Yang, Chen, and Zhang (2020) (in Chinese). However, we further illustrated how spillover networks of China’s stock market reflected the relative rise and fall of the uncertainty of investment demand and consumption demand in China during the spread of COVID-19. Yang et al. (2020) also proposed that the service industry, which has suffered due to the pandemic, is a potential threat to economic recovery in China. In addition, Huang et al. (2020) found that more difficulties would be faced by industry sectors relying on transportation services during economic recovery. Based on the spillover networks of the stock market, our findings provide evidence for these studies.

6. CONCLUSIONS

According to our empirical analysis, first, the Ke and Kg sector groups, as a whole, had stable net spillovers to the Cg sector group in all of three different periods during the breakout of COVID-19. Second, the net spillovers from the Ke and Kg groups to the Cg group rose in period 2 but fell in period 3. Third, as of period 2, the importance of the Us sector group became increasingly higher. The Us group finally played the main contributor to the spillover network of China’s stock market in period 3. We conducted further discussions from various perspectives, and all discussions validated our main result. We emphasize the need to discuss the demand change in a country. Our findings also have meaningful insights regarding economic recovery in the context of containing the spread of COVID-19. The investment demand in China suffered more than the consumption demand from the exogenous shock of COVID-19 at the beginning of the outbreak. However, when the pandemic was contained, the risk in investment demand in China was also controlled to some extent. The increasingly critical role that the Us sector group began to play revealed that the supply restriction in services is still a long-lasting threat to the next stage of Chinese economic recovery, especially under the condition that foreign demand is destroyed by COVID-19. We believe that NPIs are necessary for all countries and regions suffering from COVID-19. Thus, being aware of the overall influence of the service sector is critical for investors and policymakers globally.

Funding: This work was supported by the National Natural Science Foundation of China [71103179].

Competing Interests: The authors declare that they have no competing interests.

Acknowledgement: The authors would like to thank the editor and anonymous referees for their valuable comments.

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Appendix-A. SWS Industry securities index names corresponding with their codes.
Codes
Index names
Codes
Index names
801011
Forestry
801155
Chinese medicine
801012
Agricultural Products
801156
Health Care Service
801013
Agricultural Conglomerates
801161
electric Utilities
801014
Feed Processing
801162
Environmental Facilities & Service
801015
Fishery
801163
Gas Utilities
801016
Farming
801164
Water Utilities
801017
Husbandry
801171
Marine Ports & Service
801018
Animal Health
801172
public transit
801021
Coal Mining
801173
Airlines
801022
other Mining
801174
Airport service
801023
Oil & Gas Drilling
801175
Highways
801024
Mining Equipment & Services
801176
Marine
801032
chemical fiber
801177
Railroads
801033
Chemical materials
801178
Trucking
801034
chemical products
801181
Real Estate Management & Development
801035
Petrochemical Industry
801182
Park Exploitation
801036
Plastic
801191
Diversified Financial Service
801037
Rubber
801192
Banks
801041
Steel
801193
Capital Markets
801051
Metal New materials
801194
Insurance
801053
Gold
801202
Trading
801054
Precious Metals & Minerals
801203
retailing
801055
Industrial Metal
801204
Specialty Retail
801072
General Industrial Machinery
801205
Commercial Property Service
801073
Instrument & Apparatus
801211
Catering
801074
Special Equipment
801212
Attractions
801075
Metal Products
801213
Hotel
801076
Transporting Facilities
801214
Leisure Conglomerates
801081
Semi-conductor
801222
Software
801082
Other Electronic Products
801223
IT Services
801083
Electronical Part & Component
801231
Conglomerates
801084
Optical & Opto-electronic Products
801711
Cement
801085
Electronical Manufacturing
801712
Glass Products
801092
Automobile Services
801713
Other Construction Materials
801093
Auto Parts & Equipment
801721
Homebuilding
801094
Automobile Manufacturers
801722
Decoration
801101
Computers & Peripherals
801723
Infrastructures
801102
Communications Equipment
801724
Specialty Engineering
801111
Household Appliances
801725
Landscape engineering
801112
Audiovisuals
801731
electrical machinery
801123
Beverage
801732
Electric Automation Equipment
801124
Food Products
801733
power supply equipment
801131
Textiles
801734
High-Low-voltage Switch Equipment
801132
Apparel
801741
Aerospace Equipment
801141
Packaging & Printing
801742
Aviation Equipment
801142
Household Products
801743
Defense Equipment
801143
Paper Products
801744
Shipbuilding
801151
Chemical pharmacy
801751
Advertising & Broadcasting
801152
Biotechnology
801752
Internet Media
801153
Health Care Equipment
801761
Culture Media
801154
Health Care Distributors
801881
Other Transporting Equipment

Appendix-B. Summary of minute-per-minute returns of the SWS securities industry indices
Index
Mean(‰)
SD (%)
Skew
Kurt
JB test
AR1
ADF
801011
-0.903
16.065
0.746
12.393
13395***
-0.312***
-15.291***
801012
-0.681
5.323
-0.205
6.578
1921***
-0.167***
-14.535***
801013
-0.81
16.37
0.212
8.831
5061***
-0.245***
-16.544***
801014
-1.978
11.613
1.438
39.187
195139***
-0.027***
-15.595***
801015
-0.343
7.478
-0.501
8.214
4175***
-0.292***
-14.733***
801016
-0.169
7.627
7.071
241.454
8449653***
0.011***
-15.491***
801017
-2.167
12.145
1.729
44.891
261638***
-0.048***
-14.903***
801018
-1.939
9.717
-0.05
12.299
12805***
-0.115***
-15.709***
801021
-0.169
5.56
-0.977
37.747
179355***
0.003***
-14.786***
801022
1.28
11.014
-0.096
5.133
679***
-0.344***
-16.618***
801023
1.131
11.162
0.001
3.123
2
-0.452***
-17.112***
801024
1.362
7.237
0.04
8.987
5308***
-0.142***
-13.563***
801032
0.436
5.511
0.157
9.999
7268***
-0.066***
-14.094***
801033
-0.763
5.942
-0.425
10.867
9271***
-0.136***
-14.543***
801034
-0.034
4.086
-2.999
111.755
1756809***
0.155***
-14.632***
801035
0.543
8.122
-0.022
3.004
0
-0.433***
-15.593***
801036
0.146
6.713
3.799
114.012
1833495***
-0.053***
-15.153***
801037
0.301
5.605
-0.742
15.213
22414***
-0.11***
-14.207***
801041
0.951
9.368
0.377
24.26
67013***
-0.201***
-14.351***
801051
-0.608
5.895
-1.513
29.834
107985***
0.054***
-14.748***
801053
1.742
11.145
-0.355
70.667
678131***
-0.153***
-15.589***
801054
2.997
7.955
2.589
40.927
216983***
0.062***
-15.215***
801055
-0.042
5.526
-0.366
7.556
3154***
-0.233***
-13.098***
801072
-0.799
4.243
-1.937
76.396
799947***
0.084***
-14.131***
801073
3.452
6.1
1.167
25.485
75674***
0.018***
-15.159***
801074
-1.047
4.677
-2.233
58.968
466814***
0.06***
-14.793***
801075
0.098
5.48
-0.329
8.875
5175***
-0.141***
-13.245***
801076
-0.068
7.743
0.124
6.562
1888***
-0.332***
-15.222***
801081
0.45
10.877
-0.902
26.515
82365***
0.105***
-13.42***
801082
0.194
7.866
0.627
11.823
11760***
0.077***
-14.136***
801083
-1.317
9.022
-1.62
33.36
138049***
0.068***
-13.503***
801084
-0.144
8.405
-1.465
30.54
113585***
0*
-14.966***
801085
0.205
9.623
-0.513
24.526
68771***
0.081***
-14.303***
801092
0.746
12.032
-0.273
6.601
1964***
-0.359***
-16.821***
801093
0.464
5.12
-1.222
75.057
769773***
0.053***
-15.378***
801094
-0.855
5.687
-0.783
28.128
93863***
-0.021***
-16.051***
801101
-1.415
7.657
-2.453
55.807
416502***
0.1***
-14.612***
801102
-1.525
6.582
-3.256
105.064
1548860***
0.165***
-14.506***
801111
-1.699
7.908
-4.988
116.867
1934734***
0.082***
-13.435***
801112
0.67
8.655
-0.004
6.449
1762***
-0.261***
-13.704***
801123
0.02
5.659
0.076
14.317
18970***
0.101***
-14.104***
801124
-0.563
6.134
-0.148
17.297
30282***
-0.025***
-14.013***
801131
-0.056
4.318
-0.34
19.892
42321***
-0.108***
-13.335***
801132
-1.593
5.244
-4.632
115.477
1886105***
-0.049***
-14.272***
801141
-0.478
5.487
-0.3
9.228
5797***
-0.002***
-14.648***
801142
-0.532
4.744
-1.367
45.758
271836***
0.025***
-14.31***
801143
-0.433
6.842
-0.82
10.068
7796***
-0.112***
-15.213***
801151
0.74
5.486
0.174
45.784
271083***
0.119***
-15.87***
801152
-1.356
6.436
-1.093
25.637
76590***
0.161***
-12.885***
801153
-1.334
6.454
-1.359
26.376
82010***
0.16***
-12.518***
801154
-1.427
4.945
0.03
12.11
12290***
-0.026***
-13.718***
801155
-1.345
4.754
-2.261
53.329
378124***
0.16***
-14.2***
801156
-2.577
8.67
-0.426
17.912
33035***
0.09***
-12.896***
801161
-0.411
4.091
-0.905
24.588
69499***
-0.161***
-14.244***
801162
-0.152
4.254
-0.526
19.486
40411***
0.012***
-12.533***
801163
-0.31
5.695
-0.632
23.393
61819***
-0.038***
-15.138***
801164
-0.563
5.913
-0.219
5.001
622***
-0.338***
-13.14***
801171
-0.084
6.469
-0.147
4.522
356***
-0.354***
-13.978***
801172
0.622
6.596
-0.309
20.147
43595***
-0.274***
-14.805***
801173
0.619
9.279
-0.044
5.41
861***
-0.343***
-14.24***
801174
0.729
7.313
0.034
11.978
11938***
-0.076***
-12.674***
801175
0.723
3.825
-0.373
6.507
1904***
-0.279***
-13.566***
801176
1.587
8.194
0.751
12.524
13767***
-0.169***
-13.217***
801177
0.174
8.336
0.144
5.303
797***
-0.403***
-15.965***
801178
-1.142
4.751
-1.183
26.155
80226***
-0.044***
-14.08***
801181
-0.869
4.372
-1.865
61.322
505764***
0.075***
-14.24***
801182
-0.189
5.001
-0.036
50.9
339765***
-0.117***
-13.432***
801191
-0.07
6.451
-0.567
23.318
61325***
-0.087***
-14.984***
801192
-0.726
4.571
0.141
15.213
22100***
-0.124***
-15.312***
801193
-0.799
6.114
1.626
58.392
455934***
0.08***
-14.999***
801194
-1.106
5.455
0.029
20.385
44756***
0.027***
-15.165***
801202
0.073
5.736
-0.133
14.933
21097***
-0.234***
-14.441***
801203
0.529
4.123
-0.237
21.943
53169***
-0.104***
-13.023***
801204
-0.132
6.666
0.083
10.175
7627***
-0.214***
-15.7***
801205
0.129
7.235
0.473
15.382
22836***
-0.29***
-15.406***
801211
-3.334
11.18
0.032
6.149
1469***
-0.199***
-15.77***
801212
-0.138
8.416
0.541
16.227
26081***
-0.216***
-15.32***
801213
3.7
10.659
1.261
22.761
58767***
-0.067***
-13.902***
801214
-1.14
9.226
-0.885
34.51
147491***
-0.023***
-14.829***
801222
-0.788
6.87
-2.365
79.895
878908***
0.165***
-14.683***
801223
0.796
11.852
0.029
6.514
1829***
-0.352***
-15.129***
801231
-0.16
5.522
-0.291
34
142354***
-0.1***
-14.273***
801711
1.899
8.488
3.049
77.688
831555***
0.12***
-15.411***
801712
1.441
8.734
0.216
7.709
3311***
-0.252***
-15.216***
801713
0.895
5.733
0.907
22.886
59051***
0.016***
-15.016***
801721
0.191
9.769
0.134
4.434
315***
-0.382***
-15.273***
801722
0.785
5.303
-1.06
22.936
59520***
-0.116***
-16.152***
801723
0.678
5.343
0.459
15.852
24586***
-0.093***
-12.759***
801724
0.836
6.232
-0.05
6.752
2086***
-0.3***
-13.615***
801725
-1.986
6.082
-0.631
21.37
50208***
-0.222***
-14.937***
801731
1.57
8.162
-0.619
18.209
34482***
-0.108***
-15.183***
801732
0.149
6.248
-0.255
12.16
12463***
-0.045***
-14.955***
801733
2.507
6.899
6.863
213.396
6583022***
0.032***
-14.506***
801734
-0.391
4.735
-1.798
57.386
439925***
-0.015***
-14.019***
801741
1.939
5.806
2.174
50.854
341915***
-0.065***
-14.438***
801742
1.342
4.438
-0.222
13.912
17663***
0.096***
-13.931***
801743
1.086
5.518
0.065
16.33
26314***
-0.145***
-15.402***
801744
-0.54
9.756
0.483
98.549
1352077***
-0.143***
-16.34***
801751
-0.737
8.929
-0.352
7.031
2480***
-0.176***
-14.61***
801752
1.823
7.355
-1.207
45.102
263352***
0.132***
-13.324***
801761
-1.072
4.977
-1.975
36.741
170898***
0.034***
-13.108***
801881
0.67
7.784
0.006
7.847
3479***
-0.189***
-15.836***

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Footnotes:

1. Most of the studies on industry-specific spillover network analysis of China’s stock market chose the industry classification standard created by China Securities Index (CSI) Co., Ltd. (the CSI standard). However, the CSI standard cannot match all research targets. According to the CSI standard, the categories of “consumption goods” and “capital goods” are not parallel with each other. Companies supplying goods or services for consumption demands belong to the “consumer staples” or “consumer discretionary” sectors (level-1 categories). In contrast, companies meeting the investment demand cannot be classified as a sector. They can be classified only as an industry group, “capital goods” (a level-2 category), which belongs to the industrial sector. Thus, the spillover network analysis using the CSI standard cannot reflect the economic demand and its change correctly. The SWS standard divided the industry sectors into four sector groups, each of which is homogenous in meeting specific economic demand.

2. As early as December 27, 2019, the local government of Wuhan began to report patients with “unknown pneumonia”, and made public health responses to the infection. As of January 20, 2020, the Chinese government began to implement nationwide containment of COVID-19. On January 31, 2020, the World Health Organization (WHO) declared COVID-19 a public health emergency of international concern (PHEIC). To guarantee that all patients could be treated, the Chinese government covered all bills of pharmaceutical treatment via their budgets. In addition, to reduce the size of the pandemic, multiple non-pharmaceutical interventions (NPIs) were used by the Chinese government, including intercity travel restrictions, the early identification and isolation of suspected ill people and contact restriction measures. As a result, the outbreak was preliminarily contained in China by the end of February. Since March 18, 2020, the number of new patients has remained under ten per day. However, COVID-19 spread outside of China. On February 29, 2020, the WHO increased the assessment of the risk of spread to “very high” at a global level.