We have found many interesting features about the return and the volatility total connectedness in the last two subsections. In this subsection, the key factors that strengthen or weaken the return and the volatility total connectedness are identified.
First, it’s already very clear from previous analysis that economic sluggish periods have a sizable impact on the return and the volatility total connectedness, but still, we will put this statement to test and find out exactly how strong the impact is.
Another possible factor that can influence return/volatility total connectedness may be the “degree of concentration” in each market segment. That is, within a market segment, if some major players continue to increase in size and gradually turn the market into an oligopoly, will this change affect connectedness in this market seg-ment? On the other hand, if the major players decrease in size, what will happen to return/volatility connectedness?
To find out what forces strengthen or weaken connectedness, the following
regres-sion is run for each market segment.
Yt= β0+ β1t + β2Dt2008+ β3Dt+ β4Ct+ t ,
where Yt is the daily return/volatility total connectedness of the nine market seg-ments, which is plotted in Figure 1. D2008t is a daily dummy variable that equals unity during the 2008 global financial crisis (Sep. 2008 - May. 2009), Dt is a daily dummy variable that equals unity during economic sluggish periods (shaded area in Figure 2), and Ct is a daily time series data that captures the degree of concentra-tion for each market segments. Note that due to the uniqueness of the 2008 global financial crisis, we use Dt2008 to extract its effect from Dtin our regression. Also note that Ct is obtained by summing the market share of the top 10 companies in each industry each day. If this value is very low, then the market segment tends to be close to perfect competition. Figure 6 shows the market structure of plastic industry and transportation and shipping industry. The combined market share of the top 10 companies in the plastic industry doesn’t fluctuate much over the years, while for transportation and shipping industry, it decreases over the years from around 95% in the year 2000 to 75% in the year 2016. The complete results are shown in Appendix (see Figure A4). Finally, note that we include a linear time trend in our regression, and all the variables are daily time series data, where t goes from 1 to 4013 for all market segments except for financial and telecommunication industry
Figure 6: Market Structure (selected)
(where t goes from 1 to 3250).
For both the return and the volatility total connectedness, there are nine dif-ferent market segments to be considered, hence a total of 18 regressions to run.
Table 4 summarizes the estimated coefficients of D2008t , Dt, and Ct ( ˆβ2, ˆβ3, ˆβ4) for return total connectedness, while Table 5 does the same thing for volatility total connectedness. For inference purposes, this research uses the Newey-West standard error (Newey and West, 1987) with Quadratic Spectral kernel and the automatic bandwidth selection procedure proposed by Newey and West (1994).
We begin from Table 4. First of all, by observing the coefficients for Dt2008 (the βˆ2), it is evident that the economic crisis in 2008 has a significant influence on the return total connectedness. The coefficients in eight out of nine market segments are statistically significant under 1% confidence level, and all of them have positive signs, meaning the 2008 crisis increases the return total connectedness for all those market segments, as we suspect from the previous analysis. Moreover, five of them increase more than 10% during the 2008 crisis, with the highest increment in textile
Table 4: Return Total Connectedness Regression Results
Industry βˆ2 βˆ3 βˆ4
Financial Business 4.87% 4.94%∗∗ 2.200∗∗∗
(0.0367) (0.0212) (0.5365)
Plastic 10.66%∗∗∗ 3.50%∗∗∗ 0.898∗∗
(0.0214) (0.0096) (0.4019)
Wholesale and Retail 8.00%∗∗∗ 6.11%∗∗∗ 0.171
(0.0250) (0.0122) (0.1356)
Textile 14.20%∗∗∗ 3.46%∗∗∗ 0.081
(0.0250) (0.0114) (0.1431)
Semiconductor 11.94%∗∗∗ 4.09%∗∗∗ 0.033
(0.0214) (0.0100) (0.0736)
Telecommunication 6.08%∗∗∗ 4.72%∗∗∗ −0.058
(0.0207) (0.0118) (0.0760)
Construction 14.20%∗∗∗ −0.08% −0.726∗∗∗
(0.0289) (0.0136) (0.1195)
Transportation 8.25%∗∗∗ 3.68%∗∗∗ −0.781∗∗∗
(0.0229) (0.0104) (0.1745)
Food 13.69%∗∗∗ 2.00% −1.109∗∗∗
(0.0297) (0.0141) (0.4185)
∗∗∗ p ≤ 0.01, ∗∗p ≤ 0.05, ∗ p ≤ 0.1 Standard errors are in the parenthesis
and construction industries (14.20%). Finally, it is important to note that the only industry that is not statistically significant is, surprisingly, the financial business industry. This might be the consequence of the fact that, as we can see in Figure 3, although the return total connectedness for financial business industry increases sharply during the 2008 global financial crisis, but after the crisis (after May. 2009), it is still at a very high level, comparing to other industries (see Figure 5).
As for the coefficients for economic sluggish periods ( ˆβ3), first and foremost, even
after excluding the effect of the 2008 recession, seven out of nine industries are sig-nificant, with six of them being significant under 1% confidence level. This provides us evidence that economic sluggish periods indeed have strong influences over return total connectedness (even after excluding the 2008 crisis). Second of all, among the statistically significant coefficients, all of them have positive signs, which means that the return total connectedness tends to increase under economic sluggish periods.
Third of all, when entering economic sluggish periods, wholesale and retail trade industry has the largest amount of increase in return total connectedness (6.11%), followed by financial industry (4.94%) and telecommunication (4.72%). Recall that when using the full sample, financial business industry has the highest level of re-turn total connectedness (Table 2), thus perhaps not very surprising to see its rere-turn connectedness intensify drastically during crisis periods. What perhaps is peculiar is that when the full sample is used, both the telecommunication and the wholesale and retail trade industry have the lowest level of return total connectedness among all market segments. Yet when entering economic sluggish periods, they have the largest increment. Last but not least, the coefficients of return total connectedness are similar across market segments (around 3% to 4%), except for the wholesale and retail trade industry. This result again coincides with what we’ve observed from Figure 5, where the return total connectedness plot looks somewhat like vertical shifts of one another.
As for the coefficients for Ct ( ˆβ4), recall that both Yt and Ct are percentages
and take values between zero and one, so the coefficient for Ct in financial indus-try (2.200) means that when the combined market share of the top 10 companies increase 1%, return total connectedness for financial business industry will increase by 2.2%. It is very important to note that, among the statistically significant co-efficients, some have positive signs while the others are negative. This again gives us evidence of the distinctive nature of different market segments. That is, for fi-nancial business and plastic industry, return total connectedness will increase when the market becomes more concentrated. However, they will decrease for construc-tion, transportaconstruc-tion, and food industry. Note that these results imply that, in the financial business and plastic industry, if a company continues to grow, then the con-nectedness will increase and causes higher systemic risk when this company fails. On the other hand, in construction, transportation, and food industry, connectedness declines when a company grows and reduces the risk of a contagion.
Table 5 shows the estimated coefficients of D2008t , Dt, and Ct ( ˆβ2, ˆβ3, ˆβ4) for volatility total connectedness. First of all, for the coefficients of D2008t (the ˆβ2), six out of nine are statistically significant with positive signs, meaning the 2008 crisis increases the volatility total connectedness, with the highest increment in semicon-ductor industry (21.88%). Note that the magnitude of the increments (the ˆβ2) here is much stronger than in Table 4.
For the coefficients of economic sluggish periods ( ˆβ3), similar to its return coun-terpart (Table 4), even after excluding the effect of the 2008 recession, six out of
Table 5: Volatility Total Connectedness Regression Results
Industry βˆ2 βˆ3 βˆ4
Financial Business 15.71%∗∗∗ 10.44%∗∗∗ 5.286∗∗∗
(0.0547) (0.0316) (0.7999)
Plastic 14.19%∗∗∗ 4.09%∗∗ 3.454∗∗∗
(0.0356) (0.0160) (0.6702)
Wholesale and Retail 3.96% 7.53%∗∗∗ −0.252
(0.0462) (0.0226) (0.2513)
Textile 8.47%∗ 2.66% 0.789∗∗∗
(0.0461) (0.0211) (0.2641)
Semiconductor 21.88%∗∗∗ 1.81% 0.134
(0.0345) (0.0160) (0.1183)
Telecommunication 11.17%∗∗∗ 7.91%∗∗∗ −0.015
(0.0312) (0.0179) (0.1147)
Construction −3.65% 4.46%∗ −0.786∗∗∗
(0.0540) (0.0254) (0.2236)
Transportation 17.86%∗∗∗ −0.20% −0.531
(0.0557) (0.0254) (0.4241)
Food −4.50% 6.52%∗∗ −2.364∗∗
(0.0695) (0.0330) (0.9788)
∗∗∗p ≤ 0.01, ∗∗p ≤ 0.05, ∗p ≤ 0.1 Standard errors are in the parenthesis
nine industries are significant. This leads us to conclude that economic sluggish pe-riods, even after excluding the 2008 crisis, indeed have strong influences over both the return and the volatility total connectedness. Moreover, when entering economic sluggish periods, financial business industry has the largest growth in volatility total connectedness (10.44%), followed by telecommunication (7.91%) and wholesale and retail trade (7.53%) industry. Similar to what we have discovered, when using the full sample, both the telecommunication and the wholesale and retail trade industry
have the lowest level of volatility total connectedness. Yet when entering economic sluggish periods, they have the largest increment. Finally, recall that the coefficients of return total connectedness are similar across market segments (around 3% to 4%).
The coefficients of volatility total connectedness, on the other hand, differ a lot from one another (from 4% to 10%). This conclusion is in line with what we’ve observed from Figure 5, where the return total connectedness plot has a clear pattern while its volatility counterpart exhibits no such consistency at all.
As for the coefficients of Ct ( ˆβ4), just like Table 4, more than half of them are statistically significant, and among those coefficients, some have positive signs while the others are negative. As a consequence, for the financial business, textile and plastic industry, volatility total connectedness will increase when the market be-comes more concentrated. However, they will decrease for construction and food industry.