When choosing the VAR ordering, although it is reasonable to order companies in each market segment according to their average market share, it is still unknown to us whether the results obtained are sensitive to the choice of ordering or not. Hence this research chooses the reversed ordering (from the smallest to the largest) and four other random orderings (generated by computer) to construct the robustness test.
Figure A1 in Appendix display the results, the solid line represents the original plot,
and the dotted lines represent the reversed and four other random ordering plots. As shown from the figure, the results are extremely robust to the changes of ordering.
This conclusion is in line with Deibold and Yilmaz (2009), where they found total connectedness is robust to different orderings.
5 Concluding Remarks
This research measures the return and the volatility total connectedness across different industries in Taiwan’s stock market, both static and dynamic, then tests their relationships with the economic sluggish periods and the market structure.
For the full-sample return and volatility connectedness, we have found from 10%
(telecommunication industry) to 50% (financial business industry) of the forecast error variance comes from connectedness. Also, eight out of nine market segments have their return connectedness stronger than volatility connectedness.
As for the dynamics of the return and the volatility connectedness, using the 200-day rolling samples, we have discovered that, in general, both the return and the volatility total connectedness for all market segments tend to increase during finan-cial crises, where return total connectedness do so in small steps and its volatility counterpart experiences discrete jumps. Moreover, without any exception, return total connectedness is much “smoother” than volatility total connectedness. Finally, from Figure 5 we can see very clear that for return total connectedness, all the
mar-ket segments tend to move in a similar pattern, while volatility total connectedness displays no such consistency.
As for the reasons that cause changes in connectedness, the 2008 global financial crisis has a significant influence on both the return and the volatility total connect-edness for all market segments. Moreover, even after excluding the effect of the 2008 recession, we still find the return total connectedness for most market segments to increase 3% to 4% during economic sluggish periods while their volatility counter-part increase from 4% to 10%. Finally, for a different market segment, the changes in market structure concentration will have a different effect on its level of connect-edness. In particular, when the market becomes more concentrated, the return and the volatility total connectedness will increase for financial business, plastic, and textile industry, while they will decrease for construction, transportation, and food industry.
We have obtained various interesting results in this research, and the possible applications of these results could be enlightening, which includes:
1. Profolio Management: Time-varying diversification opportunities have been studied by numerous researchers (see Fleming et al. 2001, Kirby and Ostdiek 2012 for example). This research obtains the level of connectedness for nine market segments using the full sample. And in the dynamic analysis, we know how much they will rise during crisis periods. Since the ability to diversify is inversely related to the level of connectedness, all these information would be
valuable to achieve skillful portfolio managing.
2. Company Regulation: The 2008 global financial crisis has created renewed interest in company regulation. But for a given industry, how big can a com-pany grow without being labeled as “too big”? The answer to this question not only depends on how well connected this market segment is (which by now we know), but also on what will happen when the company gets large.
Regression results (Table 4 and 5) have shown us that for certain industry (e.g., financial industry), when a company becomes larger, the connectedness will increase, causing more risk when this company fails. On the contrary, connectedness declines when a company grows in some other industry (e.g., food industry), which reduces the risk of a contagion when it fails. Hence, pol-icy maker should have very different regulations when facing companies from different industries.
3. Economic Downturns Identification/Prediction: The results show that connectedness have intimate relation with the economic downturns, volatility total connectedness exhibits jumps when entering a recession, then it jumps back to its original level after crises, for instance. As a result, we can at least try to identify, if not predict, economic downturns using return/volatility total connectedness. Notice that there is literature on predicting financial crisis by exploiting the time-varying nature of connectedness. See Allen et al. (2012),
and Billio et al. (2012) for example.
As for future studies, there are still several intriguing questions not yet answered in this research. First of all, In the static analysis in Section 4.1, Table 2 shows that return connectedness is stronger than volatility connectedness in eight out of nine market segments. The reasons behind this result are still unknown. Second of all, as mentioned repeatedly, return total connectedness is much smoother than its volatil-ity counterpart. Why this should be so is puzzling. Thirdly, on a more technical level, using VAR based variance decomposition is not the only way to capture “total connectedness”. For example, Jorda (2005) suggests using local projection instead of a VAR model to construct impulse response functions. By using a different im-pulse response function, we’ll get a different measurement of total connectedness.
This list of questions is by no means exhaustive. The profound implication and potential application of the results in this research are exciting. Base on these findings, I hope future researchers can eventually find out new theories and results that explain the distinct behavior of the return and the volatility total connected-ness. Moreover, I wish this research can help explore new territories in portfolio management, recession prediction, company regulations, and others.
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APPENDIX
Figure A1: Robustness Check - Reversed and Random Ordering
Figure A2: Return Total Connectedness and Business Cycle
Figure A3: Volatility Total Connectedness and Business Cycle
Figure A4: Market Structure
Table A1: Ticker Numbers and Average Market Shares of the Companies Selected
Market Taiwan Stock Exchange Ticker Number Replacement for
Segment (Average Market Share) Missing Values
1303 1301 1326 1314 1319
Plastic (31.46%) (28.61%) (25.09%) (1.70%) (1.33%)
1304 1313 1310 1312 1307
(1.13%) (1.00%) (0.90%) (0.87%) (0.57%)
5903† 2912 5904† 8941† 2915 2913 2908 2614
Wholesale (33.74%) (16.93%) (10.00%) (6.44%) (4.62%) (1.63%) (1.54%) (1.50%)
and Retail 5905† 2903 6195† 5902 2905 2910 2601
(4.27%) (4.23%) (2.44%) (2.31%) (1.72%) (1.11%) (1.05%)
1216 1227 4205 4207† 1229 1702 1232
Food (47.28%) (6.56%) (5.72%) (4.42%) (4.14%) (2.01%) (1.96%)
1201 4712 1234 1210 1737†
(3.86%) (3.83%) (3.70%) (3.67%) (2.14%)
2618 2603 2610 2609 2615 5601
Transpor- (16.44%) (12.32%) (11.64%) (8.58%) (8.54%) (2.25%)
tation 5604 2606 5609† 2607 2605
(7.12%) (6.62%) (5.17%) (3.25%) (2.71%)
1402 1434 4401 1440 1409 1444 1447 1460
Textile (30.06%) (8.98%) (7.87%) (4.10%) (3.01%) (1.53%) (1.37%) (1.02%)
1476† 1451 1477† 4417† 1419
(4.33%) (3.82%) (2.97%) (2.79%) (1.81%)
2412 4904 3045 2498 3152† 5388 4908
Telecom- (25.71%) (17.58%) (9.05%) (9.03%) (3.30%) (0.80%) (0.79%)
munication 3095† 4909 6143 6152 6170
(1.65%) (1.23%) (1.17%) (0.89%) (0.80%)
2330 5346 5387 5347 2303 5326
Semi- (22.98%) (9.99%) (9.52%) (5.37%) (5.15%) (0.69%)
conductor 2454† 2311 2325 5351 2344
(4.23%) (1.93%) (1.21%) (1.07%) (0.94%)
2882 2881 5820 2886 2891
Financial (12.32%) (7.48%) (6.08%) (5.85%) (5.27%)
Business 2883 2892 2880 2885 2801
(3.59%) (3.49%) (3.45%) (2.62%) (2.57%)
5522 5508 5512 5213 4416 5514 2504
Construction (13.87%) (7.79%) (6.42%) (3.86%) (3.74%) (1.80%) (1.53%)
2501 5521 2542 5511 5520
(2.80%) (2.73%) (2.13%) (1.87%) (1.83%)
[†] represent the campanies with missing data during the time span of Jan/04/2000 to Dec/31/2004.