Co-integration

Table 3.8 reports the estimation results of the modified GARCH (1,1)-M models with dummy variables during the sample period from 2 January 1995 to 31 December 2003. For SHA (B) and SZA (SZB) estimations, only the regulatory events relevant for the A (B)-share markets are included.

Panel A of Table 3.8 presents the coefficient estimates in the conditional mean equation. All of the four coefficients for the GRACH-in-Mean item turn out to be positive, while only the coefficient for SZA is statistically significant. The results give weak evidences of the positive relationship between the conditional mean and volatility of index returns in China. With the exception of interest rate cut news, all other categories of events have statistically significant impacts on the stock market returns on the announcement days.

Consistent with Hypothesis 1, for the A and B-share markets, there are statistically significantly positive aggregate stock returns in response to the announcements of demand boosting policies and measures. The coefficients indicate that on days with this type of news announcements, stock returns increase 2.8%, 2.1%, 4.6%

and 5.7% in SHA, SZA, SHB and SZB markets, respectively. What is remarkable is that the B-share markets not only display stronger reaction but also react statistically significantly one trading day before the news is announced. As for SHB market, the positive impact of demand boosting policies and measures continues to be statistically significant one trading day after the news announcements.

Table 3.8

Estimation Results of Modified GARCH (1, 1)-M Model without Cross-market Dummies (1995-2003)

Coefficient SHA SZA SHB SZB

Panel A: Conditional Mean Equation

GARCH 0.0202 0.0223** 0.0015 0.0012

Constant -0.0050 -0.0092 -0.0073 -0.0028

DBP(-1) 0.2456 0.0931 2.0763*** 0.5978*

DBP 2.7790*** 2.0567*** 4.6420*** 5.7203***

DBP(1) -0.3951 -0.0845 2.4938*** 0.6590

DSS(-1) -0.1363 0.0920 0.3507 -0.6500

DSS -3.5743*** -3.0821*** -3.4795*** -2.9165***

DSS(1) -0.1339 -0.1849 -0.8098** -0.1110

MSE⁺(-1) 0.0125 0.1130 -0.0416 -0.0233

MSE⁺ -3.4504*** -3.2989*** 4.2305*** 3.6264***

MSE⁺(1) 0.2656 0.5473 0.1916 0.0686

MSE^- (-1) -1.2447*** -1.2443*** - -

MSE^- 4.0505*** 4.9635*** - -

MSE^-(1) 0.8501 1.0252 - -

TCC(-1) 0.7057 0.3787 -0.1053 -0.4022

TCC 1.2307*** 1.6409** 1.2241*** 0.7455**

TCC(1) -0.5648 -0.5433 0.2758 0.1758 INT(-1) -0.2212 -0.0548 -0.0138 -0.0662

INT -0.3172 0.0131 -0.0227 -0.1442

INT(1) 0.1173 0.0727 -0.0014 -0.0642

AR(1)/MA(1) - - 0.0773*** 0.0594***

LMT -0.5725*** -1.0139*** 0.0318 0.0088

t-dist. d.f. 4.1588*** 3.7874*** 2.1921*** 2.1186***

Panel C: Residual diagnostic tests

Q(8) 11.0690 12.2660 14.236 15.4870 Q²(8) 0.9232 1.8959 3.535 4.8395 ARCH-LM 0.1339 0.5587 0.6242 0.0764 Note: ***, ** and * indicate that the test statistics are significant at the 1%, 5% and 10%

levels, respectively.

As for the events related to the disciplinary action and strengthening of supervision which reduces demand, it is not surprising to see that all four submarkets show dips of returns on the event days. On average, the conditional mean returns are -3.6%, -3.1%, -3.5 and -2.9% for SHA, SZA, SHB and SZB markets, respectively. The overall findings are in line with Hypothesis 2. I also note that for SHB market the negative impact continues to be statistically significant one trading day after the announcements.

There are evidences of asymmetric reactions to news in relation to speeding up the market supply expansion of the A-share and B-share markets. For SHA and SZA markets, when there is this category of announcements, the A-share aggregate returns plunge 3.5% and 3.3%, which are consistent with Hypothesis 3. However, on the announcement days regarding the B-share market expansion, SHB and SZB markets rise 4.2% and 3.6%, respectively. This result supports Hypothesis 5 but not Hypothesis 3. It shows that an increase in the B-share market size is good news to the B-share investors.

The news indicating to show down the market supply expansion is only relevant for the A-share markets as discussed in Section 3.3.2. The coefficients on SHA and SZA markets show that the A-markets rise more than 4% on the announcement days, which are in line with Hypothesis 4. There are statistically significant negative stock returns one trading day before the news is announced.

This is particular possible as slowing down the supply expansion measures, for

example, the suspension of state share reduction program is normally used as an administrative measure to stabilize the gloomy markets.

The stock market reactions to news of transaction cost cuts are as expected in Hypothesis 6, evident in the statistically significant positive coefficients for dummy variable TCC in all submarkets. However, the relative smaller magnitudes of these coefficients, which are 0.7 to 1.6% in four submarkets, are worth noting.

By employing Wald test, Chi-square statistics indicate the null hypotheses that magnitudes for the coefficients on TCC are same as those on DBP, DSS, MSE⁺ and MSE^- are all rejected at the 1% significance level.

In contrast to statistically significant impacts of regulatory changes, all the coefficients on the interest rate cut dummy variables (including leading and lagging variables) are not statistically significant. As the interest rate cut announcement effect is not observed, I reject Hypothesis 7 and support Hypothesis 8. The seemingly insensitivity to interest rate related announcements may be explained by the phenomenon that the interest rate adjustments are usually well expected by the market through a series of policy debates and the special institutional background as discussed in Section 3.3.3. It may also be related to the small event sample size of interest rate changes in China, where bank saving and loan rates are governed by the PBOC and not market determined. While in developed countries, interest rate changes more frequently. In U.S. Federal Reserve discount rate would be adjusted to realign it to market rate or due to Federal Reserve’s monetary policy changes (Chen, Mohan and Steiner, 1999).

The insignificance of announcement effect should be interpreted with care as the empirical design of this study is only to test the interest rate cut announcement effect on stock returns. Whether the interest rate changes have mid-term or long-term effect on the stock markets is beyond the scope of this study and left for further research.

Turning to the estimates for the conditional variance equation, which are listed in Panel B of Table 3.8, the coefficients on the ARCH and GARCH items are statistically significant positive and their sums are very close to unity. The result indicates that in each submarket, conditional variance takes a long time to dissipate, that is, volatility is said to be ‘persistent’.

The coefficients for number of non-trading days are positive and statistically significant for SHA and SZA markets, showing that the conditional volatilities increase with the number of non-trading days. The coefficients are also positive for SHB and SZB markets, but not statistically significant. These findings are in line with those of Friedmann and Sanddorf-Köhle (2002).

The statistically significant negative coefficients for LMT in SHA and SZA markets suggest that the conditional variances decrease after the imposition of 10% limit on daily price change since 16 December 1996. The results for the B-share markets are inconclusive as coefficients are not statistically significant, but they certainly do not support that price limit reduces volatilities, the view held by conventional wisdom and most regulators. The ineffectiveness of price limit to

reduce variances of SHB and SZB markets is not unusual as critics have documented volatility spillover effect, i.e. price limit causes higher volatility levels on subsequent days. For example, Kim and Rhee (1997) find that price limits do not reduce volatilities on the Tokyo Stock Exchange as volatility is merely temporarily contained and then ‘spill-over’ to subsequent days. Kim (2001) indicates that Taiwan stock market is more volatile under stricter price limits and concludes that restrictive price limits do not moderate stock volatilities. Chen, Kim and Rui (2005) find that price limit is particularly more restrictive for illiquid B-shares and document a large number of occasions where B-shares hit 10% price limit while the firms’ companion A-shares do not. The evidence found by Chen, Kim and Rui (2005) that the price limit is more restrictive in the B-share markets as compared to A-share markets may be used to explain why price limit in China is unsuccessful in damping the volatilities in the B-share markets as restrictive price limits do not moderate stock volatilities due to volatility spillover (Kim, 2001). A thorough investigation of this issue by using company level data is a topic for further study.

The estimated coefficients for the degrees of freedom v are all statistically significant at the 1% level, implying the appropriateness of employing the modified GARCH (1,1)-M model assuming the standardized errors zt under distribution. And the relatively small degrees of freedom parameters for the t-distribution (4.2, 3.8, 2.2 and 2.1 for SHA, SZA, SHB and SZB, respectively) suggest that the distribution of the standardized errors departs from normal distribution.

Residual diagnostic test statistics are reported in Panel C of Table 3.8. The Ljung-Box Q(8), Q²(8) and ARCH(1)-LM test statistics are all not statistically significant, indicating that the residuals are free of ARCH effect and autocorrelation problems.

Therefore, these diagnostic test results show that the specifications of the mean and volatility equations in the modified GARCH (1,1)-M model are adequate to capture the time-varying nature of the index returns and I do not explore higher order GARCH models.