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Time-varying Informational Efficiencyin China's A-Share and B-Share MarketsXiao-Ming Li aa Department of Commerce, Massey University (Albany), PrivateBag 102904, Auckland, New ZealandPublished online: 18 Oct 2010.

To cite this article: Xiao-Ming Li (2003) Time-varying Informational Efficiency in China's A-Share and B-Share Markets, Journal of Chinese Economic and Business Studies, 1:1, 33-56, DOI:10.1080/1476528032000039730

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Journal of Chinese Economic and Business Studies, Vol. 1, No. 1, 2003, pp. 33–56

Time-varying Informational Efficiency in China’s

A-Share and B-Share Markets

XIAO-MING LI

ABSTRACT This paper employs a time-varying framework to examine the informational

efficiency of China’s A-share and B-share markets, with a focus placed on the following

issues: changing weak-form efficiency, the leverage effect, and information transmission

in return volatility. We find that the A-share markets perform better than the B-share

markets in terms of efficiency-improving; significant leverage effects exist in three of four

markets but with different signs; and no weak and strong volatility transmissions

characterise different pairs of markets. Market segmentation is also documented, as

evidenced by no co-movements in the long-run behaviour of the four Chinese share markets.

Key words: A shares; B shares; Weak-form efficiency; Return volatility; China.

JEL classifications: G14, C22.

1. Introduction

Informational efficiency is one of the three types of efficiency of financial markets,

the other two being operational efficiency and allocative efficiency. Concerned with

the availability and use of information, informational efficiency can be further clas-

sified as weak-form, semi-strong-form, and strong-form efficiencies. Weak-form

efficiency (WFE) is the easiest type of efficiency for quantitative analysis, and

thus has been extensively studied for developed and emerging financial markets.

There is growing evidence that emerging share markets in transition economies

are characterised by time-varying WFE.1 In the Chinese context, Li (2003) uses

a time-varying AR framework combined with the GARCH-type models to assess

the evolution of the Chinese share markets in terms of WFE. That study considers

two aggregate share markets: the Shanghai Stock Exchange (SHSE) and the

Shenzhen Stock Exchange (SZSE), and finds gradually improved WFE for both

of them over the period 1991–2001. While Li’s findings are interesting, it is of

further interest to probe into the time series properties of A and B shares, the two

sub-markets of the SHSE and of the SZSE, for the similar informational efficiency

Xiaoming Li, Department of Commerce, Massey University (Albany), Private Bag 102904, Auckland,

New Zealand; e-mail: x.n.li@massey.ac.nz

Journal of Chinese Economic and Business StudiesISSN 1476-5284 print: ISSN 1476-5292 online � 2003 Taylor & Francis Ltd

http://www.tandf.co.uk/journalsDOI: 10.1080/1476528032000039730

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issues, if only because the results may reveal which one, the A-share market or the

B-share market, has contributed more to overall efficiency improving in the aggre-

gate stock market.

Before starting formal statistical investigations, an account of different classes of

the Chinese shares, especially of A and B shares, may be in order. The account is

necessarily brief, however, to converse space, and more details can be found in

the existing literature.2 The shares of China’s listed companies are presently

categorised into six classes: A, B, H, red-chip, N and S, mainly in accordance

with different listing locations and the investors faced. Among them, A and B

shares are traded on the SHSE and the SZSE, both types of shares being denomi-

nated in the renminbi (RMB), but the former are traded in RMB whereas the latter

are in US dollars (Shanghai) or HK dollars (Shenzhen).3 A shares are restricted

to Chinese nationals domiciled in mainland China. B shares were available only

to foreigners, Hong Kong, Macao and Taiwan investors, as well as overseas

Chinese citizens until 31 May 2001. The original idea of devising B shares in addi-

tion to A shares for the same listed company was mainly for overseas investors to

circumvent foreign-exchange controls while making security investments in

China. However, due to problems such as isolations between Chinese and foreign

currencies, information asymmetry between domestic and foreign investors, clien-

tele bias against B shares and so on, the two share markets moved independently

most of the time, as illustrated in Figure 1. While the A-share markets grew rapidly,

the B-share markets sometimes were so faltering that the listed companies’ market

values became even lower than their net capital values. In other words, B shares

failed to play an expected role of raising capital for the listed companies, and

even became a burden on China’s security markets.

It is apparently out of tune with the workings of modern financial markets that

the same company in the same country and same market issues different classes of

shares just according to different currencies and/or investors with different residen-

cies/nationalities. Although aware of this bizarre practice, the Chinese authorities,

such as the China Securities Regulatory Commission (CSRC), were perplexed

for effective remedies. Obviously, the ultimate way out is for the two markets to

merge, but the main problem afflicting them is the restricted convertibility of the

RMB. In 2000–2001, China’s impending entry into the WTO speeded up its pre-

parations for further financial liberalisation, including an ultimately complete lift

of the ban on currency convertibility. Since then, a series of measures regarding

B shares have been initiated. They included the following. (1) As of 1 June 2001,

domestic Chinese nationals are allowed to trade B shares using ‘lawfully-acquired’

foreign exchanges. Since then, the shares have virtually become available to all

investors, albeit with US or HK dollars. (2) Those B-share-only companies are

allowed to issue A shares. (3) No newly-listed companies are permitted to issue B

shares only. The first measure can be seen as the prelude of opening the A-share

markets to all investors, domestic and foreign. The second and third measure

seem to be aimed at ultimately phasing out the B-share-only companies.

These new policies taken by the CSRC were perceived by the markets as a signal

that the merger between A and B shares seems to be underway. What immediately

followed was a buying spree of B shares in both the Shanghai and Shenzhen stock

markets. Figure 1 shows that the prices of B shares rocketed in March–July 2001. It

also shows the price discounts on B shares relative to A shares for both the SHSE

and the SZSE throughout the entire sample period. Investors went on a panic

purchase of B shares in the hope that they could make enormous profits from

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such huge price differentials between A and B shares today, should the two markets

merge tomorrow. But the expected merger did not actually happen. Then, from

August 2001, the prices of both types of shares started to fall all the way down,

according to Figure 1.

Having set up a brief background of the A- and B-share markets, we are now in a

position to pose several questions regarding their informational efficiency, which the

rest of the paper will address. How does weak-form efficiency in each of the markets

change over time? How do they react to the arrivals of good and bad news? How

seriously are they segmented in terms of the short-run interdependence of return

volatility between them and in terms of co-movements in their long-run trends?

As far as China’s A- and B-share markets are concerned, the WFE question has

been addressed in Laurence et al. (1997) and Long et al. (1999), while the

second and third questions have not yet been addressed in previous studies, to

the best of our knowledge.4 However, the statistical techniques, such as random

Figure 1. (a) The price indexes of Shanghai’s A and B shares; (b) the price indexes ofShenzhen’s A and B shares. — A share; - - - B share.

Time-varying Informational Efficiency 35

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walk and Granger causality tests, employed by the authors are, in our opinion, inap-

propriate. To show the reasons why, Section 2 presents the statistical profiles of the

data and their discussions. Section 3 describes and explains the models and estima-

tion methods we employ in this paper. Empirical results are given and interpreted in

Section 4, and concluding remarks are made in Section 5.

2. Statistical Profiles of Daily Returns on A and B Shares

In this section, we present the statistical profiles of the A- and B-share data. The

data are the daily closing price indexes of the Shanghai SE A Share (SHA), the

Shanghai SE B Share (SHB), the Shenzhen SE A Share (SZA) and the Shenzhen

SE B Share (SZB), obtained from Datastream. The A-share price indexes are in

the Chinese currency. Although B shares are traded in US/HK dollars, their

RMB-denominated price indexes are also readily available and thus were directly

used in our study for the sake of comparability. The sample covers the period

from 5 October 1992 to 24 May 2002 (with a total of 2515 observations), for

which the data on SZA and SZB are available, but for which the data on SHA

and SHB are truncated. The truncation on the SHA and SHB data seems to be

inevitable, because the relationships between the Shanghai and Shenzhen markets

for A and B shares can only be examined over the same sample period.

Table 1 reports some descriptive statistics. All of the four return series exhibit

severe leptokurtosis and are characterised by positive skewness. Not surprisingly,

the Jarque–Bera normality test statistic suggests that they are non-normal, as

evidenced by the highly significant test statistic generated in each case. The four

return series are also found to possess heteroscedasticity and autocorrelation,

given the significant ARCH test statistics and Ljung–Box statistics.

The ADF statistics indicate that there is a unit root in each of the four logarith-

mic price indexes, but not in their first differences. In other words, they all follow a

random walk process. It is tempting to take the acceptance of the random walk

Table 1. Summary statistics

Share Skewness Kurtosis J-B L-B(26) ARCH ADF CUSQ

(r) (r) (r) (r) (r) (p) (r) (p) (r)

SHA 1.661 21.12 2865.7** 120.4** 47.54** � 2.33 � 11.4** 0.408** 0.413**

(18) (17)

SHB 2.145 33.71 4390.2** 79.88** 4.179* � 1.14 � 27.0** 0.117** 0.116**

(3) (2)

SZA 0.980 16.55 3468.1** 52.43** 26.56** � 1.19 � 11.6** 0.316** 0.315**

(19) (19)

SZB 1.996 33.48 5091.6** 124.7** 10.67** � 1.63 � 15.3** 0.153** 0.153**

(8) (7)

Note: ‘p’ denotes the logarithmic price level. ‘r’ denotes the rate of return (i.e. �p). J-B denotes the

Jarque–Bera normality test statistic. L-B(26) denotes the Ljung–Box test statistic of the 26th order.

ARCH denotes the ARCH test statistic of order 1. ADF denotes the augmented Dicky–Fuller test

statistic. CUSQ denotes the CUSUMSQ test statistic. *Significant at the 5% level. **Significant at the

1% level. The figure in parenthesis are the lag lengths chosen according to the ‘t-sig’ method (see Perron,

1997).

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hypothesis for a market price index to suggest that the market is weak-form efficient.

However, this overlooks the fact that the presence of some significant autoregressive

terms in the ADF-test equation still implies the predictability of market returns. In

addition, the computation of the t-type ADF statistic is based on the OLS regres-

sion, which requires the residual also to be normal and homoscedastic, but these

two assumptions are violated in each case, according to the results of diagnostic

checks on the regression equation for the ADF test (not reported).

A further problem that has been largely ignored pertains to the possible struc-

tural change in the behaviour of the data. The last column of Table 1 provides

the CUSUMSQ statistic for each of the ADF-test regression models. One can see

that the null hypothesis of stable parameters is decisively rejected in all four

cases. Hendry and Neale (1991) and Perron (1989) showed that inference on

unit roots is affected by structural change; that is, the unit root tests tend to

under-reject the null of a unit root. If a structural change is abrupt in nature and

takes place at a certain point of time, unit root tests may still be conducted after

taking such a break into account. However, many studies on Chinese stock

market efficiency (including Laurence et al., 1997, as mentioned in Section 1)

have failed to do so when applying unit root tests. Worse still, in the cases where

structural changes take place gradually over time and where they occur at every

point of time, performing unit root tests to examine market efficiency, which is

now time-varying, would be simply out of the question.

To summarise the above discussion, unit root tests such as the ADF test do not

lend a helping hand in studying the efficient market hypothesis (EMH) for the

Chinese stock markets. We need to find an alternative framework for this purpose.

3. Research Framework

Let us start with the definition of weak-form efficiency in searching for such a frame-

work. Fama (1970) defines weak-form efficiency as follows. Security prices fully

reflect the information contained in past price movements, i.e. they do not follow

patterns that repeat and it is not possible to trade profitably purely on the basis of

historical price information. The essence of weak-form efficiency is that past returns

on a market cannot be used to predict current returns on the same market.

Mathematically, this can be formulated as:

rt ¼ b0 þXp

i¼1

birt�i þ et, et � Nð0, �2e Þ ð1Þ

where rt denotes the continuously compounded percentage returns on a share

market. If bi¼ 0 (i¼ 1, 2, . . . , p), then rt¼ b0þ et. That is, current returns rtdepend only on a constant b0 and a white-noise error term et. Since b0 and et do

not contain any information on past returns, we may say that current returns rtcannot be predicted based on past returns rt�i (i¼ 1, 2, . . . , p) and so the market

is weak-form efficient. If bi 6¼ 0, they measure the predictability of current returns

using past returns or the degree to which the market is weak-form inefficient,

according to Fama’s definition.

Equation (1) is a familiar AR( p) model, and provides a useful framework to

test the weak-form EMH (i.e. to test bi¼ 0 against bi 6¼ 0). An advantage of this

framework over the unit-root-test one is that the former can easily accommodate

Time-varying Informational Efficiency 37

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the possibility of gradual structural change – that is, the former can be used to study

time-varying WFE, whereas the latter cannot. Taking into account possible struc-

tural change over time, equation (1) may be modified to become:

rt ¼ b0t þXp

i¼1

bitrt�i þ et, et � Nð0, �2e Þ ð2Þ

and

bit ¼ �ibit�1 þ uit uit � Nð0, �2i Þ and i ¼ 0, 1, . . . , p ð3Þ

Equation (2) is known as the measurement equation, and equation (3) as the transi-

tion equation. In the measurement equation, all model parameters, bit (i¼ 0, 1,

2, . . . , p), are allowed to change at each point of time t. The transition equation

then describes the way these parameters evolve. We term it as general auto-

regression of order 1 (GAR(1)). The GAR(1) specification embraces several likely

outcomes. If �i¼ 0 and �i¼ 1, then bit are constant; if �i 6¼ 0 and �i¼ 1, then bitbecome a random walk; if �i 6¼ 0 and � 1 <�i < 1, then bit follows an AR(1) process;

and so on. Which outcome it will turn out to be, can be – and should be –

determined by the data. The assumption that bit are constant, as implicitly made

in previous studies on China’s share markets, is just one of the likely outcomes

of the transition equation (3), and is too restrictive to be able to capture the true

behaviour of market efficiency in China’s share markets.

A further advantage of employing the AR model over employing the unit-root-

test strategy is that the former can address the problem of heteroscedasticity whereas

the latter, again, cannot. Such a problem does exist in China’s share markets as

confirmed in the preceding section. In finance, heteroscedasticity, in the form of

a time-varying variance, represents time-varying volatility (risk) of the associated

market. The literature has suggested a variety of GARCH-type models from

which we can choose an appropriate one to combine with the AR model for a

particular purpose. More specifically, when considering the possible existence of

return volatility, we need to split equation (2) into two as follows:

rt ¼ b0t þXp

i¼1

bitrt�i þ et, et � Nð0, htÞ ð4Þ

ht ¼ a0 þXm

j¼1

aje2t�j þ

Xm

j¼1

aþj ðeþt�jÞ

2þXn

j¼1

cjht�j , eþt�j ¼ maxfet�j , 0g ð5Þ

with conditional variance ht replacing unconditional variance �2e . Here, the transi-

tion equation (3) is retained. Equation (5), termed the threshold GARCH (or

TGARCH) model, describes the behaviour of conditional variance ht. It also

embraces the possible leverage effect, i.e. asymmetries in the return-volatility reac-

tion to information shocks (see, for example, Campbell and Hentschel, 1992): if

aþj < 0 ð> 0Þ, past bad news (et�j < 0) has a greater (smaller) impact on the current

volatility of returns than past good news (et�j>0).

The time-varying AR model (i.e. equations (4) and (3)) can also be combined

with the TGARCH-spillover model to investigate the transmission of information

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on return volatility across different share markets. The TGARCH-spillover model is

specified as:

ht ¼ a0 þXm

j¼1

aje2t�j þ

Xm

j¼1

aþj ðeþt�jÞ

2þXn

j¼1

cjht�j þXq

j

dje20t�j ð6Þ

where e0t�j may be interpreted as past information shocks to markets other than

the own one. Statistically insignificant values of dj would imply that past information

shocks to other markets are not utilised by, or available to, participants in the own

market, as the return volatility of the own market is not affected by the shocks. In a

sense, this reflects a certain degree of market segmentation.

The above discussion arrives at a conclusion that the AR model is a superior

framework to the unit-root-test one in studying the EMH. This is especially so if

WFE is time-varying. In the case of the AR-model parameters being time-varying,

however, the Kalman Filter algorithm must be applied. For the technical details of

the algorithm, readers are referred to Harvey (1989). Very briefly here, this techni-

ques gives the minimummean-squared-error linear estimator of bit (i¼ 0, 1, 2, . . . , p)based on the observations up to and including time t, even if the disturbances are

not normal (see Maddala and Kim, 1998, p. 473). In other words, it allows learning

to occur with each additional observation. The Kalman Filter technique may be

implemented in an econometrics package TSP with the initial values bi0 and the

starting values of �i and �i (i¼ 0, 1, 2, . . . , p) provided by the user. The final esti-

mates of these values will be determined at convergence by the TSP program.

4. Empirical Results

We first estimated equation (1) for each return series, ignoring the possible insta-

bility of bi and possible non-normality and heteroscedasticity of et. In the estimation

process, the order of the AR terms, p, was determined as such a minimum value that

the associated Ljung–Box Q statistics of et up to the 26th order show no evidence of

autocorrelation (see Table 2). This was to ensure that et are not contaminated with

any information on past returns, and so such information will be embodied only in

the predictable components �bi r t�i in equation (1).

Although not reported in Table 2, some or all of the estimated parameters bi are

highly significant. Because they are assumed to be constant, this would suggest that

the four share markets are not weak-form efficient throughout the entire estimation

Table 2. Some statistics of the estimated equation (1)

Statistics RSHA (12) RSHB (1) RSZA(4) RSZB (4)

L-B (26) 26.76[0.422] 24.83[0.529] 35.71[0.097] 31.21[0.221]

CUSQ 0.418[0.000] 0.117[0.000] 0.314[0.000] 0.153[0.000]

ARCH 30.14[0.000] 7.304[0.0007] 23.36[0.000] 11.01[0.001]

J-B 3222.1[0.000] 4575.4[0.000] 3629.2[0.000] 6089.1[0.000]

Note: RSHA, RSHB, RSZA and RSZB are respectively the rate of returns on Shanghai’s A and B shares,

and Shenzhen’s A and B shares. L-B(26) is the Ljung–Box test statistic of the 26th order. CUSQ is the

CUSUMSQ test statistic. ARCH is the ARCH test statistic of order 1. J-B is the Jarque–Bera normality

test statistic. Figures beside the four return variables are their AR orders of equation (1).

Time-varying Informational Efficiency 39

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period. However, this result is doubtful as the non-normality and heteroscedasticity

problems are not properly dealt with, rendering invalid the t-statistics of bi computed

by the OLS method. Moreover, the highly significant CUSUMSQ statistics suggest

that the model parameters are not stable. To confirm this Figure 2 presents graphical

results of the CUSUMSQ test for the Shanghai A and B shares in (a) and (b), and for

the Shenzhen A and B shares in (c) and (d). That the recursive residuals exceed the

5% bound of significance indicates instability of the AR parameters bi, and one can

observe that this is true in each case. Instability appears to be more serious for the

two A-share markets than for their two B-share counterparts, according to Figure 2.

The useful messages conveyed by the ARCH and CUSUMSQ test results are

that return volatility and weak-form efficiency must have changed over the sample

period in all four share markets. Now, an immediate question of how arises. To

address this question, we next estimated the system consisting of equations (4),

(3) and (5), assuming TGARCH errors for all four return series. To make it

easier for the program to reach convergence, the smallest order p of the time-varying

AR model (i.e. equation (4)) and the smallest orders m and n of the TGARCH

Figure 2. The CUSUMSQ tests of parameter stability in equation (1) for SHA (a); SHB (b);SZA (c); SZB (d).

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model (i.e. equation (5)) were chosen, provided that the Ljung–Box test statistic

of the 26th order shows no autocorrelation, and the ARCH statistic also shows

no heteroscedasticity, in the standardised residuals eSTDt ¼ et=ffiffiffiffiht

p. The Kalman

Filter method then yielded the estimation results in Table 3.

Several messages emerge from Table 3. First, regarding the time-varying AR

model, its order turns out to be 1 for all four shares, quite different from the time-

invariant AR model considered in Table 2. As a result, there are two b

estimates, two � estimates and two � estimates for each of the four share markets.

That the time-varying AR model is of order 1 suggests that the values of returns in

the immediate previous period only can be used to infer current returns if the

AR(1) parameter is statistically significant. The estimates of bb00 reveal that

Shanghai’s B shares enjoyed a positive mean rate of daily returns (0.3996%), while

Shanghai’s A shares, Shenzhen’s A and B shares suffered negative mean rates of

daily returns (�0.0999%, �0.7739% and �0.0321%), on the starting date of our

sample (i.e. 7 October 1992, due to two observations lost). The estimated initial con-

ditions of the four AR(1) parameters ðbb10Þ including their t-values seem to suggest

that returns on Shanghai’s A- and B-share markets were predictable while those on

Shenzhens’ were not, on this starting date. These results need to be confirmed

later on, however, as the standard errors (underlying the reported t-values of bb10)

of the initial state vector (e.g. b0¼ [b00, b10]T) are computed from numeric second

derivatives of the model by the TSP program, but the standard errors used for com-

puting the confidence intervals of the AR-model coefficients (b0t and b1t) should be

the ones associated with the vector’s covariance matrix Var(b0) (see Rockinger and

Urga, 2000). So at this stage we can only roughly judge the significance of the

start-up values of the AR parameters on the t-values given in Table 3.

Table 3. Estimation results of equations (4), (3) and (5)

Share bb00 bb10 ��0 ��1 ��0 ��1 LLF

SHA � 0.0999 0.2534 1.0000 1.0000 0.0026 0.0019 � 6195.18

(� 0.657) (2.887)* (677.64)** (483.52)**

SHB 0.3996 0.2327 0.9750 0.9989 0.0215 0.0032 � 5816.67

(0.614) (2.319)* (41.74)** (750.98)**

SZA � 0.7739 0.1886 0.9650 1.0000 0.0235 0.0097 � 5950.83

(� 0.762) (1.276) (33.493)** (286.88)**

SZB � 0.0321 0.2619 1.0000 1.0000 0.0030 0.0155 � 5848.00

(� 0.239) (1.227) (669.16)** (305.05)**

Share aa0 aa1 aaþ1 cc1 L-B(26) ARCH LRT

SHA 0.0243 0.1102 � 0.0608 0.9277 38.152 0.1531 29.88**

(3.159)** (9.764)** (� 5.280)** (155.13)** [0.059] [0.696]

SHB 0.1338 0.1599 0.1185 0.8081 33.289 0.0133 23.80**

(5.651)** (8.858)** (4.646)** (64.23)** [0.154] [0.908]

SZA 0.0286 0.0996 � 0.0541 0.9321 31.177 0.0012 46.50**

(3.016)** (9.287)** (� 5.029)** (150.59)** [0.222] [0.972]

SZB 0.9248 0.2670 0.0067 0.6171 23.602 0.0270 0.040

(10.84)** (6.788)** (0.136) (22.99)** [0.599] [0.869]

Note: The effective estimation period is from October 7, 1992 to May 24, 2002. LLF is the log-likelihood

function. L-B(26) is the Ljung–Box test statistic of the 26th order. ARCH is the ARCH test statistic of

order 1. LRT is the likelihood ratio test statistic for no asymmetry between positive and negative shocks.

The figure in parentheses are t-ratios. The figures in square parentheses are p-values. *Significant at the

5% level. **Significant at the 1% level.

Time-varying Informational Efficiency 41

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Second, regarding the transition equation (3), two markets (SHA and SZB) have

their AR-model coefficients following a random walk process, given that their ��0

and ��1 estimates are unity and extremely significant. SHB’s AR-model coefficients

evolve according to an AR(1) process, as ��0 ¼ 0:9750 < 1, ��1 ¼ 0:9989 < 1 with

extremely high t-values. As for SZA, its intercept is an AR sequence

ð��0 ¼ 0:9650 < 1Þ but its first-order autocorrelation coefficient is characterised as

a random walk ð��1 ¼ 1Þ, given again huge t-values. The estimated standard errors

of uit in the transition equation, ��i ði ¼ 0, 1Þ are also reported in Table 3, but ��isstandard errors are not. This is because, in the estimation process, constraints

��i > 0 were imposed to prevent them from taking negative values; that is, they

were forced to follow non-standard distributions. Therefore, their own estimated

standard errors do not have the usual meaning and it is pointless to report them.

Each ��i determines the variability of the corresponding AR-model coefficient bbit:

the greater is ��i, the larger swing bbit will have. For example, SZA’s ��0 has the highest

value (0.0235), and so the mean rate of returns on Shenzhen’s A shares (bb0t of SZA)

must have the largest range of change among all markets’ mean rates of returns.

Another example is that SZB’s ��1 (0.0155) is the largest among the standard

errors of all markets’ AR(1) parameters, and so SZB’s AR(1) parameter bb1t (predict-

ability) is expected to have the greatest magnitude of fluctuations. In addition,

non-zero ��i make the variances of the parameters, Vit¼Var(bit) (and the resultant

confidence intervals ½bit þ 1:96ffiffiffiffiffiffiVit

p�� ½bit � 1:96

ffiffiffiffiffiffiVit

p� ¼ 2 1:96

ffiffiffiffiffiffiVit

pÞ, vary across

different points in time (these results can be confirmed by examining the time

plots of bbit in Figures 3–10).

The third group of messages pertains to the estimated TGARCH model whose

orders (m and n) determined in the above-described way are all 1 for the four

markets. The component et of the measurement equation (4) contains other sets

of information than on past returns per se that hit the market. One such set of

information includes the volatility (risk) of past returns, since the conditional var-

iance ht is found to be dependent on e2t�1 and ht�1, as evidenced by the highly sig-

nificant estimates of aa1 and cc1 in Table 3 for all four shares. Note that we did not

impose restrictions on the TGARCH-model coefficients in the estimation process,

which means that the distribution of the t-statistics is standard and so the

computed t-values are reportable. Thus, even if the AR(1) parameter bb10 is

zero, that is, even if past reruns can no longer be used to predict currency returns,

past return risks ht�1 may still be useful information in predicting current return

risks or the ‘amplification’ of current returns (i.e. ht in rt ¼ b0t þffiffiffiffiht

peSTDt ). In this

case, one may say that, even if the market has become weak-form efficient (as

bb1t ¼ 0), it is not yet semi-strong-form efficient (as ht¼ f(ht�1)).5 Put differently,

the genuine unpredictable component of the AR model is not et but eSTDt , as

the latter is purified of the ARCH effect in each case (see the seventh column

in the lower panel of Table 3).6

The last results to be observed from Table 3 are the leverage-effect results. aaþ1 ’s

t-values are statistically significant at the higher than 1% level for SHA (� 5.280),

SHB (4.646) and SZA (� 5.029), and the likelihood ratio test statistic LRT is

also significant at the higher than 1% level for each of these three markets (29.88,

23.80 and 46.50), implying that they possess asymmetries in the return-volatility

reaction to different information shocks. A noticeable difference, however, is that

the two A-share markets react to bad news more strongly than to good news

ðaaþ1 < 0Þ, whereas good news dominates bad news in affecting the volatility of

the Shanghai B-share returns ðaaþ1 > 0Þ. For the Shenzhen B-share market, no

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asymmetry between negative and positive shocks is found to exist, according to both

the insignificant t statistic (0.136) and LRT statistic (0.040).

The spillover effect of volatility in one market into the other can be understood

as information transmission in volatility across different markets. We investigated

it on a pair-wise basis for the four share markets, and the results are reported in

Table 4. Based on both the magnitude and significance of the parameter dd1, there

groups of results may be identified. In group 1, of no information transmission,

Shenzhen’s A shares do not demonstrate any spillover effect of volatility

on Shanghai’s A shares and Shenzhen’s B shares, nor do Shanghai’s A shares on

Shanghai’s B shares and Shenzhen’s B shares, since t-values in these cases are

highly insignificant. This is also true for Shenzhen’s B shares on Shanghai’s B

shares. In group 2, weak information transmission is found from SHB to SHA

and SZA, from SZB to SHA and SZA, and from SZA to SHB, according to the

very small magnitudes ðjddij < 0:01Þ, but significant t-ratios, of the corresponding

dd1. The third group shows strong information transmission that runs from SHA

to SZA (0:01 < jdd1j < 0:1 with t-statistic¼ 3.878), and from SHB to SZB

(0:1 < jdd1j with t-statistic¼ 10.77). In this last group, the spillover effects are all

positive, in contrast to group 2 where they are all negative, albeit with negligible

magnitudes. It may therefore be claimed that, in terms of volatility transmission,

the two A-share markets are, relatively speaking, less segmented from each other,

Table 4. Estimation results of equation (6)

ht of e20t�1 of aa0 aa1 aaþ1 cc1 dd1 ARCH L-B(26)

SHA SHB 0.0682 0.1174 �0.0666 0.9189 � 0.0044 0.2145 38.316

(5.333)** (9.279)** (� 5.457)** (122.96)** (� 6.258)** [0.643] [0.057]

SHA SZA 0.0243 0.1096 � 0.0613 0.9276 0.0010 0.1526 38.087

(3.156)** (9.310)** (� 5.173)** (154.73)** (0.179) [0.696] [0.059]

SHA SZB 0.0457 0.1139 � 0.0636 0.9232 � 0.0025 0.1805 38.615

(4.203)** (9.529)** (� 5.346)** (135.64)** (� 5.282)** [0.671] [0.053]

SHB SHA 0.3580 0.2431 0.2336 0.6819 � 0.0004 0.1511 31.784

(3.817)** (7.755)** (4.649)** (18.08)** (� 0.090) [0.697] [0.200]

SHB SZA 0.1395 0.1569 0.1367 0.8099 � 0.0042 0.0054 33.013

(6.346)** (8.788)** (5.195)** (65.81)** (� 8.719)** [0.941] [0.162]

SHB SZB 0.1260 0.1586 0.1213 0.8069 0.0052 0.0143 33.311

(5.315)** (8.750)** (4.709)** (64.23)** (1.270) [0.905] [0.153]

SZA SHA 0.2722 0.1558 � 0.0758 0.7894 0.0586 0.1339 37.016

(4.856)** (5.699)** (� 3.048)** (25.72)** (3.878)** [0.714] [0.075]

SZA SHB 0.0371 0.1006 � 0.0561 0.9322 � 0.0016 0.0038 31.199

(4.283)** (9.443)** (� 5.211)** (158.99)** (� 3.737)** [0.951] [0.221]

SZA SZB 0.0367 0.1028 � 0.0577 0.9311 � 0.0016 0.0033 31.348

(4.145)** (9.446)** (�5.248)** (157.07)** (� 3.979)** [0.954] [0.216]

SZB SHA 0.9051 0.2620 0.0076 0.6236 � 0.0005 0.0269 23.832

(10.43)** (6.546)** (0.153) (21.87)** (� 0.147) [0.870] [0.586]

SZB SHB 0.1657 0.0581 0.0116 0.7945 0.1532 0.0049 24.786

(5.546)** (3.761)** (0.525) (55.76)** (10.77)** [0.944] [0.531]

SZB SZA 0.9161 0.2640 0.0097 0.6215 � 0.0024 0.0270 23.676

(10.89)** (6.698)** (0.197) (23.21)** (0.000) [0.869] [0.594]

Note: This effective estimation period is from 7 October 1992 to 24 May 2002. ARCH is the ARCH test

statistic of order 1. L-B(26) is the Ljung–Box test statistic of the 26th order. The figures in parentheses

are t-ratios. The figures in square parentheses are p-values. *Significant at the 5% level. **Significant at

the 1% level.

Time-varying Informational Efficiency 43

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and so are the two B-share markets, with unidirectional spillover effects from

Shanghai to Shenzhen in both cases. A higher degree of segmentation is found to

exist between the markets in each of other pairs, as evidenced by no or very weak

information transmission. Note that the ARCH and Ljung–Box statistics are all

insignificant at the 5% or lower level, exhibiting the fact that the standardised

errors eSTDt resulting from the TGARCH-spillover specification for conditional

variance are free of the ARCH and autocorrelation problems. Thus, we have

a certain degree of confidence in the above inference. Comparing Table 4 with

Table 3, we also find that the conclusions here regarding the leverage effect

do not change qualitatively: it is still significantly negative for both SHA and

SZA, significantly positive for SHB, and not significant at all for SZB.

Since all parameters of the AR models are non-constant over time, we need to

conduct a graphical analysis of their smoothed estimates. Figures 3–6 depict the

smoothed estimates of the time-varying intercept b0t in equation (4) for the four

shares respectively. b0t, the long-run mean rate of returns on the market, may be

interpreted as appropriately capturing the long-run trend of the market. If two or

more markets are integrated, one would expect to observe some co-movements in

their long-run trends or some similar patterns of their long-run mean returns.

What we observe from the four figures, however, is that the four b0ts evolve along

utterly different time paths. For example, SHA’s long-run mean returns underwent

four discernibly different phases: rising (10.1992–02. 1997), declining slowly

(02.1997––06.1999), declining sharply (06.1999–08.2001) and declining slowly

(08.2002–05.2002), but SHB’s went through indiscernible changing phases. This

suggests that there do not exist co-movements in the two markets’ long-run mean

returns. The similar result also applies to other pairs of such returns, if the same gra-

phical inspection is conducted. Therefore, we may conclude that the four share

markets are highly segmented from each other. Restrictions such as those on the

dual listing of shares on the Shanghai and Shenzhen Stock Exchanges and those

on investing in A shares by domestic investors only and in B shares by foreign inves-

tors only have clearly contributed to these acute market segmentations. Even if a

major policy change or a big shock can alter a market’s long-run behaviour, it

cannot alter others’, at least in the same direction. As a result, the degree of diffi-

culty in regulating all share markets is increased for policy-makers. However, one

upside of the market segmentations is perhaps that, because of low correlation

between the four shares, including more than one of them in a portfolio will enhance

the effectiveness of risk reduction through diversification. Figures 4 and 5 also dis-

play that Shanghai’s B shares and Shenzhen’s A shares brought investors no returns

most times since the confidence intervals of their bb0t include zero during these

times. Figures 3 and 6 then show that investors of Shanghai’s A shares and

Shenzhen’s B shares experienced alternate periods of bullish and bearish markets,

again according to the time paths of the respective confidence intervals.

We now turn to examining the time-varying weak-form efficiency of the four

share markets. Relevant to this purpose are the four non-constant AR(1) parameters

b1t, and so the time plots of their smoothed estimates are displayed in Figures 7–10.

It was mentioned earlier that Shanghai’s A- and B-share markets might have

started up with significant return predictability. This is now confirmed by Figures 7

and 8: the initial values of their bb10 were both around 0.25 on 7 October 1992 with

high statistical significance (their lower bounds of the confidence intervals are all far

greater than zero). But the later development of the two markets witnessed an

increasing contrast between them in terms of return predictability. SHA’s bb1t

44 X.-M. Li

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Figure3.Smoothed

estimates

andco

nfiden

ceintervalsofb 0

tfortheShan

ghai

A-shareindex

.—

b 0t;---b 0

t�

1:96

ffiffiffiffiffiffiffi

V0t

p.

Time-varying Informational Efficiency 45

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Figure4.Smoothed

estimates

andco

nfiden

ceintervalsofb 0

tfortheShan

ghai

B-shareindex

.—

b0t;---b0t�

1:96

ffiffiffiffiffiffiffi

V0t

p:

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Figure5.Smoothed

estimates

andco

nfiden

ceintervalsofb0tfortheShen

zhen

A-shareindex

.—

b 0t;---b 0

t�

1:96

ffiffiffiffiffiffiffi

V0t

p.

Time-varying Informational Efficiency 47

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Figure6.Smoothed

estimates

andco

nfiden

ceintervalsofb 0

tfortheShen

zhen

B-shareindex

.—

b 0t;---b 0

t�

1:96

ffiffiffiffiffiffiffi

V0t

p:

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Figure7.Smoothed

estimates

andco

nfiden

ceintervalsofb1tfortheShan

ghai

A-shareindex

.—

b 1t;---b 1

t�

1:96

ffiffiffiffiffiffiffi

V1t

p:

Time-varying Informational Efficiency 49

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Figure8.Smoothed

estimates

andco

nfiden

ceintervalsofb 1

tfortheShan

ghai

B-shareindex

.—

b 1t;---b 1

t�

1:96

ffiffiffiffiffiffiffi

V1t

p:

50 X.-M. Li

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Figure9.Smoothed

estimates

andco

nfiden

ceintervalsofb 1

tfortheShen

zhen

A-shareindex

.—

b 1t;---b 1

t�

1:96

ffiffiffiffiffiffiffi

V1t

p:

Time-varying Informational Efficiency 51

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Figure10.Smoothed

estimates

andco

nfiden

ceintervalsofb 1

tfortheShen

zhen

B-shareindex

.—

b 1t;---b1t�

1:96

ffiffiffiffiffiffiffi

V1t

p:

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moved rapidly towards zero and crossed the horizontal axis for the first time in mid-

1993. Thereafter, it fluctuated but with the confidence interval basically covering

this axis, which can be interpreted as non-predictability. SHB’s bb1t, however,

stayed far above the horizontal axis almost throughout the past 10 years or so

with exceptions occurring only very recently in 2002, when it first touched the

axis. Closer inspection of its time plot reveals that the return predictability of

Shanghai’s B shares kept rising between January 1994 and December 1996, and

since then had declined all the way down. These observations suggest that SHA’s

returns became unpredictable much earlier than SHB’s, the latter experiencing

rising and then falling predictability.

Unlike the messages conveyed in Table 3, Figures 9 and 10 show that the SZA

and SZB markets started with significant return predictability, because their

bb1t were distinguishable from zero until 12 December 1992 and 2 April 1993

respectively. For SZA, the subsequent periods were mostly characterised as non-

predictablity, during which its AR(1) parameter had a zero value included in the

confidence interval. This is not the case for SZB, however. From Figure 10, it can

be seen that the AR(1) parameter was significantly different from zero during

seven periods covering 1244 of 2513 days, approximately 50% of the sample size.

But it is also true that since 4 April 2001, non-predictability has again featured the

SZB market.

It is of particular interest to compare the impacts of the 1994 RMB devaluation on

the two B-share markets where share trading takes place in non-Chinese currencies.

Starting from 7 January 1994, SHB’s autocorrelation coefficient went all the way

up (see Figure 8) until 9 December 1996 when it peaked at 0.35. We know that the

Chinese currency devalued sharply from 5.76 ¥/$ to 8.7 ¥/$ from 1 January 1994

onwards. Since Shanghai’s B shares are traded in US dollars this sharp devaluation

of the RMB meant a sudden drop in SHB’s US-dollar prices relative to SHA’s RMB

prices. Given that A and B shares for each listed company enjoy equal rights and

dividends, it would presumably trigger a buying spree for Shanghai’s B shares

among (mostly) the US investors, although such responses from them were delayed

4 days. During a panic buy, one expects investors to purchase even more if they

observe a large rise in prices. The soaring of the market then became self-sustaining

and, as a result, high or increasing autocorrelation is expected. This runs counter to

a fully weak-form efficient market where we expect complete and instantaneous

adjustments upon receiving good news. Interestingly, the devaluation of the RMB

did not have a similar significant impact on the correlation structure of

Shenzhen’s B shares. The AR(1) coefficient also started rising from 7 January

1994 but the rising trend ended shortly on 21 April 1994 when it reached

0.10495. Moreover, the associated confidence interval included zero throughout

this period (see Figure 10). This suggests that, although a RMB devaluation against

the US dollar would mean an equal devaluation against the HK dollar (as the latter

is pegged to the former), the HK investors were not as terribly interested as the US

investors. One possible reason might be because the ongoing intense

disputes between Chris Patton and the Chinese government over reforming

Hong Kong’s political systems had already increased political uncertainty about

Hong Kong’s future, and hence the risk in holding Shenzhen’s B shares.

Consequently, it is not surprising that no massive purchases of SZB and hence

no significant rising autocorrelation in SZB’s returns occurred.

It has been argued by Fama (1991) and Malkiel (1992) that a market may be

efficient yet predictable. In other words, if non-predictability exists, the market

Time-varying Informational Efficiency 53

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must be weak-form efficient; but if the market is predictable, this does not necessa-

rily imply that the market is weak-form inefficient. In this case, predictability may be

caused by factors other than informational inefficiency. One such important factor is

the illiquidity of the market (Lo and MacKinlay, l990). It is certainly true that the

four markets under our investigation were illiquid at their earlier stages of develop-

ment, judging from their relatively small market values then (see Exhibit 1 in

Johnson, 2002). However, it is also true that when China’s four share markets

first started trading, investors were not familiar with the price discovery mechanism,

the information-releasing systems were incomplete or not established, regulations

were not normalised or enforced effectively, and so on. All these factors would

make it unlikely for the share prices fully to reflect relevant information.

Consequently, just relying on the fact of market illiquidity would lead to an erro-

neous conclusion that the markets were efficient albeit predictable. As their

market values grew, the market illiquidity became less and less – while informational

inefficiency became more and more – relevant in causing predictability if detected.

The point here is that the presence of predictability can be at least partly attributed

to information inefficiency in the Chinese context.

Based on the above consideration, we make inference on weak-form efficiency

for China’s four share markets as follows. First, the level of weak-form efficiency

has been improving through time in at least three markets: SHA, SHB and SZA.

The Shanghai A-share market is the first to have become efficient since 3

November 1997, followed by the Shenzhen A-share market (since 3 July 1998).

The Shanghai B-share market has shown a convergence (albeit very slow) towards

efficiency since 9 December 1996. A similar trend is, however, not observed for the

Shenzhen B-share market whose AR(1) parameter jumped to the highest level

(0.51978) recently on 1 March 2001, following the official announcement that

domestic investors would be allowed to trade in B shares from 1 June 2001 (this

announcement also resulted in a pulse in SHB’s AR(1) parameter on the same

day but only to a value of 0.12912). Second, compared with the A-share markets,

the B-share markets have been relatively inefficient and slow in moving towards

efficiency. Within a time-invariant framework, Laurence et al. (1997) find the

existence of weak-form efficiency in the A-share markets but not the B-share

markets. Using a time-varying framework, we have obtained similar but more

accurate and detailed results in the sense that our results shed some light on the

evolution behaviour of WFE of the A- and B-share markets in China. The differ-

ences in WFE between the A-share and the B-share markets reveal that overseas

investors have had less information available than domestic investors about the

listed companies, the relevant policies and the economy as a whole.

5. Concluding Remarks

In this concluding section, we summarise the main findings as follows. Within a

time-varying framework, we have been able to observe how, in the past 10 years

or so, the levels of informational efficiency changed in China’s two A-share and

two B-share markets. The A-share markets have been found to perform better

than the B-share markets in that the former were quicker and earlier in becoming

weak-form efficient than the latter. Therefore, it is the A-share markets that have

contributed to what Li (2002) finds as a steady convergence of the aggregate

Shanghai and Shenzhen stock markets towards efficiency. Despite this, however,

the two A-share markets are not semi-strong-form efficient, let alone the two

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B-share markets (weak-form inefficiency automatically implies semi-strong-form

inefficiency but not vice versa), given the presence of TGARCH errors in the AR

models of all four shares.

Asymmetrical market reactions to different information shocks have been found

in the SHA, SZA and SHB markets. For the former two, bad news has a larger

impact on volatility than good news, while for the latter the opposite applies.

No leverage effect has been detected for the SZB market.

We have provided evidence in support of the common belief that China’s share

markets are highly segmented. Part of the evidence comes from the estimation

results of the TGARCH-spillover models, which indicate the existence of three

groups of, respectively, no information transmission (SHA(SZA, SHB(SHA,

SHB(SZB, SZB(SHA and SZB(SZA), weak information transmission

(SHA(SHB, SHA(SZB, SHB(SZA, SZA(SHB and SZA(SZB), and strong

information transmission (SZA(SHA and SZB(SHB). So the degree of market

segmentation in terms of information transmission in return risks varies across

different groups of share markets in pairs. Another part of the evidence stems

from the Kalman–Filter estimates of the intercepts of the AR models. We have

found that the four markers do not share a common trend in their long-run behav-

iour, suggesting that they are not integrated. Although investors could reduce risk

by holding a diversified portfolio, policy-makers may prefer more integration

between share markets in the near future to enhance the effectiveness of financial

regulations.

Notes

1. These studies include Rockinger and Urga (2000), Zalewska-Mitura and Hall (1999), and Emerson

et al. (1997).

2. See, for example, Chiao (1998), Yao (1998), Bergstrom and Tang (2001), and the website of the

China Securities Regulatory Commission at http://www.csrc.gov.cn.

3. Companies that are listed on the Hong Kong Stock Exchange, the New York Stock Exchange and the

Singapore Stock Exchange issue the so-called H, red-chip, N and S shares traded in the HK, US and

Singapore dollars respectively.

4. Bergstrom and Tang (2001) focus on why B shares have shown a substantial discount against A shares.

Heaney et al. (1999) examine share return seasonalities and price linkages of A and B shares.

5. Semi-strong form efficiency is defined as follows: security prices fully reflect all publicly available infor-

mation, i.e. market participants cannot make superior returns by ‘searching out’ information from

publicly available sources, since the information will already be incorporated into security prices

(Fama, 1970).

6. If share prices fully incorporate information on past prices and on the volatility of their changes, then

current returns may be expressed as r0

t � ðrt � b01 � b1t rt�1Þ=ffiffiffiffiht

p� �¼ eSTDt , where rt represents current

returns that do not incorporate such information. Thus only ‘pure news’ eSTDt can determine the values

of r0

t . If a market’s returns behave like r0

t as defined here, we may say that the market is not only weak-

form efficient but also satisfies at least one criterion of semi-strong-form efficiency in that current

returns r0

t fully incorporate all the other-than-past-price information as contained in ht.

References

Bergstrom, C. and Tang, E., ‘‘Price Differentials between Different Classes of Stocks: An Empirical

Study on Chinese Stock Markets,’’ Journal of Multinational Financial Management, 2001, 11,

pp. 407–426.

Campbell, J.Y. and Hentschel, L., ‘‘No News is Good News: An Asymmetric Model of Changing

Volatility in stock returns,’’ Journal of Financial Economics, 1992, 31, pp. 281–318.

Chiao, X., China Stock Markets Statistics and Analysis 1990 December – 1998 June. Beijing: China Financial

Economics Publisher, 1998.

Time-varying Informational Efficiency 55

Dow

nloa

ded

by [

Uni

vers

ity o

f B

rist

ol]

at 0

1:27

10

Nov

embe

r 20

14

Emerson, R., Hall, S.G., and Zalewska-Mitura, A. ‘‘Evolving Market Efficiency with an Application to

Some Bulgarian Shares,’’ Economics of Planning, 1997, 30, pp. 75–90.

Fama, E.F. ‘‘Efficient Capital Markets: A Review of Theory and Empirical Work,’’ Journal of Finance,

1970, 25, pp. 383–417.

Farma, E.F., ‘‘Efficient Capital Markets: II,’’ Journal of Finance, 1991, 46, pp. 1575–1617.

Harvey, A.C. Forcasting, Structure Time Series Models and the Kalman Filter. Cambridge: Cambridge

University Press, 1989.

Heaney, R.A., Powell, J.G., and Shi, J. ‘‘Share Returns Seasonalities and Price Linkages of Chinese A

and B Shares,’’ Review of Pacific Basin Financial Markets and Policies, 1999, 2, pp. 205–229.

Hendry, D.F. and Neale, A., ‘‘A Monte Carlo Study of the Effects of Structural Breaks on Tests for Unit

Roots,’’ in P. Hackl and A. Westlung, eds, Economic Structrual Change, New York: Springer Verlag,

1991, 95–119.

Johnson, R., ‘‘Investing in China Stock,’’ China & World Economy, 2002, 2, 49–57.

Laurence, M., Cai, F., and Qian, S., ‘‘Weak-form Efficiency and Causality Tests in Chinese Stock

Markets,’’ Multinational Finance Journal, 1997, 1(4), pp. 291–307.

Li, X.M., ‘‘China: Further Evidence on the Evolution of Stock Markets in Transition Economies,’’

The Scottish Journal of Political Economy, 2003, forthcoming.

Lo, A.W. and MacKinlay, C.A., ‘‘An Econometric Analysis of Nonsynchronous Trading,’’ Journal of

Econometrics, 1990, 45, pp. 181–212.

Long, M.D., Payne, J.D., and Feng, C., ‘‘Information Transmission in the Shanghai Equity Market,’’

Journal of Financial Research, 1999, 22, pp. 29–45.

Maddala, G.S. and Kim, I.-M., Unit Roots, Cointegration, and Structural Change. Cambridge: Cambridge

University Press, 1998.

Malkiel, B.G., ‘‘Efficient Market Hypothesis,’’ in K. Newman, M. Milgate, and J. Eatwell, eds, New

Palgrave Dictionary of Money and Finance. London: Macmillan, 1992.

Perron, P., ‘‘The Great Crash, the Oil Price Shock and the Unit Root Hypothesis,’’ Econometrica, 1989,

57, pp. 1361–1401.

Perron, P., ‘‘Further Evidence on Breaking Trend Functions in Macroeconomic Variables,’’ Journal of

Econometrics, 1997, 80, pp. 355–385.

Rockinger, M. and Urga, G., ‘‘The Evolution of Stock Markets in Transition Economies,’’ Journal of

Comparative Economics, 2000, 28, pp. 456–472.

Yao, C., Stock Market and Futures Market in the People’s Republic of China, Oxford: Oxford University

Press, 1998.

Zalewska-Mitura, A. and Hall, S.G., ‘‘Examining the First Stages of Market Performance: A Test for

Evolving Market Efficiency,’’ Economics Letters, 1999, 64, pp. 1–12.

56 X.-M. Li

Dow

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ded

by [

Uni

vers

ity o

f B

rist

ol]

at 0

1:27

10

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embe

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