Asian Emerging Markets and News
by
Tatsuyoshi Miyakoshi *
Graduate School of International Cultural Studies,
Tohoku University
March 3, 2005
ABSTRACT
This paper has investigated the effects of news on the conditional variances in the six
Asian emerging markets by using the composite index of news between January and
April 2000. Our results are as follows. First, more the present news is, the volatility
increase more in all markets, and the present news effects disappear within one day in
Taiwan and Thailand, but continue to appear within at least two days in the other
countries. Second, we identify Thailand and Indonesia as “news-rich” markets. Third,
we confirmed the robustness for the results obtained by the composite index of news,
by using the Japanese and the US market data. Therefore, the detail analysis for
individual news, which prevails in the advanced country markets, will be
necessary to get extra profits by responding quickly to the news.
Keywords: News; Asian emerging markets; EGARCH
JEL Classification Number: G1;C40;C22
Running head: Asian Emerging Markets and News
* This paper was presented at the 2002 seminar of Waseda University, the 2002 autumn
conference of Japan Society of Monetary Economics, the 2002 conference of the East
Asian Economic Association, and the 2003 Pacific Rim Conference. I would like to
thank Shikuan Chen, Hiroya Akiba, Shinya Takezawa, Chun-tien Hu, Junichi Shimada
and Lim Chong Yah for their valuable comments. This research was supported by the
Japan Economic Research Foundation in 1999.
Correspondence to: Tatsuyoshi Miyakoshi, Graduate School of International Cultural
Studies, Tohoku University,Kawauchi Aoba-ku,Sendai 980-8576, JAPAN
Tel:+81-22-217-7613, Fax:+81-22-217-7613, E-mail: miyakoshi@intcul.tohoku.ac.jp
1. Introduction
The empirical distributions of stock returns display fat tail, spiked
peaks, and a persistence in the variance structure. The auto-regressiveconditional-heteroskedasticity (ARCH) process of Engle (1982) provides a good
approximation to the stock return series. The more advanced model, the
generalized ARCH (GARCH) of Bollerslev (1986) and an exponential GARCH
(EGARCH) of Nelson (1991), belong to the ARCH family. Bollerslev (1987),
Nelson (1991), and Glosten et al (1993) have shown the efficacy of the ARCH
family in the markets of advanced countries in a sense that the parameters are
highly significant in-sample. Moreover, the EGARCH capture the asymmetric
turbulence that is empirically observed but is not able to be captured by the
GARCH. We can find the significant parameters for the asymmetric turbulence
still among recent researches: see Karanasos and Kim (2003), Lee et al.(2004),
and Kim et al.(2005). The EGARCH seems to be superior to the GARCH.
A next logical step in the development of an analytical framework is to
find the useful information to explain market returns. The “news” has recently
become a topic of great interest to many researchers. Ito and Roley (1987) define
the news as the deviation between announced and expected values of events. In
general, the expected values for the scheduled publicly announced information,
such as gross domestic product (GDP), unemployment rates, balance of
payment and inflation rates, are obtained from “Money Market Services” in the
United States. Most recent researches have focused on the news relating to such
scheduled public announcements (i.e., public news), because of the availability
of data.
One result of these public news analyses is as follows. The response of
stock returns to the news can be classified into two categories: first, equilibrium
adjustments, which involve a disturbance in the markets because of the arrival
of new information and a subsequent equilibrium adjustment; and second,
policy anticipation, which involves market changes due to expectations of a
monetary policy, as pointed out by Kim and Sheen(2000).1
Engle and Ng(1993) proposes a definition for the news that it is the error term of
return equation and then the previous news affects the volatility in the ARCH model.
This is a trial to interpret the ARCH structure by the news. It is literally included into a
definition by Ito and Roley (1987). However, in Engle-Ng’s framework of news, the
present news cannot influence the present volatility.
1
1
Becker, Finnerty and Kopecky (1995) found that the public news of the
USA, but not the UK, affects the prices of bonds in the advanced market. Becker,
Finnerty, and Friedman (1995) found that this response to the US news was
responsible for the linkage between US and UK stock returns. Ederington and
Lee (1995) found that the response of interest rates of the US treasure-bond
futures, which begin within 10 seconds of the news release, is basically
completed within 40 seconds after the news release. Edison (1997) found that
the US interest rates respond to six US news relating to real economic activity
and inflation.
Recently, several papers have investigated the effects of news on the
conditional variance. Kim and Sheen (2000) found the news disturbed the
market volatility. Moreover, they identified which news excited or calmed the
volatility, and suggested that these items of news be used as policy instruments.
Kim and In (2002) have investigated the disturbance effects of news on the day
of announcement, and examined the effects of US and Australian news on
Australian returns and volatility. Kim (2003) has analyzed the different effect of
bad and good news, and then extended this research to the spillover effect of
news from foreign countries. He suggests the items of bad news from foreign
countries are more significant explanatory variables than items of good news.
Thus, investors can make use of the results obtained from the news analyses
and can get extra profits by responding quickly to the news relating to the
scheduled public announcements. Compared with a large number of researches
on news in advanced markets, there are very few researches in the Asian
emerging markets except for Lee and Suh (2004) and Wang and Firth (2004).
However, the emerging markets are news-full as pointed out by Chang,
McQueen and Pinegar (1999).
The purpose of this paper is to investigate the effects of news on the
conditional variance in the Asian emerging markets: Korea, Taiwan, Thailand,
Singapore, Malaysia and Indonesia index returns during Jan 1995- Apr 2000. In
contrast to the advanced countries, the emerging markets will not have few
providers such “Money Market Services” that provides the expected values on
the scheduled public announcement, and thus investors cannot know the public
news ( the deviation between expected and actual values of scheduled public
announcements). Moreover, the scheduled public news is only a small part of
the news in the emerging markets, as there are many items of news that are not
scheduled for public or private announcement. The problem is more serious
2
than in advanced markets and then we introduce the composite index for news,
following Miyakoshi (2002). Lamoureux and Lastrapes (1990) supposed that the
information flow determines the trading volumes, so the trading volume is a
proxy for information flow, then Miyakoshi (2002) defined that the deviation
between the expected and actual trading volume is the composite index for the
news.
The findings of the paper are as follows. First, more the present news is,
the volatility increase more in all markets, and the present news effect
disappears within one day in Taiwan and Thailand, but continue to appear
within at least two days in the other countries (negative effects for the second
day), but the expected current trading volume still have negative effects on
volatility in Thailand and Malaysia. Anyways, the cause of large volatility is not
expected current trading volume but the news. Second, the news-rich markets
are the Thai and the Indonesian market and are the periods during the Asian
crisis. In particular, the detail analysis for individual news is necessary for these
markets and periods to get extra profits by responding quickly to the news. Third,
we checked the robustness for the first and second results by using the Japanese
and the US market data, and we obtained the same results, but the Japanese
market is more news-rich and the US is less than the Asian markets. Fourth, we
found that the news as defined in this paper coincide with the “NEWS” that the
mass media consider, and ascertained the validity of our methodology. Finally,
fifth, Lamoureux and Lastrapes (1990), Najand and Yung (1991), Locke and
Sayers (1993), Sharma et al. (1996), and Miyakoshi (2002) showed the current
trading volume explain the persistence of variance of variance represented by
the ARCH-type effect. They did not make clear why, though Miyakoshi(2002)
suggests the cause of the results is news included in the current trading volume.
This paper supports the suggestion of Miyakoshi.
Recent rapid financial deregulation in the Asian emerging markets has
promoted a great deal of share trading and has attracted international investors,
which has also made the markets news-rich. Therefore, the news analysis,
which prevails in the advanced country markets, will provide a good remark to
many previous papers for the Asian emerging markets, including Bekaert and
Harvey (1997), Ng(2000), and Miyakoshi (2003) by using the ARCH-type model.
The structure of the remainder of the paper is as follows. Section 2
describes the EGARCH model and provides a definition of the news, the
investigation strategy and testable hypotheses. Section 3 describes the data and
3
explains the Asian emerging markets. Section 4 discusses the empirical results.
Section 5 contains concluding remarks.
2. Methodology
2-1. EGARCH Model
In this paper, we use the EGARCH model, which overcomes two
drawbacks of the GARCH model relating to computation and the simple effects
of the past variance on the present one. See Nelson (1991). We restrict our
attention to an EGARCH (1,1) since it has been shown to be a parsimonious
representation of conditional variance that adequately fits many financial time
series.
This specification can be expressed as: 2
rt  b 0  b1rt 1   t
 | (V , 
1
,
2
: t=1,2,…,T
(1)
,...) ~ N (0,  2 )
(2)
log  t2   0  1 log  t21 2 z t 1   3 (| zt 1 | E(| zt 1 |))   4* log Vt
(3)
 t  t z t :
(4)
z t ~ N (0,1)
where rt is the rate of return (logPt-logPt-1 : Pt is a stock price), b 0 is the constant,
and Vt is the daily trading volume, which is used as a proxy variable for the
current information flow to the market.3 If we set *4=0, ignoring the trading
Although some research checks the lag length by using the Schwarz’s Bayesian
Information Criterion (SBIC) and the Akaike Information Criterion (AIC), the statistical
nature of their criteria is unknown for the ARCH. Second, if there exists a correlation
between volatility and expected return, equation (1) may be explained by time-varying
risk premiums, as proposed by Engle, Lilien and Robins (1987). However, we use the
typical model, following Najand and Yung (1991) and Sharma, Mougoue and
Kamath(1996) did.
3 We must be cautious in interpreting the results due to the possible existence of a
simultaneity bias, as pointed out by Lamoureux and Lastrapes (1990). The trading
volume may be endogenous to our system. Thus, the estimation procedures based on
the likelihood function obtained by conditioning on these variables would prove
inappropriate. If the volume is correlated with the disturbances in the stochastic part of
2
4
volume, the above equations represent a typical EGARCH (1,1).4
What is information flow? It is concerned with events or rumours
occurring in both the private and public sectors. How is it measured? We use
daily trading volume as a proxy variable for the daily information, following
Schwert (1989), Lamoureux, and Lastrapes (1990), Gallant, Rossi, and Tauchen
(1992), and Jones, Kaul, and Lipson (1994).
We assume that the current information flow Vt is generated by the
Auto-Regressive (AR) process:
lo g V
  0    lo gV
1


(5)
where t follows a white noise process. We assume that people estimate the
current information flow Vt by using the ordinary least squares (OLS) in (5),
where the lag length is selected out of 14 lags (about three weeks of trading
days) by the Schwarz’s Bayesian Information Criterion (SBIC). People can
separate the current information flow into two parts. The first is an expected
(estimated) part from the AR of the past information and the second is the

unexpected (unestimated) part. We define the unexpected part,  , to be the "
news", following Ito and Roley (1987). Hence, the current information flow

includes the news. The |  | is defined to be the size of the news. The adjusted
R-squared in (5), R , expresses the fitness for the expected part and the
1- R expresses the fitness for the news. Thus, we define 1- R
to be the
volume of news. In addition, the news-rich market is defined on an ad hoc basis
to be a market with a volume of news larger than 0.32, where there is no
the model, the estimating procedures are likely to yield inconsistent estimations of the
parameters. Lamoureux and Lastrapes (1990) and Najand and Yung (1991) try to
resolve this problem and to use a lagged trading volume. Although that trial alters the
nature of the underlying model, statistically it is not sure that lagged trading volumes
and the stochastic part are uncorrelated.
4 In the EGARCH, the persistence of variance is measured by the magnitude of  1 : the
more the magnitude approaches unity, the greater is the persistence of shocks to
volatility. The positivity or negativity of unanticipated excess returns determines
future variance, which is measured by 2 and 3 , where 3 represents a magnitude
effect. For 3>0, the innovation in log 2 is positive (negative) when the magnitude of Z
t-1 is larger (smaller) than its expected value. The 2 represents a sign effect. For 2 <0,
the innovation in conditional variance is positive (negative) when returns innovations
are negative (positive).
5
justification for choosing 0.32. Later, 0.32 is an average of all sub-periods of all
countries. We consider that the news-full markets is a market with more news
than average markets.5
We can write the log likelihood function logL and determine the
parameter  (0, 1, 2, 3, or 4) to maximize it:
M ax
 2
1
1
2
log L ( )   log 2   log    2
2
2 1
2 1 
T
(6)
Maximizing equation (6) should be considered as Quasi-Maximum Likelihood
Estimation (QMLE), since the true error term distribution is unknown. The
QMLE supposes this error term to be normal, maximize equation (6), and then
the estimated ˆTQML will be consistent and asymptotically normal with the
so-called sandwich covariance matrix, see e.g.,Bollerslev and Wooldridge (1992,
Theorem 2.1,pp.148-149):
T (ˆTQML   )  N (0, A 1 BA 1 )
2
1 T   ln f ( t ) 
A   E
T t 1     
(7)

1 T    ln f ( t )    ln f ( t )  
B    E 

 
T t 1  





where E(.) is actually the matrix of the partial derivatives on ln f(εt) by the
QMLE estimates at time t. Therefore, traditional inference procedures are
immediately available. We use QMLE in this paper.
2-2. Investigation Strategy and Testable hypotheses
In this section, we describe the investigation strategy we used, based
on the development of previous researches which showed the current trading
volume explain the persistence of variance of variance represented by the
ARCH-type effect but did not make clear why. First, we conducted the test of
Engle and Ng (1993) defined the news to be the error term of the return equation in
GARCH. Their definition differs from ours in that it does not allow today’s news to
affect the today’s volatility.
5
6
structural change, the unit root test for trading volume, and checked the
summary statistics for stock returns, which are shown in Tables 1, 2, and 3.
Thus, we statistically diagnosed whether we can use the EGARCH model for
the Asian stock returns during the periods including the 1997 Asian crisis. We
ascertain the validity of using the EGARCH for the Asian emerging markets,
checking the significant parameters for the asymmetric turbulence, which
results are shown in Table 4.
Second, to investigate whether the EGARCH can capture fully the
current information flow, this paper tests the hypothesis that  *4 >0 in equation
(3). Furthermore, in the presence of current information flow with * 4 >0,  1
was found to be smaller and statistically insignificant in Table 4. That is, the
current information flow explains the variance persistence represented by the
EGARCH effect, as pointed out by a lot of previous researches on advanced
markets.
Third, we separate current information flows into current news and
current expected trading volume, and estimate their coefficients of both
variables. The results are shown in Table 5 and 6 and the more present news
affects volatility more, but not the more expected current trading volume.
Fourth, we identify the news-rich markets and periods, by calculating
the volume of the news, which will be shown in Table 7. We stress the necessity
of news analysis for individual news in the news-rich markets and periods.
Fifth, we check the robustness of the results by using the advanced
market of Japan and USA. We ascertain the same results in Table 8.
Sixth, we consider the validity of the definition for the “news” used in
this paper in Table 9.
3. Data and Asian Emerging Markets
All data are the daily data purchased from the Data Base of Nomura
Research Institute JAPAN. In the data base, the daily return rt consisted of daily
stock closing price Pt, and volume Vt (measured in 109 shares) represents the
number of shares traded during the day for all common stocks in the market. 6
Previously, the shares traded, the trade in dollars and the number of transaction have
been used as trading volumes (trading activity). However, Lo and Wang (2001) have
offered a new concept for trading volume. They argue that the turnover (i.e., traded
shares divided by outstanding) is a nature measure of trading activity. The data for
6
7
The data covers the recent five-years, from Jan. 1, 1995 to Apr. 30, 2000. The
original data, without logarithmic transformation, are illustrated in Figure 1:
they are not revised to exclude rights off and dividend off. These are Korea
Composite Stock Price Index in Korea, Taiwan Stock Exchange Capitalization
Weighted Index in Taiwan, Stock Exchange of Thailand Index in Thailand, Strait
Times Index in Singapore, KLSE Composite Index in Malaysia and The JSX
Composite Index in Indonesia.7
Table 1 lists the country name of the sample stocks, the number of
samples, and the non-normality of the unconditional distribution of daily
returns. The table reports the mean, the standard deviation of return, the
Kendall-Stuart skewness, the excess kurtosis, and their tests. The Ljung-Box
Q-statistics Q2(20) are reported under the null hypothesis of non-serial
correlation tests in daily squared returns. At a significance level of five per cent,
the null hypotheses (skewness = 0 or excess kurtosis = 0) and non-serial
correlation are usually rejected. Thus, the time series have the typical features of
stock returns, displaying a fat tail distribution, a spiked peak, and the
persistence of variance.8 Therefore, the ARCH-type model including such
features seems to be appropriate for analyzing these series, compared with the
AR model.
Since the beginning of the Asian financial crisis on July 2, 1997, several
countries have adopted non-standard interventions, such as equity market
intervention in the Hong Kong Special Administrative Region or the use of
capital controls by Malaysia. 9 As shown in Figure 1, stock prices and trading
volumes have exhibited repeated up-and-down motions and large fluctuations
since the Asian financial crisis. These point to the structural changes of the
EGARCH represented by (1)-(4). However, because the statistical features
emerging markets are very restricted, but it is worthwhile using them when the data
will be available.
7 The Malaysian data for trading volume are missing for on Dec. 7, 1995, Feb. 2, 1996,
and Mar. 8, 1996. Hence, the data are complemented by the trading volume data of the
day before.
8 All computations in the paper have been performed with the computer package
WinRATS-32 Version 4.30.
9 These interventions raised questions about the implications for the behavior of
market participants and asset prices in the future. In addition, since the crisis, the sharp
adjustments of sovereign credit ratings for many emerging markets have raised
concerns about the accuracy and stability of the rating process. See IMF (1999, pp.
169-214).
8
relating to structural changes are unknown in the EGARCH, it is impossible to
diagnose the structural change. To escape this problem, this paper separates the
whole sample period into three sub-periods: before, during, and after the Asian
financial crisis. Each period corresponds to the following dates, respectively:
January 1, 1995 to April 30, 1997; January 1, 1997 to April 30, 1998; and January
1, 1998 to April 30, 2000.
We assume that the traditional inference procedures for structural
changes are available. Thus, we roughly confirm the date of structural change
in two ways. First, we depict the graphs of stock returns for each country. An
inspection of Figure 2 suggests that structural changes occurred around April
30, 1997 and April 30, 1998. In addition, as shown in Table 2, we implement the
Chow Test of structural changes at the same dates. Using the maximum
likelihood ratio test with dummy variables for all parameters, all of the
parameters in the EGARCH model are unchanged after that date under the
null-hypothesis, whereas at least one is changed after that date under the
alternative hypothesis. The null hypotheses are rejected at the one per cent
significance level except in the case of Taiwan. However, we assume that
structural changes occurred in all countries, including Taiwan. Moreover, we
separate the whole periods into three overlapping periods to obtain a
sufficiently large sample size.
In Table 3, we examined the Augmented Dickey-Fuller (ADF) tests for
a unit root in the trading volume with logarithms, in order to guarantee the
statistical consistency of the EGARCH with stationary variables.10 This test
mostly rejects the null hypotheses of a unit root and then statistically supports
the inclusion of trading volume into the model, except for the period during the
crisis. During the crisis, more detailed insight and advanced analytical
methodology will be needed.
On the other hand, we confirm the results by using the test of
Kwiatkowski, Phillips, Schmidt, and Shin (1992) (known as the KPSS test),
The procedure for choosing the optimal lag length was to test between a one and
fourteen-lag, by using the minimum value of SBIC. The residuals were then checked
for whiteness. If the residuals in any equation proved to be non-white, we sequentially
chose a higher lag structure until they were whitened. The whiteness is checked by
only Ljung-Box Q tests for absence of correlation for all 20 lags at 5% significance level,
following MacDonald and Taylor (1994). AIC and SBIC are not robust when there are
negative moving average errors. Recently, Ng and Perron (2001) modified them and
proposed MAIC and MBIC. These criterions seem to be useful and worthy of use in the
10
9
where stationarity is maintained as the null hypothesis. The values of the test
statistics are fairly sensitive to the choice of the lag length, with a longer lag
length likely to support the null-hypothesis of stationarity. In Table 3, even the
longer lag length of 14 periods cannot reject the null hypothesis at the one per
cent significance level. It roughly supports the hypothesis of stationarity except
for the periods following the sub-period from January 1998 to April 2000.
Nevertheless, we assume that the trading volumes are stationary for all
sub-periods and countries. In addition, we assume that the stock return is
stationary because we have taken the first difference, following previous
research.
4. Empirical Results
Table 4 reports the estimated coefficients and asymptotic t-statistics
under the QMLE from the model (1)-(4) without and with trading volume
included. The parameter 2 is significantly negative coefficient for half of all periods
and all markets. This result shows the EGARCH capture the asymmetric
turbulence that was observed but was not able to be captured by the GARCH,
supporting the use of EGARCH rather than GARCH.
The coeficient on volume, *4 , is significantly positive for Thailand and
Indonesia, irrespective to any sub-period. Moreover, in the presence of volume
with *4 >0, the magnitude of *1 is smaller than 1 or statistically insignificant,
except for Thailand during the crisis. On the other hand, these results hold for
Korea before the crisis and for Singapore during the crisis. Thus, the current
information flow explains some parts of the variance persistence represented by
the EGARCH. By using the USA and Japanese markets data, Lamoureux and
Lastrapes (1990), Najand and Yung (1991), Locke and Sayers (1993), Sharma et
al. (1996), and Miyakoshi (2002) and others have found the same results.
However, the previous researches did not investigate why the current trading
volume explained the volatility and what composition of the current trading
volume affect the volatility. Miyakoshi (2002) only suggests a news influence on
this question. Following Miyakoshi (2002)’s suggestion, we investigate this
problem.
Table 5 report the estimated coefficients under the following model in
(1),(2),(3),(8), where the current trading volume is partitioned into the expected
future.
10

part ( V̂t ) and the unexpected part (  : news), based on (5).11
log  t2   0  1* log  t21  2* z t 1   3* (| z t 1 |  E (| Z t 1 |))   41* log Vˆ t   42* log ˆt
(8)
The coefficient * 42 for news are strongly significant positive for all periods and
all markets, accompanying the smaller magnitude of * 1 compared with  1:
the more news enlarges the volatility. The coefficients for the expected current
trading volume is negative significant for only Thailand and Malaysia for all
periods. Then, by using only the period after the crisis, we investigate why the
expected current trading volume affects the volatility. The expected current
trading volume is expected by using the past trading volume which includes
the past news, and then we infer the past news as the cause.
Table 6 reports the estimated coefficients under the following model in
(1),(2),(3),(9), where the expected current trading volume V̂t and the current


and past news  t and  t 1 .
log  t2   0   1* log  t21   2* z t 1   3* (| z t 1 | E(| Z t 1 |))   41* log Vˆ t   42* log ˆt   43* log ˆt 1 (9)
In Singapore and Indonesian markets, the past news replaces the effects of
expected current trading volume, but in Thailand and Malaysia the effects
remain. Even though the past news with two lags is included, the effects also
remain, which results are not shown in the table. The past news effects on
volatility are negative for all countries.
Thus, the more present news enlarges the volatility. The past news
affects volatility negatively to adjust the overshooting. However, there is no
effect of past news in Taiwan and Thailand: the news effects remain alive in one
day. The expected current trading volume affects the volatility in only Thailand
and Malaysia, which will explained in future.
In Table 7, the news-rich markets where (1- R
) is larger than 0.32 (an
average of all sub-periods of all countries) are Taiwan, Thailand, and Indonesia in
terms of average on periods and the news-rich periods are the before and during crisis.
At least these markets and periods need the news analysis by using individual news
items in detail.
For the possible existence of a simultaneity bias in footnote 3, the trading volume is
now portioned into two items independent of the current trading volume, which will
11
11
In Table 8, we implement the robustness check of the results in Table 4, 6, and
7, by using the data from the Japanese and the USA markets after the crisis: January 1,
1998 to April 30, 2000. The inclusion of the current trading volume in EGARCH
render the EGARCH effect of *1 smaller than 1 as well as shown in Table 4.12
Moreover, the present news enlarges but the past news decreases the volatility as well
as shown in Table 6. Finally, the Japanese market is a news-rich (1- R
=0.45) but the
USA market (0.19) is not so, based on the 0.32.
Finally, in Table 9, we compare the news as defined in this paper with
the “News” that the mass media consider. The table lists the 10 biggest "News"
items for the Thai and Indonesian markets between January 1, 1998 and April
30, 2000, and provides details of their exact dates and the content considered by
the mass media. The biggest "News" item for Thailand, occurring on October
29-30, 1998, was the Siam Commercial Bank going bankrupt and requesting 1.7
billion baht of aid from the Central Bank of Thailand. This failure made people
aware of the systemic risks inherent in the financial system. In Indonesia, the
biggest "News" item, occurring on May 15, 1998, was that the Central Bank
stopped settlement work in inter-bank and foreign exchange markets. We also
examined the news in Korea, which is not a news-rich country. Its biggest
"News" item, occurring on February 7, 1998, was that the committee agreed to
introduce a Korean version of a Lay-off system. This made people and investors
anxious about the prospect of rising unemployment and a more serious Asian
economic crisis. We check the validity of the definition for news by using the
Japanese data. This advanced market data supports the definition for news.
5. Conclusions
This paper has investigated the effects of news on the conditional
resolve the problem.
12 QMLE needs the twice differential coefficients of estimated parameters with regard
to maximized likelihood. The computing program has to decide the size of the
differential, which affects the differential coefficients. The smaller the size of the
differential, the better the approximation is. However, the capacity of RATS for the
digital number restricts the differential coefficients. The digital number for the
differential is 0.1x10-6 in Miyakoshi (2002), but it is 0.1x10-4 in this paper. Then, the
asymptotic t-statistics for the estimated parameters in this paper is different a little
from them in Miyakoshi (2002, pp.224, Table 4).
12
variances in the Asian emerging markets by using the composite index of news
proposed by Miyakoshi (2002).
The findings of the paper are as follows. First, more the present news is,
the volatility increase more in all markets, and the present news effects
disappear within one day in Taiwan and Thailand, but continue to appear
within at least two days in the other countries (negative effects for the second
day). Second, the news-rich markets are the Thai and the Indonesian market.
Third, we checked the robustness for the first and second results obtained by
the composite index of news, by using the Japanese and the US market data,
which have shown the same results. Fourth, we found that the composite index
of news as defined in this paper coincide with the “NEWS” that the mass media
consider, supporting the validity of our methodology.
Recent rapid financial deregulation in the Asian emerging markets has
promoted a great deal of share trading and has attracted international investors,
which has also made the markets news-rich. Therefore, the detail analysis for
individual news, which prevails in the advanced country markets, will be
necessary to get extra profits by responding quickly to the news: in particular for the
Thai and the Indonesian market.
13
References
Becker, K.G., Finnerty, J.E., and Kopecky, K.(1995), "Domestic macroeconomic
news and foreign interest rates", Journal of International Money and
Finance 14, 763•E83.
Becker, K.G., Finnerty, J.E., and Friedman, J. (1995), " Economic News and
Equity Market linkages between the U.S. and U.K.", Journal of Banking
and Finance 19, 1191-1210.
Bekaert, G. and Harvey, C.R. (1997), " Emerging equity market volatility",
Journal of Financial Economics 43, 29-77.
Bollerslev, T. (1986), "Generalized Autoregressive Conditional
Heteroskedasticity", Journal of Econometrics 31, 307-321.
Bollerslev, T. (1987), "A Conditionally Heteroskedastic Time Series Model for
Speculative Prices and Rates of Return", The Review of Economics and
Statistics 69,542-547.
Bollerslev, T. and Wooldridge, J.M. (1992), "Quasi-Maximum Likelihood
Estimation and Inference in Dynamic Models with Time-Varying
Covariances", Econometric Reviews 11, 143-172.
Chang, E.C., McQueen, G.R., and Pinegar, J.M. (1999), "Cross-autocorrelation in
Asian stock markets", Pacific-Basin Financial Journal 7, 471-493.
Ederington, L.H. and Lee, J.H. (1995), "The Short-Run Dynamics of the Price
Adjustment to New Information", Journal of Financial and Quantitative
Analysis 30, 117-134.
Edison, H.J. (1997), "The Reaction of Exchange Rates and Interest Rates to News
Releases", International Journal of Finance and Economics 2,87-100.
Engle, R.F. (1982), " Autoregressive Conditional Heteroskedasticity with
Estimates of the Variance of United Kingdom Inflation ",
Econometrica 50, 987-1007.
Engle, R.F., Lilien, D.M. and Robins, R.P. (1987), "Estimating time varying risk
premia in the term structure: the ARCH-M model", Econometrica
55, 391-408.
Engle, R.F. and Ng, V.K. (1993), "Measuring and Testing the Impact of News on
Volatility", Journal of Finance 48, 1749-1778.
Fuller, W.A. (1976), Introduction to Statistical Time Series, John Wiley & Sons,
New York.
Gallant, R., Rossi, P.E. and Tauchen, G.(1992), " Stock Prices and Volume", The
14
Review of Financial Studies 5, 199-242.
Glosten,L.R. et al.(1993), "On the Relation Between the Expected Value and
the Volatility of the Nominal Excess Return on Stocks", The Journal of
Finance 48, 1779-1801.
IMF (1999), "International Capital Markets: Developments, Prospects, and Key
Policy Issues", International Monetary Fund.
Ito, T. and Roley, V.V. (1987), "News from the U.S. and Japan: Which moves the
Yen/Dollar Exchange Rate?", Journal of Monetary Economics
19, 255-277.
Jones, C.M., Kaul, G.K., and Lipson, M.L. (1994),"Transactions, Volume, and
Volatility", Review of Financial Studies 7, 631-651.
Kim, S-J. and Sheen, J. (2000), "International Linkage and Macroeconomic News
Effects on Interest Rate Volatility-Australia and the US-", Pacific-Basin
Financial Journal 8, 85-113.
Kim, S. and In, F. (2002), "The Influence of Foreign Stock Markets and
Macroeconomic News Announcements on Australian Financial Markets",
Pacific-Basin Financial Journal 10, 571-582.
Kim, S-J. (2003), "The Spillover Effects of US and Japanese Public Information
News in Advanced Asia Pacific Stock Markets", Pacific-Basin Financial
Journal 11, 611-630.
Kim, S-J. et al.(2005), "Dynamic Stock Market Integration Driven by the
European Monetary Union: An Empirical Analysis", Journal of Banking &
Finance, forthcoming.
Karanasos, M. and Kim, J.(2003), "Moments of the ARMA-EGARCH model",
Econometrics Journal 6,146-166.
Kwiatkowski, D., Phillips, P.C.B., Schmidt, P., and Shin, Y. (1992), "Testing the
null hypothesis of stationarity against the alternative of a unit root",
Journal of Econometrics 54, 159-178.
Lamoureux, C.G. and Lastrapes, W.D. (1990), "Heteroskedasticity in Stock
Return Data: Volume versus GARCH Effects", Journal of Finance
45, 221-229.
Lee, B-S. et al.(2004), "Information Transmission between the NASDAQ and
Asian Second Board Markets", Journal of Banking& Finance 28, 1637-1670.
Lee, B-S. and Suh,J.(2004), "What Drives Volatile Emerging Stock Market
Returns ?", Pacific-Basin Financial Journal, forthcoming.
Locke, P.R. and Sayers, C.L. (1993), "Intra-Day Futures Prices Volatility:
15
Information Effects and Variance Persistence", Journal of Applied
Econometrics 8, 15-30.
Lo, A.W. and Wang, J. (2000), "Trading Volume: definitions, Data Analysis, and
Implications of Portfolio Theory", The Review of Financial Studies
13, 257-300.
MacDonald, R. and Taylor, M.P. (1994), "The monetary model of the exchange
rate: long-run relationships, short-run dynamics and how to beat
random walk", Journal of International Money and Finance 13, 276-290.
Miyakoshi, T. (2002), " ARCH versus Information-Based Variances: Evidence
from the Tokyo Stock Market", Japan and the World Economy14,215-231
Miyakoshi, T. (2003), "Spillovers of stock return volatility to Asian equity
markets from Japan and the US", Journal of International Financial
Markets, Institutions and Money 13, 383-399.
Najand, M. and Yung, K. (1991),"A GARCH Examination of Relationship
between Volume and Price Variability in Futures Markets",
Journal of Futures Markets 11, 613-621.
Nelson, D.B. (1991), "Conditional Heteroskedasticity in Asset returns",
Econometrica 59, 347-370.
Ng, A.(2000), "Volatility spillover effects from Japan and the US to the
Pacific-Basin", Journal of International Monetary and Finance 19, 207-233.
Ng, S. and Perron, P. (2001), "Lag Length Selection and the Construction of Unit
Root Tests with Good Size and Power", Econometrica 69, 1519-1554.
Schwert, G.W. (1989), "Why does Stock Market Volatility Change Over Time?",
Journal of Finance 44, 1115-1153.
Sharma, J.L., Mougoue, M., and Kamath, R. (1996), " Heteroskedasticity in Stock
Market Indicator Return Data: Volume versus GARCH Effects", Applied
Financial Economics 6, 337-342.
Wang,S.S. and Firth,M.(2004), "Do Bears and Bulls Swim across Oceans? Market
Information Transmission between Greater China and the rest of the
World", Journal of International Financial Markets, Institutions and
Money 14, 235-254.
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