Discussion Papers In Economics
And Business

The Effects of IMF Supported-Program on the Asian Crisis

Tatsuyoshi Miyakoshi
Yoshihiko Tsukuda
Junji Shimada

Discussion Paper 07-24

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANThe Effects of IMF Supported-Program on the Asian Crisis

Tatsuyoshi Miyakoshi
Yoshihiko Tsukuda
Junji Shimada

Discussion Paper 07-24
June 2007
この研究は「大学院経済学研究科・経済学部記念事業」
基金より援助を受けた、記して感謝する。
Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANThe Effects of IMF Supported-Program on the Asian Crisis
byTatsuyoshi Miyakoshi *Yoshihiko Tsukuda**
Junji Shimada***June , 2007

ABSTRACT:
We assess the IMF supported program on the structural reforms after the Asian crisis in 1997 interms of the before-after, with-without and event study approaches with applying a time varying
parameter model to the nine Asian stock markets. All the supported countries except for Thailand( Indonesia, Korea and Philippine) remarkably improve market efficiency after the
implementation of the program, implying positive assessment of the program in the before-after

approach. Among the non-supported countries, China, Taiwan, and Malaysia do not improve
efficiency after the breakout of the crisis, providing partially positive assessment in the
with-without approach. The Thailand, Indonesia and Korean markets show the positive abnormal
returns at the dates or at the next dates of program’s announcement, providing partially positive
assessment of announcement effects in the event study approach.
Keywords: IMF supported-programs; Asian crisis; structural reforms; assessment
JEL Classification Number: C40; F33; G14; G15*

Osaka School of International Public Policy, Osaka University**

Graduate School of Economics, Tohoku University*** School of Management, Aoyama Gakuin University
Correspondence: Tatsuyoshi Miyakoshi, Prof. of Monetary Economics
Osaka School of International Public Policy, Osaka University
1-31, Machikaneyama-machi, Toyonaka, Osaka, 560-0043, Japan.
tel:+81-6-6850-5638; fax:+81-6-6850-5656,

E-mail: miyakoshi@osipp.osaka-u.ac.jp

1.

Introduction
The IMF program to settle the difficulties caused by the Asian crisis consists of amacroeconomic policy (combining exchange rate, monetary and fiscal policy) and a structuralreform policy including corporate governance. The objectives of the first policy in the IMF
supported program are to restore the stabilization of exchange rates and financial markets, and to
recover the real economy. The second policy in this program aims to lower the possibility of
future crises (i.e., a long-run policy). The Annual Reports of IMF (1998, ch.5 and 1999, ch.4)
describe the program in detail.
Several papers assessed the first policy in the IMF program. Ito (1999) and Yoo and Moon
(1999) assessed no smooth recovery in the real economy and claimed that the IMF programs that
do not address the crux of the matter were ineffective and costly. Berg (1999) stated that the issuesof short-run stabilization receded as early as 1999. However, Cerra and Sweta (2005) found thatwhile growth recovered fairly quickly after the crisis, there was evidence of permanent losses in
the levels of output in all the countries. Lane et al. (1999) presented a preliminary assessment thatthe developments toward recovery had been much more favorable in Thailand and Korea that had
been able to keep to the programs, but Indonesia had been still facing more difficult task in part
because of the severity of the underlying political crisis. Kho and Stulz (2000) found the negative
impact of IMF bailout announcements on the bank stock indices. In contrast, Lau and McInish(2003) found the positive impact on the individual bank stock returns and on the multiple event
dates.
To our knowledge, there are no papers for assessing the second policy. Since the second
policy aims at long-run effect, its effect does not appear in a short-run.1 However, it is interesting
to assess the second policy because success of this policy contributes to prevent another
Asian-type crisis. This policy is a structural reform policy including corporate governance. The
chronological highlights of IMF program on structural reforms for the supported countries ofThailand, Indonesia, and Korea are presented at Table 1. The second policy focusing on theclosure of unviable financial institutions is not considered in this paper because the closure is not

Lane et al.(1999) presented a preliminary assessment that in Korea and Indonesia, deficiencies incorporate governance were recognized at the outset but the progress had been very slow. The reform
agenda for the financial and corporate sectors is still evolving and it is too early to asses in detail themultitude of measures that were planned and implemented. However, they pointed out a number ofimportant general questions regarding program design from the experience with the first program.
1

1

a long-run but short-run policy.2 The programs for each country were modified several times in
the light of climate in its economy. However, the common strand in the policies is to improvegovernance in both the corporate and government sectors, to enhance economic efficiency andtransparency, and then to attract foreign capital through upgrading accounting, auditing anddisclosure standards. In actuality, the four supported countries (Thailand, Indonesia, Korea andPhilippines) implemented the measures along the supported programs. When the policies become
effective in a long-run, the investor's accessibility to the markets increases, the information
surrounding the markets prevails instantaneously among the investors, and then the current priceshould reflect all available information. That is, the success of the second policy in the IMF
program means that the assumptions of the efficient market hypothesis are satisfied and then themarkets become efficient.3
The purpose of this paper is to assess the second policy of the IMF program by the way of
examining whether the program contributes to the improvement of market efficiency in the
supported countries after the crisis.
First, we employ the two methodologies for assessing the programs which are the two out
of four classified by Khan (1990). The methods of evaluating the fund-supported programs areclassified into the four distinct categories by Khan (1990): (1) the before-after approach, which
compares the performance of a country during the program and that of this country prior to theprogram; (2) the with-without approach, which compares the performance of a country with theprogram and that of other countries without the program; (3) the actual-versus-target approach,which compares the actual performance under the program and that specified by the target of the
program; (4) the comparison-of-simulations approach, which compares the simulated
performance of fund program-type policies and the performance of other policies. He argues theadvantage and disadvantage about each approach. But it is difficult to single out the best approach.
Nevertheless, as Khan (1990) pointed out, approach (1) and (2) are relatively popular based on
restricted information for the program, compared with the other approaches. We apply the
before-after and with-without approaches for assessing the IMF program which supported
Thailand, Indonesia, Korea and Philippines after the break out of the Asian financial crisis. The

The last modification of the supported program for Korea was done on May 2 1998, which is not closely
related to structural reform and then deleted from Table 1.
3
See Fama (1970) and Jensen (1978) for the definition of efficiency market.
2

2

combined use of two approaches is expected to complement the shortcoming of each method.4
The time varying parameter model was introduced to investigate the dynamic efficiency in the
East- European emerging markets by Rockinger and Urga (2000, 2001). Their model can enhance
the gradual change of market efficiency. We use the time varying parameter model of Tsukuda,
Miyakoshi and Shimada (2006) which extends their model to more appropriate one for describing
the dynamic behaviors of stock returns and includes their model as a special case.
Second, we employ a recent new methodology for assessing the program’s announcement
effects which is not picked up by Khan (1990). The event study approach is applied to assess the
program’s announcement by Kho and Stulz (2000) and Lau and McInish (2003), whichinvestigate whether the IMF bailout announcements on the bank stock indices make a positive
abnormal return. The positive abnormal returns are evidences that the program’s announcements
help ameliorate systemic risk in the bailout countries.
We analyze the nine Asian stock markets in which Thailand, Indonesia, and Korea had the
support from the IMF program after the breakout of the crisis on July 2 1997, Philippine took the
program before the crisis, while Hong Kong, Singapore, China, Taiwan and Malaysia did not takethe program. All the IMF-supported countries except for Thailand remarkably improve efficiency
in the stock markets after the implementation of the program. In terms of the before-after
approach, the IMF program is successful. In terms of the with-without approach, the IMF programis effective if we choose China, Taiwan and Malaysia as the without-supported countries, while
the program does not provide the evidence for success if Hong Kong and Singapore are chosen.
However, the improvement of market efficiency in Hong Kong and Singapore after the crisis canbe attributed to their own structural reform programs. As a whole, the IMF program is positively
assessed to improve market efficiency. The announcements of the IMF-supported program for
Thailand, Indonesia and Korea have produced positive abnormal returns, ameliorating systemicrisk. While the previous researches including Kho and Stulz (2000) and Lau and McInish (2003)
analyzed the announcement effects of the macroeconomic policy with the short-run effects which
results appear soon, we have investigated the announcement effects of a structural reform policy
with the long run effects which results take long time to appear.
4

See Khan (1990, p.201 and 203): “The shortcoming of the before-after approach makes it a poor
estimator of the counterfactual, because the situation prevailing before the program is not likely to be agood predictor of what would have happened in a absence of the program….” and “The problem is that
program countries differ systematically from non-program countries prior to the program period, and thisdifference matters for performance evaluation”.
3This paper is organized as follows. In Section 2, we sketch a time varying parameter modelof Tsukuda, Miyakoshi and Shimada (2006) and explain the testing hypothesis for market
efficiency by employing the before-after approach and the with-without approach classified by
Khan (1990). In Section 3, we describe the data set and provide some preliminary analyses basedon the summary statistics. In Section 4, we estimate the market efficiency and discuss theassessment of IMF supported program. In Section 5 we employ an event study approach. Section
6 concludes.
[Insert Table 1]

2.

Before-After Approach and With-Without Approach
The paper employs the before-after approach and the with-without approach classified by

Khan (1990) for assessing the IMF supported programs to tackle the problems caused by the
Asian financial crisis in 1997. The program did not start at the same date for all countries. In fact
the program started at the different dates from country to country and was modified several times.
But we suppose that the program for each country started on July 2, 1997 when the Asian crisis
broke out. We compare the market efficiency during the program (i.e., after the crisis) with thatprior to the program (i.e., before the crisis) for the before-after approach, and compare the market
efficiency between the countries with program (Thailand, Indonesia, Korea and Philippine) andthose without program (Hong Kong, Singapore, China, Taiwan and Malaysia) for the with-without
approach.
We use a time-varying coefficient model of Tsukuda, Miyakoshi and Shimada (2006).5 Let
the return on the stock price index (Pt) at time t be rt = 100{ log Pt - log Pt-1}. The return
generating process follows a first order autoregressive (AR(1)) process:the observation equation:()

rt = b 0,t + b1,t rt -1 + ε t , ε t I t -1 ~ N 0,σ t2 , t = 1, …, T;
5(1)

This type specification was first proposed by Rockinger and Urga (2000) to analyze the market
efficiency in the East European emerging economies. The coefficient β is not restricted to one in our model
opposed to β = 1 in their model. Our model nests their model. See Tsukuda, Miyakoshi and Shimada4

where It-1 is the information set available up to time t-1. The unobservable time varying
parameters are assumed to follow the AR(1) process
the state equation:

b i,t = α i + β i b i,t −1 + γ i, 1 t * +γ i, 2 t * * + η i,t ,ηi, t ~ NID( 0, q2i )for i=0,1;(2)

where γ0, 1= γ0, 2= 0,6 t* and t** are the dummy variables such that t* = 10-3t for t < t1 (t1 denotes
the date when the Asian financial crisis broke out, i.e. July 2, 1997) and t* = 10-3t1 for t1 ≤ t, andt** = 0 for t < t1 and t** = 10-3 (t- t1) for t1 ≤ t. We have employed 10-3 for the scale adjustment,
since the number of the sample is about 2000. Figure 1 is a schematic diagram that describes thisidea. All random noises in the observation equation are assumed to be independent of ηi,t. If γ1, 2≠ 0 in (2), the model has a structural change at t = t1. The b0,t and b1,t in (1) are the time- varying

parameters which measure the predictable part based on the previous returns (i.e., previous
information).
[Figure 1]
For the noises of the observation equation (1), we assume EGARCH(1,1) model,ln σ t2 = φ0 + δ D t + φ1 ln σ t2−1 + φ2 ( ε t -1 σ t + φ3 ε t -1 σ t )(3)where Dt is the dummy variable such that Dt = 0 for t < t1 and Dt = 1 for t1 ≤ t. The dummy
variable Dt changes the level of log volatility
The model constitutes a state space representation with an observation equation of (1)and state equations of (2). Since the behavior of unobservable process {bi, t} is a main interest of(2006) for more explanation of the differences between the two models.
Since this paper is interested in the dynamic efficiency of the markets, we introduce a time trend only forthe process of {b1, t }, but do not for the process of {b0, t }65this paper, we briefly explain a basic idea of estimating {bi, t} by using the Kalman filter algorithm.
It estimates {bi, t} in two ways. The filtering estimate is the expectation of bi, t conditional on theobservations up to the period t {r1,…,rt}. The smoothing estimate which we use is the expectationof bi, t conditional on the whole observations {r1,…,rT}. Generally the latter is expected to be morereliable than the former because the latter utilizes the full information available based on thewhole samples {r1,…,rT}. We can obtain the maximum likelihood estimates of the unknown
parameters θ = (b 0, 0 , α 0 , β 0 , q 0 , b1, 0 , α 1 , β 1 , γ 1,1 , γ 1, 2 , q1 , φ 0 , φ1 , φ 2 , φ 3 ) , and the standard

asymptotic theory is applicable if the error terms are normal and the state space processes are
stationary. For initial conditions, we specify the b0,0 and b1,0 as the constant coefficients which arecalculated by the sample first-order autoregressive model from the first 20 observations.
If b1,t = 0 in (1), the returns at period t is not predictable, which in turn implies the market
efficiency in the weak form of Fama (1970). We measure the dynamic efficiency throughexamining the time trend of the smoothed estimates of b1, t. The dynamic efficiency over time
periods may be confirmed, if β 1 < 1 and the non-stochastic part ( α1 +γ1,1 t*+γ 1,2 t**) with
t*=10-3t1 goes to zero over time within the sample periods. Seeing Figure 1, we test the hypothesisof α1≥ 0 and /or γ1,1≥0 , α1 +γ1,1 10-3t1>0, and γ 1,2<0 and α1 +γ1,1 10-3t1+γ1,210-3 (tT-t1) ≈0. Needlessto say, our model cannot be extrapolated to the out-of-sample periods, because the non-stochastic
part eventually explodes to infinity. Though we have tried to incorporate the quadratic term oftime like 1/t2, the model did not show good performances.
It is significant to check whether β 1 < 1 or not. The specification of (2) includes the Random
Walk (RW) type as a special case. If β 1 = 1 , the AR type model reduces to the RW type one. Mostof previous researches including Rockinger and Urga (2000, 2001) imposed the RW type
restriction to the state equation (2) for investigating the dynamic efficiency of the markets. But
they did not test the RW type hypothesis against the AR(1) type one based upon the data from theEast European emerging markets. The appropriateness of the statistical models should bedetermined from the data we observe. As Groenewold and Fraser (1999) did, one way of choosing
the model between the AR(1) type and the RW type is to test the hypothesis:
H0 :β1 = 1 v.s.

H1 :β1 < 1.

(4)This constitutes a unit root test applied for the state equation. Although the test is apparently
similar to the Dickey and Fuller (1981) test in the framework of observable autoregressive model,
6the variable b1, t is not observable in our model. The DF test is not applicable since we do notknow the distribution of the DF test statistic in the state space model. With this caution in mind,
we formally use the DF test for the hypothesis testing of (4). More rigorously, we implement theunit root test by the methods proposed by Perron (1989,p.1380:equation (13)), because the process
has a trend break.
3. The Data and Basic Statistics

The data are compiled from the Nomura Research Institute Japan. The daily closing stock
price Pt is measured in local currency. We examine the stock price indices for the nine Asian stock
markets, consisting of the Stock Exchange of Thailand Index in Thailand, the Jakarta Composite
Index in Indonesia, the Korea Composite Stock Price Index in Korea, the Philippine Composite
Index in Philippine, the Hang Seng Index in Hong Kong, the Strait Times Index in Singapore, theShanghai B Share Index in China, the Taiwan Stock Exchange Weighted Price Index in Taiwan,
and the Kuala Lumpur Stock Exchange composite index in Malaysia.7 The sample periods are thesame for all the markets, ranging from January 1, 1995 to December 31, 2001. The number ofobservations is approximately 2000 for each country. We choose the appropriate sample periods
for computation before and after the IMF supported program started.
The stock price indices and the returns for the whole periods are illustrated in Figures 2 and3. Both the stock price indices and the returns exhibit large fluctuations over the periods. In
particular, after the Asian crisis, the indices drastically drop down and the volatilities of returns
greatly enlarge. However, it is not apparent to see from Figures 2 and 3 what the drastic changesof movement in the price indexes and the returns really mean.
First, we look carefully at the summary statistics of the data in the pre-crisis and thepost-crisis samples. Table 2 indicate the number of observations, mean, standard deviation,
skewness, excess kurtosis, the first order autocorrelation for the returns and the first order
autocorrelation for the squared returns from the both sub-samples for each country. The asterisks
in the columns 4 and 5 denote that the values are significant from zero at 5% level, meaning thenon normal distribution of returns. The asterisks in the column 7 reject the null of no first order

7 The Shanghai market is much larger than the Shenzhen market in terms of market capitalization. Eachmarket is separated into the A market (only domestic investors are allowed to trade) and the B market (notrestricted to domestic investors). However, the index of the B market is measured in Hong Kong dollars or
US dollars. We only analyze the Shanghai B market in this paper.
7

autocorrelation of the squared returns at 5% level. Table 2 reveals that the typical features of stock
returns such as fat tail, spiked peak, and the persistence of variance are observed for the Asianstock markets. Therefore, the ARCH-type model incorporating the above facts is appropriate foranalyzing the return series. The asterisks in the column 6 reject the null of no first order
autocorrelation of the returns at 5% level. The first order autocorrelations of the returns are
significant for the six out the nine markets. This fact indicates that more than half of the Asianmarkets are inefficient for the periods of January 1, 1995 to December 31, 2001.
[Insert Figure 2, Figure 3 and Table 2]

4. Empirical Results and Discussions
Preliminary Results

We compare Table 2 (a) for the pre-crisis sample with Table 2(b) for the post-crisis.
There exist significant first order autocorrelations of the returns in most of the markets. However,
if we look at the Table 2 (a) and (b) in detail, the values of autocorrelation for the post-crisis areless than those for the pre-crisis sample in all countries except for Taiwan. Since ρ(1) = 0 means
the market efficiency, the markets for the post-crisis are generally more efficient than those for the
pre-crisis.
In order to find the rough time trend of the first order autocorrelation, we calculate themoving autocorrelation of 2T+1 days (MARt for 2T+1) defined by
⎧ T
⎫ ⎧ T
⎫
MARt for (2T + 1) = ⎨ ∑ (rt ;i − rt )(rt −1;i − rt )⎬ ⎨ ∑ (rt ;i − rt ) 2 ⎬⎩i = −T⎭ ⎩i = −T⎭−(5)where r t is a mean of {rt +i }i = −T . The MARt is an analogue to the construction of the moving
Taverage from the returns. Figure 4 plots the MARt for 1001,201 and 25 for each country. Fromvisual examination of the MARt for 201 in Figure 4, we see that the moving autocorrelation in the
supported countries (Thailand, Indonesia and Korea), the quasi-supported country (Philippines)
and Singapore seem to approach to zero after the start date of the IMF program (July 2,1997).
Among the non-supported countries, the MARt of China, Hong Kong and Taiwan seem to beunchanged between the pre- and post-crisis, but the MARt of Malaysia seems to rise in the8

post-crisis period. However, from those of the MARt for 1001 and 25, we can never find theconfirmed results.
Applying the before-after approach and the with-without approach to the basic statistics,
we may preliminarily say that the IMF-supported programs have a positive effect on the
post-crisis Asian market efficiency on the basis of an intuitively reasonable but naive method of
the MAR. However, in order to confirm the preliminary results, we need more sound and rigorous
analysis of the IMF supported programs.
[Insert Figure 4]
Main Results

Table 3 shows the estimated parameters of the time-varying parameter model presentedin (1), (2) and (3).8 The estimates of β0 are close to unity for most markets. However, theestimates of β1 are far from unity expect for the Hong Kong market but rather close to zero for theremained markets. If the RW type model is true in the Asian markets, the estimates of β1 areexpected to be close to unity. We test the unit-root hypothesis of (4) for the process of {b1,t}. Table
4 shows that the null hypothesis is rejected at 5% level for all the countries expect for the Hong
Kong market. The stock prices in the Asian markets do not follow the RW type time varying
parameter models. Even though the Hong Kong market is exception, the AR type theoreticallycovers the RW type.
We have already checked for β 1 < 1 in Table 4. The visual examination in Figure 4
should be confirmed by the statistical tests for (α1≥ 0 and/or γ1,1≥0 ), (α1 +γ1,1 10-3t1>0), and (γ-3-3
1,2<0 and α1 +γ1,1 10 t1+γ1,2 10 (tT-t1) ≈0 ) in the equation (2), since the non-stochastic part of b1, t

is ( α1 +γ1,1 t*+γ 1,2 t**). See Figure 1. For example, if α1> 0 and/or γ1,1 = 0, α1 +γ1,1 10-3t1>0, and
γ1,2< 0 and α1 +γ1,1 10-3t1+γ1,2 10-3 (tT-t1) ≈0, b1,t has no time trend before the crisis but has adecreasing time trend toward zero after the crisis. Table 3 presents the estimates of equations (1)and (2). First, in terms of the before-after approach, the IMF programs for the supported andquasi-supported countries have positive effects on the market efficiency after the crisis, because
As the estimates of EGARCH terms (i.e., equation (3)) are not main interest in this paper, Table 3 did notindicate those estimates. However, those estimates suggest that the EGARC formulation is useful todescribe the process of stock returns for the Asian markets. In particular, most of the coefficients forasymmetric responses to positive and negative shocks (φ3) are significant at 5% level. The volatility
increases after the Asian crisis for 6 countries out of 9 at 10% level.

89the estimates of α1 and/or γ1,1 are significant positive and α1 +γ1,1 10-3t1>0 while those of γ1,2 are
negative and significant at 5% level, and goes to zero (α1 +γ1,1 10-3t1+γ1,2 10-3 (tT-t1) ≈0) over timewithin the sample periods for all the above mentioned countries except for Thailand. Note thatα1= γ1,1 =0 at even 10% andα1 +γ1,110-3t1+γ1,2 10-3 (tT-t1)=-0.161 for Thailand and then, theefficiency is perfect before the program (the crisis) and decreases after it. Second, we assess the
IMF program in terms of the with-without approach. The five countries of Hong Kong, Singapore,
China, Taiwan and Malaysia are non-supported countries. These countries are divided into the twogroups according to the market behaviors after the crisis; the first group consists of China andTaiwan and Malaysia, the second group of Hong Kong and Singapore. All countries in the firstgroup have negative estimates of γ1,1 but positive estimates of γ1,2, in particular the estimates of
γ1,2 for Taiwan and Malaysia are significantly positive at 5% level: α1 and γ1,1 are significantly
positive or insignificant, α1 +γ1,1 10-3t1 is negative or zero, but α1 +γ1,1 10-3t1+γ1,2 10-3 (tT-t1) is
positive. The stock markets for these countries did not improve efficiency after the crisis incontrast to the IMF supported countries where the markets improved efficiency after the crisis.
That is, as shown at the last column of Table 3, the time trend of b1,t increase roughly fromnegative to positive values across zero. This fact indicates the success of the IMF program interms of the with-without approach.
Malaysia more strictly controlled the stock markets to prevent the speculative attacks
during the crisis than the pre-crisis period. In fact, in 1998, Malaysia imposed a range of foreignexchange and capital controls that substantially insulated Malaysian financial markets fromexternal influences and effectively closed down the offshore ringgit market. See IMF (1999,
pp.180-185) in detail.
On the other hand, the second group (Hong Kong and Singapore) have the same pattern ofestimated coefficients as that of the IMF supported countries. Namely, Singapore in the second
group has insignificantly positive estimates of γ1,1, and has significantly negative estimates of γ1,2
together with positive estimate of α1 at 5% level. That is, it has a decreasing time trend toward
zero after the crisis and goes to zero over time within the sample periods as shown in the lastcolumn of Table 3. On the other hand, the Hong Kong market is always efficient. This observation
does not provide the evidence confirming the IMF program if we use Hong Kong and Singapore
as the without countries. However, we note that this observation does not necessary mean failureof the IMF programs implemented into Indonesia, Korea and Pphilippines, because other policy
measures taken by Hong Kong and Singapore might have improved the market efficiency. In fact,10

many structural reforms in Hong Kong and Singapore are executed toward market deregulation
after the crisis, following their own programs. For example, despite of equity market intervention
just after the crisis, Hong Kong Monetary Authority and the Securities and Futures Commission
lead to upgrade the market transparency and protection of investors with restrictions in 1998. See
IMF (1999, pp.170-180) in detail. It may be an interesting question to examine why the smoothing
estimates of Hong Kong and Singapore are going toward that of the efficient markets after thecrisis. But we need further studies to answer this question.
[Insert Table 3 and Table 4]

5. Event Study Approach

We define the abnormal return abrj
abr j = r j − r j , where r j is a mean of {r j +i } i+=100−100 ,(6)

where the j is the date of announcement of IMF-supported program. The rj is the moving average
from the returns, based on 201 days which is used for the analysis for MARt and yields goodsuggestions. If abrj>0 or abrj+1>0, we can preliminarily say that the program’s announcement
ameliorate the systemic risk. We consider the one day lag of program’s announcement effectbecause of psychological and physical delay. Table 5 shows that the announcements (of the
program shown in Table 1) in Indonesia and Korea indicate the significant positive abnormal
returns at the next date of some announcements, while no significant positive abnormal returns
appear in Thailand. In general, the effects of the long-run policy like structural reform naturally
appears its effects in a long run, and then its announcement’s effects based on a short-run view isdifficult to be strongly positive. However, Lau and McInish (2003) have studied theannouncements of the IMF macroeconomic policy with the short-run effects, providing positive
assessment in all supported countries.
[Insert Table 5]11In order to confirm the preliminary results, we implement the test by using the dummy
variables for the dates of program announcements or the next dates. That is, we test whether thecoefficients of dummy variables for announcement dates are positive significant in the following
AR(1) equation:

rt = a + b rt -1 + ∑ j =1 (γ j D j ,t + δ j E j ,t ) + ε t : ε t ~ N (0, σ 2 )k(7)where j is a suffix from the first to k-th announcement date. The Dj,t is a dummy variable forannouncement j, equal to 1 for day t when announcement j occurs and zero otherwise. The Ej,t isalso a dummy variable for announcement j, equal to 1 for next date t+1 to date t whenannouncement j occurs and zero otherwise. As seen at Tables 1 and 5, the two, four, and three
events (announcements of IMF programs) occur in Thailand, Indonesia and Korea, respectively.
Table 6 indicates that the Korean market shows that announcement effect is significant positive on
the day for the first event, and on the next day for the second event. In the Indonesia market, thesignificant positive effect appears on the day for the second event. There is a significant effect on
the next day for the third event in Thailand.
[Insert Table 6]
These results confirm the preliminary results. Why did the Korean market show the positive
abnormal returns replying to the announcement for the long-run policy? Yoo and Moon(1999)
concluded that the Korean economy still possesses strong fundamentals, but poor microeconomic
policies with regard to the financial sector have disastrous consequence, therefore, the economic
recovery is obtained with continuous reform efforts to a safe and sound financial sector. Based on
their conclusion, it is imagined that the Korean investors expected positively the structural reform
policy by the IMF programs, and then the positive abnormal returns appear replying to even theannouncement of long-run policies. However, further researches are necessary.

6. Conclusions

We assessed the IMF supported program for the structural reforms against the Asian crisis
12

by using three evaluation methodologies of the before-after approach (which compares theperformance in a country between the pre- and post-programs), the with-without approach (which
compares the performance in different countries with supported program and without supported
program) and the event study approach for program’s announcements (which investigates whether
the announcement produces the positive excess return). We employed a time varying parametermodel for analyzing the nine Asian stock markets. Thailand, Indonesia, and Korea took the
IMF-supported program after the crisis, Philippines took the program before the crisis, but Hong
Kong, Singapore, China, Taiwan and Malaysia did not take the program.
We assessed the IMF-supported program successful in terms of the before-after approach.
That is to say, all the IMF-supported countries except for Thailand remarkably improved
efficiency in the stock markets after the implementation of the program on July 2, 1997. This
result is confirmed by the analysis of the time varying parameter model in addition to thepreliminary analysis of moving autocorrelations.
In terms of the with-without approach, the IMF program is effective if we choose China,
Taiwan and Malaysia as the without-supported countries. The market efficiency in these
non-supported countries does not improve after the program implementation. On the other hand, if
Hong Kong and Singapore are chosen as the without-supported countries, the with-without
approach does not provide the evidence for success of the IMF programs, because this country
improved efficiency after the crisis broke out. However, thinking in mind that the improvement ofmarket efficiency in Hong Kong and Singapore can be attributed to their own structural reform
programs, we positively evaluate that the IMF program to improve market efficiency is successful
in terms of the with-without approach as a whole except for Thailand.
Also, we assess whether the program announcements help ameliorate systemic risk. Thisapproach was not classified yet by Khan (1990) but a new approach used recently by Kho andStulz (2000) and Lau and McInish (2003). All the IMF supported countries show the positive
abnormal returns replying to the announcements of the IMF-supported program. In general, theannouncement’s effects based on a short-run view are not strongly positive for the long-run policy
like structural reform. Why did the market show the positive abnormal returns? Following Yoo
and Moon(1999), the Korean investors already expected positively the structural reform policy by
the IMF programs. Then, we can suppose that the positive abnormal returns appear replying toeven the announcement of long-run policies.

13References
Berg, A. (1999),” The Asia Crisis - Causes, Policy Responses and Outcomes ”, IMF Working
Paper No. 99/138.

Cerra,V. and Saxena, S.C. (2005), “Did Output Recover from the Asian Crisis?” IMF Staff
Papers 52:1-23
Fama, E. (1970),”Efficient Capital Markets: A Review of Theory and Empirical Work”, Journal of
Finance 25:383-417.
Gronewold, N. and P. Fraser, (1999), ”Time-Varying Estimates of CAPM betas”, Mathematics
and Computers in Simulation, 48, 531-539.
IMF(1998), The Annual Report 1998, International Monetary Fund.
IMF(1999), The Annual Report 1999, International Monetary Fund.
Ito, T. (1999),”Asian Currency Crisis and the IMF”(in Japanese), The Economic Review(The Institute of Economic Research, Hitotsubashi University) 50:69-93.
Jensen ,M. C. (1978),”Some Anomalous Evidence Regarding Market Efficiency”, Journal
Financial Economics 6:95-101.
Khan, M. S. (1990),"The Macroeconomic Effects of Fund-Supported Adjustment Programs", IMF
Staff Papers, 37, 2:95-231.
Kho, B-C. and Stulz, R. M. (2000),”Banks, the IMF, and the Asian crisis”, Pacific-Basin Finance
Journal 8:135-275.
Lane, T. etc.(1999),”IMF-Supported Programs in Indonesia, Korea, and Thailand: A Preliminary
Assessment”, IMF Occasional Paper No.178.
Lau, S-T. and McInish, T. (2003),”IMF Bailouts, Contagion Effects, and Bank Security
Reurns”, International Review of Financial Analysis 12:3-23.
Perron, P.(1989),”The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis”,
Econometrica 57:1361–1401.

Rockinger, M. and Urga, G. (2000),”The Evolution of Stock Markets in Transition Economies”,
Journal of Comparative Economics 28:456-472
Rockinger, M. and Urga,G. (2001),”A Time-Varying Parameter Model to Test for Predictability
and Integration in the Stock Markets of Transaction Economies”, Journal of Business& Economic Statistics 19:73-84.
Tsukuda, Y., Miyakoshi,T. and Shimada, J. (2006),” Dynamic Efficiency in the East European
Emerging Markets”, Asia-Pacific Financial Markets 12,159-179.
Yoo, J-H. and Moon, C. W. (1999),”Korean financial crisis during 1997–1998 Causes andChallenges”, Journal of Asian Economics 10:263-277.

14

Table 1. Chronological Highlight for IMF Supported Programon Structural Reform
ThailandAugust 20,1997: structural initiatives to increase efficiency, deepen the role of the private sector in
the Thai economy, and reinforce its outward orientation, civil service reform, privatization, andinitiatives to attract foreign capital.
February 24, 1998 and May 26, 1998: Improving governance in both the corporate andgovernment sector. Further deepening the role of the private sector, including through initiatives
to attract foreign capital.

Indonesia
November 5,1997: Structural reforms to enhance economic efficiency and transparency,
including liberalization of foreign trade and investment, dismantling of domestic monopolies, andexpanding the privatization program.
January 15,1998: Limiting the monopoly of the national marketing board to rice, deregulating
domestic trade in agricultural produce, and eliminating restrictive market arrangements.
April 10, 1998: An extensive agenda of structural reforms to increase competition and efficiency
in the economy, reinforcing the commitments made in January and including the further
privatization of six major state enterprises already listed and the identification of seven newenterprises for privatization in 1998/1999.
June 24, 1998: Establishing an effective bankruptcy system, as an essential part of the corporate
debt-restructuring strategy envisaged by the June 4 agreement between the government andcreditor banks on debt restructuring.

Korea
December 4,1997: efforts to dismantle the nontransparent and inefficient ties among thegovernment, banks, and businesses, including measures to upgrade accounting, auditing, anddisclosure standards, to require that corporate financial statements be prepared on a consolidated
basis and certified by external auditors, and to phase out the system of cross guarantees withinconglomerates.
December 24,1997: speeding up the liberalization of capital and money markets, including thelifting of all capital account restrictions on foreign investors’ access to the Korean bond market by
December 31, 1997.
February 7,1998: introducing a number of measures to improve corporate transparency, including
strengthening the oversight functions of corporate boards of directors, increasing accountability toshareholders, and introducing outside directors and external audit committees.

Note: This table is made from Annual Report 1998, p.23-32, IMF.

15

Table 2. Summary statistics of the daily returns(a) Pre-crisis sample
Pre Sample

NOBS

MEAN

STD

SKEW

KURT

ρ(1)

ρ2(1)

Thailand610-0.155

1.440

0.049

1.698*

0.157*

0.113*

Indonesia

614

0.072

0.919

0.154

3.610*

0.262*

0.182*

Korea

731-0.0401.121

0.292*

0.962*

0.174*

0.075*

Philippines

619

0.001

1.152

-0.1762.455*

0.222*

0.094*

Hong Kong

617

0.107

1.169

-0.288*4.163*

0.046

0.046

Singapore

621

0.006

0.880

-0.282*2.972*

0.192*

0.176*

China

612

0.112

2.861

0.965*

18.363*0.048

0.154*

Taiwan

714

0.035

1.270

-0.396*2.766*-0.016

0.028

Malaysia

611

0.017

1.028

0.265*

3.055*

0.151*

0.222*

ρ(1)

ρ2(1)

(b) Post-crisis sample
Post Sample

NOBS

MEAN

STD

SKEW

KURT

Thailand1107-0.0522.285

0.634*

2.769*

0.141*

0.265*

Indonesia

1105-0.0602.264

0.308*

4.745*

0.183*

0.154*

Korea

1174

-0.0342.755-0.0600.710*

0.104*

0.090*

Philippines

1122-0.0961.944

1.011*

10.382*0.175*

0.091*

Hong Kong

1107-0.0262.273

0.189*

6.794*

0.015

0.365*

Singapore1132-0.0301.852

0.378*

7.231*

0.138*

0.176*

China

1093

0.025

1.566

0.076

6.178*-0.0060.250*

Taiwan1198-0.050

1.783

0.016

1.228*

0.077*

0.155*

Malaysia1108-0.0462.435

0.507*

21.855*0.042

0.491*

Notes: "NOBS", "STDEV", " KURT", "ρ(1)", and "ρ2(1)" respectively denote the number of the observations,standard deviation, excess kurtosis, the first order autocorrelation of the return process, and the first order
autocorrelation of the squared return process. The asterisks "*" represents statistical significance at the 0.05
level.

16

Table 3. Estimation results
Parameters for Equ. (1) and (2)Supported countries

ThailandIndonesia

Korea

Philippines

α0

β0

q0

α1

β1

q1

γ1,1

γ1,2

α1+
γ1,1t110-3

α1+γ1,1t110-3+γ1,2(tT-t1)10-3-0.048

0.565

0.556

-0.044-0.082

0.304

0.361

-0.306**0.177-0.161(0.025)(0.067)(0.222)(0.141)(0.203)(0.076)(0.251)(0.124)-0.001

0.933

0.087

0.262**-0.017

0.559

0.083

-0.215*

0.313

0.076

(0.001)(0.029)(0.032)(0.104)(0.073)(0.071)(0.244)(0.111)-0.023

0.226

0.451

0.057**

0.040

0.000

0.177**-0.162**0.187-0.003(0.014)(0.031)(0.076)(0.011)(0.077)(0.000)(0.015)(0.045)-0.040

0.425

0.462

0.133**-0.4360.276

0.306**-0.289**0.323-0.001(0.026)(0.070)(0.097)(0.025)(0.067)(0.051)(0.033)(0.112)α1+
γ1,1t110-3

α1+γ1,1t110-3+γ1,2(tT-t1)10-3

0.012

0.003

-0.050Non-supported countries
α0

β0

q0

α1

β1

q1

γ1,1

γ1,2

-0.001

0.969

0.069

0.011

0.881

0.000

0.001

-0.008(0.002)(0.018)(0.023)(0.011)(0.079)(0.000)(0.018)(0.011)-0.002

0.959

0.037

0.248*-0.319

0.177

0.170

-0.357** 0.354(0.002)(0.024)(0.014)(0.129)(0.269)(0.085)(0.244)(0.142)

China

0.002

0.920

0.000

0.243**-0.1760.363-0.509** 0.162** -0.070(Shanghi
A)(0.003)(0.041)(0.000)(0.046)(0.136)(0.132)(0.074)(0.067)

Taiwan-0.021

0.600

0.514

-0.095

0.027

0.001

-0.0090.138** -0.101(0.008)(0.049)(0.056)(0.079)(0.234)(0.001)(0.101)(0.039)-0.001

0.956

0.057

0.189*-0.1750.333-0.1640.261** 0.088(0.002)(0.029)(0.043)(0.111)(0.061)(0.078)(0.143)(0.062)

Hong
Kong

Singapore

Malaysia

Notes: Standard errors are in parenthesis. The estimates of α1, γ1 and γ2 with "**" indicate
significance at 5% level and with "*" indicate significance at 10% level

17

0.107

0.064

0.377

Table 4. DF test for β1 = 1.0
Thailand Indonesia Korea-5.33-13.93

Philippines Hong Kong Singapore China Taiwan Malaysia-12.47 -21.43

-1.51

-4.90

-8.65

-4.16

-19.26∧

Note: The entries denotethe values of the unit root test statistic " ( β 1 − 1) / σˆ β " where σˆ β is a∧standard error for β1 and denoted in the parenthesis below the estimates in Table 3. The orderof the lags is zero (i.e. it is not an augumented test) for the sake of simplicity. The 5% significance point of
the t statistic for β1=1 is –3.96 from Table V.B of Perron (1989, p. 1377), in view of the fact that180/332≈0.5.

Table 5.

Program’s Announcements and Abnormal returns

Dates of Announcements

Abnormal returns

Abnormal returns(at announcement date)(at next date)

Thailand

8/20/1997

γ1-0.364-0.2492/24/1998

γ2-0.555-0.432

5/26/1998
Indonesia

γ3-1.139-4.075

11/5/1997

γ1-0.637-2.349

1/15/1998

γ2-4.0356.843*

4/10/1998

γ3+-0.508

6/24/1998

γ4

1.953

-0.382

12/4/1997

γ16.951*

6.939*

12/24/1997

γ2-3.8766.825*

2/7/1998

γ3

0.736

2.445

Korea

Notes: +; The market was closed on the day when the announcement was released: the ‘next
date’ is the first trading date after that day.
*; A simple one-sided t-test, t = abrj / s, indicates significance at 5% level, where s denotes the
sample standard deviation from the full samples. The estimate of s for each market is shownin Table 2(a). The t-statistic is assumed to be distributed as N (0,1) under the null hypothesisof no announcement effect.

18

Table 6. Estimated Coefficients of Dummy Variables for Announcement’s Date
Countries

γ1
ThailandThe date γ
The next date δIndonesia
The date γ
The next date δ
Korea
The dateγ

The next date δEstimated Coefficients
γ2
γ3

γ4

-0.273-0.842-1.274(2.001)(2.000)(2.000)-0.405(1.998)-0.587(1.998)-3.904*(1.998)-0.509-5.309*1.737(1.853)(1.857)(1.852)-2.3277.501*-0.570-0.849(1.848)(1.851)(1.848)(1.848)6.701*-3.3000.184(2.177)(2.184)(2.178)6.029*

7.004*

2.082(2.178)(2.175)(2.173)

Notes: The standard errors are in the parentheses. The asterisk "*" indicatessignificance at 10% level.

Figure 1. Time Trend Structure

α1 +γ1,1 t110-3
α1 +γ1,1 t* α1 +γ1,1 t110-3 +γ 1,2 t**
t0

t1

tT

19

Figure 2.

Stock price indices(a) Supported countries
Thailand
2000
1500
1000
500
0
01/03/95

07/02/97

Indonesia

12/27/99

800
600
400
200
0
01/02/95

Korea
1000
500
07/02/97

12/23/99

Philipines

1500

001/03/95

07/02/97

03/09/00

4000
3000
2000
1000
0
01/02/95

07/02/97

12/23/99(b) Non-supported countries
Hong Kong

Singapore

20000

3000

15000
10000

2000

5000
0
01/03/95
2500
2000
1500
1000
500
0
01/03/95

1000
07/03/97

001/03/95

01/04/00

China

12/20/99

Taiwan
15000
10000
5000

07/02/97

001/05/95

12/30/99

Malaysia
1500
1000
500
0
01/03/95

07/02/97

07/02/97

12/20/99

20

07/02/97

02/21/00

Figure 3. Returns on the Stock price indices(a) Supported countries
Thailand
10
5
0
-5
-10
01/03/95

07/02/97

Indonesia

12/27/99

10
5
0
-5
-10
01/02/95

Korea
10
5
0
-5
-10
01/03/95

07/02/97

07/02/97

12/23/99

Philipines

03/09/00

10
5
0
-5
-10
01/02/95

07/02/97

12/23/99(b) Non-supported countries
Hong Kong
10
5
0
-5
-10
01/03/95

07/03/97

Singapore

01/04/00

10
5
0
-5
-10
01/03/95

China(Shanghai A)
10
5
0
-5
-10
01/03/95

07/02/97

12/30/99

07/02/97

12/20/99

Taiwan
10
5
0
-5
-10
01/05/95

Malaysia
10
5
0
-5
-10
01/03/95

07/02/97

12/20/99

21

07/02/97

02/21/00

Figure 3. Moving autocorrelations for 1001, 201 and 25 days(a) Supported countries
0.5

Thailand0.0-0.5
01/04/95
0.5

0.0

07/02/97

12/24/99

Korea

-0.5
01/03/95

07/02/97

12/22/99

Philipines

0.5
0.0

0.0
-0.5
01/04/95

Indonesia

0.5

07/02/97

03/08/00-0.5
01/03/95

07/02/97

12/22/99(b) Non-supported countries
0.5

Hong Kong

0.0-0.5
01/04/95

0.5

0.0

07/03/97

01/03/00-0.5
01/04/95

China(Shanghai A)

0.5

0.0
-0.5
01/04/95

0.5

07/02/97

12/17/99

Taiwan0.0

07/02/97

12/29/99-0.5
01/06/95

07/02/97

Malaysia⎯⎯⎯ MARt for 1001

⎯ ⎯ MARt for 201········· MARt for 25

0.0
-0.5
01/04/95

Singapore

0.5

07/02/97

12/17/99

22

02/19/00