An Examination of the Asian Crisis Part II:

Information Spillover, Herding, and Regime Shifts

Jarl G. Kallberg, Crocker H. Liu, and Paolo Pasquariello*

Stern School of Business - New York University Revision April 18, 2002

Abstract

We study the impact of capital flights on the interaction of currency and equity markets in Indonesia, the Philippines, Malaysia, South Korea, Taiwan and Thailand during the Asian crisis using the sequence of regime shifts in reduced-form linear relations between currency and equity returns, and between currency and equity return volatility estimated non-parametrically by Kallberg, Liu, and Pasquariello (2002). We relate those structural breaks to data on net monthly flow of funds from foreign investors in each of the equity markets in our sample. We test for whether the timing of those regime shifts implies a causal relationship across countries, and whether such relationship can be explained by common information shocks, portfolio rebalancing or “herding” phenomena. Our results suggest that portfolio rebalancing was a major channel for the transmission of information shocks across East Asia in 1997, but not in 1998. We provide evidence that information spillover and herding effects, rather than common information shocks, generate the propagation of the estimated structural shifts from a country to another during the Asian crisis. In particular, we find that the highest degree of herding occurred in 1998, when most of the breaks in the reduced-form return relations were observed. We also show that, when signed herding was more intense, i.e., when more equity markets were simultaneously experiencing funds’ withdrawals, the expected number of return and return volatility break events per period was bigger, and the clustering of regime shifts across countries was more likely. JEL classification: C51; G15

Keywords: Regime shifts; Asian crisis; Information spillover; Exchange rates; Herding

*Associate Professor of Finance, Associate Professor of Finance and Ph.D. candidate respectively at the Stern School of Business. We thank an anonymous referee for many suggestions that have substantially improved this paper We are also grateful to Viral Acharya, Edward Altman, Yakov Amihud, Robert Dittmar (our discussant in Bloomington), Tony Kao, Kanak Patel (our discussant in Maui), Bertrand Renaud, Vijay Singal (our discussant in New Orleans), and other seminar participants at the 1999 AREA/ AREUEA conference in Maui, the 1999 NYU Conference on Risk and Return Management for Insurance Companies, the 2000 Indiana University 6th Biennial Conference on Financial Crises, the 2001 AFA Annual Meeting in New Orleans, and the 2002 EFMA Annual Meeting in London. Please address comments to the authors at the Leonard N. Stern School of Business, New York University, Kaufman Management Education Center, Suite 9-190, 44 West 4th Street, New York, NY 10012-1126, or through email: jkallber@stern.nyu.edu, cliu@stern.nyu.edu and ppasquar@stern.nyu.edu, respectively. The usual disclaimer applies.

2

An Examination of the Asian Crisis Part II:

Information Spillover, Herding and Regime Shifts

Abstract

We study the impact of capital flights on the interaction of currency and equity markets in Indonesia, the Philippines, Malaysia, South Korea, Taiwan and Thailand during the Asian crisis using the sequence of regime shifts in reduced-form linear relations between currency and equity returns, and between currency and equity return volatility estimated non-parametrically by Kallberg, Liu, and Pasquariello (2002). We relate those structural breaks to data on net monthly flow of funds from foreign investors in each of the equity markets in our sample. We test for whether the timing of those regime shifts implies a causal relationship across countries, and whether such relationship can be explained by common information shocks, portfolio rebalancing or “herding” phenomena. Our results suggest that portfolio rebalancing was a major channel for the transmission of information shocks across East Asia in 1997, but not in 1998. We provide evidence that information spillover and herding effects, rather than common information shocks, generate the propagation of the estimated structural shifts from a country to another during the Asian crisis. In particular, we find that the highest degree of herding occurred in 1998, when most of the breaks in the reduced-form return relations were observed. We also show that, when signed herding was more intense, i.e., when more equity markets were simultaneously experiencing funds’ withdrawals, the expected number of return and return volatility break events per period was bigger, and the clustering of regime shifts across countries was more likely.

JEL classification: C51; G15

Keywords: Regime shifts; Asian crisis; Information spillover; Exchange rates; Herding

3

I. Introduction

The Asian crisis was precipitated by large-scale capital outflows from Southeast Asian

economies, prompted by concerns about over-borrowing and mounting debt burdens. The

grounds of the crisis were laid mid-decade, when there was a substantial increase in the flow

of capital to liberalizing Asian markets without a commensurate improvement in their

capacity to manage these resources.

United Nations (1998; p.2)

The Asian currency crisis of 1997-1998 began when Thailand abandoned its fixed peg

currency regime and devalued the baht on July 5, 1997. The resulting shocks to Asian and

world economies have been the subject of an intense amount of discussion and analysis,

without many clear conclusions emerging. The reasons for its intensity, the role of foreign

investment, the manner in which the crisis propagated across the economies of Asia, and its

long-term impact continue to be discussed. However, as the above quote suggests, the most

frequently mentioned culprit for the crisis is the destabilizing effect of the flight of foreign

capital. In particular, much popular press blames the “excessive speculation” and rapid

withdrawal of capital by hedge funds for the events of 1997 and 1998. Although many

academic studies suggest that portfolio rebalancing may have represented a major channel

for transmitting information shocks across markets during the Asian crisis, the available

empirical evidence is inconclusive.

This paper attempts to clarify some important aspects of the crisis through a more formal

analysis of foreign exchange markets, equity markets and flows of capital in Indonesia,

Malaysia, the Philippines, South Korea, Taiwan and Thailand. We select these six countries

because they experienced the greatest extent of economic and financial turmoil during 1997

and 1998. Furthermore, their domestic monetary authorities maintained similar currency

management policies at the time of the crisis. Each of these countries was in fact pegging its

currency to the U.S. dollar (or to a basket of currencies in which the U.S. dollar dominated)

before the crisis swept through the region. This peg was fixed in the case of Thailand; the

other countries allowed the rate to fluctuate within pre-specified, relatively narrow bands.

The combination of such protection against currency devaluations and the rapid growth

experienced by these economies in the 1990s led to rapid inflows of foreign capital. These

flows of capital declined, first in 1997 during the Asian currency crisis, then again in 1998

4

with the devaluation of the ruble and the collapse of many major hedge funds, including

Long Term Capital Management (LTCM).

The financial devastation of those once thriving markets during the second half of 1997

was remarkable. Over the interval from July to December of 1997, the domestic equity

indexes for Indonesia, Malaysia, the Philippines, South Korea, Taiwan and Thailand dropped

by 37%, 52%, 41%, 42%, 12% and 56%, respectively. The corresponding local currencies

depreciated by 56%, 35%, 36%, 46%, 17% and 46%, respectively, versus the U.S. dollar.

After July 1997, most of these countries reacted swiftly in revising their currency regimes.

Thailand abandoned its fixed exchange rate regime on July 2, 1997. Indonesia gave up the

enlarged band of currency fluctuation on August 14, 1997. On October 17, 1997, Taiwan

decided to adopt a floating exchange rate regime. Korea abandoned the defense of its

currency in November of 1997, as did Malaysia.

A companion paper, Kallberg, Liu, and Pasquariello (2002), tests for regime shifts in

reduced-form linear relations between currency and equity returns and between currency and

equity return volatility in the above mentioned Asian markets. In that paper, using a non-

parametric technique developed by Bai, Lumsdaine, and Stock (1998), we identify and

characterize regime shifts in each of these countries in 1997 and 1998 and argue that the

behavior and interaction of those currency and equity markets during the Asian crisis

resulted from significant structural breaks, as opposed to being merely a manifestation of

natural dependencies. In our data, shocks to volatility lead shocks to returns; while regime

shifts in returns occur almost a year after the currency crisis, volatility breaks take place

around the crisis period. We further show that the sequential nature of breaks in the

structural relation between currency and equity market return and volatility market returns

and volatility is consistent with information spillover effects.

Figures 1 and 2 summarize the main findings in Kallberg et al. (2002) and motivate our

analysis. There we display net monthly flow of funds from foreign investors in each equity

market for Indonesia, Malaysia, the Philippines, South Korea, Taiwan and Thailand (Figures

1A and 2A), and confidence intervals at the 5% level around those estimated break dates �k ,

ranked in order of increasing statistical significance, over two time intervals, March to

December 1997 and January to December 1998, respectively. These two intervals

5

correspond to most of the observed break-dates for equity versus currency volatility (Figures

1C and 2C) and equity versus currency returns (Figures 1B and 2B).

The observed pattern in the flows of funds for 1997 (in Figure 1A) is striking. Intense

portfolio rebalancing activity took place during the selected time period. While Malaysia

suffered the most significant outflows, countries such as South Korea, the Philippines and

Thailand experienced inflows of capital for most of that summer. Portfolio rebalancing

appears to be a major channel by which information shocks were transmitted across markets

during 1997. The picture for 1998 (Figure 2A) suggests a different interpretation. Most of

the countries in our sample experienced significant contemporaneous inflows and outflows

of capital at that time, which is when we identify structural breaks in the hypothesized return

relations. In this case, Figure 2A seems to suggest that portfolio reallocation efforts affected

the entire region during 1998, although with a different intensity for each market, i.e., that

herding may have been a driving force for the events of that year.

The timing information on the volatility break events contained in Figures 1C and 2C is

especially significant for this paper. Such evidence suggests not only that breaks in volatility

anticipated breaks in returns but also that volatility shocks in one country may have affected

the posited relations between equity and currency markets in other countries during the

crisis. To test for this possibility, we estimate the impact of the break event in country i at

time t on the reduced-form volatility relation of other countries. If information spillover

were at least partially responsible for the propagation of the event from a country to another,

we would expect the statistical power of such structural relations to improve from the

inclusion of dummies for break dates in other countries. We find some evidence of

information spillover across Indonesia, the Philippines and South Korea between 1997 and

1998, when past regime shifts in the linear relation between their domestic equity and

currency return volatility appear to have induced a statistically and economically significant

decline in equity returns.

This paper links those estimated regime shifts to the movements of foreign capital. In

particular, we seek to gain some insight into how the actions of international investors,

specifically capital flight, influenced the structure of Asian currency and equity markets

around the time of the crisis. We start by asking whether the observed sequence of regime

breaks described in Figures 1 and 2 implies a causal relation across countries. We then test

6

for whether the observed sequence of structural breaks can be explained by portfolio

rebalancing or the “herding” behavior of foreign investors. Finally, we develop a Poisson

regression model to analyze whether changes in the flows of funds were responsible for the

observed clustering in regime shifts.

Our analysis of flows of funds data provides further evidence that information spillover

or herding effects generate the observed return and volatility shocks among countries. We

show that portfolio rebalancing was a major channel for transmitting information shocks

across markets during 1997, but not in 1998 (when the highest degree of herding occurred,

and when most of the breaks in returns were instead observed). We also demonstrate that

the expected number of return and return volatility break events per period was greater, and

the clustering of those break events more likely, when signed herding was more intense (i.e.,

when more equity markets were simultaneously experiencing withdrawals).

The organization of the paper is as follows: Section II briefly reviews the main economic

explanations for the turmoil of 1997 and 1998. Section III illustrates the statistical

methodology. Section IV describes the data set, summarizes the findings of Kallberg et al.

(2002) that we use in this paper, and presents our first set of results. In Section V we study

the relation between herding behavior and cross-country spillover. Section VI concludes.

II. The economics of the Asian crisis

… any country that adopts a fixed exchange rate for stabilization reasons needs to

have a well-thought-out exit strategy. In the crisis year of 1997, the widely held implicit

strategy of waiting until a speculative attack forced a devaluation caused an overshooting of

currency depreciation and significantly lowered stock returns.

Grier and Grier (2001; p. 146)

While there is general agreement among academics and practitioners that the Asian crisis

was triggered by the devaluation of the Thai baht in July of 1997, there is less agreement

about the relative significance of the many factors that contributed to its severity.1 In this

section we briefly overview these explanations and the related economic literature. The

major contributing factors discussed here are: (i) the role of currency regimes, (ii) the

1For a further discussion of the causes of the Asian crisis, refer to Gangnes (1998) and Wu (1998).

7

interaction of foreign investment and domestic current accounts and (iii) macroeconomic

factors.

(i) Currency regimes

A major factor in the turmoil that affected the six countries in our sample was the

adoption by each of them of a pegged currency regime. This effectively virtually eliminated

the currency risk for foreign investors, but at the same time left these countries more

exposed to economic shocks or the actions of speculators than countries adopting a floating

exchange rate regime.2 The literature has argued that such circumstance may help explain the

occurrence of financial crises. Supportive empirical evidence on the adverse effects of

pegging the domestic currency can be found in Grier and Grier (2001) for 25 developing

countries in 1997, and in an earlier study of African economies by Tornell and Velasco

(1995).3 In particular, Grier and Grier (2001) use a macroeconomic model to show that the

nations in their sample that adopted pegged currency regimes suffered more than what could

have been anticipated from their fundamentals.

(ii) Foreign investment and current account balance

These implicit guarantees on the value of the local currency and the remarkable growth

rates this region experienced in the first half of the 1990s drew vast amounts of foreign

capital into the region. In 1996, the amount of foreign investment in the developing markets

of Asia totaled $56 billion.4 This pool of capital encouraged over-investment. Investment in

South East Asia countries (measured as fixed capital formation as a percent of GDP) grew

more quickly than GDP, inducing persistent deficits on the current account. In Thailand,

investment accounted for 40% of GDP from 1990 to 1996, while GDP increased by 6.4%

over this period. Over-investment in speculative real property (particularly in Thailand,

Malaysia, and Indonesia) put stress on the domestic financial systems, given the fast pace of

Asian financial market liberalization, weak regulation of the financial sector and insufficient

quality of lending oversight. Corsetti, Pesenti, and Roubini (1998) report that Thailand,

2 For example, in the first nine months of 1997, the Bank of Thailand spent an estimated $10 billion in foreign exchange reserves in an effort to defend the baht (Time, 8/25/1997). 3 Kaminsky, Lizondo, and Reinhart (1998) survey the empirical literature relating to currency crises. 4 See Van Wincoop and Yi (2001) for detailed figures and a discussion of the impact on the U.S. and Europe of foreign capital leaving Asia in 1997 and 1998.

8

Indonesia, and the Philippines experienced growth rates in bank credit in excess of 20% per

annum. Growth rates in Korea and Malaysia were only slightly lower. At the same time,

estimates of non-performing loans prior to the crisis were significant, ranging from 14% in

the Philippines to 19% in Thailand.

Since in many of these countries the foreign reserves were inadequate, foreign capital was

eventually used to fund deficits on their current account balances. Compounding these

problems was the fact that much of the foreign capital was short term in nature. This “hot”

capital is very sensitive to changes in the international economic environment. Hence, its

sudden withdrawal, as we will explore later, can disrupt an economy, especially when these

funds were used to finance long-term projects.

Furthermore, the analysis in many academic studies (e.g., Fleming, Kirby, and Ostdiek

(1998), Kaminsky, Lyons, and Schmukler (2001), and Kodres and Pritsker (2002)) also

suggests that portfolio rebalancing may have represented a major channel for transmitting

information shocks across markets during the Asian crisis. The paper by Fleming et al.

(1998) is particularly relevant for this research, as it investigates how volatility linkages of

two distinct types, a common information shock or information spillover, may result in

investors rebalancing their holdings in other markets in response to a change in the hedging

component of their demand for one market. Brown, Goetzmann, and Park (2000) address

the issue of whether the withdrawal of hedge fund capital has played an important role in the

Asian crisis, by analyzing the investment decisions of ten major currency funds during that

period. They show that changes in those funds’ positions were not highly correlated with

fluctuations in exchange rates. Lin and Kuo (2000) argue that the actions of international

hedge funds, although central to the Thai baht crisis, were not a major force in the currency

turmoil involving Indonesia, Malaysia and the Philippines. These results thus suggest that the

importance of hedge funds’ activity as a catalyst of the crisis may have been overstated.

(iii) Macroeconomic factors

As interest rates rose in the U.S. and the outlook for European markets improved, the

flow of foreign capital began to contract. Additionally, almost all project returns were

denominated in local currency, while most of the debt financing those investments was

dollar-denominated. These mismatches, together with limited forward markets, weakened

9

the domestic banking sectors. The gradual rise in the dollar in mid-1995 also reduced

exports, since the region’s exchange rates were pegged to the U.S. dollar, albeit to varying

degrees. Such currency arrangements also led to severe losses of foreign reserves, as in

Taiwan between June and October of 1997, in the Philippines in July 1997, and in South

Korea later that year. The stress from these factors, combined with the high debt burdens

culminated in the second half of 1997.5 The high interest payments now become

unsustainable as interest rates rose, the value of the domestic currency plummeted and the

values of real assets tumbled.

Kallberg et al. (2002) identify and characterize such potential interaction between equity

and currency returns, and equity and currency return volatility around the time of the Asian

crisis. In the remainder of this paper, we use their chronology of regime shifts in reduced-

form relations between currency and equity markets to test for whether the resulting

sequence of structural breaks implies a causal relationship across countries, and for whether

such regime shifts and relationships can be explained by the dynamics of flows of funds

across countries, i.e., by portfolio rebalancing or “herding” by foreign investors.

III. Methodology

Testing for regime breaks

In this section we briefly describe the empirical method used in Kallberg et al. (2002) to

test for the existence, timing and intensity of shocks in a set of linear reduced-form structural

relations between equity and currency returns, and equity and currency volatility for each of

the countries in our sample. Kallberg et al. adopted a statistical methodology originally

devised by Bai, Lumsdaine, and Stock (1998) to make statistical inference about regime

breaks, including interval estimation of the break date, with minimal restrictions on the

underlying data generation process (DPG). In particular, Bai et al.’s non-parametric

technique searches for a single break in univariate or multivariate time series models (with or

without stationary regressors) assuming only that the DPG is a stationary VAR before and

after the break, and specifies asymptotic confidence intervals for the estimated break point.

5 See Furman and Stiglitz (1998) for an analysis of the role of external debt during the Asian crisis.

10

More specifically, the reduced-form model for returns uses month t equity index returns

denominated in local currency as the dependent variable. The independent variables are the

currency returns in each of the five months t – 2 to t + 2 (to account for possible lead-lag

relations) and the equity return in month t=− 1 (to account for first-order autocorrelation in

returns). We have the following equation:

ti i

itittititt xykdxbAyy εβαλµ +�

��

�+++++=

= =−+−−+−

5

1

5

13131 )( , ( 1 )

with dt(k) equal to 1 if t is greater or equal to k, and zero otherwise. Here, k is a potential

break date, yt is the equity index return for a certain country in month t, and xt is the

corresponding currency return in month t.

Kallberg et al. (2002) also focuses on the second moments of returns, by extending the

basic model of Eq. (1) to the case of equity and currency volatility. We in fact search for

breaks in a structural relation between monthly rolled volatility for the available series of

equity returns and the corresponding monthly rolled volatility series for currency returns. We

calculate the rolled volatility as a moving standard deviation of 12 monthly returns for the

equity (σy) and currency (σx) index returns. The proposed model is then

( )[ ] txttxtytyt kdbA εβσλσσµσ +++++= −1 . ( 2 )

Many of the approaches proposed in the literature to test for a break in any of the

parameters in Eqs. (1) and (2) are based on Wald statistics. Bai et al.’s test, which is similar to

that of Quandt (1958, 1960), identifies a break date �k as the value of k that maximizes an F-

statistic � ( )F k . The date �k is then statistically significant if )ˆ(ˆ kF is greater than the

corresponding critical value (for the selected significance level). This technique also allows us

to construct confidence intervals for the estimated break dates assuming only that the

disturbances form a sequence of martingale differences.

In Kallberg et al. (2002), we used two monthly time series, currency returns and equity

returns for Indonesia, Malaysia, the Philippines, South Korea, Taiwan and Thailand, and the

specification of our model described above, to estimate break dates for volatility and return

shocks, i.e., for breaks in the linear reduced-form models of Eqs. (1) and (2). We employ the

results of that analysis in this research to examine whether the ensuing chronology of break

11

dates provides enough evidence of causal relationships across countries around the time of

the Asian crisis, i.e., whether information spillover from one country to another was

occurring during 1997 and 1998.

Herding measure

In this paper we investigate the potential causes for the information effects observed

during the Asian crisis, and described in Kallberg et al. (2002). One popular explanation is

the herding behavior of foreign investors. Capital flight, in particular the widespread

withdrawal of foreign funds from emerging equity markets, appears to have played an

important role in the Asian crisis, especially in creating pressure on the domestic currencies

and interest rates.6

The concept of herding comes into play when different markets simultaneously (or with

only a slight lag) experience flows of foreign capital in the same direction. Herding

intensifies the transmission of single-market information shocks through geographically or

fundamentally heterogeneous markets. In our analysis, inter-country herding can arise

because of distinct, yet related reasons, as in Wermers (1999). First, managers might mimic

each other’s behavior, disregarding their private information, to avoid jeopardizing their

reputation by trading differently from other managers who are acting in apparently

interrelated markets. Second, as suggested earlier, managers might make country allocation

decisions simultaneously. This could happen either because the original information shock

affecting a single market is correlated to the information sets of each of the other markets, or

because a common information shock affects all the information sets at the same time.

Third, managers could infer private information from observing trading in geographically

heterogeneous markets. Finally, institutional investors trading in different markets might

need to rebalance their aggregate holdings in these markets as a result of global portfolio

reallocation. Measuring the existence and intensity of the herding effect during the Asian

crisis is another important objective of this study.

We use a measure developed by Lakonishok, Shleifer and Vishny (1992) and adopted by

Wermers (1999). Choe, Kho, and Stulz (1999) use a similar criterion to analyze herding

6 In 1997, the Prime Minister of Malaysia, Dr. Mahathir Mohamed, called hedge fund managers the “highwaymen” of the global economy (Chancellor (2000)).

12

behavior in the Korean stock market in 1997. The measure assumes a null hypothesis that

observed herding behavior is due purely to chance. The herding measure for a given month

t, H(t), is defined as

})]([)({)]([)()( **1 tbEtbEtbEtbtH t −−−= − , ( 3 )

where b(t) is the number of countries in which we observe negative flows of funds divided

by the total number of countries for which flows of funds were available at time t.

Eq. (3) implies that either sign of H(t) is equally likely. The proxy for Et-1[b(t)], the

expected proportion of outflows during a given month, is the simple average of the observed

b(t) during the past quarter. We calculate the adjustment term E{|b*(t) − E[b*(t)]|} under

the null hypothesis that herding is only observed as a result of random chance. Hence, b*(t)

is the proportion of negative flows that we would observe by drawing xt, the number of

observed outflows at time t, from a binomial distribution B(xt, nt), where the probability of a

single sell outcome is one half, and nt is the number of countries in the sample for which

flows of funds data were available at time t. Since xt follows a binomial distribution with

probability b*(t) of success, E{|b*(t) − E[b*(t)]|}, is then easily calculated given b*(t) and nt.

Our final measures of herding are generated as averages of H(t) over selected time intervals.

When we analyze the herding measure for the chosen sample sub-periods, we can test for

systematic patterns in the aggregate behavior of fund managers who invest in the Far East.

Any significant value of H(t) signals that international investors tend to trade in different

markets together, at the same time, and in the same direction, more often than would be

expected by random and independent trading.

IV. Empirical results

Data

We use three monthly time series: currency returns, equity returns, and flow of funds, in

our analysis of Indonesia, Malaysia, the Philippines, South Korea, Taiwan, and Thailand.

We assume that the exchange rate of each local currency relative to the dollar is the key

exchange rate variable. Therefore, for the time series of monthly currency returns, we use

the spot rates that correspond to the noon buying rate for cable transfers payable in foreign

13

currencies, as recorded by the Federal Reserve Bank of New York every business day. For

each of the six markets, we use the major equity index in local currency to calculate a

monthly time series of local returns. The six equity indexes are JCI Jakarta Composite Index,

EMAS Equity Index, PSE Index, Kospi 200, the Thailand Stock Exchange Index and the

TWSE Stock Index. Monthly equity indexes for each country are from Bloomberg. Table 1A

presents summary statistics for each country. Sample periods are of different length, due to

data availability constraints, but they all end in March 1999.

To measure the extent to which international portfolio managers reallocate their wealth

across the region, we use time series data on the net monthly flow of funds in each equity

market. The flow is the difference (in millions of U.S. dollars) between total foreign

purchases and sales of domestic equities in each month. Flow of funds data are collected by

local stock market authorities, and were made available to us by a major investment bank.

Sample periods in most cases overlap the ones for equity and currency data, and again end

all in March 1999. The flow of funds data in Table 1B shows great variation across the

sample, although the means are positive for each country.

Indonesia, Malaysia, the Philippines, South Korea, and Thailand arguably suffered the

greatest degree of economic, financial, and political turmoil during 1997 and 1998. More

importantly, those countries also shared a similar exchange rate regime at or around the time

of the Asian crisis, being all unilaterally pegged to the U.S. dollar by relatively tight bands of

fluctuations. Thailand abandoned its fixed exchange rate regime on July 2, 1997. Indonesia

gave up the enlarged band of currency fluctuation on August 14, 1997. The Indonesian

rupiah crashed soon afterwards. South Korea renounced battling the increasing selling

pressure on the won in November of 1997, and so did Malaysia.

The Philippines and Taiwan were also hit by the crisis, although less severely. In the

Philippines, the Central Bank was forced to relax its previously successful band of

fluctuations for the exchange rate by the end of July 1997. Taiwan, previously known as one

of the East Asian Tigers, was able at first to fend off expectations of increased depreciation,

through its Central Bank’s policy of active foreign exchange intervention. Therefore, the

country initially appeared to be unaffected by the economic instability undermining the rest

of the region. However, on October 17, 1997, Taiwan decided to adopt a floating exchange

rate regime, and the New Taiwan Dollar subsequently fell by 30% versus the U.S. dollar.

14

Many analysts and economists seem to agree that Taiwan’s economy paid a very high price

for the comparative resilience shown throughout 1997 and 1998, in particular in the

domestic financial markets, where the steep rise of short term interest rates and the loss of

foreign reserves were eventually accompanied by a severe decline of the stock market.

Despite the major devaluations of their domestic currencies, during this time both the

Philippines and Taiwan experienced significant, albeit extremely volatile portfolio

rebalancing activity. For instance, the average absolute flows of funds in and out of Taiwan’s

domestic equity market over the interval January 1997-March 1999 was second only to South

Korea. While many other Asian equity markets were plummeting, The TWSE Stock Index

was instead rising by 13.15%, to then fall by more than 21% in 1998. Investigating the

potential role of portfolio rebalancing in explaining these disparities and the occurrence of

potential “information spillover” events from a market to another is one of the objectives of

this research.

Analyzing regime shifts

Using the database described above, in a companion paper we estimate breaks in the

assumed structural relations between local equity returns and local currency returns (Eq. (1),

in Table 2A), and between the volatility of local equity returns and the volatility of local

currency returns (Eq. (2), in Table 2B).

Tables 2A and 2B reveal that, for three of the countries in our sample, Indonesia, the

Philippines and South Korea, breaks in the hypothesized reduced-form relations described in

Eqs. (1) and (2) do occur in 1997 and 1998, and are statistically significant at less than 5%

for both returns and return volatility. Indonesia and the Philippines, where the domestic

central banks strictly controlled the corresponding currencies and market regulations were

poor or nonexistent, experienced probably the greatest extent of political turmoil ensuing

from the Asian crisis. The troubles of South Korea surprised many investors and

commentators, in light of the enormous economic and social progress made by the country

in the previous fifty years. Regime shifts in the lead-lag relation between equity and currency

returns were registered for Malaysia, Thailand, and Taiwan as well. Nonetheless, for these

three countries breaks in return volatility cluster in the fall of 1994, during the Mexican Peso

crisis.

15

Furthermore, breaks in the hypothesized structural relations between equity and currency

return volatility appear to consistently anticipate the corresponding break event in the

returns’ relations for most of the countries in our sample. More specifically, in our sample of

Asian countries, past regime shifts in the linear relation between equity and currency return

volatility appear to have induced a statistically and economically significant decline in equity

returns, even after controlling for the occurrence of a break in the relation between equity

and currency returns of Eq. (1). This evidence is consistent with the results of French et al.

(1987) for the U.S. markets, and shows that, in Asia, volatility shocks influence future

returns.

Regime shifts and information spillover across countries

In our companion paper we argued not only that, during the events of 1997 and 1998, a

negative relationship between volatility and returns characterized many of the Asian

countries under investigations but also that the observed sequence of breaks (in Table 2)

appeared to imply a causal relationship from volatility to returns. An intuitive explanation of

this phenomenon is that any information shock that affects the volatility of a single market’s

equity index returns, the volatility of the exchange rate, or the relation between the two, may

eventually induce a shock in returns through the adjustment of the predicted component of

the index volatility itself.

Significant cross-country correlations could channel those information shocks from one

country into other markets, with market frictions, differences in economic fundamentals,

and constraints to hedging influencing the intensity of the spillover phenomenon.7 The

sequential nature of those estimated structural breaks suggests that information spillover

effects created return and volatility linkages between Asian markets. Information alters

expectations in one market and affects returns and volatility in other markets through

changes in hedging demand. The effect of these changes is protracted over time and delayed

by the existence of market frictions. The existence of such linkages in East Asia has often

been related to the availability of hedging and portfolio rebalancing opportunities among

7 The available empirical evidence on the state of the economies involved in the Asian crisis (e.g., Eichengreen, Rose, and Wyplosz (1996), Corsetti et al. (1998), or Barro (2001)) leads us to exclude the possibility that idiosyncratic shocks occurred in each of the countries for which the statistic F is statistically significant could explain the chronology of regime shifts in the reduced-form relations between equity and currency markets.

16

local equity markets around the time of the Asian turmoil. Indeed, the previously mentioned

studies of Fleming et al. (1998) and Kodres and Pritsker (2002) have argued that portfolio

rebalancing represented a major channel for transmitting information shocks across markets

during 1997 and 1998. In the remainder of this paper, we address the closely related issues

of whether the sequence of breaks reported in Tables 2A and 2B implies a causal

relationship across countries, and of whether cross-country spillover at or around the time of

the Asian turmoil can be attributed to herding behavior by foreign investors.

We start by using the timing information on the volatility break events contained in Table

2B to explain whether the observed sequence of structural breaks implies their transmission

across countries. Such evidence suggests that breaks in volatility anticipated breaks in

returns, and that volatility shocks affected future returns for most of the nations that have

experienced regime shifts in the posited relations between equity and currency markets

during the crisis. The timing of the structural breaks in volatility will allow us to test for the

existence of information spillover by estimating the impact of the break event in country i at

time t on the structural relation itself.8 From Table 2B we identify two sets of countries that

experienced a (quasi) contemporaneous volatility break event. The first set consists of

Thailand, Malaysia, and Taiwan in the fall of 1994. The second set comprises Indonesia,

South Korea and the Philippines in 1997-98. We then estimate the regression

( )[ ] ( ) tkdxtktdxtbytAytK

ityii i

εσαβσλσσµσ +++++−+==1

ˆˆ1 . ( 4 )

Eq. (4) tests whether the statistical power of Eq. (2) can be improved by including a

dummy for break dates in the other countries in the group. Thus, d( �k i) is equal to zero when

t is below the lower limit of the confidence interval for the volatility break registered in

country i, and one otherwise. If information spillover were at least partially responsible for

the propagation of the event from a country to another, we would expect the estimated αs

to be statistically significant. Table 3 reports the results of our analysis. To correct for

heteroskedasticity and autocorrelation in the residuals of the above regression, statistical

significance of the coefficients is measured using Newey-West standard errors. The

clustering of volatility breaks in the fall of 1994 does not seem to be characterized by

8 We are grateful to Robert Dittmar for suggesting this analysis of the break chronology.

17

information spillover. Indeed, all the coefficients for break events in other countries are

statistically indistinguishable from zero, except for the parameter measuring the propagation

of a quasi-contemporaneous volatility shock from Malaysia to Thailand (α1 in Table 4). The

results for the second set of countries are strikingly different. Many of the coefficients for

other countries’ volatility-adjusted break event dummies are significant at the 10% level or

less, in particular suggesting the occurrence of spillover from Indonesia to the reduced-form

volatility relations of the Philippines and (more weakly) of South Korea. Therefore, the

cross-country propagation variables in Table 3 appear to help explaining the estimated

sequence of volatility breaks occurring in Asia during 1997 and 1998 and the ensuing

statistically and economically significant declines in equity returns.

Prima facie, these findings seem to suggest that, after controlling for past regime shifts in

the linear relation between their domestic equity and currency return volatility, Malaysia,

Taiwan and Thailand in the fall of 1994 did not experience spillover of information from

other markets in the region, while volatility shocks to Indonesia affected the Philippines and

South Korea during the Asian crisis. However, the simultaneity of many of the spillover

dummies prevents us from being able to distinguish between a rapid propagation of

information innovations from a country to another and a common information shock, and

reduces the statistical power of the analysis of Eq. (4). Moreover, such dummies do not

explicitly account for one of the possible sources of spillover of information across

countries, portfolio rebalancing, nor do they distinguish it from the widespread withdrawal

of foreign funds that would accompany a common information shock. Therefore, in the

remainder of the paper we concentrate our attention on the analysis of flows of funds and

explore more directly their relationship to the observed sequence of regime shifts between

equity and currency markets across the Asian countries in our sample.

Analysis of flows of funds

To relate the timing of regime breaks in Asia to portfolio rebalancing activity, we employ

our flow of funds data and the estimated break dates in Tables 3 and 4.9 Figures 1 and 2

9 A word of caution on the use of such data is necessary. The difference between total foreign purchases and sales of domestic equities in each month accounts only for the trading activity of foreign investors in the domestic spot equity markets. Speculative forces can nonetheless deeply affect financial markets even while

18

display our monthly flows of funds data for Indonesia, Malaysia, the Philippines, South

Korea, Taiwan, and Thailand, and confidence intervals at the 5% level around those

estimated break dates �k , ranked in order of increasing statistical significance, over two time

intervals, March to December 1997 and January to October 1998 respectively. These two

intervals correspond to most of the observed break-dates for equity versus currency volatility

(Figures 1C and 2C) and equity versus currency returns (Figures 1B and 2B).

The observed pattern in the flows of funds for 1997 (in Figure 1A) is striking. Intense

portfolio rebalancing activity occurred during the selected time period. While Malaysia

suffered the most significant outflows, countries such as South Korea, the Philippines and

even Thailand experienced inflows of capital for most of that summer. These findings,

coupled with the evidence that most of the volatility shocks accompanying the Asian crisis

(in Table 2B) occurred between August and October 1997, are consistent with information

spillover effects. Portfolio rebalancing appears to be a major channel by which information

shocks were transmitted across markets during 1997. The picture for 1998 (Figure 2A) is not

as clear. Most of the countries in our sample experienced significant contemporaneous

inflows and outflows of capital at that time, with Malaysia again acting as the bellwether.

This is when our empirical model identifies structural breaks in the hypothesized return

relations. In this case, Figure 2A seems to suggest that portfolio reallocation efforts affected

the entire region during 1998, although with a different intensity for each market, i.e., that

herding may have been a driving force for the events of that year.

Table 4 reports summary results on the herding measure H(t) described in Eq. (3) for four

different time intervals. Over the longest time period, from May 1993 (when we have flows

of funds for at least three countries) to March 1999, the figures are significant at the 1%

level, indicating the presence of herding behavior. Breaking the time period into three sub-

periods shows that herding occurred primarily during 1998 and early 1999, when the t-

statistic for H(t) is equal to 2.12 and significant at (slightly more than) the 5% level. In the

inducing little or no change in those flows of funds. For example, our dataset ignores the flow of loans from foreign banks to domestic borrowers that, as previously mentioned, characterized many East Asian economies during the early 1990s. Furthermore, if speculators choose to trade in derivatives (like swaps) to take a negative stance on a particular country, thus exercising a downward pressure on the corresponding domestic markets, their actions would cause only marginal fluctuations in the flows of funds to that country. Unfortunately, evidence on such activity is too scant, and often just anecdotal, to be employed in the statistical analysis that follows. We thank an anonymous referee for these observations.

19

interval surrounding the Mexican Peso crisis, between 1993 and the end of 1995, evidence of

herding in our sample of Asian markets is weaker. In 1996 and 1997, the herding measure is

instead barely different from zero, as is the corresponding t-test. These results suggest that,

consistently with the analysis of Figures 1 and 2, herding was strongest in 1998, i.e., in the

period during which most regime shifts in returns occurred.

V. Herding and clustering of break events

One of the most puzzling characteristics of the financial crises of the past decade is the

speed at which they appear to move from one country to the other, as the results of our

investigation into how currency and equity markets interact during stress periods seem to

confirm. As previously mentioned, and as evident from the bottom panels of Figures 1 and

2, breaks in the relation between currency and equity returns and return volatility are

concentrated in relatively short periods of time, even after we account for the statistical

uncertainty surrounding our estimates. Figures 1 and 2, and the analysis of Table 4, make it

apparent that capital-flow reversals from international investors played a significant role in

the clustering of estimated breaks in the reduced-form relations of Eq. (1) and (2) across

Asian countries during 1997 and 1998. This should not be surprising, given the large

holdings of emerging markets’ publicly available equity by international investors and the

mounting evidence of institutional panic and herding around the time when the crises

occurred. Most significantly for our paper, Kaminsky et al. (2001) observe that mutual fund

investments were very responsive and volatile during most of the crises of the 1990s. In

particular, they argue that the events of 1997 and 1998, although initiated in a single country

(Thailand and Russia respectively), rapidly propagated to other markets, due to the large and

widespread withdrawals of funds from Asia, Latin America, and Eastern Europe, thus

providing support for cross-country spillover effects.

In this section we explore this issue in more depth. In the analysis we have presented so

far, breaks in the estimated relations between equity and currency return volatility at or

around the time of the Asian crisis cluster over a few months of 1997. We also found that,

during the same time interval, the herding measure H(t) was barely different from zero.

Breaks in the reduced-form relation between equity and currency returns were instead

concentrated in 1998, when H(t) was large and significant. This evidence seems to suggest

20

that herding played a role in the clustering of return breaks, but less so in the clustering of

volatility breaks.

To translate this intuition into a more rigorous statistical investigation, we define some

new variables. The first is It , the number of countries that at time t are experiencing a

statistically significant break in their reduced-form relation between equity and currency

markets; more specifically It is the number of countries for which t falls in the corresponding

break confidence interval, as specified in Tables 2A and 2B. Hence, It measures the extent of

concentration of break events across the countries in our sample for each period t. We

compute It for both the sequence of return breaks and the sequence of return volatility

breaks. In the remainder of this section, we develop an empirical model to test whether

herding explains the variability of these series, which proxy for clustering of volatility or

return break events.

To that end, we use the flows of funds data described in Section IV to derive a measure

of the intensity of the herding behavior of international investors at each point in time t. As

previously mentioned, our dataset covers net purchases (or sales) by foreign investors in the

domestic spot equity markets for each of the countries in the sample. We define HM1t as the

number of countries that at time t were experiencing a net outflow of funds from

international investors.10 HM1t is a measure of signed herding, therefore focusing on the

extent of net withdrawals across Asia at each point in time, which Kaminsky et al. (2001) have

already identified as a likely culprit in the events of 1997 and 1998.

We intend to verify whether the herding proxy HM1t can explain the clustering of the

break events reported in Tables 2A and 2B. The easiest approach would be to use linear

regressions. However, because the break is, by construction, a unique event, and because

these events are concentrated over relatively few dates, the discrete variables It for returns

and volatility are characterized by a preponderance of zeros and small values. Hence, such

data appear to be better analyzed by a specification that accounts for those properties. In

particular, the econometric literature suggests the use of the Poisson regression model.

10 As previously mentioned, our dataset covers net purchases (or sales) by foreign investors in the domestic spot equity markets for each of the six countries in our sample. However, although such data starts on January 1992, both equity return and flows data are simultaneously available for all of those six countries only starting from March of 1996. To accommodate these limitations, the variables It and HM1t are computed using only the subset of countries for which both flows of funds data and regime shift information were available at time t.

21

According to this approach, each variable It is assumed to be drawn from a Poisson

distribution with parameter λ t. The primary equation of the model is then

( ) �,2,1,0!

Pr ===−

ii

eitt I

t Itλλ

, ( 5 )

where i is the number of countries experiencing a break event at time t. The variable λ t is

related to the signed herding regressor HM1t in terms of a log-linear model, i.e., lnλ t = B’Rt ,

where B = [constant, β] and Rt = [1, HM1t].11 We estimate the model of Eq. (5) by maximum

likelihood for the full sample 01/1992-03/1999. Our parameter of interest is clearly β. If

estimates of β are positive and significant, then the expected number of break events per

period E[It|HM1t] is bigger, and the clustering of break events more likely, when herding is

more intense, i.e., when HM1t is higher. It is easy to show that the partial derivative of the

conditional expected number of break events per period E[It|HM1t] with respect to the

corresponding herding measure is given by λ tβ. In Table 5 we report the results of the

estimation of Eq. (5) for It computed with respect to the breaks in the reduced-form

equation (2) for return volatility (listed in Table 2B) and with respect to the breaks in the

reduced-form equation (1) for return (listed in Table 2A).

The results from Table 5 support our initial hypothesis that signed herding was a relevant

factor in explaining the estimated clustering of return break events during 1998. In the third

column of Table 5, the coefficient β is positive and statistically significant at the 1%

confidence level, as is the chi-squared statistic. Interestingly, signed herding appears to have

played an important role in the clustering of volatility break events observed in 1994 (as

hinted by Table 4) and 1997 as well. Indeed, the estimated coefficient β is again positive and

significant, although the evidence for the model’s fit is much less compelling, suggesting

lower explanatory power for HM1t in the model for volatility.

We perform a parallel analysis using an alternative, more general measure of herding,

HM2t , defined as the maximum of HM1t and the number of countries that at time t were

experiencing a net inflow of funds from international investors. HM2t is a proxy for unsigned

herding because it captures the extent by which international investors in different countries

11 For more on this topic, see Greene (1997), especially Chapter 19.

22

act together independently of the direction of their flows. We estimate Eq. (5) using HM2t for

both return- and volatility-based It . Unsigned herding appears to explain the concentration

of regime shifts in returns, but not the clustering of return volatility breaks during both the

Mexican Peso and the Asian crises. Indeed, the model for volatility (in the second column of

Table 5) is clearly rejected by the data. Nonetheless, unsigned herding significantly improves

the fit of Eq. (5) with respect to the clustering of return breaks, generating two different R2s

between 40% and 50%.12 Furthermore, using the resulting estimates for β in the fourth

column of Table 5 to compute λ tβ over the mean values for Rt, we find that, on average,

about one additional country suffers a regime shift in the reduced-form return relation of

Eq. (1) when the number of countries experiencing flows of funds of the same sign increases

by 3 units.13

To provide more intuition for these results, in Figure 3 we plot the probabilities for

various outcomes of the clustering variable It , in particular for It = 0, 1, 2, and 3, computed

according to the following recursion:

( )

( ) .3,2,1Pr

0Pr0

=���

�===

=== −

ii

iP

eP

tit

tt

PI

I

-1itt

t

λ

λ

( 6 )

The probabilities in Eq. (6) are estimated using the coefficients from the first and third

columns of Table 5, for both the measures of volatility break clustering (Figure 3A) and

return break clustering (Figure 3B), together with the corresponding dependent variable

itself and the signed herding measure HM1t. The correlation between those Its and HM1t is

evident. As a consequence of the good fit provided by the model of Eq. (5), the probability

of no regime shifts drops, the expected number of countries suffering a break increases, and

the clustering probabilities P2t and P3

t rise significantly when signed herding is more intense,

i.e., when the percentage of funds’ outflows across Asian equity markets is higher.

In short, our analysis suggests that the likelihood of observing a concentration of regime

shifts in the reduced-form models of interaction between equity and currency returns and

12 More precise definitions of the two different R2s used to measure the fit of the log-linear model are in the note to Table 5. 13 In Table 5, none of these measures is however statistically significant, even though β is, because of the standard errors of the mean regressors.

23

return volatility posited in this paper in more than one country over a certain period of time

is an increasing function of signed herding, i.e., of widespread withdrawals from the

corresponding equity markets. Nonetheless, as in Table 4, unsigned herding appears to be

less successful in explaining the clustering of return volatility breaks during 1994 and 1997.

These findings are consistent with our previous assertion that, as a result of herding behavior

by international investors, the Asian financial markets experienced cross-country spillover

during the recent crisis events.

VI. Conclusions

In this study we investigate the role of portfolio rebalancing and herding in the Asian

crisis. In a companion paper, we specified reduced-form linear relations between equity

index returns and lead-and-lag currency returns, and tested whether any of those relations in

six Asian countries experienced a regime shift during the events of 1997 and 1998. The

resulting estimated sequence of structural breaks in the posited relations between currency

and equity markets allowed us distinguish whether market linkages between countries are

due to common information shocks or to information spillover effects. Most of the

estimated breaks in the hypothesized return and return volatility structural relations are not

simultaneous, even when the uncertainty surrounding our estimates is taken into account.

The sequential nature of these regime shifts indicates that information spillover effects

created linkages across the Asian markets during 1997 and 1998.

In this paper, we address the issue of whether the observed sequence of breaks implies a

causal relationship across countries, and of whether cross-country spillover at or around the

time of the Asian turmoil can be attributed to herding behavior by foreign investors. Our

analysis shows no evidence of information spillover for Malaysia, Taiwan, and Thailand in

1994, when they all experience a simultaneous volatility break event. A common information

shock seems a more likely explanation for the set of volatility breaks that occurred in those

countries. We find instead some evidence of information spillover across Indonesia, the

Philippines, and South Korea between 1997 and 1998.

Our analysis suggests that portfolio rebalancing was a major channel for transmitting

information shocks across markets during 1997, but not in 1998. Over the entire time

period, the herding measure used in our analysis is significant at the 1% level, revealing the

24

presence of herding behavior. We nonetheless find that the lowest degree of herding occurs

between 1996 and 1997, when most of the breaks in volatility were observed, but that

herding was particularly intense in 1998, i.e., around the time when most of the breaks in the

posited reduced-form return relations were detected.

Finally, using a Poisson regression model, we provide additional support for the

hypothesis that the Asian markets were affected by cross-country spillover at or around the

time of the crisis, by showing that the expected number of return and return volatility break

events per period was bigger, and the clustering of those break events more likely, when

signed herding was more intense, i.e., when more equity markets were simultaneously

experiencing funds’ withdrawals.

25

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27

Table 1 Descriptive statistics This table displays descriptive statistics (mean and standard deviation) for time series of equity indexes and local currency returns (Table 1A), and flows of funds (Table 1B) for each of the countries included in the study. Equity returns are computed from local equity indexes’ monthly time series obtained from Bloomberg. Currency returns are calculated from the exchange rate versus the U.S. dollar. The minus sign represents devaluation of the local currency. Currency data are from the Federal Reserve Bank of New York. Flows of funds are in millions of U.S. dollars and are calculated as the difference between the total foreign purchases and sales of domestic stocks in each month of the sample.

1A) Equity and currency data

Equity returns Currency returns Countries Mean Stdev Mean Stdev Sample Size Indonesia 0.06% 11.88% −5.56% 25.05% Jan 96 - Mar 99 Malaysia 0.19% 10.19% −0.62% 3.40% Nov 92 - Mar 99 Philippines −0.29% 10.06% −0.91% 3.77% Nov 94 - Mar 99 South Korea 0.21% 10.52% −0.55% 5.01% Jan 90 - Mar 99 Taiwan 1.02% 9.38% −0.34% 1.99% Apr 93 - Mar 99 Thailand −0.67% 11.84% −0.62% 5.23% Mar 93 - Mar 99

1B) Flows of funds data

Flows of funds Countries Mean Stdev Sample Size Indonesia 60.67 146.88 Mar 96 - Mar 99Malaysia 86.52 648.09 Jan 93 - Mar 99 Philippines 154.15 184.61 Jan 95 - Mar 99 South Korea 229.77 445.79 Jan 92 - Mar 99 Taiwan 100.78 335.04 Jan 96 - Mar 99 Thailand 64.55 432.00 May 93 - Mar 99

28

Table 2 Analysis of structural breaks This table, from Kallberg et al. (2002), reports estimated break dates k^ for the structural relation between equity and currency returns (Table 2A)

ti i

ititttiitt xykdxbAyy εβαλµ +�

��

�+++++=

= =−+−−−

5

1

5

13131 )ˆ( , ( 1 )

and between equity and currency return volatility (Table 2B) ( )[ ] txttxtytyt kdbA εβσλσσµσ +++++= −1 , ( 2 )

respectively. The Median column in the Tables shows the estimated break date �k , computed using the Wald statistic F described in Kallberg et al. (2002). For both Eqs. (1) and (2) we test the null hypothesis that the post-break coefficient changes are not significantly different from zero, i.e., that no break occurred in the sample period, by comparing the maximum value in the estimated time series )(ˆ kF to the 5% quantile of its limiting distribution. The null hypothesis is rejected when the maximum value for )(ˆ kF , reported in the Max-Wald column, is lower than the critical value for the selected significance level. Bekaert et al. (1998) compute a table with critical values for max )(ˆ kF (for dimensions up to 68), approximating the limiting distribution of the F process with partial sums of normal random variables for each possible dimension of the test statistic )(ˆ kF . From such table we use the asymptotic 1% (5%) critical value of 27.02 (22.21) for Eq. (1) (where 7 parameters are allowed to break) and of 16.37 for Eq. (2) (where 2 parameters are allowed to break). The 2.5th and 97.5th Percentile columns display estimated lower and upper bands, respectively, for the confidence intervals for the “true” break dates, computed with quantiles of the Picard (1985) distribution.

2A) Analysis of equity indexes monthly returns Country 2.5th Percentile Median 97.5th Percentile Max-Wald p-value Indonesia May-98 Jun-98a Jun-98 24.19 < 0.05 Malaysia Oct-97 Feb-98b May-98 35.64 < 0.01 Philippines Feb-98 Apr-98b May-98 47.33 < 0.01 South Korea Dec-97 Mar-98a May-98 26.52 < 0.05 Taiwan Jan-94 Feb-94a Feb-94 23.93 < 0.05 Thailand Jan-98 Mar-98b Apr-98 27.45 < 0.01 2B) Analysis of equity indexes monthly return volatility Country 2.5th Percentile Median 97.5th Percentile Max-Wald p-value Indonesia May-97 Jun-97c Jun-97 70.70 < 0.01 Malaysia Jun-94 Oct-94c Jan-95 23.56 < 0.01 Philippines Jul-98 Aug-98c Aug-98 63.61 < 0.01 South Korea Sep-97 Nov-97c Dec-97 17.67 < 0.01 Taiwan Sep-94 Oct-94c Oct-94 32.55 < 0.01 Thailand Sep-94 Oct-94c Oct-94 21.22 < 0.01 a Significant at the 5% level (for a critical value of 22.21). b Significant at the 1% level (for a critical value of 27.02) or less. c Significant at the 1% level (for a critical value of 16.37) or less.

Table 3 Evaluation of information spillover: volatility This table displays the results of tests for chronologically sequential relations between equity volatility breaks. From Table 2B we identify two sets of countries that experienced a contemporaneous volatility break. The first set consists of Malaysia, Taiwan and Thailand in mid-1994. The second set comprises Indonesia, the Philippines and South Korea in 1997-98. We then estimate the following regression:

( )[ ] ( ) tkdxtktdxtbytAytK

ityii i

εσαβσλσσµσ +++++−+==1

ˆˆ1 , ( 4 )

where d( �k i) is equal to zero when t is earlier than the lower limit of the confidence interval for the volatility break registered in country i, and one otherwise. The first column of the table shows the regression adjusted R2, R2a , for the structural relationship of Eq. (4) for each of the countries in the sample. We identify the break dates �k and �k i through the Wald statistic described in Eq. (5). Break dates in our sample are collected in Table 2B. Country i is where a break contiguous to �k has been recorded, according to the results reported in Table 2B. The Contiguous breaks columns displays, for each country in the sample, the two markets where a contiguous break was recorded. We use S if the break is simultaneous, P if country i breaks before �k and D if country i breaks after �k . The last three columns report the corresponding estimated coefficients for the information spillover dummies. To correct for heteroskedasticity and autocorrelation in the residuals of the above regression, statistical significance of the coefficients is measured using Newey-West standard errors.

Contiguous breaks Spillover dummy coefficients Country R2

a i = 1 i = 2 α1 α2

Malaysia 96.63% Thailand (S) Taiwan (S) 0.022 −0.007Taiwan 82.46% Malaysia (S) Thailand (S) −0.036 0.057 Thailand 95.82% Malaysia (S) Taiwan (S) 0.188c 0.050 Indonesia 92.45% Philippines (D) South Korea (D) 0.042b 0.262b Philippines 92.11% Indonesia (P) South Korea (P) 0.125c −0.021 South Korea 94.33% Philippines (D) Indonesia (P) 0.003 0.090a

a Significant at the 15% level. b Significant at the 10% level. c Significant at the 5% level or less.

Table 4 Analysis of the average herding measure for selected periods This table displays the results of tests for the null hypothesis that herding is observed in selected time periods only by random chance, i.e., that the average herding measure H(t) developed by Lakonishok et al. (1992) has mean equal to zero across the six countries in our sample, Indonesia, Malaysia, the Philippines, South Korea, Taiwan and Thailand. H(t) is computed as

})]([)({)]([)()( **1 tbEtbEtbEtbtH t −−−= − , ( 3 )

where b(t) is the number of countries in which we observe negative flows of funds over the total number of flows of funds available at time t. Flows of funds are in millions of U.S. dollars and are calculated as the difference between the total foreign purchases and sales of domestic stocks in each month of the sample. The proxy for Et-1[b(t)], the expected proportion of sellers during a given month, is the simple average of the observed b(t) during the past quarter. The adjustment term E{|b*(t)-E[b*(t)]|} is calculated under the null hypothesis that herding is only observed as a result of random chance. Hence, b*(t) is the proportion of negative flows that we would observe by drawing xt, the number of observed outflows at time t, from a binomial distribution B(xt, nt), where the probability of a single sell outcome is one half, and nt is the number of countries in the sample for which flows of funds data were available at time t. Since xt follows a binomial distribution with probability b*(t) of success, E{|b*(t)-E[b*(t)]|} easily follows given b*(t) and nt. Our final measures of herding are generated as averages of H(t) over selected time intervals.

Analysis of the herding measure H(t) Periods Mean H(t) Median H(t) Stdev H(t) t-statistic

05/93 – 03/99 6.55% 6.25% 19.07% 2.83* 05/93 – 12/95 6.99% 6.25% 19.85% 1.90a 01/96 – 12/97 2.05% −4.51% 13.95% 0.72 01/98 – 03/99 12.89% 12.15% 23.56% 2.12a

a Significant at the 10% level. * Significant at the 1% level or less.

31

Table 5 Herding and spillover effect This table displays the results of the estimation of the Poisson regression model for the number of simultaneous break events I in each period t computed using the confidence intervals for breaks reported in Table 2B (for equity versus currency return volatility) and Table 2A (for equity versus currency returns). The probability of i such events in period t is given by

( ) �,2,1,0!

Pr =−

== ii

tItte

i tIobλ

λ. ( 5 )

The variable λt is formulated in terms of a log-linear model, i.e., lnλ t = B’Rt , where the coefficient vector B = [constant, β], HM1t and HM2t are the measures of signed and unsigned herding, respectively, described in the text, and where Rt = [1, HM1t] or Rt = [1, HM2t]. The model of Eq. (5) is estimated via maximum likelihood. For each of the estimated coefficients (the constant and the herding parameter β) we report the corresponding t-statistic. The two R2 statistics help assess the improvement of the fit resulting from using λ t instead of the mean value for y to predict It. The Pearson R2

P is computed as R2P = 1 – [Σt(It - λ t)2 / λ t] / [Σt(It – Ibar)2 / Ibar], where Ibar is the mean of I

over the sample. The Deviance R2D is instead obtained as R2

D = 1 – [ΣtItln(It / λ t)] / [ΣtItln(It / Ibar)]. The goodness-of-fit statistics χ2 tests the hypothesis that the coefficients of the regression in Eq. (5) are all zero. We also report the partial derivative of the conditional expected number of break events per period E[It|HM1t] with respect to the corresponding herding measure, HM1t. This measure, given by λ tβ, is computed at the mean of HM1t. The clustering measure is computed using the six countries in our sample, Indonesia, Malaysia, the Philippines, South Korea, Taiwan and Thailand. The model is estimated over the interval from 01/1992 to 03/1999.

Poisson model for clustering of breaks Volatility breaks Return breaks

Statistics HM1t HM2t HM1t HM2t Constant −2.0315* −1.4831* −1.8561* −7.3703*

(t-stat) (-5.516) (-2.657) (-5.649) (-5.526) β 0.2544a 0.0289 0.3043* 1.3438*

(t-stat) (1.948) (−0.081) (2.757) (5.522)

Pearson R2P −7.38% 0.49% −16.17% 39.69%

Deviance R2D 5.50% 0.05% 5.89% 49.71%

χ2 3.426a 0.033 6.803a 57.440* (p-value) (0.0642) (0.8563) (0.0107) (0.0000)

∂E[I|HM]/∂HM 0.0526 −0.0060 0.0839 0.3707 (t-stat) (1.248) (-0.180) (1.504) (1.242)

a Significant at the 10% level. * Significant at the 1% level or less.

Figure 1 Flows of funds, return, and volatility breaks in East Asia: March 1997 to January 1998 Figure 1A reports monthly flows of funds data for the six countries in our sample, Indonesia, Malaysia, the Philippines, South Korea, Taiwan and Thailand. Flows of funds are in millions of U.S. dollars and are calculated as the difference between the total foreign purchases and sales of domestic stocks in each month of the sample. Figures 1B and 1C display confidence intervals at the 5% significance level around the estimated break date �k , i.e., the one that maximizes the Wald statistic )(ˆ kF over the sample interval, for the structural relations between equity and currency return and volatility respectively. The confidence intervals are computed according to Bai et al. (1998). The confidence interval measure of 6 (left axis) is associated with the country for which we measure the most significant )ˆ(ˆ kF in the sample. The confidence interval measure

of 1 (left axis) is associated to the country for which we measure the least significant )ˆ(ˆ kF in the sample. A B C

- 2 0 0 0

- 1 5 0 0

- 1 0 0 0

- 5 0 0

0

5 0 0

1 0 0 0

1 5 0 0

$ M

ill.

M a r - 9 7 A p r - 9 7 M a y - 9 7 J u n - 9 7 J u l - 9 7 A u g - 9 7 S e p - 9 7 O c t - 9 7 N o v - 9 7 D e c - 9 7T i m e

M a l a y s i a I n d o n e s i a

T h a i l a n d P h i l i p p i n e s

T a i w a n S o u t h K o r e a

0

1

2

3

4

5

6

7

M a r - 9 7 A p r - 9 7 M a y - 9 7 J u n - 9 7 J u l - 9 7 A u g - 9 7 S e p - 9 7 O c t - 9 7 N o v - 9 7 D e c - 9 7T i m e

S o u t h K o r e a

I n d o n e s i a

0

1

2

3

4

5

6

7

M a r - 9 7 A p r - 9 7 M a y - 9 7 J u n - 9 7 J u l - 9 7 A u g - 9 7 S e p - 9 7 O c t - 9 7 N o v - 9 7 D e c - 9 7T i m e

T h a i l a n d

S o u t h K o r e a

M a l a y s i a

33

Figure 2 Flows of funds, return, and volatility breaks in East Asia: April to December 1998 Figure 2A reports monthly flows of funds data for the six countries in our sample, Indonesia, Malaysia, the Philippines, South Korea, Taiwan and Thailand. Flows of funds are in millions of U.S. dollars and are calculated as the difference between the total foreign purchases and sales of domestic stocks in each month of the sample. Figures 2B and 2C display confidence intervals at the 5% significance level around the estimated break date �k , i.e., the one that maximizes the Wald statistic )(ˆ kF over the sample interval, for the structural relations between equity and currency return and volatility respectively. The confidence intervals are computed according to Bai et al. (1998). The confidence interval measure of 6 (left axis) is associated with the country for which we measure the most significant )ˆ(ˆ kF in the sample. The confidence interval measure

of 1 (left axis) is associated to the country for which we measure the least significant )ˆ(ˆ kF in the sample. A B C

- 6 0 0

- 4 0 0

- 2 0 0

0

2 0 0

4 0 0

6 0 0

8 0 0

1 0 0 0

1 2 0 0

1 4 0 0

$ M

ill.

J a n - 9 8 F e b - 9 8 M a r - 9 8 A p r - 9 8 M a y - 9 8 J u n - 9 8 J u l - 9 8 A u g - 9 8 S e p - 9 8 O c t - 9 8T i m e

M a l a y s i a I n d o n e s i a

T h a i l a n d P h i l i p p i n e s

T a i w a n S o u t h K o r e a

0

1

2

3

4

5

6

7

J a n - 9 8 F e b - 9 8 M a r - 9 8 A p r - 9 8 M a y - 9 8 J u n - 9 8 J u l - 9 8 A u g - 9 8 S e p - 9 8 O c t - 9 8T i m e

P h i l i p p i n e s

S o u t h K o r e a

0

1

2

3

4

5

6

7

J a n - 9 8 F e b - 9 8 M a r - 9 8 A p r - 9 8 M a y - 9 8 J u n - 9 8 J u l - 9 8 A u g - 9 8 S e p - 9 8 O c t - 9 8T i m e

P h i l i p p i n e s

T h a i l a n d

S o u t h K o r e a

I n d o n e s i a

M a l a y s i a

Figure 3 Probabilities of clustering of break events: Volatility and return Figure 3 displays the time series of probabilities for the following outcomes of the clustering variable It = 0, 1, 2, and 3, computed according to the recursion

( )

( ) ,3,2,1Pr

0Pr0

=���

�===

=== −

ii

iP

eP

tit

tt

PI

I

-1itt

t

λ

λ

( 6 )

for both the series of volatility breaks (3A) and the series of breaks in returns (3B), using the estimates for the constant term and the herding coefficient β for HM1t from Table 5 and the log-linear model lnλ t = B’Rt where B = [constant, β] and Rt = [1, HM1t]. On the left axis, we plot (dark line) the number of simultaneous breaks It, and (thin line) the explanatory variable HM1t , while on the right axis we plot Pt0, Pt1, Pt2, and Pt3. A) Volatility Breaks Clustering B) Return Breaks Clustering

0

1

2

3

4

5

6

Jan-92Apr-92Jul-92Oct-92Jan-93Apr-93Jul-93Oct-93Jan-94Apr-94Jul-94Oct-94Jan-95Apr-95Jul-95Oct-95Jan-96Apr-96Jul-96Oct-96Jan-97Apr-97Jul-97Oct-97Jan-98Apr-98Jul-98Oct-98Jan-99

0.00

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.

IP1P3HM1P2P0

0

1

2

3

4

5

6

Jan-92Apr-92Jul-92Oct-92Jan-93Apr-93Jul-93Oct-93Jan-94Apr-94Jul-94Oct-94Jan-95Apr-95Jul-95Oct-95Jan-96Apr-96Jul-96Oct-96Jan-97Apr-97Jul-97Oct-97Jan-98Apr-98Jul-98Oct-98Jan-99

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

IP1P3HM1P2P0