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Journal of International Money and Finance21 (2002) 295–350
www.elsevier.com/locate/econbase
The dynamics of emerging market equity flows
G. Bekaerta,d, C.R. Harveyb,d,∗, R.L. Lumsdainec,d
a Columbia University, New York, 10027 USAb Duke University, Durham, NC 27708, USA
c Deutsche Bank, London, EC2N 2DB UKd National Bureau of Economic Research, Cambridge, MA 02138, USA
Abstract
We study the interrelationship between capital flows, returns, dividend yields and worldinterest rates in 20 emerging markets. We estimate a vector autoregression with these variablesto measure the degree to which lower interest rates contribute to increased capital flows andshocks in flows affect the cost of capital among other dynamic relations. We precede the VARanalysis by a detailed examination of endogenous break points in capital flows and the othervariables. These structural breaks are traced to the liberalization of emerging equity markets.Our evidence of structural breaks calls into question past research which estimates VAR mod-els over the full sample. After a liberalization, we find that equity flows increase by 1.4% ofmarket capitalization. We also show that shocks in equity flows initially increase returns whichis consistent with a price pressure hypothesis. While the effect is diminished over time, therealso appears to be a permanent impact. This is consistent with our finding that our proxy forthe cost of capital, the dividend yield, decreases. Finally, our analysis of the transition dyna-mics from pre-liberalization to post-liberalization suggests that when capital leaves, it leavesfaster than it came in. These results may help us understand the dynamics of the recent crisesin Latin America and East Asia. 2002 Published by Elsevier Science Ltd.
JEL classification: F3; F4; G1; E4
∗ Corresponding author. Tel.:+1-919-660-7768; fax:+1-919-660-8030.E-mail address: [email protected] (C.R. Harvey).
0261-5606/02/$ - see front matter 2002 Published by Elsevier Science Ltd.PII: S0261 -5606(02 )00001-3
296 G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
1. Introduction
With a number of recent crises in emerging markets, the role of foreign capitalin developing countries is under intense scrutiny once again. One country, Malaysia,imposed capital controls on October 1, 1998, in an effort to thwart the perceiveddestabilizing actions of foreign speculators. After a decade of capital market liberaliz-ations and increased portfolio flows into developing countries, this process may nowbe stalled or even reversed. The goal of this paper is to explore the dynamics, causesand consequences of capital flows in 20 emerging markets over the last 20 years.Importantly, we explicitly investigate the role of the recent financial liberalizationprocess in these dynamics.
Our work is related to two literatures. First, there is a growing body of researchthat studies the joint dynamics of capital flows and equity returns [see for example,Warther, 1995; Choe et al., 1999, Froot et al., 2001; Clark and Berko, 1997; Edelenand Warner, 1999; Stulz, 1999]. The first hypothesis of interest is whether foreigninvestors are “ return chasers” , in the terms of Bohn and Tesar (1996), that is, areflows caused by changes in expected returns? A related hypothesis is that inter-national investors are momentum investors, leading to a positive relation betweenpast returns and flows. The second set of hypotheses focuses on the effect of flowson returns. Both Froot et al. (focusing on 28 emerging markets) and Clark and Berko(focusing on Mexico) find that increases in capital flows raise stock market prices,but the studies disagree on whether the effect is temporary or permanent. If theincrease in prices is temporary, it may be just a reflection of “price pressure” , whichhas also been documented for mutual fund flows and stock indices [Warther, 1995;Shleifer, 1986]. If the price increase is permanent, it may reflect a long-lastingdecrease in the cost of equity capital associated with the risk sharing benefits ofcapital market openings in emerging markets.
Our work is also related to a second literature on capital market liberalizationsand the integration process in emerging markets [see Bartolini and Drazen, 1997;Bekaert and Harvey, 1995, 2000a,b; Henry, 2000a,b; Kim and Singal, 2000]. Duringthe sample period, many emerging markets removed capital controls, which oftenwent hand in hand with other reforms in the domestic financial system, trade lib-eralization, macro-economic stabilization programs (especially in Latin-America)and large scale privatizations [see Bekaert and Harvey, 2000b for detailed time lineson important structural changes in emerging countries]. These structural changescomplicate any empirical analysis of emerging markets during this period, since theycould cause permanent or at least long-lasting changes in the data-generating pro-cesses. In Bekaert et al. (2002), we use the structural break methodology of Bai,Lumsdaine, and Stock (hereafter BLS) (1998) to “date” when market integrationoccurred and document structural changes in a number of financial and economictime-series.1
1 Kawakatsu and Morey (1999) also use endogenous break point techniques to date stock marketopenings and examine stock market efficiency before and after the opening.
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The main tool of analysis in this paper is a vector-autoregressive (VAR) frame-work as in Froot et al. (2001), but with a number of differences. First, we add twovariables to the bivariate set-up of returns and equity flows in Froot et al.: the worldinterest rate and local dividend yields. The low level of US interest rates has oftenbeen cited as one of the major reasons for increased capital flows to emerging mar-kets in 1993 [see World Bank, 1997; as well as Calvo et al. (1993, 1994) and Fernan-dez-Arias, 1996] and our framework will allow us to trace out the effects of anunexpected reduction in world interest rates on capital flows to emerging markets.We are ultimately interested in the effects of structural reforms in emerging marketson local returns and particularly, on the local cost of capital. The inclusion of theworld interest rate helps in that endeavor in that it removes the effect of exogenousglobal determinants of capital flows. We add dividend yields to the VAR as our costof capital measure, since they capture potential permanent price effects induced byincreased foreign capital after liberalizations better than average returns [see Bekaertand Harvey, 2000a] and they also serve as an indicator of expected returns allowingdifferentiation of the ‘ return chasing’ and ‘momentum investing’ hypotheses.
Second, we precede our VAR analysis with a detailed endogenous break pointanalysis of our three main time series (net equity flows as a proportion of localmarket capitalization, log returns and the log dividend yield) using the novel tech-niques in BLS (1998) and Bai and Perron (1998a,b). This analysis helps pin downa relevant time-period over which to conduct the VAR analysis but is also interestingin its own right. For example, we study the transition dynamics of some of ourvariables around the break points. Such analysis is particularly important given thatrecent events in South-East Asia indicate that the integration process may now behalted and reversed. Studying capital flow dynamics and their impact on the localmarket may therefore yield predictions for the likely effects of the recent re-impo-sition of capital controls in some countries. Also, if capital market liberalizationsinduce one-time portfolio rebalancing on the part of global investors, one may expectnet flows to increase substantially after a liberalization and then to decrease again[see Bachetta and van Wincoop (2000) for a formal model generating such dynam-ics]. The Bai-Perron statistics look for multiple breaks in a time series and mayuncover such dynamics. We also test this prediction directly.
Third, although equity flows are our main focus, we also investigate how theyrelate to bond flows. For example, increased equity flows may substitute fordecreased bond flows or both may increase simultaneously as a result of a generalfinancial liberalization package.
We find that equity capital flows increase after liberalization but level out threeyears after their liberalization. This provides evidence that foreign investors rebalancetheir portfolios towards the newly available emerging market assets. Our analysis ofthe transition dynamics suggests that the movement of equity capital is much fasterwhen it leaves than when it enters. In general, we find sharply different results ifour models are estimated over the entire sample—which ignores a fundamental non-stationarity in the data—versus a post-break (liberalization) sample. One of our mainfindings is that unexpected equity flows are indeed associated with strong short-livedincreases in returns. However, we also find that they lead to permanent reductions
298 G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
in dividend yields. As we explain later, the dividend yield change may reflect achange in the cost of capital. Hence, the reduction in the dividend yield suggeststhat additional flows reduce the cost of capital and that the actual return effect isnot a pure price pressure effect.
The paper is organized as follows. The second section presents the vector autore-gressive empirical model that we use to interpret the relation between expectedreturns, dividend yields and capital flows. The third section explores the methodsthat we use to establish structural breaks in the time-series of interest. The fourthsection describes the data. The results are presented in the fifth and sixth sections.The final section offers some concluding remarks.
2. The econometric framework
2.1. Variables and main hypotheses
Since there is currently no well-accepted model of transition dynamics around aliberalization, we conduct our empirical analysis in the context of vector autore-gressions (VARs). For part of our analysis, these VARs are reduced-form represen-tations of an unspecified structural model, but some of the empirical hypotheses wewant to test require a structural interpretation of the VARs. In our primary empiricalsystem, we generalize the set-up of Froot et al. (2001), who run a bivariate VARon the ratio of net capital flows to market capitalization and market returns. Theirmain identifying structural restriction is that the shock to flows may affect returnsbut not vice versa. In addition, past returns only affect current returns through theireffects on flows. Two interesting questions can be investigated in this framework:
1. What is the effect of an unexpected shock to capital flows on current returns andwhat are its dynamics (that is, does it die out or is it permanent)? The dynamiceffects of the shock serve to distinguish the price pressure hypothesis from thepermanent change in the cost of capital hypothesis.
2. Do past returns affect current capital flows? In particular, Bohn and Tesar (1996)argue that capital flows are motivated by capital “chasing” high expected returns,rather than portfolio rebalancing motives. One issue here is that high past returnsneed not signal high future returns, unless momentum is an important determinantof expected return (see Bohn and Tesar (1997). In our framework, we will beable to distinguish the expected return-updating hypothesis from the momentumhypothesis.
In contrast to previous work, our primary VAR will contain four variables. LetYt � [it,nft,dyt,rt]�, where it is the world interest rate, nft is the net equity capital flowdivided by market capitalization, dyt is the log-dividend yield and rt is the loggedequity return.
The presence of the world interest rate allows a more subtle testing of the hypoth-eses in (1). It has often been argued that the emerging markets received a lot of US
299G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
capital in 1993 because investors were chasing higher yielding assets with interestrates in the US reaching historical lows. It should be mentioned that there may begood reasons for an inverse link between US interest rates and capital flows to emerg-ing markets. For example, the low US interest rates may have increased the Amer-icans’ wealth and therefore increased their risk tolerance, leading them to rebalancetowards riskier emerging market securities. Whatever the reason, our framework willallow a direct test of the magnitude and dynamics of a decrease in the world interestrate on capital flows to emerging markets.
With the world interest rate in the system, we can now divide the effect of highercapital flows on returns into two components. Capital flow increases induced bylower world interest rates may, for example, be less likely to lead to permanent priceincreases than capital flow increases that are not caused by world factors, but alsomay reflect portfolio rebalancing after a capital market liberalization.
The addition of the log-dividend yield is motivated by the work of Bekaert andHarvey (2000a). They argue that the extreme volatility in emerging market returnsimplies that changes in the cost of capital can be better assessed by investigatingchanges in dividend yields. In any rational pricing model, dividend yields will bedecreasing in the growth rate of dividends (cash flows) and increasing in the discountrate. Because of their low variability, dividend yields will capture permanent priceeffects induced by cost of capital changes more accurately than average returns.Bekaert and Harvey document that liberalizations tend to lead to small drops individend yields. One problem is that a lower dividend yield may also reflect animprovement in growth opportunities. Nevertheless, our set-up will allow us to testthe effect of a change in the world interest rate, and/or capital flows on the dividendyield in emerging markets and contrast that with the effect on returns. In addition,given that the dividend yield is a good proxy for expected returns, which is alsoborne out in the predictability tests for emerging markets by Harvey (1995), itsinclusion allows for a proper test of the Bohn-Tesar hypotheses mentioned above.
To perform tests of the main hypotheses, we use impulse response analysis basedon a structural interpretation of the VAR. Consider, without loss of generality, afirst-order VAR, suppressing the constant:
Yt � AYt�1 � et, (1)
with all eigenvalues of A having moduli less than one so that the VAR is stationary.We compute impulse responses, IR(i,j,k) � ∂e�iYt�k /∂e∗t,j, where e∗t,j are the “struc-
tural” shocks and ei is an indicator variable selecting the ith variable, e.g. e1 =[1,0,0,0]. We look at one standard deviation shocks. The structural shocks, e∗
t,j, aredetermined by the ordering in the VAR, that is, et � P�e∗t , where P is an upper-triangular matrix and e∗t are uncorrelated structural shocks, such that � �E[et e�t] � P�P.
The ordering of the variables is as defined above, with the world interest ratecoming first. Hence, the world interest residual is implicitly assumed not to be affec-ted by the other shocks in the system. As in Froot et al. (2001), we order flowsbefore returns, but we insert the dividend yield in the middle. Dividend yields andreturns are contemporaneously negatively correlated, but a shock to the dividend
300 G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
yield may reflect a near-permanent price change due to the liberalization process ora change in expected returns, and it is therefore natural to order dividend yieldsbefore returns. We are interested in:
1. the effects of the world interest rate, it, on the ratio of net capital flows to marketcapitalization, returns and dividend yields,
2. the impact of flows on returns and dividend yields to test the price pressure versuspermanent impact hypotheses, and
3. the effect of past returns and dividend yields on flows to test ‘ return chasing’ and‘momentum investing’ .
For this last response, our setup removes the contemporaneous correlation betweenreturns or dividend yields and flows, ascribed potentially to price pressure effects,because flows are ordered before these two variables. For example, a shock to thedividend yield not contemporaneously correlated with capital flows may reflect achange in growth opportunities or a change in expected returns which may affectfuture foreign capital inflows.
We also report impulse responses of shocks to the world interest rate, currentcapital flows, returns, and dividend yields on two cumulative variables: the averagelong-run return and the change in US holdings.
First, we define rt�k,k � 1k�k�1
i � 0 rt�1�i as the per period cumulative long-horizon
return. Next, consider the definition of net capital flows to capitalization:
nft �ft
mcapt
,
where ft is the net capital flow and mcapt is the equity market capitalization. Thecumulative capital flow to market capitalization is:
cft,t+k � �k
i � 1
nf t+i
�ft+1
mcapt+1
� % �ft+k
mcapt+k
�1
mcapt+k�ft+1 ×
mcapt+k
mcapt+1
� % � ft+k�.
So, cft,t+k accumulates the flows occurring between t and t+k and allows each flowto change value as a result of the market return.
Now consider the definition of the cumulative holdings in the local market:
301G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
ht � ��t
i�0
fimcapt
mcapi� 1
mcapt
� �t
i�0
fimcapi
� �t
i�0
nfi.
So holdings accumulate the flows from the beginning of time (notice the counterbegins at i = 0) allowing for the market return and express them as a proportion ofcurrent market capitalization. From this analysis, it is immediate that
cft,t+k � ht+k�ht,
is the change in holdings. Whereas impulse responses on the VAR variables die outin any stationary VAR, these cumulative effects represent the permanent effect onreturns and capital flows when we let k go to infinity.
2.2. VAR dynamics
Before we test the main hypotheses, we document the information in the VARsregarding the dynamic relations between our four variables using two different stat-istics.
First, we investigate dynamic regression coefficients between the various variables,for example, what is the correlation between world interest rates today and capitalflows or returns in the future? Formally, we investigate:
bi,j,k �Cov [e�iYt+k, e�jYt]
Var[e�jYt], (2)
where ei are indicator vectors, e.g. e1 = [1,0,0,0].Given that the VAR is stationary, Var[Yt] = C(0) can be computed as
Vec[C(0)] � [I�A�A]�1vec(�), (3)
where � � E[et e�t] and I is the identity matrix. The VAR fully summarizes theshort-run and long-run dynamics of Yt ; e.g.
E[YtYt�k] � C(k) � AkC(0). (4)
With this information, projection coefficients at all horizons can be computed.With the long-run variables introduced above, changes in holdings and long-run
returns, we can investigate the relation between these variables on the one hand andworld interest rates and capital flows on the other hand. For example, we compute:
302 G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
gk �Cov [rt+k,k, nft]
Var[nft]
�
1ke�4[I � A � % � Ak�1]C(0)e�2
e�2C(0)e2,
(5)
where e2 (e4) are indicator variables selecting flows (returns). Also, [I+A+%+Ak�1]= [I�Ak][I�A]�1. We can also let k go to infinity here. Analogously, we can investi-gate the long-run beta of changes in holdings with respect to current interest rates,returns and dividend yields.
Second, whereas the regression coefficients provide useful summary information,they are univariate relations that may hide intricate dynamic patterns. For example,there may be a positive relation between current capital flows and future returns, butpart of this correlation may come indirectly through the effect of world interest rateson capital flows. It would be interesting to see whether there is still a relation betweencapital flows and future returns, controlling for the world interest rate effect. Simi-larly, are capital flows mostly predictable by external variables like world interestrates, or by internal variables (returns and dividend yields) that may proxy forexpected returns for example? This question has been addressed before [see Calvoet al., 1993, 1994; and Fernandez-Arias, 1996], but in the context of our VAR it isparticularly simple to implement. We conduct a series of Granger-causality tests,testing whether world interest rates Granger-cause the other variables, and whetherreturns and dividend yields Granger-cause capital flows or vice versa. If liberalizationis a gradual process and is pre-announced, dividend yields may decrease and returnstemporarily increase before flows pick up. In this case, returns and dividend yieldsmay Granger-cause flows. Hence, it may be hard to distinguish this effect frommomentum investing (positive feedback trading), since this would also imply thatpositive returns predict higher flows.
2.3. The dynamics of capital flows and breaks
It does not make much sense to conduct this analysis over the full sample of data,given that many of the markets that we study may have undergone an integrationprocess somewhere in the middle of the sample. If the market truly went from seg-mented to integrated, the dynamics of all the variables in our VAR except the worldinterest rate would be affected. Consider Fig. 1 which sketches what a standardmodel of risk sharing [see the description in Bekaert et al., 2002] would predict,namely “a permanent change in prices leading to a new regime of lower expectedreturns” . Interestingly, this process is associated with short-term increases in returns(the return to integration) and long-run decreases in expected returns, plus a perma-nent decrease in dividend yields. This pattern would more generally result from“ investor base broadening” , the spreading of risks among more investors (see Stulz,1999, for a summary of the scarce evidence to date on this). If flows drive pricestemporarily away from fundamentals, the price effect ought to be temporary (“ theprice pressure hypothesis” ). This behavior can be examined in the context of our
303G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
Fig. 1. Asset prices and market integration.
VAR by contrasting the effects of capital flows on rt, rt+k,k and dyt. We expect apositive (negative) contemporaneous relation between returns (dividend yields) andunexpected flows that is permanent under the first but transitory and reversed underthe second hypothesis.
Fig. 1 also suggests a potential scenario for capital flows. Flows are of coursevirtually zero before the liberalization. After the liberalization, which may be gradual,large inflows occur as foreign investors include the emerging market into their port-folios. However, once the rebalancing is accomplished, net flows need no longer bepositive. Bachetta and van Wincoop (2000) model the dynamics of capital flows byallowing for a gradual decrease in a tax on investments into an emerging marketand show how capital flows can over-shoot. Of course, this is a simple story andthere are many competing models for the behavior of emerging market capital flows
304 G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
including models that predict a foreign lending boom followed by the inevitablecrash! [see, for example, Calvo and Mendoza, 1998.]
To deal with this problem, we apply the most recent methodology on break dateinference. We first apply the methodology developed in BLS to all of our univariateseries, and also use it to determine a break date for the joint system. This breakpoint analysis then determines the post-break period to which we will apply ourVAR analysis described above. The break point analysis also reveals a period oftransitions and the transition dynamics are of interest in their own right.
A disadvantage of the BLS methodology is that it only allows for one break. Thereare a number of reasons why there may be more than one break especially in thenet flows series. As indicated above, flows may be temporarily high to effect aportfolio rebalancing after capital market integration. There may be a second breakat the end of this process. Recently, we have seen a reversal of capital flows witha number of well-publicized crises in Mexico 1994–1995, and in South-East Asiain 1997–98. Even much before this, the debt crisis may have caused some LatinAmerican markets to become effectively segmented from the rest of the world,although capital flows before then were small. Therefore we also apply the techniquesof Bai and Perron (1998a,b) which allow for multiple breaks, but only apply tounivariate series. Additional details are presented in the econometrics section.
We also investigate the dynamics of capital flows around a potential liberalizationbreak using a very simple regression procedure. If the capital flow story of Fig. 1is accurate, mean equity flows should be higher after the break than before, butdecrease again after portfolio re-balancing is completed. In other words, let D1t bea dummy that comes on after the break and D2t the dummy that comes on threeyears after the break, then in the regression
nft � a � bD1t � cD2t � et, (6)
we would find b to be positive, and c to be negative. We choose three years becauseof the time it takes from announcement to effective implementation of a marketliberalization. Bekaert and Harvey (2000a) provide evidence that liberalizations areoften gradual.
Finally, to examine the transition dynamics, we compute a statistic we call the“ transition half-life statistic” (THL). The THL statistic is measured as follows. Con-sider the point in time at which the break occurs and imagine the current realizationof the variable is at the unconditional mean before the break. Now consider fore-casting k periods in the future using the new dynamics. If the VAR is stationary,eventually the forecasts will reach the new unconditional mean. How fast they willget there depends on the persistence of the system and how far away the post-breakmean is from the starting point (pre-break mean). Our THL statistic records the timeit takes (in months) to reach half the distance between old and new mean. We canalso reverse the computation. That is, we compute the THL statistic starting fromthe new mean going to the old mean using the pre-break dynamics. Comparing thetwo statistics is informative about the different dynamics before and after the break.We will compute this statistic for capital flows and dividend yields. For capital flows,a bold interpretation of the pre-break THL statistic is that it reveals something about
305G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
how capital flows will react when the integration process is reversed, as is recentlyhappening in a few countries. We do not compute the THL statistic for returns,because we conjecture that the measurement of mean returns is too noisy to makethe computation valuable.
3. Data
Our data consists of capital flows, interest rates, dividend yields, and returns. Oursource of monthly data on capital flows is the US Treasury International Capital(TIC) reporting system.2 For the 20 emerging markets we study, we are able tocalculate the net US flows for stocks and bonds for 17 countries. The Treasury doesnot track data on Jordan, Nigeria, and Zimbabwe. We use the International FinanceCorporation’s Emerging Markets Database as a source of the US dollar returns andthe dividend yields. The world interest rate is constructed as a GDP weighted averageof the short-term government Treasury bills in the G-7 countries.3 The interest rateand GDP data are from Datastream.
With so many countries and relatively small sample periods, a country by countryanalysis may both lack power and prevent us from presenting the results in an intelli-gible way. Therefore we present most of our results using country groupings. Weuse three different types of groupings. The first set is aimed primarily at noisereduction. We aggregate results over all countries with three weighting schemes:equally weighted, value weighted (using the market capitalization of the equity mar-ket from the IFC) and volatility weighted. The volatility weighting constructs weightsusing the inverse of the sum of squared residuals of all the regressions in the VARfor a particular country relative to the inverse sum of the squared residuals overall countries. Hence, the “noisiest” VARs are down-weighted. Before applying thisprocedure, we re-scaled the residuals, so that at a global, all-country level, capitalflows accounted for 40% of the total variance, returns and dividend yields each 25%,and interest rates, 10%. If the coefficients (like impulse responses) are independentacross countries, this aggregation would lead to a reduction of the typical country-specific standard error by a factor of over four, since there are 17 countries in oursample. Hence, even if the country-specific standard errors are double the size ofthe coefficients, we would obtain significance.
The second grouping is geographical. We contrast the results for six Latin Amer-ican countries (Argentina, Brazil, Chile, Colombia, Mexico and Venezuela) and sixAsian countries (Indonesia, Korea, Malaysia, the Philippines, Taiwan and Thailand).Although these results may be sensitive to country-specific outliers, the contrastsbetween the development models of Latin-America and South-East Asia and therecent crises in both areas make this grouping meaningful.
2 See Tesar and Werner (1994, 1995) for a description and analysis of these data.3 See the data appendix for additional details on the construction of the dividend yields and the world
interest rate.
306 G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
Our last groupings focus on the characteristics of capital flows. Table 1 summar-izes some of the characteristics we use in the selection process. First, we investigatethe magnitude of equity and bond flows. We calculate the return-adjusted cumulativeequity flows divided by market capitalization. This is a measure of US ownershipin the country. We present average ownership over the full sample as well as the1990s. The largest average ownership is found for Mexico (since 1990) followed byBrazil and Argentina. The country with the largest average ownership in Asia isThailand in the 1990s. To allow comparison with bond flows, where return adjust-ments and market capitalizations are not available, we also calculate the cumulativeequity flows to GDP without return adjustments. In this analysis, Mexico, Chile andMalaysia have the largest cumulative U.S. equity flows to GDP. Bond flows to GDPare presented in the next column. The highest averages of the cumulative bond flowsto GDP are found in Mexico, Venezuela and Argentina. Clearly, cumulative net bondflows are much smaller than equity flows. By adding cumulative equity and bondflows, we obtain a measure of total external financing through portfolio capital. Wealso report sample averages and averages in the 1990s. There are two countries withnet negative external financing, Chile and Taiwan, both, not surprisingly, countrieswith stringent capital controls. Mexico, Venezuela and Malaysia have the highestexternal financing to GDP. Generally, both equity and bond flows are larger in thenineties than for the full sample, and total external financing is larger in the ninetiesfor 15 out of 17 countries.
We now consider different groups of countries based on this information for the1990s sample. First, we rank the countries according to the importance of externalfinance. In addition to the three countries previously mentioned (Mexico, Venezuelaand Malaysia), we add Argentina, Brazil and Korea to the top group. The bottomsix countries are, in addition to Chile and Taiwan, India, Greece, the Philippines andPakistan, all with less than 0.35% of GDP in external financing through US bondand equity flows.
Finally, we want to select countries that primarily rely on equity capital and coun-tries that primarily rely on fixed income. This is not trivial to do, since for somecountries the absolute flows may be very small and, in particular, for bonds, theymay be negative. Our approach was to rank countries based on the difference betweenthe average cumulative post-1989 equity and bond flows to GDP. The bottom sixcountries are deemed bond-reliant, the top six equity reliant. We exclude countrieswhen they do not place in the top 10 ranked according to cumulative bond flows orcumulative equity flows to GDP, respectively. The countries relying primarily onequity are Chile, the Philippines, Malaysia, Portugal, Thailand and Korea. Weexcluded Taiwan because of its insignificant absolute equity flows. Although Mexico,Brazil and Argentina are top countries in terms of equity flows to GDP, they appearin the fixed income group because of their substantial positive fixed income flows.The other three countries are Venezuela, Pakistan, and Indonesia. Because of therelative non-importance of bond flows in general, it is possible that countries groupedin the “ rely on bonds” category receive more equity than bond capital.
At the bottom of the table, we present country groupings. We find that LatinAmerican countries tend to have high US equity ownership. Asian countries have
307G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
Tab
le1
Sum
mar
yan
alys
isof
net
equi
tyflo
ws
and
net
bond
flow
s
Cou
ntry
Ave
rage
ofcu
mul
ativ
eA
vera
geof
cum
ulat
ive
Ave
rage
ofcu
mul
ativ
eA
vera
geof
tota
lE
quity
flow
H0:
Equ
ityflo
ws
H0:
Bon
dflo
ws
equi
tyflo
ws/
equi
tyeq
uity
flow
s/G
DP
bond
flow
s/G
DP
cum
ulat
ive
exte
rnal
and
bond
flow
dono
tG
rang
erdo
not
Gra
nger
capi
taliz
atio
nfin
anci
ngto
GD
Pco
rrel
atio
nca
use
bond
flow
sca
use
equi
tyflo
ws
Full
Post
-199
0Fu
llPo
st-1
990
Full
Post
-199
0Fu
llPo
st-1
990
P-va
lue
P-va
lue
Arg
entin
a0.
0093
0.08
370.
0035
0.00
900.
0040
0.01
040.
0074
0.01
940.
055
0.55
80.
114
Bra
zil
0.05
190.
1271
0.00
370.
0093
0.00
300.
0077
0.00
670.
0170
0.01
30.
000
0.16
5C
hile
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330.
0578
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900.
0226
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0326
�0.
0642
�0.
0236
�0.
0416
�0.
049
0.03
60.
897
Col
ombi
a0.
0137
0.03
230.
0017
0.00
44�
0.00
010.
0022
0.00
150.
0065
0.18
90.
000
0.00
0G
reec
e0.
0012
0.01
730.
0008
0.00
23�
0.00
09�
0.00
09�
0.00
010.
0014
0.04
60.
262
0.01
9In
dia
0.00
480.
0112
0.00
080.
0019
�0.
0010
�0.
0009
�0.
0002
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110.
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0.00
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000
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nesi
a0.
0302
0.07
640.
0027
0.00
690.
0023
0.00
580.
0050
0.01
27�
0.00
70.
834
0.18
1Jo
rdan
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
Kor
ea0.
0417
0.07
810.
0037
0.00
880.
0016
0.00
560.
0053
0.01
440.
071
0.00
00.
000
Mal
aysi
a0.
0115
0.02
630.
0088
0.02
10�
0.00
580.
0004
0.00
300.
0214
�0.
135
0.99
60.
763
Mex
ico
0.11
860.
2411
0.01
040.
0253
0.01
960.
0466
0.03
000.
0719
0.10
30.
973
0.00
0N
iger
ian.
a.n.
a.n.
a.n.
a.n.
a.n.
a.n.
a.n.
a.n.
a.n.
a.n.
a.Pa
kist
an0.
0058
0.01
480.
0007
0.00
180.
0006
0.00
140.
0012
0.00
320.
110
0.00
00.
100
Phili
ppin
es0.
0176
0.04
440.
0046
0.01
15�
0.00
52�
0.01
01�
0.00
060.
0014
0.03
50.
832
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8Po
rtug
al0.
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0.07
010.
0028
0.00
70�
0.00
16�
0.00
330.
0012
0.00
370.
043
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wan
0.00
100.
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0.00
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0004
�0.
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�0.
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�0.
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�0.
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0.02
20.
973
0.95
3T
haila
nd0.
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0.06
670.
0026
0.00
59�
0.00
05�
0.00
010.
0021
0.00
58�
0.06
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urke
y0.
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0.04
770.
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280.
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0.16
90.
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enez
uela
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390.
0327
0.01
440.
0338
0.00
700.
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0.89
2Z
imba
bwe
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
(con
tinu
edon
next
page
)
308 G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
Tab
le1
(con
tinu
ed)
Cou
ntry
Ave
rage
ofcu
mul
ativ
eA
vera
geof
cum
ulat
ive
Ave
rage
ofcu
mul
ativ
eA
vera
geof
tota
lE
quity
flow
H0:
Equ
ityflo
ws
H0:
Bon
dflo
ws
equi
tyflo
ws/
equi
tyeq
uity
flow
s/G
DP
bond
flow
s/G
DP
cum
ulat
ive
exte
rnal
and
bond
flow
dono
tG
rang
erdo
not
Gra
nger
capi
taliz
atio
nfin
anci
ngto
GD
Pco
rrel
atio
nca
use
bond
flow
sca
use
equi
tyflo
ws
Full
Post
-199
0Fu
llPo
st-1
990
Full
Post
-199
0Fu
llPo
st-1
990
P-va
lue
P-va
lue
Full
0.02
380.
0581
0.00
340.
0083
�0.
0008
0.00
040.
0026
0.00
870.
027
Asi
a0.
0223
0.04
900.
0037
0.00
91�
0.00
31�
0.00
440.
0006
0.00
47�
0.01
3L
atin
0.03
530.
0888
0.00
480.
0119
0.00
130.
0059
0.00
610.
0178
0.05
3R
ely
onst
ocks
0.02
570.
0572
0.00
520.
0128
�0.
0073
�0.
0119
�0.
0021
0.00
09�
0.01
6R
ely
onbo
nds
0.03
520.
0890
0.00
360.
0089
0.00
720.
0174
0.01
080.
0263
0.04
7T
opex
tern
al0.
0380
0.09
120.
0051
0.01
240.
0060
0.01
720.
0111
0.02
960.
019
Bot
tom
exte
rnal
0.00
890.
0246
0.00
270.
0067
�0.
0083
�0.
0171
�0.
0057
�0.
0103
0.06
0
Full
repr
esen
tsan
equa
lly-w
eigh
ted
aver
age
acro
ssal
lco
untr
ies.
Asi
ais
aneq
ually
-wei
ghte
dav
erag
eac
ross
six
Asi
anco
untr
ies
(Ind
ones
ia,
Kor
ea,
Mal
aysi
a,th
ePh
ilipp
ines
,T
aiw
anan
dT
haila
nd),
and
Lat
inis
equa
lly-w
eigh
ted
aver
age
acro
sssi
xL
atin
Am
eric
anco
untr
ies
(Arg
entin
a,B
razi
l,C
hile
,C
olom
bia,
Mex
ico
and
Ven
ezue
la).
We
also
repo
rteq
ually
-wei
ghte
dac
ross
the
six
coun
trie
sth
atre
lym
ost
oneq
uity
finan
cing
(Chi
le,
the
Phili
ppin
es,
Mal
aysi
a,Po
rtug
al,
Tha
iland
and
Kor
ea),
equa
lly-w
eigh
ted
acro
ssth
esi
xco
untr
ies
that
rely
mos
ton
bond
finan
cing
(Ven
ezue
la,
Mex
ico,
Arg
entin
a,Pa
kist
an,
Indo
nesi
aan
dB
razi
l),
equa
lly-w
eigh
ted
acro
ssth
esi
xco
untr
ies
that
have
the
high
est
prop
ortio
nof
exte
rnal
finan
cing
,th
atis
,th
ebo
ndpl
useq
uity
capi
tal
flow
sto
GD
P(M
exic
o,M
alay
sia,
Ven
ezue
la,A
rgen
tina,
Bra
zil
and
Kor
ea)
and
the
six
coun
trie
sw
ithth
elo
wes
tpr
opor
tion
ofex
tern
alfin
anci
ngto
GD
P(C
hile
,T
aiw
an,
Paki
stan
,th
ePh
ilipp
ines
,G
reec
ean
dIn
dia)
.T
heco
rrel
atio
nsar
ees
timat
edov
erth
efu
llsa
mpl
e.T
hebr
eak
date
sfo
rea
chco
untr
yar
epr
esen
ted
inT
able
2.T
heG
rang
er-c
ausa
lity
test
sar
efr
oma
biva
riat
eV
AR
ofeq
uity
retu
rns
and
bond
retu
rns
estim
ated
over
the
full
sam
ple.
309G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
had sharply negative bond outflows in the 1990s perhaps due in part to the Asiancrisis. Latin American countries tend to rely more on equity financing than Asiancountries. The countries grouped in the “ relying on bonds” category are generallycountries that heavily rely on external portfolio capital, including equity capital. Asa proportion of market capitalization, these countries have higher U.S. holdings thanthe “stock reliant” countries.
Table 1 presents some additional information on the relation between bond andstock flows. Given that our analysis focuses on equity flows, it is important to knowwhether bond flows are substitutes for equity flows. We present the correlation ofthe net capital flows which is, in general, small. The highest correlation is found forIndia and Colombia. The largest negative correlations are found for Turkey andMalaysia. In the majority of the countries, the correlation is positive, and of the fivenegative correlations, three are in South-East Asian countries.
We also present Granger-causality tests based on a bivariate system of stock flowsto market capitalization and bond flows to GDP. We can reject the hypothesis at the5% level that equity flows do not Granger-cause bond flows in Brazil, Chile, Colom-bia, India, Korea, Pakistan, Portugal and Thailand. We can reject the hypothesis atthe 5% level that bond flows do not Granger-cause equity flows in Colombia, Greece,Indonesia, Korea, Mexico, Portugal and Thailand. Hence, there are five countries forwhich there are significant predictive relations in both directions, potentially suggest-ing the effect of a third variable on general capital flows or persistence in capitalflows after a capital market liberalization. There is no support for a consistent patternwhere bonds always lead equity (or vice versa) or where bonds and equity areclear substitutes.
Fig. 2 presents the results from the impulse response analysis based on the bivari-ate VAR. We examine value- and equally-weighted impulse responses as well asthe regional groupings. There are two interesting observations. First, positive shocksto stock (bond) flows are followed by positive responses in bond (stock) flows.Again, this is potentially consistent with gradual portfolio rebalancing towardsemerging markets in general (both equities and bonds, after a capital market liberaliz-ation or induced by changes in world interest rates, for example). Second, notice thedistinction between Latin America and Asia. A shock to equity flows in Latin Amer-ica has a much larger short-term impact on bond flows than it does in Asia. Moreover,there is an initial slightly negative effect of bond flows on stock flows in Latin
Fig. 2. The relation between net bond and net equity flows.
310 G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
America whereas the effect in Asia is positive. From the second period onwards,the effects are positive and very similar in magnitude across the two regions. Fromboth graphs, it is clear that the effects die out within six months. We also conducteda break point analysis of bond and equity flows. In the countries that experiencedsignificant breaks in equities and bond capital flows, the break in bonds precededthe break in equities in four countries (Argentina, Korea, Malaysia and Thailand).The break in equities preceded the break in bonds in five countries (Brazil, Colombia,Greece, India and the Philippines). With the exception of one country, the Philip-pines, the equity and bond breaks are clustered closely in time for all of thesecountries.4
Taken together, our results suggest that bond and equity flows are not substitutesbut both increased in the nineties with the liberalization process.
4. Breaks and initial pre and post-break analysis
4.1. Break analysis
To select an appropriate break date for use in subsequent analysis, we rely onseveral pieces of information. First, we use the results of the univariate break analysisfor three series: returns, dividend yields and net equity flows. We compute themedian break date, the 90% confidence interval for the date in months as well as astatistic that provides a test of the null hypothesis that no break occurred. Detailedresults on all break tests are reported in Appendix Tables 1–3.
Second, we conduct a multivariate analysis of breaks using the BLS framework.We investigate three specifications. The first is a trivariate system with equity capitalflows, dividend yields and returns. The second specification is a quadravariate systemthat includes an equation for the world interest rate. However, the coefficients in theworld interest rate equation are not allowed to break in the estimation but the depen-dence of the other variables in the system on the world interest rate may break. Thethird specification adds the bond capital flows to the system of equations to see ifthe breaks we find are affected by this addition. For 13 of the 17 countries, themultivariate breaks fall within the range of the univariate breaks for either bond orequity flows. For four countries, (Greece, Mexico, Portugal and Taiwan), the breakdates correspond to the break dates in dividend yields or returns, but the confidenceintervals for the breaks are always tighter in the multivariate estimation consistentwith what the theory would predict. Finally, the break dates from the three multivari-ate systems are very close to each other.
Third, we conduct the Bai-Perron tests that allow for multiple breaks. In a numberof countries more than one break occurs. For example, in the dividend yield esti-mation for Mexico there are three significant breaks: January 1983, July 1986 andMarch 1991. The first date closely follows the onset of the Latin American debt
4 See Appendix Table 1.
311G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
crisis. The second date closely follows the abolition of the official exchange rateand coincides with major debt restructuring. The final date closely follows the privat-ization of Telmex and the beginning of the NAFTA negotiations. For the flows esti-mation, we find little evidence in favor of multiple breaks, although this may be dueto low power. Argentina shows two breaks in equity flows, with the second onebeing detected in the BLS tests. There are three breaks for equity flows in Korea,partially reflecting the gradual lifting of foreign ownership. Finally, there are twobreak dates for bond flows in Taiwan, one preceding and one following the liberaliz-ation plan of January 1991. Hence, there appears to be a pattern in the multiplebreak analysis that is linked to important events tied to either deliberalization orliberalization of capital markets.
Finally, we link the statistical breaks to the economic events detailed in Bekaertand Harvey (2000b). The end-result is presented in Table 2, where we also brieflyindicate the motivation for each break date. All the dates we choose are dates foundby one of the break analyses we conducted, but the final choice uses exogenousinformation on regulatory reforms to make sure we use a relevant break date. Werely heavily on the break dates that arise from the quadravariate system as well asthe Bai-Perron breaks for dividend yields and equity flows. For example, the Bai-Perron break for equity capital flows in Thailand is August 1988. This closely corre-sponds to the opening of the market to foreigners by the creation of the Alien Boardfor trading in late 1987. The quadravariate and quintravariate date for Taiwan isApril 1988. This closely corresponds to the lifting of exchange controls. Pakistan isparticularly interesting. Most consider the official liberalization to be February 1991.The Bai-Perron break in dividend yields is earlier, in December 1990. However,an examination of the chronology shows that the liberalization was announced inNovember 1990.5
4.2. The impact of breaks on unconditional means
Table 3 presents an analysis of both the means and standard deviations of the fourcountry-specific variables that we study in the VARs: dividend yields, log returns,net bond flows to GDP and net equity flows to equity market capitalization. Individ-ual country results and results for our country groupings are presented for the fullsample, as well as the pre- and post-break periods using the dates in Table 2.
There are a number of interesting differences between the pre-break and post-break periods. The dividend yields are sharply lower on average (2.5 in post-breakand 4.7 in pre-break). The yields decrease in 13 of 17 countries. This is consistentwith the idea that expected returns decrease after a liberalization. Although returnsare much noisier than dividend yields, we also observe that the average return
5 We also closely examined the results of estimations with a smaller trimming factor, which suggestedbreaks near the end of the sample that are associated with the Asian crisis. Since these dates are verymuch near the end of our sample, we did not exclude post-break observations.
312 G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350T
able
2Se
lect
ion
ofbr
eak
date
s
Cou
ntry
Dat
eSe
ries
Rea
son
Arg
entin
aM
ay92
dy-B
PA
lso
dy-U
.C
lose
toE
q.flo
ws-
BP
(Dec
92).
Follo
ws
intr
oduc
tion
ofA
rgen
tine
Fund
(Oct
91)
whi
chis
the
first
time
US
inve
stor
sco
uld
acce
ssth
ism
arke
tin
mod
ern
times
.
Bra
zil
Mar
90Q
uad
Clo
seto
dy-U
(Nov
90)
and
Eq.
Flow
s-U
(Jan
90).
Offi
cial
isM
ay91
.E
xact
lyco
inci
des
with
intr
oduc
tion
ofC
ollo
rPl
an(M
ar90
).
Chi
leN
ov90
dy-B
PA
bout
one
year
from
Offi
cial
(Jan
92).
Qua
did
entifi
esth
ede
btcr
isis
.Sh
ortly
follo
ws
intr
oduc
tion
ofre
form
pack
age
(Apr
90),
maj
orA
DR
(Jul
y90
)an
dre
new
alof
And
ean
Pact
(Nov
90).
Col
ombi
aO
ct91
dy-B
P1C
lose
toO
ffici
al(F
eb91
).E
q.Fl
ows-
Ufo
llow
s(J
uly
93).
Exa
ctm
onth
that
Peso
dere
gula
ted
(Oct
91).
Inad
ditio
n,ex
act
mon
thth
atfo
reig
nco
untr
yfu
nds
are
allo
wed
toha
veup
to10
%of
owne
rshi
pan
dfo
reig
nfir
ms
are
perm
itted
tore
mit
100%
ofpr
ofits
.
Gre
ece
Jul
88Q
uad
Als
ody
-BP
date
.O
ffici
al(D
ec87
).C
lose
lyco
inci
des
with
date
offir
stA
DR
(Aug
88).
Indi
aA
pr92
Qua
dB
etw
een
dy-U
(Jan
92)
and
Eq.
Flow
s-U
(May
93).
Offi
cial
isN
ov92
.C
lose
lyfo
llow
ses
tabl
ishm
ent
ofSe
curi
ties
Exc
hang
eB
oard
(Jan
92).
Inad
ditio
n,th
efir
stA
DR
isFe
b92
.
Indo
nesi
aM
ay92
dy-B
PO
ffici
alSe
p89
.E
quity
and
bond
flow
sbr
eak
muc
hla
ter.
Firs
tA
DR
laun
ched
Feb
92.
Fore
ign
Boa
rdfo
rtr
adin
gby
fore
igne
rses
tabl
ishe
din
July
92.
Jord
anA
pr92
dy-B
PO
ffici
alis
Dec
95.
Clo
sely
prec
edes
the
liftin
gof
cont
rols
onou
tbou
ndan
din
boun
ddi
rect
inve
stm
ents
;al
low
ance
ofpr
ivat
eho
ldin
gof
fore
ign
exch
ange
and
othe
rfin
anci
alas
sets
,an
dpr
ovis
ion
ofm
arke
tac
cess
tofo
reig
nfin
anci
alin
stitu
tions
in19
93.
Kor
eaO
ct92
Eq.
Flow
s-B
P3C
lose
tody
-BP
(Feb
91).
Offi
cial
isJa
n92
.Fo
reig
nsca
now
nup
to10
%of
stoc
ksin
sele
cted
indu
stri
es(J
an92
).M
ore
indu
stri
esin
clud
edin
May
92.
Mal
aysi
aFe
b91
dy-B
PE
xact
lyco
inci
des
with
the
Priv
atiz
atio
nM
aste
rPl
an(F
eb91
).Pr
eced
esth
eO
utlin
ePe
rspe
ctiv
ePl
anin
Jun
91w
hich
enco
urag
edfo
reig
nin
vest
men
tan
dpr
ivat
isat
ion.
Offi
cial
date
isea
rlie
rD
ec88
.(c
onti
nued
onne
xtpa
ge)
313G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350T
able
2(c
onti
nued
)
Cou
ntry
Dat
eSe
ries
Rea
son
Mex
ico
Mar
91dy
-BP3
Les
sth
ana
year
from
Eq.
Flow
s-U
(Jul
90).
Clo
sely
follo
ws
Tel
mex
priv
atiz
atio
nin
Dec
90an
dth
ein
itiat
ion
ofN
AFT
Ata
lks
inFe
b90
and
bond
flow
sin
Mar
90.
Offi
cial
isea
rly,
May
89.
Nig
eria
Jun
95dy
-UO
ffici
alis
Aug
95bu
tea
rlie
rin
1995
the
Bud
get
calle
dfo
rth
eop
enin
gof
mar
kets
tofo
reig
npo
rtfo
lioin
vest
ors.
Hen
ce,
the
mar
ket
mig
htha
vean
ticip
ated
the
offic
ial
intr
oduc
tion
ofth
eN
iger
ian
Inve
stm
ent
Dec
ree
inA
ug95
.
Paki
stan
Dec
90dy
-BP1
Ver
ycl
ose
toO
ffici
alin
Feb
91.
Inde
ed,
the
anno
unce
men
tof
the
liber
aliz
atio
nsth
atw
ere
impl
emen
ted
inFe
b91
was
mad
ein
Nov
90.
Phili
ppin
esJu
l90
Eq.
Flow
s-U
Clo
seto
dy-U
(Oct
91).
Offi
cial
isJu
n91
.Fo
llow
sm
ajor
bank
rest
ruct
urin
gag
reem
ent
inFe
b90
.Fi
rst
AD
Ris
Mar
91.
Port
ugal
Feb
89Q
uad
Clo
seto
dy-U
.O
ffici
alis
July
86.
Port
ugal
Fund
laun
ched
inA
ug87
.Pr
eced
espr
ivat
izat
ion
law
inM
ar90
.
Tai
wan
Apr
88Q
uad
Als
ody
-Uda
te.
Exc
hang
eco
ntro
lslif
ted
inJu
l87
.O
ffici
alis
late
r,in
Jan
91.
Tha
iland
Aug
88E
q.Fl
ows-
BP
Offi
cial
Sep
87w
hen
Alie
nB
oard
intr
oduc
ed.
dy-U
isJa
n90
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314 G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350T
able
3Pr
e-po
stan
alys
isof
the
vari
able
sus
edin
vect
orau
tore
gres
sion
s
Cou
ntry
Div
iden
dyi
eld
Net
bond
flow
s/G
DP
Net
equi
tyflo
ws/
mca
pL
ogre
turn
s
Mea
nSt
d.de
v.M
ean
Std.
dev.
Mea
nSt
d.de
v.M
ean
Std.
dev.
Arg
entin
aFu
ll1.
559
1.37
20.
0001
80.
0008
80.
0007
50.
0054
70.
0116
0.21
38Pr
e1.
125
1.31
8�
0.00
002
0.00
008
�0.
0002
30.
0045
20.
0159
0.24
58Po
st2.
639
0.78
30.
0006
60.
0015
40.
0032
00.
0067
40.
0011
0.09
63
Bra
zil
Full
5.49
44.
403
0.00
007
0.00
064
0.00
085
0.00
205
0.00
830.
1602
Pre
7.02
94.
078
0.00
000
0.00
004
0.00
024
0.00
119
0.00
560.
1566
Post
3.06
93.
775
0.00
019
0.00
103
0.00
180
0.00
268
0.01
260.
1664
Chi
leFu
ll4.
946
2.02
2�
0.00
027
0.00
191
0.00
034
0.00
139
0.01
770.
0966
Pre
5.77
92.
022
�0.
0002
60.
0008
80.
0002
10.
0013
80.
0188
0.10
67Po
st3.
441
0.76
0�
0.00
030
0.00
299
0.00
057
0.00
139
0.01
580.
0756
Col
ombi
aFu
ll4.
655
3.11
90.
0001
10.
0008
70.
0005
70.
0020
60.
0206
0.07
97Pr
e7.
377
2.50
3�
0.00
003
0.00
011
�0.
0000
50.
0008
00.
0224
0.05
80Po
st2.
269
0.73
70.
0004
40.
0015
00.
0012
00.
0026
80.
0187
0.09
71
Gre
ece
Full
5.59
33.
299
0.00
002
0.00
027
0.00
020
0.00
185
0.00
540.
0948
Pre
6.17
33.
915
�0.
0000
10.
0000
5�
0.00
010
0.00
200
�0.
0038
0.08
65Po
st4.
926
2.24
20.
0000
50.
0003
90.
0005
50.
0016
20.
0161
0.10
29
Indi
aFu
ll2.
613
1.35
0�
0.00
065
0.00
240
0.00
014
0.00
041
0.00
880.
0793
Pre
3.17
21.
186
�0.
0010
20.
0026
80.
0000
10.
0000
80.
0167
0.07
25Po
st1.
247
0.45
60.
0002
50.
0010
30.
0004
50.
0006
6�
0.01
030.
0916
Indo
nesi
aFu
ll1.
368
0.69
50.
0000
70.
0003
30.
0014
70.
0025
9�
0.01
910.
1300
Pre
0.27
60.
159
0.00
000
0.00
004
0.00
101
0.00
142
�0.
0150
0.10
45Po
st1.
633
0.48
10.
0002
30.
0005
90.
0016
40.
0029
2�
0.02
070.
1391
Kor
eaFu
ll3.
819
2.84
60.
0001
10.
0006
80.
0006
90.
0018
80.
0035
0.09
98Pr
e4.
632
1.59
30.
0000
00.
0002
70.
0002
40.
0009
00.
0102
0.08
85Po
st1.
593
0.54
90.
0004
00.
0012
00.
0019
30.
0030
0�
0.01
480.
1247
(con
tinu
edon
next
page
)
315G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
Tab
le3
(con
tinu
ed)
Cou
ntry
Div
iden
dyi
eld
Net
bond
flow
s/G
DP
Net
equi
tyflo
ws/
mca
pL
ogre
turn
s
Mea
nSt
d.de
v.M
ean
Std.
dev.
Mea
nSt
d.de
v.M
ean
Std.
dev.
Mal
aysi
aFu
ll1.
990
0.62
40.
0001
50.
0019
50.
0002
00.
0006
5�
0.00
020.
0960
Pre
2.16
00.
398
�0.
0000
70.
0011
80.
0002
20.
0004
40.
0061
0.08
60Po
st1.
870
0.72
20.
0005
60.
0028
60.
0001
80.
0007
8�
0.00
550.
1037
Mex
ico
Full
4.29
84.
172
0.00
032
0.00
178
0.00
111
0.00
404
0.01
320.
1337
Pre
5.74
34.
496
0.00
015
0.00
195
0.00
077
0.00
254
0.01
550.
1457
Post
1.50
60.
422
0.00
064
0.00
132
0.00
175
0.00
592
0.00
890.
1076
Paki
stan
Full
4.49
62.
619
0.00
002
0.00
024
0.00
039
0.00
229
0.00
420.
0828
Pre
7.70
90.
789
0.00
000
0.00
000
0.00
000
0.00
020
0.01
010.
0299
Post
2.76
51.
882
0.00
006
0.00
040
0.00
069
0.00
303
�0.
0005
0.10
73
Phili
ppin
esFu
ll1.
540
1.47
60.
0001
20.
0010
50.
0005
40.
0023
90.
0175
0.10
39Pr
e2.
263
1.90
6�
0.00
008
0.00
028
0.00
005
0.00
313
0.04
430.
1042
Post
0.91
30.
534
0.00
047
0.00
162
0.00
087
0.00
162
�0.
0009
0.10
01
Port
ugal
Full
2.45
61.
128
�0.
0000
20.
0001
60.
0010
10.
0039
70.
0206
0.10
45Pr
e1.
006
0.54
40.
0000
00.
0000
20.
0001
50.
0006
90.
0515
0.17
96Po
st2.
802
0.93
9�
0.00
003
0.00
023
0.00
129
0.00
452
0.01
080.
0628
Tai
wan
Full
0.99
40.
541
�0.
0001
70.
0004
30.
0000
30.
0002
80.
0131
0.13
31Pr
e1.
677
0.70
1�
0.00
003
0.00
007
0.00
002
0.00
026
0.04
000.
1469
Post
0.83
20.
334
�0.
0003
30.
0005
80.
0000
40.
0002
90.
0046
0.12
79
Tha
iland
Full
5.52
33.
311
0.00
004
0.00
043
0.00
030
0.00
098
0.00
560.
0962
Pre
7.68
02.
860
�0.
0000
10.
0001
30.
0003
50.
0010
00.
0165
0.07
36Po
st3.
005
1.55
70.
0001
10.
0006
10.
0002
40.
0009
6�
0.00
700.
1162
(con
tinu
edon
next
page
)
316 G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
Tab
le3
(con
tinu
ed)
Cou
ntry
Div
iden
dyi
eld
Net
bond
flow
s/G
DP
Net
equi
tyflo
ws/
mca
pL
ogre
turn
s
Mea
nSt
d.de
v.M
ean
Std.
dev.
Mea
nSt
d.de
v.M
ean
Std.
dev.
Tur
key
Full
4.34
92.
205
0.00
001
0.00
013
0.00
049
0.00
240
0.01
770.
1803
Pre
7.27
52.
917
0.00
000
0.00
002
�0.
0000
60.
0005
20.
0535
0.21
91Po
st3.
708
1.35
20.
0000
20.
0002
00.
0006
60.
0027
20.
0064
0.16
59
Ven
ezue
laFu
ll1.
648
0.91
40.
0001
50.
0028
70.
0000
10.
0039
60.
0083
0.14
09Pr
e1.
394
0.91
40.
0002
10.
0032
9�
0.00
010
0.00
158
0.02
660.
1383
Post
1.89
00.
847
0.00
001
0.00
146
0.00
015
0.00
557
�0.
0123
0.14
18
All
coun
trie
sFu
ll3.
373
2.12
30.
0000
20.
0010
00.
0005
30.
0022
70.
0092
0.11
92Pr
e4.
263
1.90
0�
0.00
007
0.00
065
0.00
016
0.00
133
0.01
970.
1201
Post
2.35
91.
081
0.00
020
0.00
115
0.00
101
0.00
277
0.00
140.
1134
Lat
inA
mer
ica
Full
3.76
72.
667
0.00
009
0.00
149
0.00
061
0.00
316
0.01
330.
137
Pre
4.74
12.
555
0.00
001
0.00
106
0.00
014
0.00
200
0.01
750.
142
Post
2.46
91.
221
0.00
027
0.00
164
0.00
145
0.00
416
0.00
740.
114
Asi
aFu
ll2.
539
1.58
20.
0000
50.
0008
10.
0005
40.
0014
60.
0034
0.11
0Pr
e3.
115
1.27
0�
0.00
003
0.00
033
0.00
032
0.00
119
0.01
700.
101
Post
1.64
10.
696
0.00
024
0.00
124
0.00
082
0.00
159
�0.
0074
0.11
9
Rel
yon
stoc
ksFu
ll3.
379
1.90
10.
0000
20.
0010
30.
0005
10.
0018
80.
0108
0.09
95Pr
e3.
920
1.55
4�
0.00
007
0.00
046
0.00
020
0.00
126
0.02
460.
1064
Post
2.27
10.
844
0.00
020
0.00
159
0.00
085
0.00
205
�0.
0003
0.09
72
Rel
yon
bond
sFu
ll3.
144
2.36
30.
0001
30.
0011
20.
0007
60.
0034
00.
0044
0.14
36Pr
e3.
879
1.95
90.
0000
60.
0009
00.
0002
80.
0019
10.
0098
0.13
68Po
st2.
250
1.36
50.
0003
00.
0010
50.
0015
40.
0044
8�
0.00
180.
1264
(con
tinu
edon
next
page
)
317G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
Tab
le3
(con
tinu
ed)
Cou
ntry
Div
iden
dyi
eld
Net
bond
flow
s/G
DP
Net
equi
tyflo
ws/
mca
pL
ogre
turn
s
Mea
nSt
d.de
v.M
ean
Std.
dev.
Mea
nSt
d.de
v.M
ean
Std.
dev.
Top
exte
rnal
Full
3.13
52.
389
0.00
016
0.00
147
0.00
060
0.00
301
0.00
750.
1407
Pre
3.68
12.
133
0.00
005
0.00
113
0.00
019
0.00
186
0.01
330.
1435
Post
2.09
51.
183
0.00
041
0.00
157
0.00
150
0.00
411
�0.
0017
0.12
34
Bot
tom
exte
rnal
Full
3.36
41.
885
�0.
0001
60.
0010
50.
0002
70.
0014
40.
0111
0.09
84Pr
e4.
462
1.75
3�
0.00
024
0.00
066
0.00
003
0.00
117
0.02
100.
0911
Post
2.35
41.
035
0.00
004
0.00
117
0.00
053
0.00
143
0.00
410.
1009
All
coun
trie
sre
pres
ents
aneq
ually
-wei
ghte
dav
erag
eac
ross
all
coun
trie
s.A
sia
iseq
ually
-wei
ghte
dac
ross
six
Asi
anco
untr
ies
(Ind
ones
ia,
Kor
ea,
Mal
aysi
a,th
ePh
ilipp
ines
,T
aiw
anan
dT
haila
nd),
and
Lat
inis
equa
lly-w
eigh
ted
acro
sssi
xL
atin
Am
eric
anco
untr
ies
(Arg
entin
a,B
razi
l,C
hile
,C
olom
bia,
Mex
ico
and
Ven
ezue
la).
We
also
repo
rteq
ually
-wei
ghte
dac
ross
the
six
coun
trie
sth
atre
lym
ost
oneq
uity
finan
cing
(Chi
le,
the
Phili
ppin
es,
Mal
aysi
a,Po
rtug
al,
Tha
iland
and
Kor
ea),
equa
lly-w
eigh
ted
acro
ssth
esi
xco
untr
ies
that
rely
mos
ton
bond
finan
cing
(Ven
ezue
la,
Mex
ico,
Arg
entin
a,Pa
kist
an,
Indo
nesi
aan
dB
razi
l),
equa
lly-w
eigh
ted
acro
ssth
esi
xco
untr
ies
that
have
the
high
est
prop
ortio
nof
exte
rnal
finan
cing
,th
atis
,th
ebo
ndpl
useq
uity
capi
tal
flow
sto
GD
P(M
exic
o,M
alay
sia,
Ven
ezue
la,
Arg
entin
a,B
razi
lan
dK
orea
)an
dth
esi
xco
untr
ies
with
the
low
est
prop
ortio
nof
exte
rnal
finan
cing
toG
DP
(Chi
le,
Tai
wan
,Pa
kist
an,
the
Phili
ppin
es,
Gre
ece
and
Indi
a).
The
mea
nsan
dst
anda
rdde
viat
ions
are
estim
ated
over
the
full
sam
ple,
the
pre-
brea
ksa
mpl
ean
dth
epo
st-b
reak
sam
ple.
The
brea
kda
tes
for
each
coun
try
are
pres
ente
din
Tab
le2.
318 G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
decreases after the breaks. Note that return volatility actually declines from 41.6%to 39.3% on an annualized basis.
The capital flows also exhibit differences. The equity capital flows to capitalizationratio increases five-fold after the break. Increases occur in 15 of 17 countries. Simi-larly, the bond flows to GDP ratio increases from a negative number in the pre-break period to a positive number in the post-break period. Increases occur in 13 of17 countries. It is also interesting to note that while the volatility of equity returnsand dividend yields decreases after our breaks, the volatility of both the equity andbond capital flows increases. Indeed, the volatility of the bond and equity flow ratiosdoubles from pre to post-break.
4.3. Mean dynamics of capital flows
We estimated the regression specified in eq. (6) for each individual country andpooled across all countries for the various country groups introduced in Section 3.The pooled regression allows for fixed effects, and corrects for temporal heterosked-asticity. The results are in Table 4. We report the regressions both for equity flowsand bond flows. The equity results are consistent with the liberalization inducing alarge portfolio rebalancing that induces large capital flows just after the break, butless after the transition period (which we fixed at three years). On an annualizedbasis, equity flows increase by 1.4% of local market capitalization, but then drop by0.55% after three years. Bond flows continue to increase throughout.
One might expect this phenomenon to be artificially driven by the Asian crisis.During late 1997 and 1998, large amounts of foreign capital left Asia and this wasnot driven by a liberalization-induced portfolio rebalancing. However, the overallphenomenon we find does not occur for Asia, where equity flows on average continueto increase three years after the liberalization. The positive/negative pattern howeveris very strong in Latin-America. Not surprisingly the result also strongly appears forthe 6 countries relying most on foreign capital, whereas it does not show for theleast foreign portfolio capital reliant countries. The fact that the result shows up forthe countries that rely more heavily on bonds and not for those that rely most heavilyon stocks is probably due to the geographical composition of these groups, withLatin-American countries dominating the former, but not the latter.
4.4. Transition dynamics
The results of our THL statistic using the quadravariate VAR are summarized inFig. 3 for capital flows and dividend yields. We can interpret these results as indica-tive of how flows and dividends react when the markets become integrated and howthey might react when the integration process is reversed. The pre-mean/new dynam-ics plot illustrates that for almost half the countries, capital comes in very fast, sothat adjustment happens very quickly, usually within one month. This may provideindirect evidence of portfolio rebalancing. But there are also some notable excep-tions. In Chile, Colombia, Greece, India, Korea, the Philippines, Portugal, Thailand,and Venezuela the transition lasts at least five months. More striking is that in 15
319G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350T
able
4C
hang
esin
capi
tal
flow
saf
ter
brea
ks
Cou
ntry
Net
equi
tyflo
ws
Net
bond
flow
s
Inte
rcep
tB
reak
3-yr
spo
stIn
terc
ept
Bre
ak3-
yrs
post
Arg
entin
a�
0.00
023
0.00
613
�0.
0052
6�
0.00
002
0.00
053
0.00
029
�0.
694.
04�
3.13
�2.
342.
920.
87
Bra
zil
0.00
030
0.00
135
0.00
019
�0.
0000
10.
0001
20.
0001
42.
132.
060.
27�
0.98
1.54
0.89
Chi
le0.
0002
10.
0001
50.
0003
5�
0.00
026
�0.
0004
20.
0006
31.
710.
520.
86�
3.09
�1.
161.
09
Col
ombi
a�
0.00
005
0.00
124
0.00
007
�0.
0000
40.
0002
00.
0005
2�
0.53
2.75
0.11
�3.
661.
591.
92
Gre
ece
�0.
0001
00.
0002
40.
0005
9�
0.00
001
�0.
0000
10.
0001
1�
0.57
1.30
1.92
�2.
52�
1.04
2.23
Indi
a0.
0000
10.
0002
70.
0003
2�
0.00
102
0.00
102
0.00
047
1.49
3.24
2.02
�3.
153.
162.
19
Indo
nesi
a0.
0008
00.
0015
9�
0.00
132
0.00
000
0.00
017
0.00
011
3.03
2.65
�1.
970.
662.
040.
75
Kor
ea0.
0002
40.
0010
20.
0014
10.
0002
40.
0010
20.
0014
12.
244.
191.
702.
244.
191.
70
Mal
aysi
a0.
0002
20.
0002
5�
0.00
050
�0.
0000
7�
0.00
004
0.00
112
3.19
1.19
�2.
27�
0.63
�0.
152.
35
Mex
ico
0.00
077
0.00
350
�0.
0042
70.
0001
50.
0006
3�
0.00
022
3.25
2.70
�3.
171.
002.
00�
0.65
Paki
stan
0.00
000
0.00
026
0.00
071
0.00
000
0.00
001
0.00
008
�0.
042.
101.
20�
0.20
1.11
1.14
(con
tinu
edon
next
page
)
320 G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
Tab
le4
(con
tinu
ed)
Cou
ntry
Net
equi
tyflo
ws
Net
bond
flow
s
Inte
rcep
tB
reak
3-yr
spo
stIn
terc
ept
Bre
ak3-
yrs
post
Phili
ppin
es0.
0000
50.
0008
9�
0.00
010
�0.
0000
8�
0.00
010
0.00
105
0.10
1.40
�0.
24�
2.68
�1.
093.
42
Port
ugal
0.00
015
0.00
042
0.00
106
0.00
000
�0.
0000
70.
0000
61.
472.
231.
72�
3.00
�3.
401.
99
Tai
wan
0.00
002
�0.
0000
20.
0000
6�
0.00
003
�0.
0001
1�
0.00
027
0.39
�0.
451.
15�
4.53
�1.
69�
1.82
Tha
iland
0.00
035
�0.
0000
3�
0.00
012
�0.
0000
1�
0.00
005
0.00
025
2.20
�0.
15�
0.63
�1.
19�
0.76
2.54
Tur
key
�0.
0000
60.
0008
9�
0.00
026
0.00
000
0.00
005
�0.
0000
5�
0.77
3.41
�0.
58�
1.17
1.29
�1.
14
Ven
ezue
la�
0.00
010
0.00
066
�0.
0007
90.
0002
1�
0.00
022
0.00
004
�0.
601.
13�
0.61
0.89
�0.
820.
13
All
coun
trie
s0.
0011
6�
0.00
046
0.00
012
0.00
022
7.56
�2.
752.
823.
51
Asi
a0.
0004
60.
0000
30.
0001
80.
0004
04.
720.
232.
553.
51
321G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
Tab
le4
(con
tinu
ed)
Cou
ntry
Net
equi
tyflo
ws
Net
bond
flow
s
Inte
rcep
tB
reak
3-yr
spo
stIn
terc
ept
Bre
ak3-
yrs
post
Lat
in0.
0021
9�
0.00
154
0.00
014
0.00
022
5.97
�3.
721.
431.
50
Rel
yon
stoc
ks0.
0004
30.
0002
4�
0.00
007
0.00
054
3.82
1.39
�0.
823.
86
Rel
yon
bond
s0.
0023
6�
0.00
168
0.00
021
0.00
006
6.06
�3.
972.
750.
68
Top
exte
rnal
0.00
224
�0.
0015
40.
0002
10.
0002
96.
18�
3.69
2.33
2.08
Bot
tom
exte
rnal
0.00
032
0.00
031
0.00
008
0.00
026
3.31
2.55
1.05
2.27
Bas
edon
are
gres
sion
ofne
teq
uity
flow
san
dne
tbo
ndflo
ws
ontw
oin
dica
tor
vari
able
s.T
hene
teq
uity
(bon
d)flo
ws
repr
esen
tth
ene
teq
uity
(bon
d)flo
ws
betw
een
the
Uni
ted
Stat
esan
dea
chem
ergi
ngm
arke
t.T
heflo
wda
taar
em
onth
lyfr
omth
eD
epar
tmen
tof
the
Tre
asur
y.T
hefir
stin
dica
tor
take
sth
eva
lue
ofon
eaf
ter
abr
eak.
The
seco
ndin
dica
tor
take
son
the
valu
eof
one
thre
eye
ars
afte
rth
ebr
eak.
The
rear
ese
ven
pool
edes
timat
ions
:al
lco
untr
ies,
six
Asi
anco
untr
ies
(Ind
ones
ia,K
orea
,M
alay
sia,
the
Phili
ppin
es,
Tai
wan
and
Tha
iland
),si
xL
atin
Am
eric
anco
untr
ies
(Arg
entin
a,B
razi
l,C
hile
,Col
ombi
a,M
exic
oan
dV
enez
uela
),th
esi
xco
untr
ies
that
rely
mos
ton
equi
tyfin
anci
ng(C
hile
,th
ePh
ilipp
ines
,M
alay
sia,
Port
ugal
,T
haila
ndan
dK
orea
),th
esi
xco
untr
ies
that
rely
mos
ton
bond
finan
cing
(Ven
ezue
la,M
exic
o,A
rgen
tina,
Paki
stan
,Ind
ones
iaan
dB
razi
l),t
hesi
xco
untr
ies
that
have
the
high
estp
ropo
rtio
nof
exte
rnal
finan
cing
,th
atis
,th
ebo
ndpl
useq
uity
capi
tal
flow
sto
GD
P(M
exic
o,M
alay
sia,
Ven
ezue
la,
Arg
entin
a,B
razi
l,an
dK
orea
)an
dth
esi
xco
untr
ies
with
the
low
est
prop
ortio
nof
exte
rnal
finan
cing
toG
DP
(Chi
le,T
aiw
an,P
akis
tan,
the
Phili
ppin
es,G
reec
ean
dIn
dia)
.We
dono
tre
port
the
inte
rcep
tsin
the
grou
pes
timat
ions
.T
hebr
eak
date
sfo
rea
chco
untr
yar
epr
esen
ted
inT
able
2.A
llre
gres
sion
sha
vehe
tero
sked
astic
ity-c
onsi
sten
tst
anda
rder
rors
.T
hepo
oled
regr
essi
ons
allo
wfo
rfix
edef
fect
s.T
heva
lues
belo
wea
chco
effic
ient
repr
esen
tth
et-
stat
istic
.
322 G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
Fig. 3. Transition dynamics.
of the 17 countries, the transition implied by the post-mean/old dynamics is faster(with Brazil moving from 1 to 2 months), suggesting that when capital leaves, itleaves faster than it came! This is an interesting finding in light of the recent criseswhich resulted in capital flight from many emerging markets. Indeed, our empiricalfindings use data from long before these crises.
In 13 of the 17 countries, a dividend yield decrease followed the liberalization(structural change), indicating a decrease in the cost of capital. In contrast to theevidence on capital flows, this decrease in the cost of capital seems to take sometime (more than 1 month in all countries besides Korea, and a median of 4 months),but in the reverse experiment, this process would take even longer (median of 10months). One interpretation of this finding is that liberalizations have made dividendyields less persistent.
5. VAR dynamics
5.1. Dynamic regression coefficients
Table 5 reports the results of our analysis of dynamic regression coefficients. Wereport the coefficients at three horizons, k = 12 (one year), k = 36 (three years), andk = 60 (five years). We also report the cumulative responses over these horizons, andthe infinite cumulative response. To give an idea of the cross-sectional distribution ofthe coefficients across countries, we order the coefficients from low to high andreport the coefficients for the third and 15th country (there are 17 countries). Wealso report the equally-weighted average over the six Latin American, over the sixAsian and over all countries. The table only reports the post-break analysis. Asreported in a previous version of the paper [Bekaert et al., 1999], many of the resultsare not robust across time periods demonstrating once again that capital liberaliza-tions have caused breaks in the dynamic relations between our variables that cannotbe ignored.
The first three panels investigate slope coefficients with the world interest rate asthe regressor. These reflect the long-run correlations between capital flows, dividendyields or returns at time t+k and the world interest rate at time t. The relation betweenfuture capital flows and current world interest rates varies significantly across coun-tries. The equally-weighted response is positive, but the relation is negative for both
323G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
Tab
le5
Dyn
amic
anal
ysis
Res
pons
eSa
mpl
ek=
12k=
36k=
60cu
mk=
12cu
mk=
36cu
mk=
60In
finity
Futu
reca
pita
lflo
ws
Post
3rd
coun
try
−0.0
194
−0.0
155
−0.0
047
−0.4
688
−0.7
078
−1.1
395
−1.5
049
tow
orld
inte
rest
15th
coun
try
0.03
270.
0057
0.00
190.
8146
1.05
141.
0640
1.06
47ra
tes
Equ
alw
eigh
t0.
0061
0.03
350.
0974
0.04
350.
4887
1.97
611.
7526
Lat
inA
mer
ica
−0.0
099
−0.0
060
−0.0
019
−0.0
176
−0.2
356
−0.3
197
−0.3
635
Asi
a−0
.005
1−0
.000
7−0
.000
3−0
.135
6−0
.178
4−0
.188
3−0
.154
2
Futu
redi
vide
ndPo
st3r
dco
untr
y−1
7.37
−8.6
5−7
.04
−254
.19
−639
.35
−878
.84
−192
1.20
yiel
dsto
wor
ld15
thco
untr
y12
.90
4.94
3.12
143.
1133
7.66
387.
3843
8.63
inte
rest
rate
sE
qual
wei
ght
9.75
29.0
880
.52
93.4
053
2.26
1774
.50
−432
.55
Lat
inA
mer
ica
−5.4
9−5
.15
−3.7
7−6
0.82
−191
.63
−298
.70
−484
.82
Asi
a−2
.06
−1.4
6−1
.26
−20.
08−5
8.44
−91.
34−2
05.8
3
Futu
rere
turn
sto
Post
3rd
coun
try
−1.6
042
−0.5
033
−0.3
056
−2.4
314
−1.2
862
−0.8
134
−70.
7920
wor
ldin
tere
stra
tes
15th
coun
try
1.29
790.
5596
0.46
891.
4222
1.27
630.
8150
96.3
130
Equ
alw
eigh
t−0
.272
5−1
.122
1−3
.203
1−0
.096
9−0
.480
3−1
.104
1−1
2.21
10L
atin
Am
eric
a0.
0433
0.13
530.
0993
−0.0
364
0.06
250.
0852
9.48
85A
sia
0.93
030.
3931
0.28
681.
3551
0.82
310.
6259
115.
8200
Futu
redi
vide
ndPo
st3r
dco
untr
y−1
6.95
−1.7
5−0
.58
−445
.25
−563
.99
−567
.52
−565
.10
yiel
dsto
capi
tal
15th
coun
try
15.5
215
.45
5.90
176.
8354
6.87
919.
7319
37.0
0flo
ws
Equ
alw
eigh
t0.
88−6
.57
−28.
4816
.76
−34.
97−4
24.6
2−7
7.44
Lat
inA
mer
ica
−4.4
6−0
.28
0.30
−116
.65
−153
.65
−150
.99
−123
.13
Asi
a8.
2012
.95
13.5
210
2.04
370.
8169
2.88
4358
.00
(con
tinu
edon
next
page
)
324 G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
Tab
le5
(con
tinu
ed)
Res
pons
eSa
mpl
ek=
12k=
36k=
60cu
mk=
12cu
mk=
36cu
mk=
60In
finity
Futu
rere
turn
sto
Post
3rd
coun
try
−0.1
451
−0.0
212
−0.0
041
−0.1
238
−0.0
674
−0.0
446
−12.
6570
capi
tal
flow
s15
thco
untr
y3.
3869
1.79
171.
6850
5.05
914.
2570
2.64
4714
1.28
00E
qual
wei
ght
1.42
841.
4957
2.29
632.
4706
1.75
631.
7887
−133
2.80
00L
atin
Am
eric
a−0
.022
50.
0265
0.00
800.
1033
0.05
590.
0397
2.42
57A
sia
0.87
400.
4617
0.39
813.
3117
1.47
201.
0542
185.
1700
Futu
reca
pita
lflo
ws
Post
3rd
coun
try
−0.0
0015
−0.0
0001
0.00
000
−0.0
0588
−0.0
0462
−0.0
0543
−0.0
2422
todi
vide
ndyi
elds
15th
coun
try
0.00
044
0.00
015
0.00
009
0.00
801
0.01
374
0.01
609
0.01
565
Equ
alw
eigh
t0.
0002
30.
0004
60.
0011
20.
0023
00.
0102
50.
0282
6−0
.015
13L
atin
Am
eric
a0.
0000
60.
0000
60.
0000
30.
0001
90.
0018
30.
0028
50.
0037
0A
sia
−0.0
0001
0.00
002
0.00
002
−0.0
0069
−0.0
0032
0.00
010
0.00
334
Futu
reca
pita
lflo
ws
Post
3rd
coun
try
−0.0
0030
−0.0
0011
−0.0
0006
−0.0
0125
−0.0
0437
−0.0
0408
−0.0
0627
tore
turn
s15
thco
untr
y0.
0004
30.
0000
70.
0000
70.
0096
70.
0137
30.
0171
30.
0141
8E
qual
wei
ght
0.00
009
0.00
012
0.00
016
0.00
484
0.00
727
0.01
055
−0.2
2599
Lat
inA
mer
ica
−0.0
0007
−0.0
0004
−0.0
0002
0.00
650
0.00
507
0.00
434
0.00
356
Asi
a0.
0000
4−0
.000
010.
0000
00.
0028
00.
0027
60.
0026
40.
0066
5
For
our
anal
ysis
ofdy
nam
icre
gres
sion
coef
ficie
nts,
we
repo
rtth
eco
effic
ient
sat
thre
eho
rizo
ns,
k=12
(1ye
ar),
k=36
(thr
eeye
ars)
,an
dk=
60(fi
veye
ars)
.W
eal
sore
port
the
cum
ulat
ive
resp
onse
sov
erth
ese
hori
zons
,an
dth
ein
finite
cum
ulat
ive
resp
onse
.T
oin
vest
igat
eth
ecr
oss-
sect
iona
ldi
stri
butio
nof
the
coef
ficie
nts
acro
ssco
untr
ies,
we
orde
rth
eco
effic
ient
sfr
omlo
wto
high
and
repo
rtth
eco
effic
ient
sfo
rth
eth
ird
and
15th
coun
try
(the
rear
e17
coun
trie
s).
We
also
repo
rtth
eeq
ually
-wei
ghte
dav
erag
eov
erth
esi
xL
atin
Am
eric
an,o
ver
the
six
Asi
anan
dov
eral
lco
untr
ies.
The
brea
kda
tes
are
prov
ided
inT
able
2.
325G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
the Latin American and Asian countries. For example, a 1% decrease in the worldinterest rate is associated with a cumulative 36-month increase of US holdings ofthe local equity market equal to 0.24% in Latin America and 0.18% in South-EastAsia in the post-break period.
The relation between the world interest rate and future dividend yields equally-weighted across the countries is at first positive but the cumulative infinite responseis negative. The responses in Latin America and Asia are always negative, indicatingthat lower interest rates are associated with future increases in dividend yields. Thisis surprising under the “push” hypothesis where lower interest rates drive developedmarket capital into emerging markets and drive up prices there, hence lowering divi-dend yields. Of course, we report long-run effects, and the “push” effect may bevery short-lived. The effects we document die out very slowly, which is largely dueto the large persistence of the dividend yield in most countries. Note that the coef-ficients seem large primarily because of the log-transformation of dividend yields.To get an approximate regression coefficient for the level of the dividend yield, oneshould multiply the reported coefficients by the average dividend yield level (0.035).
For the implicit regression of future returns on the world interest rate, we dividethe cumulative effects by k, to obtain a per period return effect (except, of course,for k = �). Not surprisingly, given the noisiness in returns, the coefficients showalso a wide range across countries. Nevertheless, they are consistently positive forboth Latin America and Asia. Hence, decreases in world interest rates are associatedwith long-run decreases in returns, not increases as we might expect. The samecaveats we mentioned above apply. In addition, we should add that a true test ofthe “push” hypothesis should focus on the effects of an unexpected shock to worldinterest rates, especially since world interest rates are quite predictable. Such ananalysis follows later when we consider impulse responses.
The following panels consider the long-run relation between capital flows andfuture dividend yields and returns. The relation between capital flows and futuredividend yields also shows a lot of cross-sectional dispersion, with positive coef-ficients for Asia and negative ones for Latin America. Hence, the slope coefficientsdo not clearly reveal a permanent cost of capital effect from increased capital flows.The correlations between capital flows and returns are largely positive; higher flowsare correlated with higher future returns but the coefficients are quite small.
The next panel considers an implicit regression of future capital flows on currentlog-dividend yields. The cumulative responses can now be interpreted as the changein total holdings over this period. The coefficients are generally small for the samereason previous coefficients involving dividend yields were large — the log-trans-formation. For an average dividend yield level of 0.035, one should multiply thecoefficients by 28.6. If higher dividend yields proxy for higher expected returns, andthe return chasing hypothesis is true, one would expect a positive contemporaneousrelation, which may persist because of positive autocorrelation in dividend yieldsand flows. This is indeed the case for both Latin-America and Asia at the five-yearhorizon, but the effects are very small. It remains true when the country-by-countryresults are averaged at the five-year horizon but not when the infinite cumulativeresponse is computed.
326 G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
The last panel investigates the correlation between current returns and future capi-tal flows. At the horizons that we consider, we would not expect to see much of aneffect, even if foreigners are momentum traders. Indeed, we find very small effects,that are positive when cumulated for Asia and Latin-America, but negative whenaveraged over all countries.
In general, the slope coefficients do not lead to strong conclusions about univariatecorrelation patterns between the various variables. This motivates investigating morecomplex patterns, either partial regression coefficients as in the Granger-causalityanalysis that follows, or impulse responses which control for the predictability ofcausal factors.
5.2. Granger-causality tests
Granger-causality tests are reported in Table 6. The first three columns investigatethe predictive power of a much discussed external factor (a “push” factor), the worldinterest rate, on equity flows, dividend yields and returns. The statistical results areweak. This is not surprising before the break if markets were truly segmented pre-venting free capital flows. The only significant result is that in Korea we can rejectthat the world interest rate fails to Granger-cause returns. But even in the POSTperiod, significant results are rare. Except for single cases of near or below 5%rejections of the no Granger-causality hypotheses in Korea, the Philippines, and Tur-key, the only country where the world interest rate played a significant role predictingcapital flows, dividend yields and returns simultaneously is Brazil.
These results may indicate that the world interest rate is not an important predictorof capital flows and returns and that the previous literature (e.g. Froot et al., 2001)justifiably ignored it, but it may also reflect the short sample periods we have avail-able. When looking at impulse responses in the next sub-section, our various countrygroupings may yet reveal the interest rate to be an important external determinantof capital flows.
The price pressure hypothesis would suggest that increased capital flows tempor-arily induce high returns which are reversed afterwards in which case we wouldexpect capital flows to Granger-cause returns. In the portfolio rebalancing story, onthe other hand, even before the full liberalization is implemented, anticipation shouldhave already led to permanent price increases, making it less obvious that we willsee significant results in the post period. As Fig. 1 indicates, returns show the mostinteresting dynamics during the transition period. If there is an effect on the dividendyield, the effect may represent a more permanent change in the cost of capital. Thestatistical evidence on Granger-causality is not overwhelming. We reject the hypoth-esis of no predictability of returns by capital flows at the 5% level for only sixcountries: Brazil (pre-break), Greece (pre-break), Korea (post-break), Malaysia (fullperiod), Pakistan (post-break), and Thailand (pre-break).
In three of the six countries where capital flows predict returns, they also predictdividend yields significantly. In three other countries (Indonesia, the Philippines andVenezuela), we also record a significant relation between capital flows and divi-dend yields.
327G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350T
able
6G
rang
erca
usal
ityte
sts
Prob
abili
tyva
lues
from
the
null
hypo
thes
isth
at:
Cou
ntry
Wor
ldin
tere
stW
orld
inte
rest
Wor
ldin
tere
stN
eteq
uity
Net
equi
tyR
etur
nsdo
not
Div
iden
dyi
elds
rate
sdo
not
rate
sdo
not
rate
sdo
not
flow
sdo
not
flow
sdo
not
Gra
nger
caus
edo
not
Gra
nger
Gra
nger
caus
eG
rang
erca
use
Gra
nger
caus
eG
rang
erca
use
Gra
nger
caus
ene
teq
uity
caus
ene
tne
teq
uity
divi
dend
yiel
dsre
turn
sdi
vide
ndyi
elds
retu
rns
flow
seq
uity
flow
sflo
ws
Arg
entin
aFu
ll0.
951
0.65
20.
283
0.92
70.
438
0.81
90.
955
Pre
0.98
10.
940
0.18
30.
978
0.39
50.
687
0.90
3Po
st0.
316
0.62
40.
172
0.80
40.
064
0.83
20.
557
Bra
zil
Full
0.93
60.
911
0.92
10.
002
0.57
50.
621
0.09
3Pr
e0.
940
0.96
20.
885
0.00
00.
008
0.02
40.
157
Post
0.09
00.
028
0.07
10.
029
0.45
40.
305
0.73
3
Chi
leFu
ll0.
691
0.16
20.
348
0.59
50.
956
0.52
60.
924
Pre
0.80
60.
268
0.12
10.
208
0.99
10.
586
0.60
7Po
st0.
406
0.98
60.
340
0.08
20.
938
0.69
70.
234
Col
ombi
aFu
ll0.
146
0.29
80.
051
0.58
10.
310
0.91
30.
000
Pre
0.29
90.
797
0.75
70.
349
0.29
90.
982
0.12
7Po
st0.
191
0.66
40.
134
0.80
20.
415
0.95
90.
015
Gre
ece
Full
0.53
50.
093
0.76
50.
496
0.12
90.
481
0.94
7Pr
e0.
909
0.32
70.
734
0.72
00.
024
0.18
80.
792
Post
0.63
50.
826
0.45
30.
898
0.57
50.
994
0.99
4
Indi
aFu
ll0.
901
0.58
80.
978
0.87
90.
051
0.50
70.
774
Pre
0.31
20.
665
0.99
90.
996
0.90
00.
730
0.12
1Po
st0.
570
0.24
00.
141
0.92
70.
090
0.81
20.
842
Indo
nesi
aFu
ll0.
995
0.30
80.
290
0.18
70.
511
0.30
70.
669
Pre
0.09
90.
195
0.37
20.
040
0.20
10.
495
0.83
3Po
st0.
999
0.92
00.
513
0.16
30.
754
0.38
40.
566
(con
tinu
edon
next
page
)
328 G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350T
able
6(c
onti
nued
)
Prob
abili
tyva
lues
from
the
null
hypo
thes
isth
at:
Cou
ntry
Wor
ldin
tere
stW
orld
inte
rest
Wor
ldin
tere
stN
eteq
uity
Net
equi
tyR
etur
nsdo
not
Div
iden
dyi
elds
rate
sdo
not
rate
sdo
not
rate
sdo
not
flow
sdo
not
flow
sdo
not
Gra
nger
caus
edo
not
Gra
nger
Gra
nger
caus
eG
rang
erca
use
Gra
nger
caus
eG
rang
erca
use
Gra
nger
caus
ene
teq
uity
caus
ene
tne
teq
uity
divi
dend
yiel
dsre
turn
sdi
vide
ndyi
elds
retu
rns
flow
seq
uity
flow
sflo
ws
Kor
eaFu
ll0.
741
0.95
30.
049
0.93
40.
010
0.00
00.
981
Pre
0.14
40.
998
0.03
01.
000
0.61
40.
518
0.98
2Po
st0.
999
0.07
00.
558
0.28
70.
010
0.00
00.
890
Mal
aysi
aFu
ll0.
948
0.87
60.
340
0.41
30.
023
0.09
20.
125
Pre
0.75
90.
442
0.46
20.
280
0.19
40.
114
0.42
3Po
st0.
901
0.78
80.
592
0.46
80.
095
0.21
30.
033
Mex
ico
Full
0.99
80.
051
0.61
70.
682
0.88
40.
960
0.73
4Pr
e0.
871
0.11
10.
189
0.32
60.
302
0.99
60.
455
Post
0.57
50.
214
0.72
60.
623
0.64
70.
994
0.51
0
Paki
stan
Full
0.34
60.
426
0.14
20.
000
0.00
00.
466
0.04
8Pr
e0.
545
0.09
00.
458
0.94
30.
702
0.49
50.
862
Post
0.53
20.
277
0.20
50.
000
0.01
50.
844
0.17
9
Phili
ppin
esFu
ll0.
015
0.44
40.
597
0.41
50.
831
0.56
30.
499
Pre
0.38
30.
249
0.68
50.
008
0.32
00.
353
0.00
1Po
st0.
052
0.24
00.
456
0.83
30.
976
0.42
30.
180
Port
ugal
Full
0.69
20.
438
0.57
00.
958
1.00
00.
988
0.96
4Pr
e0.
527
0.88
90.
903
0.96
30.
795
0.73
80.
977
Post
0.75
00.
133
0.28
10.
820
0.98
30.
985
0.90
7
Tai
wan
Full
0.59
80.
697
0.81
90.
956
0.65
10.
759
0.40
8Pr
e0.
650
0.30
30.
421
0.49
10.
847
0.70
30.
599
Post
0.69
20.
740
0.80
30.
723
0.30
10.
782
0.47
2
329G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
Tab
le6
(con
tinu
ed)
Prob
abili
tyva
lues
from
the
null
hypo
thes
isth
at:
Cou
ntry
Wor
ldin
tere
stW
orld
inte
rest
Wor
ldin
tere
stN
eteq
uity
Net
equi
tyR
etur
nsdo
not
Div
iden
dyi
elds
rate
sdo
not
rate
sdo
not
rate
sdo
not
flow
sdo
not
flow
sdo
not
Gra
nger
caus
edo
not
Gra
nger
Gra
nger
caus
eG
rang
erca
use
Gra
nger
caus
eG
rang
erca
use
Gra
nger
caus
ene
teq
uity
caus
ene
tne
teq
uity
divi
dend
yiel
dsre
turn
sdi
vide
ndyi
elds
retu
rns
flow
seq
uity
flow
sflo
ws
Tha
iland
Full
0.99
20.
265
0.58
50.
005
0.25
10.
006
0.38
3Pr
e0.
996
0.15
90.
349
0.32
60.
001
0.06
90.
137
Post
0.56
20.
490
0.58
90.
013
0.20
20.
187
0.53
8
Tur
key
Full
0.95
10.
928
0.04
00.
996
0.33
10.
303
0.49
0Pr
e0.
427
0.99
10.
037
0.05
40.
148
0.70
50.
524
Post
0.95
30.
651
0.00
90.
984
0.23
00.
427
0.09
0
Ven
ezue
laFu
ll0.
990
0.16
70.
296
0.28
80.
526
0.93
80.
546
Pre
0.82
30.
580
0.32
30.
019
0.05
40.
893
0.75
9Po
st0.
977
0.96
70.
830
0.30
30.
386
0.86
90.
398
Gra
nger
caus
ality
anal
ysis
isba
sed
ona
quad
rava
riat
esy
stem
ofw
orld
inte
rest
rate
s,ne
teq
uity
capi
tal
flow
s,di
vide
ndyi
elds
and
equi
tyre
turn
s.T
heV
AR
sar
ees
timat
edov
erth
efu
llsa
mpl
e,th
epr
e-br
eak
sam
ple
and
the
post
-bre
aksa
mpl
e.T
hebr
eak
date
sfo
rea
chco
untr
yar
epr
esen
ted
inT
able
2.
330 G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
Do endogenous factors play a large role in determining capital flows? If that isthe case, returns and/or dividend yields ought to have predictive power for capitalflows. Interestingly, there is little predictive power of dividend yields (only Colom-bia, Malaysia, Pakistan, and the Philippines), and returns predict capital flows onlyin Brazil (pre-break), Korea and Thailand.
The lack of significant predictive relations between our variables, revealed bycountry-specific Granger-causality tests, stresses again the need to focus on cross-sectionally averaged results. To analyze some of the predictive relations further, wereport three partial regression coefficients in Table 7. The first is the coefficient inthe flow equation on past flows. Froot et al. (2001) note that the predictive powerof flows for future returns may in fact be due to both a strong contemporaneousrelation between flows and returns (see next section) and the persistence in flows.Our estimates suggest a monthly persistence coefficient of around 0.135, which ismuch higher than the monthly persistence implied by Froot et al.’s daily model.
The second coefficient is the coefficient on returns in the flows equation. Positivecoefficients suggest feedback trading or may be the result of returns anticipatingpositive news about future market reforms that will bring in foreign capital. Whereasthe country by country results showed little significance (and are not reported), thecoefficients are predominantly positive as they are in Froot et al. (2001). One countryfor which a gradual liberalization story would have much appeal, Korea, records anegative coefficient.
Finally, we report the flow coefficient in the return equation. As in Froot et al.(2001), we find predominantly positive and large coefficients. A 1% increase inforeign holdings leads on average to a 4.74% increase in returns next month (seeTable 7). Froot et al. argue that this is not inconsistent with the price pressure hypoth-esis (which would require a positive contemporaneous effect and negative long-runeffect) given that flows are persistent. The response could also reflect a return tointegration, as we discussed before, although one would expect much of the increasein foreign holdings to be anticipated and, hence, not induce large ex post pricechanges. In the impulse response analysis in the next section, we look at the dynamiceffects of unexpected shocks, including the contemporaneous relations between vari-ables.
6. Tests of the main hypotheses
The impulse response analysis of the quadravariate VAR is presented in Figs. 4–6. For each country, we estimate the VAR on three samples: full sample, pre-breakand post-break. We then aggregate the impulse responses across various countrygroups using equal weights. To help interpret the evidence, in light of the hypothesesformulated in Section 2, we provide two additional tables. Table 8 presents an analy-sis of impulse responses on changes in equity holdings and long-run returns, whereasTable 9 reports some important contemporaneous betas. When rescaled, these betasconstitute the impulse responses at time 0 and they also appear in Figs. 4–6. Bothtables focus exclusively on the post-break period.
331G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
Tab
le7
Ana
lysi
sof
VA
Rco
effic
ient
s(p
ost-
brea
kes
timat
ion)
VA
Req
uatio
nE
qual
Val
ueA
sian
Lat
inR
ely
onR
ely
onT
opB
otto
mR
esid
ual
3rd
15th
wei
ghte
dw
eigh
ted
equi
tybo
nds
exte
rnal
exte
rnal
wei
ghts
coef
ficie
ntco
effic
ient
finan
cing
finan
cing
Equ
ityflo
ws
on0.
1320
0.13
600.
1133
0.12
490.
0781
0.15
710.
1280
0.20
840.
1351
�0.
0245
0.29
02la
gged
flow
s
Equ
ityflo
ws
on0.
0019
0.00
230.
0009
0.00
350.
0012
0.00
360.
0034
0.00
040.
0016
�0.
0005
0.00
49la
gged
retu
rns
Ret
urns
on4.
7378
6.67
469.
1445
1.92
158.
2919
1.28
165.
1814
4.51
666.
5033
�0.
9226
10.6
367
lagg
edeq
uity
flow
s
We
repo
rtth
eV
AR
coef
ficie
ntde
note
din
the
first
colu
mn
from
coun
try
spec
ific
estim
atio
nsba
sed
inth
epo
st-b
reak
sam
ple.
Equ
alw
eigh
ted
isan
equa
lw
eigh
ting
ofal
l11
7co
untr
ies.
Val
uew
eigh
ted
repr
esen
ts17
coun
trie
sw
eigh
ted
byth
eir
mar
ket
capi
taliz
atio
nin
Dec
embe
r19
91.
Asi
are
pres
ents
six
Asi
anco
untr
ies.
Lat
inre
pres
ents
six
Lat
inA
mer
ican
coun
trie
s.R
ely
onst
ocks
are
the
six
coun
trie
sth
atte
ndto
rely
onst
ocks
mor
eth
anbo
nds
for
exte
rnal
finan
cing
sinc
e19
89.
Rel
yon
bond
sar
eth
esi
xco
untr
ies
whi
chte
ndto
rely
mor
eon
bond
sfo
rex
tern
alfin
anci
ng.
Top
exte
rnal
are
the
six
coun
trie
sw
hich
have
the
larg
est
prop
ortio
nof
cum
ulat
ive
net
bond
plus
equi
tyflo
ws
toG
DP.
Bot
tom
exte
rnal
are
the
six
coun
trie
sw
ithth
esm
alle
stpr
opor
tion
ofcu
mul
ativ
ene
tbo
ndpl
useq
uity
flow
sto
GD
P.Fo
rth
e3r
dan
d15
thco
effic
ient
,w
era
nkth
eco
effic
ient
sac
ross
coun
trie
s,el
imin
ate
“out
liers
”,th
atis
the
smal
lest
and
high
est
two
and
repo
rtth
era
nge
from
the
rem
aind
er(m
inan
dm
ax),
(the
3rd
and
15th
coef
ficie
ntgi
ven
ther
ear
e17
coun
trie
s).
332 G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
Table 8Cumulative impulse response analysis on post break sample
Country group k=12 k=36 k=60
A. Interest rate shock on cumulative flowsEqually weighted 0.00020 0.00069 0.00158Value weighted 0.00011 0.00010 0.00026Asia 0.00031 0.00040 0.00041Latin 0.00003 0.00033 0.00044Rely on stocks 0.00033 0.00048 0.00054Rely on bonds 0.00006 0.00108 0.00340Top external 0.00008 0.00024 0.00028Bottom external 0.00036 0.00143 0.003883rd coefficient �0.00022 �0.00045 �0.0006315th coefficient 0.00101 0.00145 0.00172
B. Dividend yield shock on cumulative flowsEqually weighted 0.00004 0.00077 0.00253Value weighted 0.00042 0.00074 0.00112Asia �0.00012 �0.00003 0.00003Latin �0.00037 �0.00032 �0.00025Rely on stocks �0.00014 �0.00001 0.00008Rely on bonds 0.00014 0.00189 0.00667Top external �0.00019 �0.00021 �0.00017Bottom external 0.00013 0.00197 0.006803rd coefficient �0.00077 �0.00133 �0.0015515th coefficient 0.00100 0.00184 0.00216
C. Return shock on cumulative flowsEqually weighted 0.00012 0.00002 �0.00010Value weighted 0.00012 0.00002 �0.00001Asia 0.00011 0.00011 0.00011Latin 0.00022 0.00004 0.00000Rely on stocks 0.00011 0.00011 0.00010Rely on bonds 0.00020 �0.00004 �0.00031Top external 0.00014 �0.00002 �0.00004Bottom external 0.00009 �0.00001 �0.000293rd coefficient �0.00015 �0.00021 �0.0002815th coefficient 0.00044 0.00045 0.00046
D. Interest rate shock on average long-run returnsEqually weighted 0.00054 �0.00018 �0.00061Value weighted 0.00071 0.00007 �0.00008Asia 0.00079 0.00016 0.00002Latin 0.00125 0.00035 0.00015Rely on stocks �0.00017 �0.00046 �0.00042Rely on bonds 0.00137 �0.00031 �0.00147Top external 0.00156 0.00049 0.00027Bottom external 0.00009 �0.00084 �0.001833rd coefficient �0.00220 �0.00130 �0.0010215th coefficient 0.00284 0.00130 0.00084
(continued on next page)
333G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
Table 8 (continued)
Country group k=12 k=36 k=60
E. Net equity flows shock on average long-run returnsEqually weighted 0.00097 0.00040 0.00030Value weighted 0.00127 0.00049 0.00033Asia 0.00080 0.00038 0.00028Latin 0.00122 0.00041 0.00025Rely on stocks 0.00144 0.00061 0.00043Rely on bonds 0.00086 0.00036 0.00034Top external 0.00182 0.00072 0.00047Bottom external 0.00053 0.00027 0.000303rd coefficient �0.00079 �0.00032 �0.0001915th coefficient 0.00189 0.00091 0.00071
Notes to the table: We analyze cumulative impulse response functions for two variables: average longrun returns and change in equity holdings. The change in equity holdings is simply the cumulative sumof net equity capital flows. For the average long-run returns we examine shocks in capital flows andworld interest rates. For the change in equity holdings we measure the effect of a shock in world interestrates, returns and dividend yields. The effects are calculated by summing the impulse response until k(in months) not including the contemporaneous effect. Equal weighting is an equal weighting of all 17countries. Value weighting represents 17 countries weighted by their market capitalization in December1991. Asia represents six Asian countries. Latin represents six Latin American countries. Rely on stocksare the six countries that tend to rely on stocks more than bonds for external financing since 1989. Relyon bonds are the six countries which tend to rely more on bonds for external financing. Top external arethe six countries which have the largest proportion of cumulative net bond plus equity flows to GDP.Bottom external are the six countries with the smallest proportion of cumulative net bond plus equityflows to GDP. For the 3rd and 15th coefficient, we rank the coefficients across countries, eliminate“outliers” , that is the smallest two and highest two and report the range from the remainder (min andmax), (the 3rd and 15th coefficient given there are 17 countries). The VARs are estimated on the post-break sample. The break dates are detailed in Table 2.
6.1. The effects of world interest rate shocks
The effects of a negative world interest rate shock differ greatly between the pre-break and the post-break periods. Given that we should only expect any effect post-liberalization, we only consider the post-break period. The contemporaneous effectsof a shock in world interest rates on capital flows are mixed with positive covariancesdominating (see Table 9) but after one period a negative shock generally leads tosmall increases in net equity flows (Fig. 4 panel (a)). This is definitely the case forAsia but less so for Latin America. The countries that benefit the most are thosewith the highest degree of external financing. In all cases, the effects die out quickly.From Table 8, we see that a negative interest rate shock is associated with increasedholdings at five year horizons for all groupings. The impact is greatest for countriesthat rely on bonds rather than stocks. While there is a large positive increase inholdings for Asian countries up to one year out compared to Latin American coun-tries, by five years, the impact is very similar across the regions. A 0.3% decreasein world interest rates leads to an 0.04% increase in the proportion of the equitymarket held by US investors.
334 G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
Tab
le9
Impo
rtan
tco
ntem
pora
neou
sef
fect
s
Coe
ffici
ent
Equ
alV
alue
Asi
anL
atin
Rel
yon
Rel
yon
Top
exte
rnal
Bot
tom
Res
idua
lw
eigh
ted
wei
ghte
deq
uity
bond
sfin
anci
ngex
tern
alw
eigh
tsfin
anci
ng
nfon
i0.
0623
�0.
0571
0.05
950.
0322
0.04
530.
0793
�0.
0216
0.13
940.
0761
dyon
i12
.182
814
.451
218
.558
013
.368
214
.180
214
.605
418
.708
84.
2519
11.1
440
ron
i�
8.66
53�
12.9
565
�10
.712
8�
11.9
839
�8.
2843
�14
.377
3�
15.5
770
�3.
4362
�7.
6166
dyon
nf�
4.53
55�
5.59
96�
10.0
817
�1.
6151
�11
.646
8�
0.95
37�
7.21
08�
3.56
36�
5.88
19r
onnf
3.71
905.
3209
6.54
922.
3367
8.47
07�
0.36
187.
7257
3.88
895.
6144
ron
dy�
0.29
45�
0.27
24�
0.29
88�
0.29
85�
0.30
70�
0.29
28�
0.30
91�
0.27
53�
0.32
25
The
coef
ficie
nts
repo
rted
are
base
don
the
cont
empo
rane
ous
beta
sim
plie
dby
the
caus
alor
deri
ngof
our
VA
Rin
the
post
-bre
akes
timat
ion.
Tha
tis
,th
eyar
eta
ken
from
the
mat
rix
B,
whe
re�
=BH
B�,
whe
reB
islo
wer
tria
ngle
and
Hha
sth
eva
rian
ces
ofth
est
ruct
ural
shoc
ksal
ong
itsdi
agon
al.
Equ
alw
eigh
ted
isan
equa
lw
eigh
ting
ofal
l11
7co
untr
ies.
Val
uew
eigh
ted
repr
esen
ts17
coun
trie
sw
eigh
ted
byth
eir
mar
ket
capi
taliz
atio
nin
Dec
embe
r19
91.
Asi
are
pres
ents
six
Asi
anco
untr
ies.
Lat
inre
pres
ents
six
Lat
inA
mer
ican
coun
trie
s.R
ely
onst
ocks
are
the
six
coun
trie
sth
atte
ndto
rely
onst
ocks
mor
eth
anbo
nds
for
exte
rnal
finan
cing
sinc
e19
89.
Rel
yon
bond
sar
eth
esi
xco
untr
ies
whi
chte
ndto
rely
mor
eon
bond
sfo
rex
tern
alfin
anci
ng.T
opex
tern
alar
eth
esi
xco
untr
ies
whi
chha
veth
ela
rges
tpr
opor
tion
ofcu
mul
ativ
ene
tbo
ndpl
useq
uity
flow
sto
GD
P.B
otto
mex
tern
alar
eth
esi
xco
untr
ies
with
the
smal
lest
prop
ortio
nof
cum
ulat
ive
net
bond
plus
equi
tyflo
ws
toG
DP.
The
vari
able
sar
e:th
ew
orld
inte
rest
rate
(i),
net
equi
tyflo
ws
(nf)
,di
vide
ndyi
elds
(dy)
and
retu
rns
(r).
335G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
Fig. 4. The impact of world interest rate shocks on equity flows, dividend yields and returns.
The analysis of dividend yields in Fig. 4 panel (b) reveals that a negative shock inthe interest rate is associated with lower dividend yields in Asian and Latin Americancountries. The only incidence of higher dividend yields is for countries that use littleexternal financing. In terms of magnitude, Table 9 is informative. The contempor-aneous beta is over 10.0, meaning that a 1% decrease in the world interest rate leadsto a drop in the level of the dividend yield of about 10 times the average dividendyield, that is 35 bp. The effect persists for quite a long time, especially in Asia.
The analysis of returns in Fig. 4 panel (c) suggests a positive effect only contem-poraneously in all countries except those that do not rely on external financing. Thisis consistent with a portfolio effect (higher capital flows to emerging markets) beinginduced by a low world interest rate. It may reflect a pure short-term price pressureeffect or the return to integration (see above) if market liberalizations happen tocoincide with periods of lower world interest rates.
Our analysis of long-run returns in Table 8 suggests that negative interest rateshocks increase long-run returns over a one-year horizon but the effect is often elim-inated by three years. Generally, both the dividend yield effects and the expectedreturn effects do not show clear enough results to distinguish between long-termbeneficial effects (lower cost of capital) or short-term price pressure effects thatmight reverse themselves.
336 G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
6.2. The effects of equity flow shocks
We examine the impact of positive equity flows shocks on dividend yields andreturns in Fig. 5. We use this figure to illustrate the dramatic differences betweenthe pre- and post-break analysis. Perhaps the most powerful graph that we presentis the impulse response of a positive shock in equity flows on dividend yields. Inthe pre-break period, it is hard to see any consistent effect. However, in the postbreak period, there is a sharply lower dividend yield. Contemporaneously (see Table9), the effect is on the order of about 20 basis points per 1% increase in foreignholdings (recall that because of the log-transformation, the coefficients must be multi-plied by 0.035, the mean dividend yield, to obtain the effect on the level). In addition,the effect is very persistent. Dividend yields drop the most for Asian countries butalso drop for Latin American countries. The drop in dividend yields is very strongfor those countries that rely on external financing. Interestingly, the countries thatactively use bond financing have a larger drop in dividend yields than those countriesthat rely more on equity financing. Following Bekaert and Harvey’s (2000a) argu-ment that changes in dividend yields closely follow changes in the cost of equitycapital, this analysis suggests that increased capital flows decrease the cost of equitycapital. It is important to realize that the shock to capital flows is net of the resultof world interest rate changes.
The impulse response analysis of capital flows on returns in Fig. 5 is consistentwith the portfolio rebalancing hypothesis. In the post-break period, returns increaseonly in the very short term. These results are consistent with the dividend yieldresults. A shock in equity capital flows increases the price level which leads to higherreturns in the short-term and permanently lower dividend yields. The contempor-aneous beta is around 6. This is of the same order of magnitude as the estimate forMexico in Clark and Berko (1997), but much smaller than the estimate in the dailyflow/return model in Froot et al. (2001). None of these studies separate the interestrate effect from the capital flows effect. The cumulative effect (see Table 8) remainspositive, but becomes significantly smaller at the 60-month horizon suggesting some,but incomplete, reversal of prices. The largest impact is on countries that have arelatively large reliance on external financing.
6.3. The effects of return and dividend yield shocks
We now turn to the return chasing hypothesis. Bohn and Tesar (1996) distinguishtwo hypotheses. The “ return updating” hypothesis links expected returns to capitalflows. The “momentum” hypothesis predicts positive capital flows after positivereturns. We revisit this last hypothesis by looking at realized returns and measuringthe impact of a positive shock in returns on the capital flows (see Fig. 6 panelA). In our framework, the VAR ordering implies that the return shock omits thecontemporaneous correlation with both capital flows and dividend yields and hencereflects an unusual unexpected high return, not contemporaneously correlated withforeign capital shocks or permanent cost of capital changes. We find a positiveresponse of flows to returns in all regions but it is most dramatic for Latin American
337G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
Fig. 5. The impact of equity flow shocks on dividend yields and returns in three samples.
338 G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
Fig. 6. The impact of returns and dividend yield shocks on equity flows.
countries. We find little or no impact on capital flows for those countries that havesmall external financing. The impulse response analysis supports the hypothesis thatcapital flows are, in part, momentum driven. That this is an entirely short-run effectis confirmed by the cumulative responses reported in Table 8, Panel C. The positivereturn shocks lead to higher holdings in the short-term but not in the long term.
Fig. 6 Panel (b) reports the effects of dividend yields on capital flows, where thedividend yield shock is net of a contemporaneous capital flow effect. A positiveshock in the dividend yield is associated with higher flows after a few periods for thevolatility-weighted, equally-weighted and the value-weighted set of countries after aninitial negative response. For the Latin American and the Asian set, the impulseresponses basically become zero. The former results may be consistent with theshort-run momentum results and a longer term “expected return” -chasing effect. Inthe very short term, a higher dividend yield may simply reflect a negative unexpectedreturn, which leads to reduced short-term capital flows. However, higher dividendyields may be associated with higher expected returns in the future. Positive effectson capital flows are only visible after two periods and then only for a subset of ourgroupings. The countries not relying on external finance display a consistently posi-tive effect. Table 8, Panel B cumulates these effects to measure the total change inholdings. The cumulative effects, for example, show that the positive long-run effectson capital flows in South-East Asia suffice to overturn the initial negative effect. ForLatin American and stock reliant countries, the cumulative effect is slightly negative.
339G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
7. Conclusions
The goal of this paper is to develop a better understanding of the relation betweencapital flows and asset prices. We apply the latest structural break econometrics toidentify liberalizations in 20 emerging markets and then examine a VAR in pre- andpost-break periods on four variables: the world interest rate, net equity capital flows,ex post returns and a proxy for expected returns, the dividend yield.
Our results can be grouped into two main categories. Our first set of findingsconcern the break dynamics. We find that after a liberalization equity capital flowsincrease by 1.4% of market capitalization on an annual basis. However, three yearspost-liberalization, the equity flows are reduced. This is consistent with a modelwhereby investors rebalance their portfolios towards emerging markets. Also, com-paring the pre with post-break dynamics, we document that in almost all the countrieswe examine, when capital leaves it leaves much faster than it came. These intriguingresults may shed light on the recent crises in Latin America and Asia and the roleof capital flight.
Our second set of results revisits a number of important hypotheses in a structuralVAR framework. The fundamental nonstationarity in the data — structural breaksinduced by liberalizations — ignored by previous research, is not without conse-quences. We illuminate significant differences between the pre-break analysis andthe post-break analysis.
Our analysis yields three main findings. First, the “push effect” from world interestrates to capital flows appears consistently when we cumulate impulse responseswhereas contemporaneously interest rates and capital flows show no consistent corre-lation pattern. A 0.3% decrease in interest rates eventually increases foreign holdingsby about 0.04% of market capitalization, a small effect. Interest rate decreases dogenerate strong but very short-lived increases in returns.
Second, unexpected shocks to equity flows have a strongly positive contempor-aneous effect on returns, in line with the findings of Clark and Berko (1997) andFroot et al. (2001). The effect immediately dies out but there is only incompletereversal suggesting some of the effect is permanent. This is consistent with ourfinding that positive shocks in net equity capital flows lead to lower dividendyields — our proxy for expected returns. Following Bekaert and Harvey’s (2000a)argument that dividend yield changes reveal information about the cost of equitycapital, the equity capital flow shocks lead to a lower cost of capital in many coun-tries. We find that this relation is dramatically strengthened if we estimate our VARson the post-break sample. Although part of the initial effect may be due to “pricepressure” , our results suggest part of the response is near permanent and beneficial.
Third, we revisit the Bohn and Tesar (1996) argument that capital flows can bedriven by expected ‘ return chasing’ and by momentum investing. We find evidencethat positive returns shocks are followed by increased short-term equity capital flows,indicating a momentum effect. Using the dividend yield as a proxy for long-runexpected returns, we find only weak evidence for the expected return chasing hypoth-esis.
There are, of course, many caveats to our analysis. Ideally, we need an economic
340 G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
theory that captures the evolution from segmented to integrated financial markets.In the absence of such a theory, we rely on vector autoregressions to characterizethe behavior of important financial aggregates. In addition, the break methodologyimplicitly assumes that every break is permanent and hence theoretically ignores thatthe next break may be rationally anticipated by market participants. If this is thecase, a regime switching model, such as the one presented in Bekaert and Harvey(1995) may be a superior modeling approach. Although we have not studied thisissue in detail, we believe that structural break tests can probably be used to detectpersistent changes in regime and so are less incompatible with a regime-switchingmodel than theory may lead one to believe.
Acknowledgements
We appreciate the comments of Josh Coval, Luc Laeven, Michael Pagano, MarkSeasholes, Linda Tesar, Frank Warnock, Jeff Wurgler, as well as the participants atthe 1998 Conference on Globalization, Capital Markets Crises and Economic Reformat Duke University, the 2000 Western Finance Association meetings in Sun Valley,the University of Virginia and Erasmus University. A preliminary version of thispaper circulated under the title, “Structural breaks in emerging market capital flows” .We owe significant thanks to our RA, Chris Lundblad for his help. Lumsdaineacknowledges the support of the National Fellows Program at the Hoover Institution,Stanford University. Bekaert gratefully acknowledges financial support from an NSFgrant. The opinions expressed are the authors’ and do not represent those of DeutscheBank or its affiliates.
Technical appendix
BLS (Testing for a single break with multiple time-series that break at the sametime)
The techniques in Bai et al. (1998) (BLS) enable us to investigate structural breaksin the relationship between capital flows, returns, and dividends and to constructconfidence intervals around an estimated break date. For this part of the analysis,we assume that the data are generated by a stationary vector autoregression and thereis at most one structural break. One of the key results in BLS is that the precisionwith which a potential break date is estimated (as given by the width of the associatedconfidence interval) is a function not of the number of observations but of the numberof series in a multivariate framework that experience the same break date. In addition,they show that including series that have no break in the VAR, such as the worldinterest rate in our application, while reducing the power of the tests to detect acommon break, will not increase the width of the confidence intervals. In an earlierpaper (Bekaert et al., 2002, hereafter BHL), we provide empirical examples of howthese techniques can be used to draw inference.
341G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
App
endi
xT
able
1U
niva
riat
ebr
eak
test
s
Cou
ntry
Stat
istic
5th
%ile
Med
ian
95th
%ile
#of
lags
Stat
istic
5th
%ile
Med
ian
95th
%ile
#of
lags
Log
retu
rns
Log
divi
dend
yiel
dA
rgen
tina
8.00
Jun
84Ju
l89
Aug
941
8.84
Oct
90M
ay92
Dec
931
Bra
zil
8.17
Sep
82A
pr87
Nov
911
9.25
May
88N
ov90
May
933
Chi
le24
.75∗∗
∗M
ay81
Feb
83N
ov84
220
.48∗∗
∗Ja
n81
Sep
82M
ay84
3C
olom
bia
14.1
7∗∗A
pr90
Feb
92D
ec93
114
.98∗∗
Jun
92Fe
b94
Oct
952
Gre
ece
8.38
∗Fe
b81
Nov
85A
ug90
015
.29∗∗
Dec
83Ju
n86
Dec
881
Indi
a6.
23O
ct86
Apr
92O
ct97
012
.67
Jul
89Ja
n92
Jul
943
Indo
nesi
a21
.75∗∗
∗D
ec96
Apr
97A
ug97
220
.68∗∗
∗A
pr92
May
92Ju
n92
2Jo
rdan
5.94
Feb
79A
pr82
Jun
850
30.1
8∗∗∗
Nov
91A
pr92
Sep
921
Kor
ea10
.62∗∗
Sep
92N
ov94
Jan
970
92.4
9∗∗∗
Dec
90Fe
b91
Apr
911
Mal
aysi
a12
.53∗∗
Jun
95Ju
l96
Aug
970
10.3
2∗M
ay90
Feb
91N
ov91
1M
exic
o22
.69∗∗
∗Ja
n86
Oct
87Ju
l89
311
.92∗∗
Oct
84M
ay86
Dec
871
Nig
eria
2.56
Jun
87A
ug97
35.
55Fe
b94
Jun
95O
ct96
1Pa
kist
an13
.01∗
Jan
92D
ec93
Nov
952
29.7
7∗∗∗
Jan
96M
ar96
May
962
Phili
ppin
es13
.10∗∗
Nov
86A
ug87
May
881
10.4
9A
ug90
Oct
91D
ec92
3Po
rtug
al17
.19∗
Feb
87M
ay88
Aug
894
19.4
9∗∗∗
Jan
89A
pr89
Jul
892
Tai
wan
7.94
∗Fe
b87
Feb
90Fe
b93
018
.26
Jul
87A
pr88
Jan
894
Tha
iland
21.3
8O
ct93
Nov
94D
ec95
415
.33∗∗
Mar
89Ja
n90
Nov
901
Tur
key
2.70
Aug
90Ja
n97
020
.23
Aug
89O
ct89
Dec
891
Ven
ezue
la8.
96A
pr89
Feb
92D
ec94
112
.04
Jul
90M
ar92
Nov
932
Zim
babw
e7.
17M
ay91
Mar
953
6.59
Feb
92Ju
n93
Oct
941
(con
tinu
edon
next
page
)
342 G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350A
ppen
dix
Tab
le1
(con
tinu
ed)
Cou
ntry
Stat
istic
5th
%ile
Med
ian
95th
%ile
#of
lags
Stat
istic
5th
%ile
Med
ian
95th
%ile
#of
lags
Net
equi
tyflo
ws
toeq
uity
capi
taliz
atio
nN
etbo
ndflo
ws
toG
DP
Arg
entin
a37
.50∗∗
∗Ja
n95
Jul
95Ja
n96
244
.00∗∗
∗Ju
l92
Apr
93Ja
n94
0B
razi
l30
.74∗∗
∗D
ec88
Jan
90Fe
b91
19.
44M
ay89
Nov
92M
ay96
0C
hile
11.3
1∗Fe
b85
Jan
88D
ec90
12.
08M
ay84
0C
olom
bia
24.1
2∗∗∗
Nov
92Ju
l93
Mar
940
55.0
8∗∗∗
Sep
93Fe
b94
Jul
943
Gre
ece
18.8
9∗∗∗
Feb
90M
ar92
Apr
941
15.2
9∗∗A
pr93
Nov
94Ju
n96
0In
dia
82.2
0∗∗∗
Feb
93M
ay93
Aug
930
23.4
8∗∗∗
Aug
94M
ay95
Feb
962
Indo
nesi
a5.
40M
ay93
Dec
950
29.6
2∗∗∗
Jul
94Ju
n95
May
962
Jord
anK
orea
34.8
9∗∗∗
Jun
95Ja
n96
Aug
962
24.4
2∗∗∗
May
89D
ec90
Jul
920
Mal
aysi
a18
.68∗∗
Feb
93Fe
b94
Feb
953
15.0
3∗∗Ju
l91
Jul
93Ju
l95
0M
exic
o7.
77N
ov87
Jul
90M
ar93
113
.59∗∗
Mar
87M
ar90
Mar
930
Nig
eria
Paki
stan
6.10
May
95D
ec96
1n.
a.Ph
ilipp
ines
26.6
2∗∗∗
Sep
87Ju
l90
Mar
930
54.7
2∗∗∗
Sep
95N
ov95
Jan
964
Port
ugal
4.67
Sep
91Ju
n95
031
.13∗∗
∗Se
p94
May
95Ja
n96
4T
aiw
an5.
31Fe
b92
Jun
951
21.9
3∗∗Ju
n91
Nov
91A
pr92
4T
haila
nd34
.91∗∗
∗Ja
n96
Jul
96Ja
n97
429
.07∗∗
∗A
ug94
May
95Fe
b96
0T
urke
y5.
05Se
p94
Nov
960
39.7
0∗∗∗
May
92Ju
l92
Sep
924
Ven
ezue
la3.
34D
ec92
Aug
960
1.62
Dec
900
Zim
babw
e
Uni
vari
ate
brea
kte
sts
base
don
the
met
hods
ofB
aiet
al.
(199
8).
Tes
tis
com
pute
dfr
oman
auto
regr
essi
on,
whe
reth
enu
mbe
rof
lags
isch
osen
byth
eB
IC,
and
isth
em
axim
umov
eral
lpo
ssib
lebr
eak
date
sk∗ �
1�k�
T�
k∗of
anF-
stat
istic
test
ing
the
hypo
thes
isth
atal
lco
effic
ient
sin
the
auto
regr
essi
onbr
eak
atda
tek.
k∗is
the
trim
min
gva
lue,
take
nto
be0.
15T
,w
here
Tis
the
sam
ple
size
.C
ritic
alva
lues
for
the
stat
istic
sar
egi
ven
inB
HL
(199
9).
The
estim
ated
brea
kda
teis
give
nby
the
colu
mn
labe
led
“Med
ian”
,w
ithco
rres
pond
ing
90%
confi
denc
ein
terv
algi
ven
byth
eco
lum
nsla
bele
d“5
th%
ile”
and
“95t
h%
ile”.
Bla
nks
inth
ese
latte
rtw
oco
lum
nsin
dica
teth
atth
ees
timat
edco
nfide
nce
inte
rval
exce
eded
the
sam
ple
size
.Si
gnifi
canc
eof
the
brea
kte
sts
atth
e10
,5
and
1%le
vels
are
deno
ted
by∗ ,
∗∗,
and
∗∗∗ ,
resp
ectiv
ely.
343G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350A
ppen
dix
Tab
le2
Mul
tivar
iate
brea
kte
sts
Tri
vari
ate
test
s:Q
uadr
avar
iate
test
s:Q
uint
rava
riat
ete
sts:
Equ
ityflo
ws,
divi
dend
yiel
ds,
retu
rns
Wor
ldin
tere
stra
tes,
equi
tyflo
ws,
divi
dend
Wor
ldin
tere
stra
tes,
bond
flow
s,eq
uity
flow
s,yi
elds
,re
turn
sdi
v.yi
elds
,re
turn
s
Cou
ntry
Stat
istic
5th
%ile
Med
ian
95th
#of
Stat
istic
5th
%ile
Med
ian
95th
#of
Stat
istic
5th
%ile
Med
ian
95th
#of
%ile
lags
%ile
lags
%ile
lags
Arg
entin
a38
.25∗∗
∗M
ar95
Apr
95M
ay95
140
.42∗∗
Aug
94Se
p94
Oct
941
126.
84∗∗
May
94Ju
n94
Jul
943
Bra
zil
175.
7∗∗∗
Feb
90M
ar90
Apr
903
198.
68∗∗
∗Fe
b90
Mar
90A
pr90
325
7.1∗∗
∗Fe
b90
Mar
90A
pr90
3C
hile
84.3
5∗∗∗
Mar
85A
pr85
May
853
91.1
5∗∗∗
Feb
85M
ar85
Apr
853
149.
24∗∗
∗D
ec85
Jan
86Fe
b86
3C
olom
bia
79.4
3∗∗∗
Apr
94M
ay94
Jun
942
90.7
7∗∗∗
Apr
94M
ay94
Jun
942
128.
07∗∗
∗A
pr94
May
94Ju
n94
2G
reec
e50
.35∗∗
∗Ju
n88
Jul
88A
ug88
270
.8∗∗
Jun
88Ju
l88
Aug
883
99.8
6∗∗Ju
n88
Jul
88A
ug88
3In
dia
78.6
∗∗∗
Feb
93M
ar93
Apr
933
73.4
4∗∗M
ar92
Apr
92M
ay92
311
7.68
∗∗M
ar93
Apr
93M
ay93
2In
done
sia
140.
72∗∗
∗A
pr97
May
97Ju
n97
219
1.93
∗∗∗
Apr
97M
ay97
Jun
973
203.
82∗∗
∗A
pr97
May
97Ju
n97
3Jo
rdan
Kor
ea96
.6∗∗
∗D
ec95
Jan
96Fe
b96
312
2.89
∗∗∗
Aug
95Se
p95
Oct
953
591.
9∗∗∗
Mar
96A
pr96
May
963
Mal
aysi
a70
.25∗∗
∗M
ay96
Jun
96Ju
l96
381
.97∗∗
∗Ju
n96
Jul
96A
ug96
313
6.14
∗∗∗
Jul
96A
ug96
Sep
963
Mex
ico
67.5
3∗∗∗
Aug
87Se
p87
Oct
873
106.
13∗∗
∗A
ug87
Sep
87O
ct87
314
0.33
∗∗∗
Sep
87O
ct87
Nov
873
Nig
eria
Paki
stan
31.5
2∗∗N
ov96
Dec
96Ja
n97
153
.77∗∗
∗N
ov96
Dec
96Ja
n97
113
5.36
∗∗∗
Jul
97A
ug97
Sep
971
Phili
ppin
es52
.46
Jul
88A
ug88
Sep
883
99.4
∗∗∗
Feb
88M
ar88
Apr
883
163.
4∗∗∗
Nov
88D
ec88
Jan
893
Port
ugal
89.8
4∗∗∗
Apr
89M
ay89
Jun
893
106.
07∗∗
∗Ja
n89
Feb
89M
ar89
317
2.18
∗∗∗
May
96Ju
n96
Jul
963
Tai
wan
59.1
3∗∗M
ar88
Apr
88M
ay88
310
9.69
∗∗∗
Mar
88A
pr88
May
883
178.
77∗∗
∗M
ar88
Apr
88M
ay88
3T
haila
nd11
7.18
∗∗∗
May
96Ju
n96
Jul
963
132.
3∗∗∗
May
96Ju
n96
Jul
963
180.
52∗∗
∗M
ay96
Jun
96Ju
l96
3T
urke
y14
4.53
∗∗∗
Dec
96Ja
n97
Feb
973
182.
85∗∗
∗D
ec96
Jan
97Fe
b97
322
9.4∗∗
∗D
ec96
Jan
97Fe
b97
3V
enez
uela
128.
14∗∗
∗Ju
l96
Aug
96Se
p96
315
2.34
∗∗∗
Jul
96A
ug96
Sep
963
183.
38∗∗
∗A
ug96
Sep
96O
ct96
3Z
imba
bwe
Mul
tivar
iate
brea
kte
sts
base
don
the
met
hods
ofB
aiet
al.(
1998
),te
stin
gth
enu
llhy
poth
esis
ofno
stru
ctur
alch
ange
agai
nst
the
alte
rnat
ive
ofa
sing
lebr
eak.
The
test
isco
mpu
ted
from
aV
AR
,w
here
the
num
ber
ofla
gsis
chos
enby
the
BIC
,an
dis
anal
ogou
sto
the
univ
aria
tete
sts
inT
able
2.In
the
quad
rava
riat
ean
dqu
intr
avar
iate
test
s,on
lyth
ere
turn
s,di
vide
ndyi
elds
,an
dflo
ws
are
allo
wed
tobr
eak,
that
is,
the
test
does
not
let
the
vari
able
sin
the
wor
ldin
tere
stra
teeq
uatio
nbr
eak.
See
also
note
sto
Tab
le2.
344 G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
App
endi
xT
able
3T
ests
that
allo
wfo
rm
ultip
lebr
eaks
Log
retu
rns
Log
divi
dend
yiel
dN
eteq
uity
flow
sto
equi
tyN
etbo
ndflo
ws
toG
DP
capi
taliz
atio
n
Cou
ntry
5th
Med
ian
95th
Sign
if5t
hM
edia
n95
thSi
gnif
5th
Med
ian
95th
Sign
if5t
hM
edia
n95
thSi
gnif
%ile
%ile
%ile
%ile
%ile
%ile
%ile
%ile
Arg
entin
a1
NB
not
Mar
92M
ay92
Nov
931.
0%M
ar92
Dec
92Fe
b93
2.5%
Aug
80M
ay81
Sep
8110
.0%
Arg
entin
a2
Oct
94A
ug95
Dec
952.
5%Fe
b93
Mar
93A
pr93
10.0
%B
razi
l1
Feb
84M
ay87
Jun
9010
.0%
Jan
91no
tN
Bno
tN
Bno
tB
razi
l2
Feb
95no
tN
Bno
tN
Bno
tC
hile
1O
ct81
Mar
83Ja
n85
1.0%
Nov
90no
tN
Bno
tN
Bno
tC
hile
2N
ov94
not
NB
not
NB
not
Col
umbi
a1
Feb
90Fe
b92
Jun
932.
5%Ja
n91
Oct
91D
ec91
5.0%
NB
not
NB
not
Col
ombi
a2
Oct
93Fe
b94
Jun
952.
5%N
Bno
tN
Bno
tG
reec
eN
Bno
tD
ec86
Jul
88Se
p90
1.0%
Feb
90M
ar92
May
9210
.0%
NB
not
Indi
aN
Bno
tN
Bno
tA
pr93
May
93Ju
n93
1.0%
NB
not
Indo
nesi
aN
Bno
tFe
b92
Apr
92Ju
n92
1.0%
NB
not
Mar
94Fe
b96
Oct
9610
.0%
Jord
an1
not
Oct
91A
pr92
Jul
921.
0%N
oda
taN
oda
taJo
rdan
2no
tA
ug94
Feb
95N
ov95
1.0%
No
data
No
data
Kor
ea1
NB
not
Sep
89Fe
b91
Apr
911.
0%Se
p95
Jul
96O
ct96
1.0%
Oct
90N
ov90
Dec
901.
0%K
orea
2A
ug86
Dec
86A
ug87
10.0
%N
B10
.0%
Kor
ea3
Aug
91O
ct92
Dec
9210
.0%
NB
10.0
%M
alay
sia
NB
not
Jul
90Ja
n91
Feb
935.
0%N
Bno
tN
Bno
tM
exic
o1
Apr
84M
ar86
Oct
875.
0%Ja
n82
Jan
83Ju
l83
5.0%
NB
not
NB
not
Mex
ico
2Se
p94
May
95Ju
n96
5.0%
Apr
86Ju
l86
Dec
865.
0%N
Bno
tN
Bno
tM
exic
o3
Aug
90M
ar91
Feb
925.
0%N
Bno
tN
Bno
tN
iger
iaM
ar95
May
95O
ct96
1.0%
NB
not
No
data
No
data
Paki
stan
1N
Bno
tO
ct90
Dec
90A
pr91
5.0%
NB
not
NB
not
Paki
stan
2A
pr95
Mar
96Ju
n96
5.0%
NB
not
NB
not
(con
tinu
edon
next
page
)
345G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
App
endi
xT
able
3(c
onti
nued
)
Log
retu
rns
Log
divi
dend
yiel
dN
eteq
uity
flow
sto
equi
tyN
etbo
ndflo
ws
toG
DP
capi
taliz
atio
n
Cou
ntry
5th
Med
ian
95th
Sign
if5t
hM
edia
n95
thSi
gnif
5th
Med
ian
95th
Sign
if5t
hM
edia
n95
thSi
gnif
%ile
%ile
%ile
%ile
%ile
%ile
%ile
%ile
Phili
ppin
es1
May
87A
ug87
Jun
8810
.0%
NB
not
NB
not
NB
not
Phili
ppin
es2
Jan
88Ju
l89
Jan
9010
.0%
NB
not
NB
not
NB
not
Port
ugal
NB
not
NB
not
NB
not
Sep
90Ja
n92
Aug
921.
0%T
aiw
an1
Apr
86O
ct87
Oct
8810
.0%
NB
not
NB
not
Mar
90A
pr90
May
902.
5%T
aiw
an2
Nov
90O
ct91
May
932.
5%T
haila
nd1
NB
not
Feb
89Ja
n90
Mar
902.
5%Ju
l87
Aug
88M
ar89
1.0%
NB
not
Tha
iland
2Se
p92
Apr
93Se
p94
10.0
%T
urke
yFe
b93
May
94M
ay96
10.0
%Ju
n89
Sep
89Se
p90
10.0
%D
ec91
not
Mar
92Ju
n92
Jul
96no
tV
enez
uela
NB
not
NB
not
NB
not
NB
not
Zim
babw
eN
Bno
tN
Bno
tN
oda
tano
tno
data
Mul
tiple
brea
kte
sts
use
the
repa
rtiti
onm
etho
dsof
Bai
and
Perr
on(1
998a
,b)
whi
chal
low
for
am
axim
umof
5br
eaks
with
15%
trim
min
g.A
llte
sts
are
perf
orm
edas
sum
ing
2la
gsin
the
auto
regr
essi
on.
Est
imat
edbr
eak
date
sar
egi
ven
inth
eco
lum
nla
belle
d“m
edia
n”,
alon
gw
ithco
rres
pond
ing
confi
denc
ein
terv
als
inth
eco
lum
nsla
belle
d“5
th%
ile”
and
“95t
h%
ile”.
The
sign
ifica
nce
leve
lre
port
sth
elo
wes
tsi
gnifi
canc
ele
vel
for
whi
chth
ere
part
ition
proc
edur
efo
und
each
spec
ific
date
tobe
sign
ifica
nt.
NB
=N
oB
reak
.
346 G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
For this part of the analysis, it is assumed that there is at most one break. It isalso assumed that the errors in the VAR have 4+� moments for some � 0. Thegeneral form of the regression is (equation 2.2 from BLS):
Yt � (G�t�In)q � dt(k)(G�t�In)S�Sd � et, (A1)
where Yt is n×1 (defined earlier), G�t is a row vector containing a constant and plags of Yt, In is an n×n identity matrix, dt(k) = 0 for t k and dt(k) = 1 for t�k. qand d are parameter vectors with dimension r = n(np+1). S is a selection matrixcontaining zeros and ones and having column dimension r and row dimension equalto the number of coefficients which are allowed to change (�r; i.e., S is full rowrank).
The procedure for determining when a potential break occurred involves estimat-ing (A1) for all possible break dates k∗+1�k�T�k∗, where k∗ represents a trimmingvalue, often taken to be 15% of the sample size, T. At each possible break date, anF-statistic is computed, testing the significance of Sd, and is denoted F(k). Then thestatistic testing for structural change is equal to
maxk∗
�1�k�T�k∗F(k).
BLS show that this statistic converges via the functional central limit theorem tomax F∗, a (known) function of Brownian motion. More details and critical valuesare provided in BHL and BLS.
A confidence interval with asymptotic coverage of at least 95% is given by (eq.2.19 in BLS):
CI � (k̂�[�k]�1,k̂ � [�k] � 1),
where k̂ is the estimated break date, [·] denotes the “greatest least integer” , and
�k � c[(Sd̂)�S(Q̂1��̂�1k̂ )S�(Sd̂)]�1,
where c=7.63, �̂k̂ is the estimator of the variance-covariance matrix of the OLSresiduals under the alternative, given k̂, and Q̂1 � 1
T�T
t�1GtG�t.
BP (Testing for multiple breaks in a single series)
The assumption of a single structural change seems less palatable in light of theAsian crisis. In addition, as we indicated above, some theoretical models of thedynamics of capital flows [see, e.g. Bachetta and van Wincoop (2000)] may alsolead to multiple breaks in the net flows as a percentage of market capitalization.Therefore, we investigate whether returns, dividend yields, and capital flows experi-enced multiple structural breaks, using techniques recently developed by Bai andPerron (1998a,b). Because multiple break analogs to the VAR framework of BLShave not yet been developed, we consider each series separately.
Rather than assuming the number of breaks is known a priori, Bai and Perronprovide econometric tests to determine the number of breaks. The necessary assump-
347G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
tions in Bai and Perron are not particularly restrictive and admit a wide variety oflinear specifications to identify the break dates, and construct confidence intervalsaround the estimated break dates. We use one of their set-ups that has serially uncor-related errors but allows for lagged dependent variables. As in Lumsdaine and Papell(1997), it is also assumed that the breaks are asymptotically distinct (intuitively, ifa large downward spike is immediately followed by a large upward one, this wouldbe considered one break, rather than two). Bai and Perron also show that the esti-mation of a single break when the underlying series has two breaks in its data-generating process results in consistent estimation of the break fraction for one ofthe breaks. In fact, the procedure consistently estimates the break of the larger magni-tude. Hence, our work assuming single breaks may still detect useful dates.
To implement the procedure for investigating multiple breaks, we follow the rec-ommendations in Bai and Perron (1998b).6 First, we use their double maximum teststo test the null hypothesis that there is no break versus the alternative that there isat least one break. The statistic is given by
max1�m�M
F∗T,
where F∗T is a modified F-statistic given by equation (A2) in the technical appendix
below and M is the upper bound on the number of breaks. Critical values are givenin Bai and Perron (1998a,b), for various values of M and k∗, the trimming value.7
Second, if there is evidence of at least one break, we implement their repartitionprocedure, which is based on comparing the sum of squared residuals from estimationof the ‘best’ (in a minimum sum of squared residuals sense) l-break model to thebest l+l-break model, beginning with l = 1. The number of breaks m is the first valueof l for which the test fails to reject the null hypothesis of l breaks in favor of thealternative of l+1 breaks. Finally, confidence intervals are then computed aroundthe break dates estimated using the m-break model (formulas are given in Bai andPerron (1998b)).
We consider the full structural change model of Bai and Perron (that is, whereall coefficients are allowed to change). Using notation analogous to (A1), the modelcan be written as
Yt � �m � 1
i � 1
dt(ki)G�tdi � et,
where m is the number of breaks and, as in model (A1), Gt consists of a constant andlags of Yt, and dt(ki) is an indicator variable equal to 0 when t ki and 1 otherwise.
A maximal F-test is used to test the hypothesis of no structural change versus thealternative of m breaks. The null hypothesis is thus
6 We are grateful to Pierre Perron for supplying us with the Bai-Perron programs.7 The trimming value refers to the number of observations at either end of the sample where it is
assumed that no break has occurred; in earlier literature this was most often chosen to be 0.15T or 0.01Twhere T is the sample size.
348 G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
H0: d1 � % � dm+1
and can be expressed as Rd = 0 where
R � �1 �1 0 % 0 0 0
0 1 �1 % 0 0 0
� � � � � � �
0 0 0 % 1 �1 0
0 0 0 % 0 1 �1�.
Then the F-statistic is (this corresponds to equation (12) in Bai and Perron (1998a))
sup(l
1,%,l
m)��
F∗T(l1,%,lm;r) �
1T�T�(m � 1)r
mr �d̂�R�(RV̂(d̂)R�)�1Rd̂, (A2)
where d̂ is an estimate of d, and d � (d�1,%,d�m � 1)�, V̂ is the estimate of its covari-ance matrix, li is the fraction of the sample at which break i occurs, � is the spaceof all possible m-partitions and r is the number of columns in G.
One drawback of this approach is that the choice of how many lags to includemust now be determined exogenously, rather than by using an information criterion.However, the BP procedures do allow for robust (heteroskedasticity and autocorre-lation consistent) covariance estimation and for different variances of the errors ineach of the break periods, something the BLS test theory permits but is not allowedin current implementation. There is a cost associated with this flexibility, however;in general the BP tests appear to have less power to detect breaks than the BLStests. Thus it is important to consider both sets of results together when identifyingpossible break dates.8
Data appendix
Dividend yields
There are a few instances of zeros in the 12-month moving sum of dividends thatthe IFC reports. We investigated these zeros by looking at each individual firm’sdividends. There seems to be an issue of when the IFC recorded dividends. Forexample, in Korea, there are dividends paid in January 1988 and no dividends appearin the individual company files until March 1989. After that, there is no dividend
8 When we restrict the BP test to at most use one break, the number of lags of the BLS tests, and donot allow for robust covariance estimation or different variances, we replicate the break dates, confidenceintervals, and significance levels of the BLS tests, except for Indonesia and Portugal, two countries withvery short samples, where the break dates differ slightly.
349G. Bekaert et al. / Journal of International Money and Finance 21 (2002) 295–350
paid by an individual company until April 1990. It is not surprising that we findzero entries in January and February 1989 and in March 1990. In order to avoidzero entries which appear to be a result of the timing of the recording of dividends,rather than a canceling of dividends, we carried forward some past dividends toreplace these holes in the data.
For Korea, we calculate the dividend on the index in December 1988 and use thatvalue as the numerator for the dividend yield calculation for January and February1989. The values (in percent) for these two months are 0.5511 and 0.5273, respect-ively. In February 1990, we calculate the dividend on the index and use that valuein the numerator for March 1990. The value is 1.0919.
For Indonesia, we calculate the value of the dividend in June 1991. That value isused in the numerator for July 1991 through February 1992. The dividend yieldsare: 0.1200, 0.1380, 0.1745, 0.1936, 0.1799, 0.1718, 0.1496 and 0.1311.
For Taiwan, we calculate the value of the dividend in February 1990 and use thatin the numerator for March 1990 through April 1991. The dividend yields for thisperiod are: 0.2250, 0.2600, 0.3340, 0.4545, 0.4186, 0.6355, 0.8307, 0.6825, 0.5314,0.5037, 0.5831, 0.4703, 0.4677 and 0.4048.
World interest rates
The nominal interest rate for the G-7 countries is calculated by aggregating indi-vidual countries’ short-term interest rates weighted by using countries’ previous quar-ter’s share in G-7 GDP. The following interest rates are employed: Canada 90-dayTreasury bill (IFS 60C), France 90-day bill (IFS 60C), Germany 90-day bill (IFS60C), Italy 180-day bill (IFS 60B), Japan commercial paper from 1975–1976 (IFS60B) and the Gensaki rate from 1977–1997 (IFS GBD3M), United Kingdom 90-daybill (IFS 60C), and the United States 90-day bill (IFS 60C).
References
Bachetta, P., van Wincoop, E., 2000. Capital flows to emerging markets Liberalization, overshooting andvolatility. In: Edwards, S. (Ed.), Capital Inflows and the Emerging Economies. University of ChicagoPress and NBER, Cambridge, MA, pp. 61–98.
Bai, J., Perron, P., 1998a. Computation and analysis of multiple structural change models, Unpublishedworking paper, Boston University, Boston, MA.
Bai, J., Perron, P., 1998b. Estimating and testing linear models with multiple structural changes. Econo-metrica 66, 47–78.
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