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Stock market integration and risk premium Empirical evidence for

emerging economies of South Asia

Ilyes Abid ab Olfa Kaabia b Khaled Guesmi cb

a Department of Finance Meacutetis Lab EM-Normandie Franceb EconomiX-CNRS (UMR 7235) University of Paris Ouest Nanterre La Defense Francec Department of Finance IPAG Lab IPAG Business School France

a b s t r a c ta r t i c l e i n f o

Article historyAccepted 12 November 2013

JEL classi 1047297cation

G12

F31

C32

Keywords

Time-varying integration

Asian markets

Risk premium

ICAPM

GDC-GARCH

This article investigates the dynamics of regional 1047297nancial integration and its determinants in an internationalsetting We test a conditional version of the International Capital Asset Pricing Model (ICAPM) accounting for

the deviations from Purchasing Power Parity (PPP) as well as temporal variations in both regional and local

sources of risk Using data from 1047297ve major South Asian markets (Malaysia Thailand Singapore Indonesia and

Sri Lanka) our results support the validity of an ICAPM and indicate that the risk is regionally priced Further-

more we show that changes in the degree of regional stock market integration are explained principally by

the US term premium and the level of market openness whatever the measure of currency risk Finally

and as expected the degree of stock market integration varies considerably over time and from one market to

another As intense market integration induces both bene1047297ts and risks our 1047297ndings should have signi1047297cant

implications for economicpolicies and market regulations in emerging frontier-emergingand transition countries

particularly for countries from the same region

copy 2013 Elsevier BV All rights reserved

1 Introduction

While the empirical literature has shown the potential bene1047297ts of

international diversi1047297cation into stock markets global investors often

face both direct and indirect barriers (Bekaert and Harvey 1995)

Geographical distance between domestic and foreign markets is often

an important barrier limiting most cross-border investment opportuni-

ties The heterogeneous characteristics (eg level of 1047297nancial market

development and trade openness) among the different economic re-

gions also matter greatly Financial integration is1047297rst of all thegradual

elimination of direct and indirect barriers that impede free movement

of goods services and capital These stylized facts have given rise to

the establishment of several large geographical centers that offer very

different risk-return pro1047297les

Grouping by major geographical clusters should lead to 1047297nancialintegration as well as to the validity of the law of one price under the

impetus of trade and investment between countries in the same region

We would expect adjustments in the foreign exchange markets for this

law to be applied However as far as international portfolio diversi1047297ca-

tion in emerging countries is concerned the hypothesis of unique price

of risk across markets is usually violated insofar as exchange rate

regimes are likely to be subject to more or less stringent regulations im-

posed by local authorities Several studies have examined the dynamics

of regional integration in emerging markets Errunza and Losq (1985)

introduce a pricing structure called ldquomild segmentationrdquo where access

to the various asset classes is not the same for two types of investors

investors not subject to legal restrictions on holding assets have access

to all securities while investors subject to reference restrictions are

only able to conduct transactions on a subset of assets Their empirical

results show that emerging markets are neither strictly segmented

nor perfectly integrated In a different way Claessens and Rhee (1994)

apply Stehles (1977) procedure to study the risk-return linkages in 16

emerging markets Their empirical 1047297nding contradicts the hypothesis

of integration in most of the markets whereas the segmentation

hypothesis cannot be rejected in any of the markets

Phylaktis and Ravazzolo (2002) derive the covariances of excess

returns on the stock markets for 1980 and 1998 using Asset PricingModels They establish expressions for the excess returns of the local

and foreign stock markets as a function of the real interest rate divi-

dends paid and other variablessuch as lagged returns and theexchange

rates so as to 1047297nd the determinants of returns in each country and also

to derive the variances and covariances of the excess returns the idea is

to 1047297nd variables that help to explain movements in the stock markets of

Hong Kong Indonesia Korea Malaysia Philippines Singapore Taiwan

and Thailand They 1047297nd that variations in dividends paid are a signi1047297-

cant source of variance in stock returns An interesting result that arises

is that co-movements in output growth are directly related to stock

prices The paper unearths a close connection between Thailand and

Economic Modelling 37 (2014) 408ndash416

Corresponding author

E-mail addresses ilyesabidem-normandiefr (I Abid) kaabiaolfayahoofr

(O Kaabia) Khaledguesmiipagfr (K Guesmi)

0264-9993$ ndash see front matter copy 2013 Elsevier BV All rights reserved

httpdxdoiorg101016jeconmod201311015

Contents lists available at ScienceDirect

Economic Modelling

j o u r n a l h o m e p a g e w w w e l s e v i e r c o m l o c a t e e c m o d



the US and a high degree of integration between Korea Taiwan and

Japan

Adler and Qi (2003) extendthe model of Bekaert and Harvey (1995)

which basically combines the domestic and international versions of an

Asset Pricing Model (ICAPM) to test the power of domestic factors

relative to that of common factors to explain expected returns and

empirically infers segmentation when the weight of the domestic

factors is high So Adler and Qi (2003) investigate the evolution of the

process of integration between the Mexican and North American equitymarkets between 1991 and 2002 They show that the degree of market

integration is higher at the end of the period than at the beginning and

that Mexicos currency risk is priced Furthermore there is signi1047297cant

asymmetric volatility which is strongly related to the asymmetric

volatility of the Mexican equity market return process

Carrieri et al (2007) extend the model of Errunza and Losq (1985)

They study the integration levels of eight emerging markets over the

period 1977ndash2000 They show that the local pricing factor continues

to be relevant in the valuation of emerging-market assets but none of

the markets considered is completely segmented from the world

market Furthermore Chambet and Gibson (2008) estimate a multifac-

tor asset pricing model of partial integration an extension of that of

Errunza and Losq (1985) for 25 emerging markets and show that

some markets still remain segmented

Guesmi and Nguyen (2011) inspired by the model of Bekaert and

Harvey (1995) use a conditional version of an ICAPM to evaluatethe dy-

namics of the global integration process of four emerging market re-

gions (Latin America Asia Southeastern Europe and the Middle East)

into the world market They show that the integration degree in the

fouremerging market regionsvarieswidelythrough timeover the period

1996ndash2008 and that this can be explained by the regional factors Al-

though the general trend is toward increasing 1047297nancial integration

emerging market areas seem to be still signi1047297cantly segmented from

the global market

Guesmi (2012) investigates the evolution of the South-East Asian

stock market integration with the regional one and deduces that with

the exception of Singapores market emerging markets are not strongly

integrated in the study area These results were con1047297rmed by those of

Petri (1993) Frankel and Wei (1995) and Frankel and Romer (1999)They show that the geographical proximity effects are not signi1047297cant

in the Southeast Asian region

More recently Berger and Pozzi (2013) suggest a measure of 1047297nan-

cial integration based on the conditional variances of the country-

speci1047297c and common international risk premiums in equity excess

returns The authors show that Germany France the UK the US and

Japan exhibit several shorter periods of disintegration over the period

1970ndash2011 They conclude that stock market integration is measured

as a dynamic process that is 1047298uctuating in the short run while gradually

increasing in the long run

In our work we investigate the issue through a longitudinal study of

the South Asian region using monthly data from 199601 to 200712

Our study differs from previous ones by considering intra-regional

integration instead of global integration and by taking into accountthe currency risk in addition to the sources of global and domestic

risks The international asset-pricing model we use is built so as to

characterize the changes in market integration through time due to

the impacts of the gradual removal of barriers to emerging market

investments We also examine the portions of the returns explained

by regional and domestic risk factors respectively by carrying out a

decomposition of the total risk premium

The present study contributes to the literature by developing a

regime-switching ICAPM with a slip condition Speci1047297cally expected

return canslip from a perfectly segmented regimeto a perfectly integrat-

ed one or vice versa depending on the number of national and regional

factors that may in1047298uence the process of regional 1047297nancial integration

It is true that this model was inspired by that of Bekaert and Harvey

(1995) but it has been extended using a multivariate GDC-GARCH

model to take into account the asymmetric responses of expected

returns to different shocks

One of the advantages of our approach is to authorize the prices of

domestic and world market risks betas and correlations to vary asym-

metrically through time It is clear that this will help us to understand

the dynamics of interdependencies and correlations between South

Asian stock markets in order to facilitatedecision-making In fact inves-

tors are normally risk-averse they are concerned about market down-

turns more than upturns Consequently this risk-aversion behaviorwill be re1047298ected in market prices resulting in greater market responses

to downturns than upturns

Asymmetry can be justi1047297ed differently by the presence of informa-

tion barriers the behavioral standpoint of investor psychology (Verma

and Verma 2010) other sources of market segmentation (Bekaert and

Harvey 1995) heterogeneous transaction costs (Anderson 1997) the

coexistence of different shareholders and noise trading (De Grauwe

and Grimaldi 2006) Also another source of asymmetry effects is indus-

try concentration and imperfectly competitive behavior It implies that

wholesalers or middlemenwith power over price may exercise pricing

strategies that result in a slow andincomplete pass-throughof increases

in the international price and a fast and complete transmission of

decreases in the international price to prices upstream

Although asymmetric adjustment may also be the outcome of

market imperfections it is plausible that price support policies result

in positive and negative changes in the international price affecting

the domestic market in different ways Moreover the effect of positive

shock and negative shock is different

Our 1047297ndings clearly show that the degree of market integration of

the1047297ve emerging markets varies over the period 1996ndash2007Moreover

the US term premium and the level of market openness mainly explain

the degree of integration in emerging markets Even though this degree

reaches high values during periods of turmoil and exhibits an upward

trend toward the end of the estimation period Hence emerging mar-

kets still remain substantially segmented from the regional market

Also the total risk premium decomposition shows that the variance

risk related to the local market index (the local risk factor) explains

more than 70 of thetotalriskpremiumon average forthe1047297ve emerging

marketsTracking the integration level is a critical task It is important to

know if the emerging countries in the Asia region are globally or region-

ally integratedfor most of thesample period In fact if there are regional

integration and if suddenly during an Asian crisis the intraregional

correlations between the countries rise dramatically this may lead to

contagion effects

Our analysis is relevant for both policymakers and investorsthat pay

a particular attention to stock markets and their degree of integration

Also analyzing the links between stock markets is of particular interest

for 1047297nancial players Portfolio managers look at stock market 1047298uctua-

tions to infer the trend of each market and make diversi1047297cation deci-

sions Moreover studying the degree of integration becomes a central

issue for the world economy during turmoil periods In fact comparing

the impact of the 1047297nancial crisis on the degree of integration providesuseful information about possible substitution strategies between

stock classes In particular the integration level plays a key role regard-

ing hedging possibilities and impacts asset allocation and their risk-

return trade-off

The remainder of the article is organized as follows Section 2

presents the empirical methodology Section 3 describes the data

Section 4 presents and discusses the results and Section 5 draws the

appropriate conclusions

2 Empirical approach

Our empirical asset pricing model takes as its point of departure

that of Bekaert and Harvey (1995) and is inspired by the theoretical

models of partial integration of Black (1974) Stulz (1981) Cooper

409I Abid et al Economic Modelling 37 (2014) 408ndash416



and Kaplanis (2000) Hardouvelis et al (2006) De Santis et al

(2003) Carrieri et al (2007) and Tai (2007) All these authors

con1047297rm the partial integration hypothesis and time-varying world

market integration for most individual markets Exchange rate risk

is also found to be priced in the context of both developed and

emerging markets

In our study we adopt a partially integrated conditional ICAPM

with three sources of systematic risk that globally re1047298ect 1047298uctua-

tions in regional stock market national stock market and exchangerate Generally the conditional mean excess return can be written

as

E t minus1 Rc i t

frac14 pit minus1 α reg t minus1Cov Rc it R

c reg t =Ωt minus1

thornXl

kfrac141

α kt minus1Cov Rc it R

c kt =Ωt minus1

thorn 1minus pit minus1

α it minus1Var R

c it =Ωt minus1

eth1THORN

where E t minus 1(Rit c ) is the excess return issued in country i condition-

ally on a set of informationΩt minus 1 that is available to investors up to

time t minus 1 Exponent c indicates that returns are expressed in the

currency of the referencecountry Rreg t c is the return on theregional

market portfolio Rc kl is the return on the exchange rate of the

currency of country k against the currency of the reference country

c Cov is the conditional covariance between the security returnsand the region market returns α reg t minus 1 refers to the conditionally

expected regional price of covariance risk l is the number of mar-

kets included in the sample α it minus 1 is the conditionally expected

local price of variance risk α kt minus 1 expresses the expected price of

the exchange risk for currency k pit minus 1 is the conditional probabil-

ity of transition between segmentation and integration states

which falls within the interval [01] and can be thus interpreted

as a conditional measure of integration of market i into the regional

market If p it minus 1 = 1 only the covariance risk is priced and the

strict segmentation hypothesis is rejected If pit minus 1 = 0 the

unique source of systematic risk is the variance and the pricing

relationship in a strictly segmented market applies

Furthermore Eq (1) can be written as a risk premium decomposi-

tion More speci1047297

cally the total risk premium (TPRM ) can be brokendown into three components

TPRM it frac14 RPRM it thorn EPRM it thorn LPRM it

where the 1047297rst component is called the regional risk premium (RPRM )

and is given by TPRM it = α reg t minus 1Covt minus 1(Rit c Rreg t

c Ωt minus 1) pit minus 1 The

second one is the exchange rate risk premium (EPRM ) expressed as

follows EPRM it frac14 pit minus1suml

kfrac141α kt minus1Cov Rc

it Rc kt =Ωt minus1

and the third

one refers to the local risk premium (LPRM) written as LPRM it =

(1 minus pit minus 1)α it minus 1Vart minus 1(Rit c Ωt minus 1)

The following Eqs (2) (3) and (4) describe the expected return

on the regional market portfolio and the expected returns for Asia

country and currency

E t minus1 Rc reg t

frac14 α reg t minus1Vart minus1 R

c reg t =Ωt minus1

thorn α M t minus1Covt minus1 R

c reg t R

c M t =Ωt minus1

thornα T t minus1Covt minus1 R

c reg t R

c T t =Ωt minus1

thorn α S t minus1Covt minus1 R

c reg t R

c S t =Ωt minus1

thornα I t minus1Covt minus1 R

c reg t R

c I t =Ωt minus1

thorn α N t minus1Covt minus1 R

c reg t R

c N t =Ωt minus1

eth2THORN

E t minus1 Rc it

frac14 pit minus1

α reg t minus1Covt minus1 Rc it R

c reg t =Ωt minus1


c it R

c M t =Ωt minus1


c it R

c T t =Ωt minus1


c it R

c S t =Ωt minus1


c it R

c I t =Ωt minus1


c it R

c N t =Ωt minus1

26664

37775

thorn 1minus pit minus1 α it minus1Vart minus1 R

c it =Ωt minus1

eth3THORN

E t minus1 Rc kt

frac14 α M t minus1Covt minus1 R

c kt R

c M t =Ωt minus1

thorn α T t minus1Covt minus1 R

c kt R

c T t =Ωt minus1

thornα S t minus1Covt minus1 R

c kt R

c S t =Ωt minus1

thorn α I t minus1Covt minus1 R

c kt R

c I t =Ωt minus1

thornα N t minus1Covt minus1 R

c kt R

c N t =Ωt minus1

eth4THORN

pit minus1 frac14 Exp minus ν 0 thorn vprime

1 F it minus1

eth5THORN

with i = M (Malaysia) T (Thailand) S (Sri Lanka) I (Indonesia) andN (Singapore) Rc

M t Rc T t Rc

S t Rc I t and Rc

N t are respectively the real

exchange rate returns of the 1047297ve markets under study α reg t minus 1

α M t minus 1 α T t minus 1 α S t minus 1 α I t minus 1 and α N t minus 1 refer to the expected

prices of the exchange rate risk Exp () denotes an exponential function

|∙| is the absolute valueν 0 and ν 1 are respectively a constant and a vec-

tor of region-speci1047297c parameters F it minus 1 is a vector of region-speci1047297c

predetermined information variables related to convergence toward a

regional market at time t minus 1

The risk prices are modeled as a function of information variables as

follows

α reg t minus1 frac14 Exp δprime

reg F reg t minus1

α it minus1 frac14 Exp γ

prime

i F it minus1 α kt minus1 frac14 δ

prime

k F reg t minus1

eth6THORN

where F reg t minus 1 and F it minus 1 are respectively a set of regional and local

variables

The estimated model consists of a system of eleven equations (1047297ve

equations of excess returns for each country i one equation of excess

returns for the region and1047297ve equations of real exchange rate indices)

More precisely the econometric speci1047297cation of the model to be

estimated ie Eqs (2) (3) and (4) is characterized by the following

system of equations

er reg t frac14 α reg t minus1hregreg t thornα M t minus1hregM t thornα T t minus1hregT t thornα S t minus1hregS t

thornα I t minus1hregI t thornα N t minus1hregN t thorn ε reg t er it frac14 pit minus1

α reg t minus1hireg t thornα M t minus1hiM t thornα T t minus1hiT t thornα S t minus1hiS t

thornα I t minus1hiI t thornα N t minus1hiN t

thorn

1minus pit minus1

α it minus1hiit thorn ε it

r kt frac14 α M t minus1hkM t thornα T t minus1hkT t thornα S t minus1hkS t thornα I t minus1hkI t thornα N t minus1hkN t thorn ε kt

eth7THORN

with er t frac14 r M t r T t r S t r I t r N t

prime r t frac14 r M t r T t r S t r I t r N t

prime So r t frac14er reg t er t prime r t prime

prime r ef er s to the (11 times 1) vec tor of e xc es s r e-

turns which are assumed to be normally distributed Also ε t frac14ε reg t ε M t ε T t ε S t ε I t ε N t ε M t ε T t ε S t ε I t ε N t =Ωt minus1

N 0 H t eth THORN is a

vector of unexpected excess returns given the set of information

Ωt minus 1 and H t is a conditional variancendashcovariance matrix of excessreturns following a multivariate GDC-GARCH process1 given by

H t frac14 Dt Rt Dprime

t thornΦotimesΘt eth8THORN

where

Dt frac14 dijt

dijt frac14

ffiffiffiffiffiffiθiit

p foralli dijt frac14 0foralline j

Θt frac14 θijt

θijt frac14 ω ij thorn aprime

iε t minus1ε prime

t minus1a j thorn g primeiH t minus1 g i foralli jai g iforalli frac14 1 hellip11are 11 1eth THORNvectors of parametersΦ frac14 φ ijφ ii frac14 0foralliφ ij frac14 φ ji

1 This multivariate frameworkis more suitable than thebivariate onefor takingintoac-

count the dynamic interactions between all the variables included in the system

410 I Abid et al Economic Modelling 37 (2014) 408ndash416



The dynamic correlation structure Rt is speci1047297ed by Tse and Tsui

(2002) as follows Rt = (1 minus θ1 minus θ2)R + θ1Ψt minus 1 + θ2Rt minus 1 with

0 le θ1 + θ2 b 1where R = ( ρij) is a symmetric (11 times 11) positive

de1047297nite matrix with ρii = 1 and Ψt minus 1 is the (11 times 11) correlation

matrix2 of ε τ for τ = t minus M t minus M + 1hellipt minus 13 Its ijth element is

given by

Ψ i jt minus1 frac14 XM

mfrac141

uit minusm u jt minusm ffiffiffiffiffiffiffiffiffiffiffi ffiffiffiffiffiffiffiffiffiffiffiffi ffiffiffiffiffiffiffiffiffiffi ffiffiffiffiffiffiffiffiffiffiffiffi ffiffiffiffiffiffiffiffiffiffi ffiffiffiffiffiffiXM

mfrac141

u2

it minusm

XM

mfrac141

u2

jt minusm

v uut eth9THORN

where uit frac14 ε it ffiffiffiffiffiffi

hiit

p The matrix Ψ t minus 1 can be expressed as Ψ t minus 1 =

Bt minus 1minus1 Lt minus 1Lt minus 1primeBt minus 1

minus1 in which Lt minus 1 = (ut minus 1helliput minus M )isa(11 times M)

matrix and Bt minus 1 is a (11 times 11) diagonal matrix with ith diagonal ele-

ment given by sumM

hfrac141u

2it minush

1=2

where ut = (u1t u2t hellip u11t ) prime

The estimation of the vector of unknown parameters is carried out

by the Quasi-Maximum Likelihood Estimation (QMLE) method as

proposed by Bollerslev and Wooldridge (1992) to avoid the problem

of non-normality in excess returns Given the highly non-linear struc-ture of the model and thelarge unknown parameter number thesimul-

taneous estimation of the model is not feasible So we follow the

common literature ie Hardouvelis et al (2006) and Guesmi and

Nguyen (2011) to estimate the system (Eq (7)) in two steps and

thus study theregional integration process of the1047297ve emerging markets

(M T S I and N )In the 1047297rst stage we estimate a subsystem of six equa-

tions for excessreturns on regional and individual markets and1047297ve real

exchange rates plus the relevant variancendashcovariance elements of

Eq (8) This stepallows us to obtain the conditional variancesof region-

al market and real exchange rate their conditional covariances as well

as the prices of regional market and exchange rate risks In the second

stage we estimate the price of local market risk and the time-varying

level of integration for each emerging market in the system (Eq (7))

We maintain the same prices of regional market and exchange raterisks across different emerging markets by imposing the estimators

obtained from the 1047297rst stage

3 Data

31 Stock market and exchange rate returns

The market indices for Malaysia Thailand Singapore Indonesia and

Sri Lanka are obtained from Thomson Datastream International from

January 1996 through December 20074 We use monthly stock returns

in excessof the one-month Eurodollar interest rate which is considered

as a risk-free rate Monthly stock returns are calculated from stock

market indices with dividends reinvested

Real exchange rates represent the value of the local currency againstthe US dollar and are extracted from the IMFs International Financial

Statistics (IFS) and the US Federal Reserve databases The real effective

exchange rate index is the geometric average of bilateral real exchange

rates among the countries under consideration

32 Regional and local informational variables

As regional instrumental variablesare used to explain changes in the

prices of regional markets and foreign exchange risks we use the

dividend yield of the region in excess of the 30-day Eurodollar interest

rate (RIDY) the regional market index return (RRENT) and the region

term spread (RPRM)

As local instrumental variables we consider the dividend yield of a

market portfolio (DDIV) the return on the stock market index in excess

of the 30-day Eurodollar interest rate (RSRI) and the variation in the in-

1047298ation rate (DINF) Data are extracted from MSCI and Datastream

International

33 Financial integration instrumental variables

Fluctuations in the regional stock market constitute a source of

systematic risk within the context of an ICAPM model with partial inte-

gration The theory suggests that this risk is relevant and priced so we

hint at a number of instrumental variables that may help to describe

the prices of risk The commonly used variables are summarized below

List of integration instrumental variables

Determinant variables Measurements References

Market openness (MO) Total trade with the world

nominal GDP

Bekaert and Harvey (1997

2000) Rajan and Zingales

(2001) Bhattacharya and

Daouk (2002) Carrieriet al (2007)

Stock Market

Development (SMD)

Market valuenominal GDP Levine and Zervos (1998)


1997) Bekaert et al

(2002) and Carrieri et al

(2007)

Industrial Production (IP) log (Industrial Production) King and Levine (1992

1993) Savides (1995) and

Odedokun (1996)

In1047298ation Rate (IR) (CPIt minus CPIt minus 1) CPIt minus 1 Boyd et al (2001)

US Term Spread (UTS) Ln (US Treasury 10 year

bond minus USriskfree30 day

rate)

Harvey (1995) and

Hardouvelis et al (2006)

Dividend Yield

Differential (DYD)

DY of country i-DY world

with DY = dividendprice


2000) and Hardouvelis

et al (2006)

Exchange Rate Volatility

(ERV)

Conditional volatility

generated from an AR(1)

with GARCH(11) errors on

log exchange rate expressed

in USD

Jorion (1991) De Santis

and Gerard (1998) and

Bollerslev and Wooldridge

(1992)

Economic Growth Rate

(EGR)

Ln (Gross Domestic Product) King and Levine (1992


Odedokun (1996)

Current Account De1047297cit

(CAD)

Ln (export minus import) Guesmi (2011)

Market Returns (MR) Ln (Pt P t minus 1) Bekaert and Harvey (1997

2000)

Interest Rate (IR) Ln (short term interest rate

TB rate or interbank rate)

Arouri (2006) and Carrieri

et al (2007)

Difference in Industrial

Production (DIP)

IP country i-IP G7 Gurley and Shaw (1967)

King and Levine (1993)

and Arouri (2006)

34 Descriptive analysis of data

Table 1 presents the descriptive statistics for stock market and real

exchange rate returns The average stock returns are negative for the

considered countries and range from minus0017 (Sri Lanka) to minus0006

(Indonesia) Thailand is the least volatile market with a standard devia-

tion of 0071 While the highest market is that of Singapore (0113) for

which the skewness coef 1047297cients are negative denoting that the return

distributions are skewed toward the left and that the probability of

observing extreme negative returns is higher than that of a normal

distribution The kurtosis coef 1047297cients are signi1047297cant and greater than

three in all cases and thus reveal theleptokurtic behavior of return dis-

tributions Altogether the non-normality of all the return series is clear-

ly con1047297rmed by the JarquendashBera test Besides the Engle (1982) test

2 A necessarycondition to ensurethe positivityof bothΨt minus 1 and Rt is that M ge N = 13 For a complete review of the choice of the parameter M see Duchesne and Lalancette

(2003)4 Oursample excludesthe episodesof thelastGlobal FinancialCrisisthatcouldgenerate

biased estimates




highlights the existence of ARCH effects in all the returns series which

obviously supports our decision to model the conditional volatility of

returns by a GARCH-type process

Also all the exchange rate returns are positive and range from an

average of 0034 (Malaysia) to 0655 (Thailand) Their return distribu-

tions deviate signi1047297cantly from normality The JarquendashBera test statistic

strongly rejects the hypothesis of normally distributed returns More-

over we 1047297nd the presence of ARCH effects for all the series Similar to

stock returns the LjungndashBox test of order 12 reveals that exchange

rate returns are subject to serial correlation

4 Empirical results

41 Regional market prices and foreign exchange risks

We report in Table 2 the regional market prices and real exchange

rate risks respectively in panels A and B

It appears from Panel A that the price of currency risk for Malaysia

and Thailand is explained by three variables (RIDY) (RRENT) and

(RPRM) For Singapores market the price of currency risk is mainlydetermined by (RRENT) and (RPRM) Also the price of exchange rate

risk for Indonesia is mainly and positively determined by (RIDY) and

(RRENT)

Also it appears that the price of regional market risk (in Panel B) is

also signi1047297cantly and positively explained by all the regional variables

Moreover we investigate the economic signi1047297cance of the risk

factors considered by testing the null hypotheses that the prices of

risk are equal to zero or constant respectively TheWald test results re-

ported in Table 3 indicate the rejection of these null hypotheses at 1

level for all the markets considered Also the hypothesis that the price

of currency and local risk are equal to zero or constant can also be

rejected at the 1 signi1047297cance level These 1047297ndings effectively concur

with those of previous studies including for example Adler and

Dumas (1983) Hardouvelis et al (2006) and Carrieri et al (2007)

42 Financial integration factors

To identify the determinants of the 1047297nancial integration we

estimate the model (Eq (7)) jointly for all studied markets and for

each factor at a time using the Multivariate Nonlinear Least Squares

Method Following Bhattacharya and Daouk (2002) we impose the

same coef 1047297cients on the system (Eq (7)) to estimate the determinant

factors coef 1047297cients (ν 0 and ν 1) of stock market integration in emerging

market returns This assumption allows us to capture the impactof each

candidate factor on the integration of individual markets Referring to

previous studies (Bekaert and Harvey 1997 Grif 1047297n 2001 Karolyi and

Stulz 2002) we use the US dollar as the reference currency (column

(I) of Table 4) However when taking into account the regional integra-

tion the benchmark portfolio is that of the regional market this sug-

gests that the estimation results may be sensitive to a benchmark

currency at a regional level if the member countries have different cur-rencies In the considered countries Thailand has the largest share of

GDP and its currency (Baht ) is most commonly used in international

and regional trade Therefore we considerthe Baht as thenew reference

currency instead of the US dollar to study the impact of changing the

reference currency on the estimation of 1047297nancial integration determi-

nants So we re-estimate the system (Eq (7)) for each integration

factor The results are presented in column (II) In addition we use a

real effective exchange rate (REER) index as a proxy of the bilateral ex-

change rates presented in column (III) For each emerging market the

REER index is computed as the geometricweighted average of countries

regional members exchange rates against the US dollar where the

weights are the share of each country in the foreign trade with the

rest of the world By construction the REER index also allows for

cross-country comparisons of changes in trade competitiveness

Table 1

Descriptive statistics of return series

Mean Std dev Skewness Kurtosis JB Q(12) ARCH(6)

Panel A Excess returns on stock market indices

Malaysia minus0014 0072 0941+ 5395++ 55332+++ 6886+++ 0403+++

Singapore minus0009 0113 minus0075 6660 79957+++ 10197+++ 0016+++

Sri Lanka minus0017 0144 0961 5614 62749+++ 52018+++ 0072+++

Thailand minus0008 0071 0442 4312 14926+++ 5843+++ 0472+++

Indonesia minus0006 0121 0312 5322 17116+++ 7853+++ 0972+++

Panel B Real exchange rate returns

Malaysia 0034 0007 0703 2587 12914+++ 4179+++ 0093+++

Singapore 0269 0038 1109 3594 31689+++ 5555+++ 0169+++

Sri Lanka 0291 0045 1905 4965 11342+++ 3002+++ 0260+++

Thailand 0655 0075 1362 3575 46543+++ 2772+++ 0322+++

Indonesia 0036 0008 0903 3587 14114+++ 2159+++ 0223+++

NotesThis table shows thebasicstatisticsand thestochastic properties forstockreturns in excessof theEurodollar rates at 1 month andthe exchange rate + ++ and +++ indicate that

the null hypothesis of normality of no autocorrelation and of no ARCH effect is rejected at the 10 5 and 1 rate respectively

Table 2

Regional market prices and real exchange rate risks

Constant RIDY RRENT RPRM

Panel A Price of exchange rate risk

Malaysia 0311 0024 minus0050 0033

(0146) (0005) (0020) (0007)

Singapore 0113 00022 minus0022 0012

(0044) (00054) (0005) (0001)

Sri Lanka 0546 0012 minus0056 0018

(0129) (0014) (0002) (0017)

Thailand 0122 0014 minus005 0013

(0111) (0001) (0001) (0026)

Indonesia 0111 0015 minus006 0017

(0134) (0003) (0004) (0025)

Panel B Price of regional market risk

Asia 006 0061 0007 0004

(0011) (0072) (00005) (0001)

Note

and

indicate that the coef 1047297cients are signi1047297cant at the 5 and 1 levels

Table 3

Speci1047297cation test for prices of regional and exchange rate risks

Null hypothesis χ2 p-Value

The price of market risk of the South Asian

region is equal to zero H 0

α reg

= 0

11123 00000

The price of market risk of the South East

Asian region is constant H 0α reg = 1

224111 00000

The price of exchange rate risk of the South

Asian market is jointly zero H 0α k = 0

114152 0000


Asian market is jointly constant H 0α k = 1

111455 0000

Note indicates that the coef 1047297cients are signi1047297cant at the 1 levels




Theresults show that a higherdegree of marketopennessleadsto an

increase in the exposure of national markets to global risk factors

Besides this factor affects positively the evolution of regional 1047297nancial

integration in the case of the different currency speci1047297cations (columns

I II and III) Bekaert et al (2002 2005) Rajan and Zingales (2001) and

Bhattacharya and Daouk (2002) document that higherdegree of market

openness Thus as the markets become more open to foreign trade and

capital 1047298ows their level of economic integration rises and asset

exchanges become signi1047297cant Consequently the degree of market

openness can be a potential factor in promoting 1047297nancial integration

Moreover the US Term Spread is found to have signi1047297cant impacts

on the evolution of 1047297nancial integration in the case of the three currencyspeci1047297cations The US Term Spread has substantial effects on valuation

and on 1047297nancial asset allocation in an international context Adler and

Qi (2003) use theinterest rate spread as a factor of 1047297nancial integration

and 1047297nd that this variable affects the mobility of international capital

1047298ows that in turn leads to changes in the level of market integration

If we consider the regional market return factor the estimated coef-

1047297cientsare signi1047297cant for thethree speci1047297cationsand for theconsidered

countries Moreover they are positive for all the markets indicating a

positive correlation between the increase of regional stock returns and

intra-regional 1047297nancial integration Levine et al (2000) show that indi-

cators of economic growth are positively related to the stock markets

integration

To conclude we note that the main results remain the same despite

the change in base currency due to the dependence of these currencieson the dollar

43 Regional integration

We shall focus on thedynamicsof stock marketintegration reported

in Fig 1 and estimated using two factors the US term premium (UTS)

andthe levelof marketopenness(MO) In fact since there is a numerical

convergence problem at the estimation stage when we have more than

two unknown parameters only two information variables are used to

capture the evolution of market integration On the light of the previous

analysis and in regard to the better statistical results of the Bayesian

Information Criterion (BIC) we choose two retain the US termpremium

(UTS) and the level of market openness (MO) as information variables

At1047297rst sight we noticethat Singapore Malaysia and Thailand exhib-

it the same feature displaying high integration degrees approaching

70 at the end of the sample It appears clearly that from the beginning

of the 2000s there was a general increase in the case of the precited

countries This may be explained by the regional cooperation process

Such cooperation pursues both market-sharing and resource-pooling

strategies and achieves greater economic integration We also remark

that the increase in the degree of integration for Malaysia is higher

than that for Singapore and Thailand

Moreover the Malaysian market reached the highest integration

level exceeding 70 It is clearly the most integrated market in the

South Asian region This result was expected since Malaysia is one of the most important 1047297nancial markets in the South Asian region The

Malaysian market tends to compensate for the shortcomings of local

markets which are insuf 1047297ciently open and which liaise with less devel-

oped neighboring marketssuch as Thailand to transfer technologies and

services not available on the domestic market

TheSri Lankan and Indonesianmarkets show a farlower regional in-

tegration level thanthe other countries in theregionduring 2000ndash2007

The graphical inspection (Fig 1) shows that the intra-regional 1047297nancial

integration does not register any particulartrend upward or downward

This 1047297nding may be related to the no signi1047297cant interdependence

between Sri Lankan and Indonesian stock markets and the other Asian

countries

To complete our analysis we report in Table 5 the dynamics of stock

market integration levelsWith an average level of about 0512 Thailand is the least integrated

country within the regional market even if its process of 1047297nancial inte-

gration has begun with structural reforms aimed at stimulating the

private sector and the opening of markets to foreign investors in the

late 1980s

The Singapore market has an average of 601 followed by the

Malaysian one with an average of 553 and the Sri Lankan market

with an average of 531 We can deduce that with the exception of

theIndonesian and SriLankan markets thedegree of integration hasbe-

come very important in the study area from the 2000s Petri (1993)

1047297nds that the effects of geographical proximity are not signi1047297cant in

the Asian region indicating that the strategy of developing Asian coun-

tries turned to the conquest of foreign markets These results are veri-

1047297ed by Frankel and Romer (1999) and Guesmi (2012) In fact they

Table 4

Robustness tests of the choice of currency reference

Bilateral exchange rates against the

dollar (I)

Bilateral exchange rates against region

currency (II)

Real effective exchange rate index (III)

v0 v1 v0 v1 v0 v1

Trade Openness minus1944 (008) 4486 (2073) 7480 (2431) 5654 (1654) 5530 (1637) 11127 (3142)

Stock Market Development 7764 (2339) 13057 (3614) 8914 (2825) 0 789 (0028) minus2342 (1499) 3603 (3469)

National Industrial Production minus027 (0739) 00115 (0373) 0285 (0762) minus1214 (0384) minus0286 (0641) 2399 (0315)

World Industrial Production 1080 (0114) minus5589 (4761) minus0243 (0067) minus0005 (0002) minus0073 (0167) 0811 (0664)

Differences in Industrial ProductionGrowthRates minus0283 (0425) 0255 (0549) minus0383 (0525) 0155 (0749) minus0129 (0169) 0045 (0775)

In1047298ation Rate minus0230 (0607) minus0048 (0103) 0063 (0353) 2010 (0073) minus0064 (0803) 0702 (0526)

Exchange Rate Volatility minus4960 (5620) minus1250 (5545) minus0143 (0432) 0020 (0001) 2384 (0889) 0001 (0875)

Economic Growth Rate 0704 (0073) minus1464 (0081) 1230 (2920) minus1563 (7345) 1519 (1659) 0201 (1654)

Dividend Yield on the Local Market Index 0495 (1043) minus4597 (0893) 0807 (0953) minus1732 (0619) 0646 (0644) minus7198 (0732)

Dividend Yield on the Regional Market Index 0288 (0474) 0001 (0030) 0213 (0343) minus0023 (0364) 0161 (0132) 0025 (0415)

Dividend Yield on the World Market Index 0080 (0180) minus0140 (0760) 0569 (0730) minus4050 (0987) 1569 (1320) minus3750 (1450)

Differences in Dividend Yield 0043 (0213) 0075 (0078) 1060 (1230) 0030 (0155) 0437 (0664) minus2849 (0862)

US risk free 30 day rate minus0201 (0540) 0822 (0423) 0507 (1053) minus4597 (0892) 0339 (0140) minus0153 (0192)

US Treasury 10 year bond minus0143 (0432) 0020 ( 001) 0158 ( 0471) minus0254 (0162) minus5031 (0744) 5346 (0767)

US term spread 0263 (0093) 0100 (0021) minus0383 (0024) 0165 (0017) minus0090 (0008) 0016 (0005)

Current Account De1047297cit minus0290 (0771) minus0023 (0364) minus0042 (0032) minus0254 (0943) minus0490 (0766) minus0040 (minus0449)

Local Market Returns 0498 (0475) minus4596 (5147) 0078 (0184) 0137 (0755) 0035 (0008) 0008 (0047)

Regional Market Returns 11706 (1643) 6180 (0951) 7480 (2431) 6045 (1546) 4530 (0637) 8273 (1102)

World Market Returns minus0021 (0054) minus0041 (0543) 8179 (1258) 0892 (0008) 3042 (2049) 3036 (3496)

World Interest Rate minus0383 (0524) 0155 (0748) 0285 (0762) minus1214 (0384) minus0286 (0641) 2399 (0315)

Notes We estimate the system (Eq (7)) for all countries and consider one candidate factor for 1047297

nancial integration at a time Columns (I) (II) and (III) report the estimation resultsrespectively for the bilateral exchange rates against the US dollar the bilateral exchange rates against the Baht and the REER The numbers in parentheses are the associated standard

deviations and indicate signi1047297cance at the 10 5 and 1 levels respectively




show that intra-regional trade integration in Asia is more in1047298uenced by

the rapid growth of the country than by a genuine commitment to eco-

nomic integration Moreover there is no obvious indication of intensi-

1047297ed regional 1047297nancial market integration Nonetheless this seems to

reveal a close correspondence between measures of 1047297nancial integra-

tion and the extent of the development of 1047297nancial markets in general

The high-income economies of Singapore are fairly highly integrated

with regional capital markets The recent paceof liberalization in South

Asia post-crisis is also intensifying the extent of the countrys regional

and international 1047297nancial integration The lower-middle-income

Southeast Asian countries Thailand and Indonesia as well as Sri Lanka

are relatively less 1047297nancially integrated though evidence suggests a

gradual movement toward enhanced integration The evidence on

Malaysia is mixed (a low integration level until 2000 and an upward

trend throughout the rest of the period) also there is no evidence on

Sri Lanka The fact of not having a common trend for the markets

under consideration is due to the short period of the study These

1047297ndings may be due to the non-inclusion of smaller economies like

Cambodia and Vietnam that are relatively integrated with the Asian

regional market thanks to their liberalization politics and 1047297nancial

market deregulation

In order to examine the relevance of the local risk price in the valu-

ation of 1047297nancial assets issued by Asian markets we use the robust

Wald test (Table 6) to check the nullity of the coef 1047297cients associated

with the information variables The results from the Wald test clearly

reject the hypotheses according to which the local risk prices are indi-

vidually equal to zero In parallel the assumptions of constant local

risk price are rejected for the considered markets These 1047297ndings are

11Malaysia 12 Singapore

3

4

5

6

7

8

96 97 98 99 00 01 02 03 04 05 06 07

Integration HP-Filtered Integration HP-Filtered


Integration HP-Filtered

3

4

5

6

7

8

96 97 98 99 00 01 02 03 04 05 06 07

13 Sri Lanka 14 Thailand

02

04

06

08

10

96 97 98 99 00 01 02 03 04 05 06 07

2

3

4

5

6

7

8

96 97 98 99 00 01 02 03 04 05 06 07

15 Indonesia

3

4

5

6

7

8

9

96 97 98 99 00 01 02 03 04 05 06 07

Fig 1 Dynamic integration of emerging markets into the South Asian regional market




consistent with those of previous studies including that of Carrieri et al

(2007) Tai (2007) inthe sense that the local riskis a relevantsource of

risk in the valuation of 1047297nancial assets issued by emerging markets in

the Asian region Also the exposure to these local markets changes

over time

44 Formation of total risk premium

Table 7 indicates that the regional and local risk premiums are

signi1047297cantly different from zero at the 1 level for all the emerging

marketsstudied Malaysia has the highest total risk premiummarket

(11909) followed by Sri Lanka (115) Indonesia (8592)

Singapore (6847) and Thailand (5189) The Exchange riskpremiums

are on average greater than the regional ones for all the countries The

contribution of currency risk premium (EPRM) is also higher for

Malaysia Singapore and Indonesia the exchange risk premium is the

main component of the total risk premium for these economies Our re-sults con1047297rm those of Arouri (2006) Phylaktis and Ravazzolo (2002)

Carrieri et al (2007) and Guesmi (2012) who show that currency risk

is the most important risk factor

Finally throughout the study period the premium associated with

the exchange risk is statistically and economically signi1047297cant for the

1047297ve economies studied However the contribution of the exchange pre-

mium to the total premium is more pronounced for Malaysia Singapore

andIndonesiaThe contribution of thelocal risk factor is also statistically

signi1047297cant but economicallyweak Forthe rest of countries thetotal risk

premium is mainly determined by the regional market risk factor

(Arouri 2006 Guesmi 2012)

Table 8 presents an analysis of the models residuals in terms of

normality autocorrelation and conditional heteroscedasticity

It appears that normality of the estimated residuals can be accepted

for Malaysia Singapore Sri Lanka and the regional market The 1982

Engles test for conditional heteroscedasticity of the standardized

residuals indicates that ARCH effects no longer exist in all cases thus

revealing the appropriateness of the GARCH modeling approach Such

evidence against normality warrants the use of QML testingprocedures

5 Conclusion

We developed a conditional ICAPM in the presence of exchange rate

risk to identify factors that mayin1047298uence thedegree of 1047297nancial integra-

tion for 1047297ve major markets in Southeast Europe The 1047297ndings are then

used to study the dynamics of 1047297nancial integration Our empirical anal-

ysis is conducted on the basis of a nonlinear framework which relies on

the multivariate GDC-GARCH model

By allowing the prices of risk and the level of market integration tovary through time we show that the degree of trade openness and var-

iation in the US term premium are the most important determinants of

regional 1047297nancial integration Moreover the degree of market integra-

tion admitsfrequentchanges over thestudy periodand itsdynamic pat-

terns differ greatly across the markets under consideration The average

premium for global risk appears to be only a small fraction of the aver-

age of the total premium These results thus suggest that diversi1047297cation

into emerging market assets continues to produce substantial pro1047297ts

and that the asset pricing rules should re1047298ect a state of partial integra-

tion Our investigation which addresses the evolution and formation

of total risk premiums con1047297rms this empirically

Table 5

Dynamics of stock market integration

Panel A Parameters of the market integration measure

Constant MO UTS

Sri Lanka 0196 (0035) 0132 (0031) minus0156 (0003)

Malaysia 0277 (001) 0151 (0066) 0155 (0053)

Singapore 0561 (0059) 0061 (0002) 0117 (0007)

Thailand 0181 (0222) 0307 (0013) minus0052 (0002)

Indonesia 0221 (0342) 0207 (0011) 0032 (0001)

Panel B Statistics of market integration

p mean p max p min

Sri Lanka 0531 (0092) 0846 0214

Malaysia 0553 (0130) 0788 0314

Singapore 0601 (0115) 0790 0312

Thailand 0512 (0114) 0767 0266

Indonesia 0525 (008) 0844 0361

Notes The numbers in parentheses are the associated standard deviations and indicate that the coef 1047297cients are signi1047297cant at the 10 5 and 1 levels respectively

Table 6

Speci1047297cation test of price of local risk


Is the local risk price in Thailand zero H 0α T = 0 18113 0000

Is the local risk price in Thailand constant H 0α T = 1 84234 0000

Is the local risk price in Singapore zero H 0α N = 0 67211 0000

Is the local risk price in Singapore constant H 0α N = 1 99488 0000

Is the local risk price in Sri Lanka zero H 0α S = 0 22555 0000

Is the local risk price in Sri Lanka constant H 0α S = 1 21600 0000

Is the local risk price in Malaysia zero H 0α M = 0 18711 0000

Is the local risk price in Malaysia constant H 0α M = 1 22110 0000

Is the local risk price in Indonesia zero H 0α I = 0 387182 0000

Is the local risk price in Indonesia constant H 0α I = 1 70393 0000

Note

indicates that the coef 1047297cients are signi1047297cant at the 1 level

Table 7

Decomposition of the total risk premium

LPRM () RPRM () EPRM () TPRM ()

Malaysia 1120+++ 4412+++ 6377+++ 11909+++

(0130) (0120) (0244) (0170)

Singapor e 1389+++ 2145+++ 2953+++ 6487+++

(0149) (0812) (0011) (0151)

Sri Lanka 1111+++ 5203+++ 5186+++ 11500+++

(0152) (0028) (0178) (0125)

Thailand 1000+++ 1745+++ 2444+++ 5189+++

(0166) (0150) (0131) (0213)

Indonesia 1022+++ 3751+++ 3819+++ 8592+++

(0225) (0143) (0122) (0203)

Note+++ indicates thatthe average risk premiums are signi1047297cantlydifferent fromzero at

the 1 level with respect to the two-sided Student-t test




References

Adler M Dumas B 1983 International portfolio selection and corporation 1047297nance asynthesis J Financ 38 925ndash984

Adler M Qi R 2003 Mexicos integration into the North American Capital marketEmerg Econ Rev 4 91ndash120

Anderson NH 1997 Intuitive physics understanding and learning of physical relationsIn Ward TB Smith SM Vaid J (Eds) Creative thought an investigation of con-ceptual structures and processes APA Washington pp 231ndash265

Arouri MH 2006 Are stock markets integrated Evidence from a partially segmented

ICAPM with asymmetric effects Front Finance Econ 2 70ndash94Bekaert G Harvey CR 1995 Time-varying world market integration J Financ 50 (2)

403ndash444Bekaert G Harvey CR 1997 Emerging equity market volatility J Financ Econ 43 29ndash77Bekaert G Harvey CR 2000 Foreign speculators and emerging equity markets

J Financ 55 565ndash613Bekaert G Harvey CR Lumsdaine R 2002 The dynamics of emerging market equity

1047298ows J Int Money Financ 21 295ndash350Bekaert G Harvey CR Lumsdaine R 2005 Does 1047297nancial liberalization spur growth

J Financ Econ 77 3ndash55Berger T Pozzi L 2013 Measuring time-varying 1047297nancial market integration an unob-

served components approach J Bank Financ 37 463ndash473Bhattacharya U Daouk H 2002 Theworldprice of insidertradingJ Financ 5775ndash108Black F 1974 International capital market equilibrium with investment barriers

J Financ Econ 1 337ndash352Bollerslev T Wooldridge JM 1992 Quasi-maximum likelihood estimation and infer-

ence in dynamic models with time-varying covariances Econ Rev 11 143ndash172Boyd RD JohnstonMEUsry JL Fralick CE Sosnicki AA FieldsB 2001 Lysine level

required to optimize the growth performance to Paylean in PIC pigs J Anim Sci 79(Suppl1) 66 (Abstr)

Carrieri F Errunza V Hogan K 2007 Characterizing world market integration throughtime J Financ Quant Anal 42 (04) 915ndash940

Chambet A Gibson R 2008 Financial integration economic instability and tradestructure in emerging markets J Int Money Financ 27 654ndash675

Claessens S Rhee M 1994 The effect of barriers to equity investment in developingcountries In Frankel Jeffrey A (Ed) The Internationalization of Equity MarketsUniversity of Chicago Press Chicago and London pp 231ndash275

Cooper IA Kaplanis E 2000 Partially segmented international capital markets amp inter-national capital budgeting J Int Money Financ 19 309ndash329

De Grauwe P Grimaldi M 2006 Exchange rate puzzles a tale of switching attractorsEur Econ Rev 50 1ndash33

De Santis G Gerard B 1998 How big is the premium for currency risk J Financ Econ49 375ndash412

De Santis G Gerard B Hillion P 2003 The relevance of currency risk in the EMU J Econ Bus 55 427ndash462

Duchesne P Lalancette S 2003 On testing for multivariate ARCH effects in vector timeseries models La Rev Can Stat 31 275ndash292

Engle R 1982 Autoregressive conditional heteroskedasticity with estimates of the vari-ance of UK in1047298ation Econometrica 50 987ndash1008

Errunza V Losq E 1985 International asset pricing under mild segmentation theoryand test J Financ 40 105ndash124

Frankel J Romer D 1999 Does trade cause growth Am Econ Rev 89 379ndash399

Frankel J Wei S 1995 Emerging currency blocs In Genberged H (Ed) The Interna-tional Monetary System Its Institutions and Its Future Springer Verlag Berlinpp 111ndash143

Grif 1047297n MW 2001 Complex cases CAMHS Staff Seminar presented at Flinders MedicalCentre Adelaide (February)

Guesmi K 2011 What drive the regional integration of emerging stock markets EconBull 31 (3) 2603ndash2619

Guesmi K 2012 Characterizing South-east Asian stock market integration through timeInt J Bus 17 (1) 100ndash112

Guesmi K Nguyen Duc Khuong 2011 How strong is the global integration of emergingmarket regions An empirical assessment Econ Model 28 2517ndash2527

Gurley J Shaw E 1967 Financial structure and economic development Econ Dev CultChang 34 (2) 333ndash346

Hardouvelis GA Malliaropulos D Priestley R 2006 EMU and European stock marketintegration J Bus 79 (1) 365ndash373

Harvey C 1995 Predictable risk and returns in emerging markets Rev Financ Stud 8773ndash816

Jorion P 1991 The pricing of exchange rate risk in stock market J Financ Quant Anal363ndash376

Karolyi AG Stulz RM 2002 Are 1047297nancial assets priced locally or globally NBER Working Papers 8994 National Bureau of Economic Research Inc

King R Levine R 1992 Financial indicators and growth in a cross section of countriesWorking Paper 819 Policy Research World Bank

King R LevineR 1993 Finance andgrowth Schumpeter might be right Q J Econ 108717ndash737

Levine R Loayza N Beck T 2000 Financial intermediation and growth causality andcauses J Monet Econ 46 (1) 31ndash77

Levine R Zervos A 1998 Stock markets banks andeconomicgrowth AmEconRev 88(3) 537ndash558

Odedokun M 1996 Alternative econometric approaches for analyzing the role of the1047297nancial sector in economic growth time-series evidence from LDCs J Dev Econ50 119ndash146

Petri Peter A 1993 The East Asian trading bloc an analytical history In Frankel Jeffrey A Kahler Miles (Eds ) Regional ism and Rival ry (A Nation al Bureau of Economic Research Conference Report) University of Chicago Press Chicagopp 21ndash52

Phylaktis K Ravazzolo F 2002 Measuring 1047297nancial and economic integration withequity prices in emerging markets J Int Money Financ 21 879ndash904

Rajan R Zingales L 2001 The 1047297rm as a dedicated hierarchy a theory of the origins andgrowth of 1047297rms Q J Econ CXVI 805ndash852

Savides A 1995 Economic growth in Africa World Dev 23 (3) 449ndash458Stehle R 1977 An empirical test of the alternative hypotheses of national and interna-

tional pricing of risky asset J Financ 33 493ndash502Stulz R 1981 A model of international asset pricing J Financ Econ 9 383ndash406Tai C-S 2007 Market integration and contagion evidence from Asian emerging stock

and foreign exchange markets Emerg Mark Rev 8 (4) 264ndash283Tse YK Tsui KC 2002 A multivariate GARCH model with time-varying correlations

J Bus Econ Stat 20 (3) 351ndash362Verma P Verma R 2010 Response asymmetry of Latin American stock markets to the

US money market Glob Econ Financ J 3 (2) 133ndash147

Table 8

Residuals analysis

Skewness Kurtosis JB Q(12) ARCH(6)

Mal aysia 1172+ 5441++ 67786+++ 13392 0196

Singapore minus0382 5843 51282+++ 16801 0190

Sri Lanka 1418 15368 952563+++ 9739 0285

Thailand 0291 3247 2356 5873 0062

Indonesia 0333 7666 22356+++ 7765 0333

Region 1514 16244 10131+++ 13456 0115

Notes Numbers in parentheses are the associated standard deviations JB Q(12) and

ARCH(6) are respectively the empirical statistics of the JarquendashBera test for normality

the LjungndashBoxtestfor serial correlationof order12 andEngles (1982)test for conditional

heteroscedasticity+ ++ and +++ indicatethat thenull hypothesis ofnormality andzero

autocorrelation is rejected at the 10 5 and 1 levels respectively




the US and a high degree of integration between Korea Taiwan and

Japan

Adler and Qi (2003) extendthe model of Bekaert and Harvey (1995)

which basically combines the domestic and international versions of an

Asset Pricing Model (ICAPM) to test the power of domestic factors

relative to that of common factors to explain expected returns and

empirically infers segmentation when the weight of the domestic

factors is high So Adler and Qi (2003) investigate the evolution of the

process of integration between the Mexican and North American equitymarkets between 1991 and 2002 They show that the degree of market

integration is higher at the end of the period than at the beginning and

that Mexicos currency risk is priced Furthermore there is signi1047297cant

asymmetric volatility which is strongly related to the asymmetric

volatility of the Mexican equity market return process

Carrieri et al (2007) extend the model of Errunza and Losq (1985)

They study the integration levels of eight emerging markets over the

period 1977ndash2000 They show that the local pricing factor continues

to be relevant in the valuation of emerging-market assets but none of

the markets considered is completely segmented from the world

market Furthermore Chambet and Gibson (2008) estimate a multifac-

tor asset pricing model of partial integration an extension of that of

Errunza and Losq (1985) for 25 emerging markets and show that

some markets still remain segmented

Guesmi and Nguyen (2011) inspired by the model of Bekaert and

Harvey (1995) use a conditional version of an ICAPM to evaluatethe dy-

namics of the global integration process of four emerging market re-

gions (Latin America Asia Southeastern Europe and the Middle East)

into the world market They show that the integration degree in the

fouremerging market regionsvarieswidelythrough timeover the period

1996ndash2008 and that this can be explained by the regional factors Al-

though the general trend is toward increasing 1047297nancial integration

emerging market areas seem to be still signi1047297cantly segmented from

the global market

Guesmi (2012) investigates the evolution of the South-East Asian

stock market integration with the regional one and deduces that with

the exception of Singapores market emerging markets are not strongly

integrated in the study area These results were con1047297rmed by those of

Petri (1993) Frankel and Wei (1995) and Frankel and Romer (1999)They show that the geographical proximity effects are not signi1047297cant

in the Southeast Asian region

More recently Berger and Pozzi (2013) suggest a measure of 1047297nan-

cial integration based on the conditional variances of the country-

speci1047297c and common international risk premiums in equity excess

returns The authors show that Germany France the UK the US and

Japan exhibit several shorter periods of disintegration over the period

1970ndash2011 They conclude that stock market integration is measured

as a dynamic process that is 1047298uctuating in the short run while gradually

increasing in the long run

In our work we investigate the issue through a longitudinal study of

the South Asian region using monthly data from 199601 to 200712

Our study differs from previous ones by considering intra-regional

integration instead of global integration and by taking into accountthe currency risk in addition to the sources of global and domestic

risks The international asset-pricing model we use is built so as to

characterize the changes in market integration through time due to

the impacts of the gradual removal of barriers to emerging market

investments We also examine the portions of the returns explained

by regional and domestic risk factors respectively by carrying out a

decomposition of the total risk premium

The present study contributes to the literature by developing a

regime-switching ICAPM with a slip condition Speci1047297cally expected

return canslip from a perfectly segmented regimeto a perfectly integrat-

ed one or vice versa depending on the number of national and regional

factors that may in1047298uence the process of regional 1047297nancial integration

It is true that this model was inspired by that of Bekaert and Harvey

(1995) but it has been extended using a multivariate GDC-GARCH

model to take into account the asymmetric responses of expected

returns to different shocks

One of the advantages of our approach is to authorize the prices of

domestic and world market risks betas and correlations to vary asym-

metrically through time It is clear that this will help us to understand

the dynamics of interdependencies and correlations between South

Asian stock markets in order to facilitatedecision-making In fact inves-

tors are normally risk-averse they are concerned about market down-

turns more than upturns Consequently this risk-aversion behaviorwill be re1047298ected in market prices resulting in greater market responses

to downturns than upturns

Asymmetry can be justi1047297ed differently by the presence of informa-

tion barriers the behavioral standpoint of investor psychology (Verma

and Verma 2010) other sources of market segmentation (Bekaert and

Harvey 1995) heterogeneous transaction costs (Anderson 1997) the

coexistence of different shareholders and noise trading (De Grauwe

and Grimaldi 2006) Also another source of asymmetry effects is indus-

try concentration and imperfectly competitive behavior It implies that

wholesalers or middlemenwith power over price may exercise pricing

strategies that result in a slow andincomplete pass-throughof increases

in the international price and a fast and complete transmission of

decreases in the international price to prices upstream

Although asymmetric adjustment may also be the outcome of

market imperfections it is plausible that price support policies result

in positive and negative changes in the international price affecting

the domestic market in different ways Moreover the effect of positive

shock and negative shock is different

Our 1047297ndings clearly show that the degree of market integration of

the1047297ve emerging markets varies over the period 1996ndash2007Moreover

the US term premium and the level of market openness mainly explain

the degree of integration in emerging markets Even though this degree

reaches high values during periods of turmoil and exhibits an upward

trend toward the end of the estimation period Hence emerging mar-

kets still remain substantially segmented from the regional market

Also the total risk premium decomposition shows that the variance

risk related to the local market index (the local risk factor) explains

more than 70 of thetotalriskpremiumon average forthe1047297ve emerging

marketsTracking the integration level is a critical task It is important to

know if the emerging countries in the Asia region are globally or region-

ally integratedfor most of thesample period In fact if there are regional

integration and if suddenly during an Asian crisis the intraregional

correlations between the countries rise dramatically this may lead to

contagion effects

Our analysis is relevant for both policymakers and investorsthat pay

a particular attention to stock markets and their degree of integration

Also analyzing the links between stock markets is of particular interest

for 1047297nancial players Portfolio managers look at stock market 1047298uctua-

tions to infer the trend of each market and make diversi1047297cation deci-

sions Moreover studying the degree of integration becomes a central

issue for the world economy during turmoil periods In fact comparing

the impact of the 1047297nancial crisis on the degree of integration providesuseful information about possible substitution strategies between

stock classes In particular the integration level plays a key role regard-

ing hedging possibilities and impacts asset allocation and their risk-

return trade-off

The remainder of the article is organized as follows Section 2

presents the empirical methodology Section 3 describes the data

Section 4 presents and discusses the results and Section 5 draws the

appropriate conclusions

2 Empirical approach

Our empirical asset pricing model takes as its point of departure

that of Bekaert and Harvey (1995) and is inspired by the theoretical

models of partial integration of Black (1974) Stulz (1981) Cooper









emerging markets




as

E t minus1 Rc i t


c reg t =Ωt minus1

thornXl

kfrac141


c kt =Ωt minus1


α it minus1Var R

c it =Ωt minus1

eth1THORN






























and the third






E t minus1 Rc reg t


c reg t =Ωt minus1


c reg t R

c M t =Ωt minus1


c reg t R

c T t =Ωt minus1


c reg t R

c S t =Ωt minus1


c reg t R

c I t =Ωt minus1


c reg t R

c N t =Ωt minus1

eth2THORN

E t minus1 Rc it

frac14 pit minus1


c reg t =Ωt minus1


c it R

c M t =Ωt minus1


c it R

c T t =Ωt minus1


c it R

c S t =Ωt minus1


c it R

c I t =Ωt minus1


c it R

c N t =Ωt minus1

26664

37775


c it =Ωt minus1

eth3THORN

E t minus1 Rc kt


c kt R

c M t =Ωt minus1


c kt R

c T t =Ωt minus1


c kt R

c S t =Ωt minus1


c kt R

c I t =Ωt minus1


c kt R

c N t =Ωt minus1

eth4THORN


1 F it minus1

eth5THORN


M t Rc T t Rc

S t Rc I t and Rc










follows


reg F reg t minus1


prime


prime

k F reg t minus1

eth6THORN


variables






system of equations





thorn

1minus pit minus1



eth7THORN











where

Dt frac14 dijt

dijt frac14



Θt frac14 θijt














given by


mfrac141


mfrac141

u2

it minusm

XM

mfrac141

u2

jt minusm

v uut eth9THORN


hiit





ment given by sumM

hfrac141u

2it minush

1=2




















3 Data

















term spread (RPRM)





International










nominal GDP





Stock Market

Development (SMD)



1997) Bekaert et al


(2007)



Odedokun (1996)




rate)

Harvey (1995) and


Dividend Yield

Differential (DYD)





et al (2006)


(ERV)





in USD




(1992)


(EGR)



Odedokun (1996)


(CAD)



2000)




et al (2007)


Production (DIP)



and Arouri (2006)
















biased estimates














4 Empirical results








(RRENT)








































Table 1




Malaysia minus0014 0072 0941+ 5395++ 55332+++ 6886+++ 0403+++


Sri Lanka minus0017 0144 0961 5614 62749+++ 52018+++ 0072+++

Thailand minus0008 0071 0442 4312 14926+++ 5843+++ 0472+++

Indonesia minus0006 0121 0312 5322 17116+++ 7853+++ 0972+++


Malaysia 0034 0007 0703 2587 12914+++ 4179+++ 0093+++

Singapore 0269 0038 1109 3594 31689+++ 5555+++ 0169+++

Sri Lanka 0291 0045 1905 4965 11342+++ 3002+++ 0260+++

Thailand 0655 0075 1362 3575 46543+++ 2772+++ 0322+++

Indonesia 0036 0008 0903 3587 14114+++ 2159+++ 0223+++



Table 2




Malaysia 0311 0024 minus0050 0033

(0146) (0005) (0020) (0007)


(0044) (00054) (0005) (0001)

Sri Lanka 0546 0012 minus0056 0018

(0129) (0014) (0002) (0017)


(0111) (0001) (0001) (0026)


(0134) (0003) (0004) (0025)


Asia 006 0061 0007 0004

(0011) (0072) (00005) (0001)

Note

and


Table 3





α reg

= 0

11123 00000



224111 00000



114152 0000



111455 0000



























integration



































countries






late 1980s










Table 4



dollar (I)


currency (II)


v0 v1 v0 v1 v0 v1















































market deregulation









3

4

5

6

7

8

96 97 98 99 00 01 02 03 04 05 06 07




3

4

5

6

7

8

96 97 98 99 00 01 02 03 04 05 06 07


02

04

06

08

10

96 97 98 99 00 01 02 03 04 05 06 07

2

3

4

5

6

7

8

96 97 98 99 00 01 02 03 04 05 06 07

15 Indonesia

3

4

5

6

7

8

9

96 97 98 99 00 01 02 03 04 05 06 07









over time





























5 Conclusion


















Table 5



Constant MO UTS

Sri Lanka 0196 (0035) 0132 (0031) minus0156 (0003)

Malaysia 0277 (001) 0151 (0066) 0155 (0053)

Singapore 0561 (0059) 0061 (0002) 0117 (0007)

Thailand 0181 (0222) 0307 (0013) minus0052 (0002)

Indonesia 0221 (0342) 0207 (0011) 0032 (0001)


p mean p max p min

Sri Lanka 0531 (0092) 0846 0214

Malaysia 0553 (0130) 0788 0314

Singapore 0601 (0115) 0790 0312

Thailand 0512 (0114) 0767 0266

Indonesia 0525 (008) 0844 0361


Table 6













Note


Table 7



Malaysia 1120+++ 4412+++ 6377+++ 11909+++

(0130) (0120) (0244) (0170)

Singapor e 1389+++ 2145+++ 2953+++ 6487+++

(0149) (0812) (0011) (0151)

Sri Lanka 1111+++ 5203+++ 5186+++ 11500+++

(0152) (0028) (0178) (0125)

Thailand 1000+++ 1745+++ 2444+++ 5189+++

(0166) (0150) (0131) (0213)

Indonesia 1022+++ 3751+++ 3819+++ 8592+++

(0225) (0143) (0122) (0203)






References
















































Table 8

Residuals analysis


Mal aysia 1172+ 5441++ 67786+++ 13392 0196

Singapore minus0382 5843 51282+++ 16801 0190

Sri Lanka 1418 15368 952563+++ 9739 0285

Thailand 0291 3247 2356 5873 0062

Indonesia 0333 7666 22356+++ 7765 0333

Region 1514 16244 10131+++ 13456 0115














emerging markets




as

E t minus1 Rc i t


c reg t =Ωt minus1

thornXl

kfrac141


c kt =Ωt minus1


α it minus1Var R

c it =Ωt minus1

eth1THORN






























and the third






E t minus1 Rc reg t


c reg t =Ωt minus1


c reg t R

c M t =Ωt minus1


c reg t R

c T t =Ωt minus1


c reg t R

c S t =Ωt minus1


c reg t R

c I t =Ωt minus1


c reg t R

c N t =Ωt minus1

eth2THORN

E t minus1 Rc it

frac14 pit minus1


c reg t =Ωt minus1


c it R

c M t =Ωt minus1


c it R

c T t =Ωt minus1


c it R

c S t =Ωt minus1


c it R

c I t =Ωt minus1


c it R

c N t =Ωt minus1

26664

37775


c it =Ωt minus1

eth3THORN

E t minus1 Rc kt


c kt R

c M t =Ωt minus1


c kt R

c T t =Ωt minus1


c kt R

c S t =Ωt minus1


c kt R

c I t =Ωt minus1


c kt R

c N t =Ωt minus1

eth4THORN


1 F it minus1

eth5THORN


M t Rc T t Rc

S t Rc I t and Rc










follows


reg F reg t minus1


prime


prime

k F reg t minus1

eth6THORN


variables






system of equations





thorn

1minus pit minus1



eth7THORN











where

Dt frac14 dijt

dijt frac14



Θt frac14 θijt














given by


mfrac141


mfrac141

u2

it minusm

XM

mfrac141

u2

jt minusm

v uut eth9THORN


hiit





ment given by sumM

hfrac141u

2it minush

1=2




















3 Data

















term spread (RPRM)





International










nominal GDP





Stock Market

Development (SMD)



1997) Bekaert et al


(2007)



Odedokun (1996)




rate)

Harvey (1995) and


Dividend Yield

Differential (DYD)





et al (2006)


(ERV)





in USD




(1992)


(EGR)



Odedokun (1996)


(CAD)



2000)




et al (2007)


Production (DIP)



and Arouri (2006)
















biased estimates














4 Empirical results








(RRENT)








































Table 1




Malaysia minus0014 0072 0941+ 5395++ 55332+++ 6886+++ 0403+++


Sri Lanka minus0017 0144 0961 5614 62749+++ 52018+++ 0072+++

Thailand minus0008 0071 0442 4312 14926+++ 5843+++ 0472+++

Indonesia minus0006 0121 0312 5322 17116+++ 7853+++ 0972+++


Malaysia 0034 0007 0703 2587 12914+++ 4179+++ 0093+++

Singapore 0269 0038 1109 3594 31689+++ 5555+++ 0169+++

Sri Lanka 0291 0045 1905 4965 11342+++ 3002+++ 0260+++

Thailand 0655 0075 1362 3575 46543+++ 2772+++ 0322+++

Indonesia 0036 0008 0903 3587 14114+++ 2159+++ 0223+++



Table 2




Malaysia 0311 0024 minus0050 0033

(0146) (0005) (0020) (0007)


(0044) (00054) (0005) (0001)

Sri Lanka 0546 0012 minus0056 0018

(0129) (0014) (0002) (0017)


(0111) (0001) (0001) (0026)


(0134) (0003) (0004) (0025)


Asia 006 0061 0007 0004

(0011) (0072) (00005) (0001)

Note

and


Table 3





α reg

= 0

11123 00000



224111 00000



114152 0000



111455 0000



























integration



































countries






late 1980s










Table 4



dollar (I)


currency (II)


v0 v1 v0 v1 v0 v1















































market deregulation









3

4

5

6

7

8

96 97 98 99 00 01 02 03 04 05 06 07




3

4

5

6

7

8

96 97 98 99 00 01 02 03 04 05 06 07


02

04

06

08

10

96 97 98 99 00 01 02 03 04 05 06 07

2

3

4

5

6

7

8

96 97 98 99 00 01 02 03 04 05 06 07

15 Indonesia

3

4

5

6

7

8

9

96 97 98 99 00 01 02 03 04 05 06 07









over time





























5 Conclusion


















Table 5



Constant MO UTS

Sri Lanka 0196 (0035) 0132 (0031) minus0156 (0003)

Malaysia 0277 (001) 0151 (0066) 0155 (0053)

Singapore 0561 (0059) 0061 (0002) 0117 (0007)

Thailand 0181 (0222) 0307 (0013) minus0052 (0002)

Indonesia 0221 (0342) 0207 (0011) 0032 (0001)


p mean p max p min

Sri Lanka 0531 (0092) 0846 0214

Malaysia 0553 (0130) 0788 0314

Singapore 0601 (0115) 0790 0312

Thailand 0512 (0114) 0767 0266

Indonesia 0525 (008) 0844 0361


Table 6













Note


Table 7



Malaysia 1120+++ 4412+++ 6377+++ 11909+++

(0130) (0120) (0244) (0170)

Singapor e 1389+++ 2145+++ 2953+++ 6487+++

(0149) (0812) (0011) (0151)

Sri Lanka 1111+++ 5203+++ 5186+++ 11500+++

(0152) (0028) (0178) (0125)

Thailand 1000+++ 1745+++ 2444+++ 5189+++

(0166) (0150) (0131) (0213)

Indonesia 1022+++ 3751+++ 3819+++ 8592+++

(0225) (0143) (0122) (0203)






References
















































Table 8

Residuals analysis


Mal aysia 1172+ 5441++ 67786+++ 13392 0196

Singapore minus0382 5843 51282+++ 16801 0190

Sri Lanka 1418 15368 952563+++ 9739 0285

Thailand 0291 3247 2356 5873 0062

Indonesia 0333 7666 22356+++ 7765 0333

Region 1514 16244 10131+++ 13456 0115














given by


mfrac141


mfrac141

u2

it minusm

XM

mfrac141

u2

jt minusm

v uut eth9THORN


hiit





ment given by sumM

hfrac141u

2it minush

1=2




















3 Data

















term spread (RPRM)





International










nominal GDP





Stock Market

Development (SMD)



1997) Bekaert et al


(2007)



Odedokun (1996)




rate)

Harvey (1995) and


Dividend Yield

Differential (DYD)





et al (2006)


(ERV)





in USD




(1992)


(EGR)



Odedokun (1996)


(CAD)



2000)




et al (2007)


Production (DIP)



and Arouri (2006)
















biased estimates














4 Empirical results








(RRENT)








































Table 1




Malaysia minus0014 0072 0941+ 5395++ 55332+++ 6886+++ 0403+++


Sri Lanka minus0017 0144 0961 5614 62749+++ 52018+++ 0072+++

Thailand minus0008 0071 0442 4312 14926+++ 5843+++ 0472+++

Indonesia minus0006 0121 0312 5322 17116+++ 7853+++ 0972+++


Malaysia 0034 0007 0703 2587 12914+++ 4179+++ 0093+++

Singapore 0269 0038 1109 3594 31689+++ 5555+++ 0169+++

Sri Lanka 0291 0045 1905 4965 11342+++ 3002+++ 0260+++

Thailand 0655 0075 1362 3575 46543+++ 2772+++ 0322+++

Indonesia 0036 0008 0903 3587 14114+++ 2159+++ 0223+++



Table 2




Malaysia 0311 0024 minus0050 0033

(0146) (0005) (0020) (0007)


(0044) (00054) (0005) (0001)

Sri Lanka 0546 0012 minus0056 0018

(0129) (0014) (0002) (0017)


(0111) (0001) (0001) (0026)


(0134) (0003) (0004) (0025)


Asia 006 0061 0007 0004

(0011) (0072) (00005) (0001)

Note

and


Table 3





α reg

= 0

11123 00000



224111 00000



114152 0000



111455 0000



























integration



































countries






late 1980s










Table 4



dollar (I)


currency (II)


v0 v1 v0 v1 v0 v1















































market deregulation









3

4

5

6

7

8

96 97 98 99 00 01 02 03 04 05 06 07




3

4

5

6

7

8

96 97 98 99 00 01 02 03 04 05 06 07


02

04

06

08

10

96 97 98 99 00 01 02 03 04 05 06 07

2

3

4

5

6

7

8

96 97 98 99 00 01 02 03 04 05 06 07

15 Indonesia

3

4

5

6

7

8

9

96 97 98 99 00 01 02 03 04 05 06 07









over time





























5 Conclusion


















Table 5



Constant MO UTS

Sri Lanka 0196 (0035) 0132 (0031) minus0156 (0003)

Malaysia 0277 (001) 0151 (0066) 0155 (0053)

Singapore 0561 (0059) 0061 (0002) 0117 (0007)

Thailand 0181 (0222) 0307 (0013) minus0052 (0002)

Indonesia 0221 (0342) 0207 (0011) 0032 (0001)


p mean p max p min

Sri Lanka 0531 (0092) 0846 0214

Malaysia 0553 (0130) 0788 0314

Singapore 0601 (0115) 0790 0312

Thailand 0512 (0114) 0767 0266

Indonesia 0525 (008) 0844 0361


Table 6













Note


Table 7



Malaysia 1120+++ 4412+++ 6377+++ 11909+++

(0130) (0120) (0244) (0170)

Singapor e 1389+++ 2145+++ 2953+++ 6487+++

(0149) (0812) (0011) (0151)

Sri Lanka 1111+++ 5203+++ 5186+++ 11500+++

(0152) (0028) (0178) (0125)

Thailand 1000+++ 1745+++ 2444+++ 5189+++

(0166) (0150) (0131) (0213)

Indonesia 1022+++ 3751+++ 3819+++ 8592+++

(0225) (0143) (0122) (0203)






References
















































Table 8

Residuals analysis


Mal aysia 1172+ 5441++ 67786+++ 13392 0196

Singapore minus0382 5843 51282+++ 16801 0190

Sri Lanka 1418 15368 952563+++ 9739 0285

Thailand 0291 3247 2356 5873 0062

Indonesia 0333 7666 22356+++ 7765 0333

Region 1514 16244 10131+++ 13456 0115



















4 Empirical results








(RRENT)








































Table 1




Malaysia minus0014 0072 0941+ 5395++ 55332+++ 6886+++ 0403+++


Sri Lanka minus0017 0144 0961 5614 62749+++ 52018+++ 0072+++

Thailand minus0008 0071 0442 4312 14926+++ 5843+++ 0472+++

Indonesia minus0006 0121 0312 5322 17116+++ 7853+++ 0972+++


Malaysia 0034 0007 0703 2587 12914+++ 4179+++ 0093+++

Singapore 0269 0038 1109 3594 31689+++ 5555+++ 0169+++

Sri Lanka 0291 0045 1905 4965 11342+++ 3002+++ 0260+++

Thailand 0655 0075 1362 3575 46543+++ 2772+++ 0322+++

Indonesia 0036 0008 0903 3587 14114+++ 2159+++ 0223+++



Table 2




Malaysia 0311 0024 minus0050 0033

(0146) (0005) (0020) (0007)


(0044) (00054) (0005) (0001)

Sri Lanka 0546 0012 minus0056 0018

(0129) (0014) (0002) (0017)


(0111) (0001) (0001) (0026)


(0134) (0003) (0004) (0025)


Asia 006 0061 0007 0004

(0011) (0072) (00005) (0001)

Note

and


Table 3





α reg

= 0

11123 00000



224111 00000



114152 0000



111455 0000



























integration



































countries






late 1980s










Table 4



dollar (I)


currency (II)


v0 v1 v0 v1 v0 v1















































market deregulation









3

4

5

6

7

8

96 97 98 99 00 01 02 03 04 05 06 07




3

4

5

6

7

8

96 97 98 99 00 01 02 03 04 05 06 07


02

04

06

08

10

96 97 98 99 00 01 02 03 04 05 06 07

2

3

4

5

6

7

8

96 97 98 99 00 01 02 03 04 05 06 07

15 Indonesia

3

4

5

6

7

8

9

96 97 98 99 00 01 02 03 04 05 06 07









over time





























5 Conclusion


















Table 5



Constant MO UTS

Sri Lanka 0196 (0035) 0132 (0031) minus0156 (0003)

Malaysia 0277 (001) 0151 (0066) 0155 (0053)

Singapore 0561 (0059) 0061 (0002) 0117 (0007)

Thailand 0181 (0222) 0307 (0013) minus0052 (0002)

Indonesia 0221 (0342) 0207 (0011) 0032 (0001)


p mean p max p min

Sri Lanka 0531 (0092) 0846 0214

Malaysia 0553 (0130) 0788 0314

Singapore 0601 (0115) 0790 0312

Thailand 0512 (0114) 0767 0266

Indonesia 0525 (008) 0844 0361


Table 6













Note


Table 7



Malaysia 1120+++ 4412+++ 6377+++ 11909+++

(0130) (0120) (0244) (0170)

Singapor e 1389+++ 2145+++ 2953+++ 6487+++

(0149) (0812) (0011) (0151)

Sri Lanka 1111+++ 5203+++ 5186+++ 11500+++

(0152) (0028) (0178) (0125)

Thailand 1000+++ 1745+++ 2444+++ 5189+++

(0166) (0150) (0131) (0213)

Indonesia 1022+++ 3751+++ 3819+++ 8592+++

(0225) (0143) (0122) (0203)






References
















































Table 8

Residuals analysis


Mal aysia 1172+ 5441++ 67786+++ 13392 0196

Singapore minus0382 5843 51282+++ 16801 0190

Sri Lanka 1418 15368 952563+++ 9739 0285

Thailand 0291 3247 2356 5873 0062

Indonesia 0333 7666 22356+++ 7765 0333

Region 1514 16244 10131+++ 13456 0115































integration



































countries






late 1980s










Table 4



dollar (I)


currency (II)


v0 v1 v0 v1 v0 v1















































market deregulation









3

4

5

6

7

8

96 97 98 99 00 01 02 03 04 05 06 07




3

4

5

6

7

8

96 97 98 99 00 01 02 03 04 05 06 07


02

04

06

08

10

96 97 98 99 00 01 02 03 04 05 06 07

2

3

4

5

6

7

8

96 97 98 99 00 01 02 03 04 05 06 07

15 Indonesia

3

4

5

6

7

8

9

96 97 98 99 00 01 02 03 04 05 06 07









over time





























5 Conclusion


















Table 5



Constant MO UTS

Sri Lanka 0196 (0035) 0132 (0031) minus0156 (0003)

Malaysia 0277 (001) 0151 (0066) 0155 (0053)

Singapore 0561 (0059) 0061 (0002) 0117 (0007)

Thailand 0181 (0222) 0307 (0013) minus0052 (0002)

Indonesia 0221 (0342) 0207 (0011) 0032 (0001)


p mean p max p min

Sri Lanka 0531 (0092) 0846 0214

Malaysia 0553 (0130) 0788 0314

Singapore 0601 (0115) 0790 0312

Thailand 0512 (0114) 0767 0266

Indonesia 0525 (008) 0844 0361


Table 6













Note


Table 7



Malaysia 1120+++ 4412+++ 6377+++ 11909+++

(0130) (0120) (0244) (0170)

Singapor e 1389+++ 2145+++ 2953+++ 6487+++

(0149) (0812) (0011) (0151)

Sri Lanka 1111+++ 5203+++ 5186+++ 11500+++

(0152) (0028) (0178) (0125)

Thailand 1000+++ 1745+++ 2444+++ 5189+++

(0166) (0150) (0131) (0213)

Indonesia 1022+++ 3751+++ 3819+++ 8592+++

(0225) (0143) (0122) (0203)






References
















































Table 8

Residuals analysis


Mal aysia 1172+ 5441++ 67786+++ 13392 0196

Singapore minus0382 5843 51282+++ 16801 0190

Sri Lanka 1418 15368 952563+++ 9739 0285

Thailand 0291 3247 2356 5873 0062

Indonesia 0333 7666 22356+++ 7765 0333

Region 1514 16244 10131+++ 13456 0115





























market deregulation









3

4

5

6

7

8

96 97 98 99 00 01 02 03 04 05 06 07




3

4

5

6

7

8

96 97 98 99 00 01 02 03 04 05 06 07


02

04

06

08

10

96 97 98 99 00 01 02 03 04 05 06 07

2

3

4

5

6

7

8

96 97 98 99 00 01 02 03 04 05 06 07

15 Indonesia

3

4

5

6

7

8

9

96 97 98 99 00 01 02 03 04 05 06 07









over time





























5 Conclusion


















Table 5



Constant MO UTS

Sri Lanka 0196 (0035) 0132 (0031) minus0156 (0003)

Malaysia 0277 (001) 0151 (0066) 0155 (0053)

Singapore 0561 (0059) 0061 (0002) 0117 (0007)

Thailand 0181 (0222) 0307 (0013) minus0052 (0002)

Indonesia 0221 (0342) 0207 (0011) 0032 (0001)


p mean p max p min

Sri Lanka 0531 (0092) 0846 0214

Malaysia 0553 (0130) 0788 0314

Singapore 0601 (0115) 0790 0312

Thailand 0512 (0114) 0767 0266

Indonesia 0525 (008) 0844 0361


Table 6













Note


Table 7



Malaysia 1120+++ 4412+++ 6377+++ 11909+++

(0130) (0120) (0244) (0170)

Singapor e 1389+++ 2145+++ 2953+++ 6487+++

(0149) (0812) (0011) (0151)

Sri Lanka 1111+++ 5203+++ 5186+++ 11500+++

(0152) (0028) (0178) (0125)

Thailand 1000+++ 1745+++ 2444+++ 5189+++

(0166) (0150) (0131) (0213)

Indonesia 1022+++ 3751+++ 3819+++ 8592+++

(0225) (0143) (0122) (0203)






References
















































Table 8

Residuals analysis


Mal aysia 1172+ 5441++ 67786+++ 13392 0196

Singapore minus0382 5843 51282+++ 16801 0190

Sri Lanka 1418 15368 952563+++ 9739 0285

Thailand 0291 3247 2356 5873 0062

Indonesia 0333 7666 22356+++ 7765 0333

Region 1514 16244 10131+++ 13456 0115













over time





























5 Conclusion


















Table 5



Constant MO UTS

Sri Lanka 0196 (0035) 0132 (0031) minus0156 (0003)

Malaysia 0277 (001) 0151 (0066) 0155 (0053)

Singapore 0561 (0059) 0061 (0002) 0117 (0007)

Thailand 0181 (0222) 0307 (0013) minus0052 (0002)

Indonesia 0221 (0342) 0207 (0011) 0032 (0001)


p mean p max p min

Sri Lanka 0531 (0092) 0846 0214

Malaysia 0553 (0130) 0788 0314

Singapore 0601 (0115) 0790 0312

Thailand 0512 (0114) 0767 0266

Indonesia 0525 (008) 0844 0361


Table 6













Note


Table 7



Malaysia 1120+++ 4412+++ 6377+++ 11909+++

(0130) (0120) (0244) (0170)

Singapor e 1389+++ 2145+++ 2953+++ 6487+++

(0149) (0812) (0011) (0151)

Sri Lanka 1111+++ 5203+++ 5186+++ 11500+++

(0152) (0028) (0178) (0125)

Thailand 1000+++ 1745+++ 2444+++ 5189+++

(0166) (0150) (0131) (0213)

Indonesia 1022+++ 3751+++ 3819+++ 8592+++

(0225) (0143) (0122) (0203)






References
















































Table 8

Residuals analysis


Mal aysia 1172+ 5441++ 67786+++ 13392 0196

Singapore minus0382 5843 51282+++ 16801 0190

Sri Lanka 1418 15368 952563+++ 9739 0285

Thailand 0291 3247 2356 5873 0062

Indonesia 0333 7666 22356+++ 7765 0333

Region 1514 16244 10131+++ 13456 0115









References
















































Table 8

Residuals analysis


Mal aysia 1172+ 5441++ 67786+++ 13392 0196

Singapore minus0382 5843 51282+++ 16801 0190

Sri Lanka 1418 15368 952563+++ 9739 0285

Thailand 0291 3247 2356 5873 0062

Indonesia 0333 7666 22356+++ 7765 0333

Region 1514 16244 10131+++ 13456 0115







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