macroeconomic factors of exchange rate volatility

Macroeconomic factorsof exchange rate volatility

Evidence from four neighbouring ASEANeconomies

Chong Lee-LeeFaculty of Management, Centre for Multimedia Banking,

Investment and Accounting, Multimedia University, Selangor, Malaysia, and

Tan Hui-BoonThe University of Nottingham Malaysia Campus, Semenyih, Malaysia

Abstract

Purpose – The purpose of this study is to examine the factors of exchange rate volatility from themacroeconomic perspective for four neighbouring ASEAN economies.

Design/methodology/approach – This study has scrutinised the link between macroeconomicfactors and exchange rate volatility in both the short and the long run by applying econometricstechniques.

Findings – This study further suggests the link between macroeconomic factors and exchange ratevolatility in both the short and the long run for the selected economies. The empirical results, however,indicate that a set of common factors seems to influence the exchange rate volatility, whereby the stockmarket is a great influence commonly found across countries. The Indonesian rupiah seems to be themost sensitive to the innovations in macroeconomic factors, while the Singapore dollar is the least.

Research limitations/implications – The macroeconomic factors are believed to be the forcesbehind exchange rate volatility through the presumable rigidities of their exchange rates, resultingfrom the managed float exchange rate system adopted by those countries. Their capital markets arevital in maintaining exchange rate stability, hence suggesting the imperative role of respectiveauthorities and market players in managing a viable capital market.

Originality/value – Little attention has been given to developing countries’ experiment with theirexchange rate systems due to their presumed rigid volatility. This study adopts a more sophisticatedapproach in measuring the volatility of the exchange rate and examines the underlying factors ofexchange rate volatility instead of the level of exchange rate.

Keywords Exchange rate mechanisms, Macroeconomics, GARCH specification, Econometrics

Paper type Research paper

1. IntroductionThroughout the years, the global economy has been transformed from a simplifiedfinancial architecture to a complex intertwined set of financial systems. From theBretton Woods system to the advent of flexible exchange rate systems in 1973 until thepresent days, the environment of international markets had experienced substantialchanges in the form of excessive variability in exchange rates, greater capital mobilityand punctuated by a series of financial crises worldwide in recent years. For example,the Asian crisis-1997 had hampered growth of many Asian countries and differentresponses had been adopted by the crisis-hit countries in ameliorating their economies.Thailand, Indonesia and South Korea all agreed to receive the International Monetary

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/1086-7376.htm

SEF24,4

266

Studies in Economics and FinanceVol. 24 No. 4, 2007pp. 266-285q Emerald Group Publishing Limited1086-7376DOI 10.1108/10867370710831828

Fund (IMF) rescue programme whereby Malaysia had shied away from the offer.Different remedial measures were taken by the affected economies in managing theireconomic contraction and erratic exchange rate movement.

Asian economies in general have transformed tremendously since the 1970s and theexchange rate arrangements implemented by the countries have come a long waycompared with the past since the eruption of the Asian financial crisis-1997.Developing countries are more exposed to both internal and external disturbances thanever. A better understanding of exchange rate volatility therefore might providegreater economic stability. Previously, most researches have scrutinised determinantsof the level of exchange rate. Meese (1990) who studied the currency fluctuations in thepost-Bretton Woods era found that the changes of macroeconomic variables alonecould not explain major currencies movements. By using a long-span data, MacDonaldand Taylor (1994) however noticed relationships between macroeconomic variablesand exchange rate. A recent study by Rapach and Wohar (2002) meanwhile producedmixed results for the monetary model of exchange rate determination.

Movements of exchange rate are always a concern for various parties. Ininternational currency markets, exchange rate plays a significant role and thevariability of exchange rate, whichever way it sways, tends to give a significant impacton the economy. Since, the adoption of flexible exchange rate system in 1973, exchangerate movements have been excessive and the bulk of the previous studies on thissubject matter are based on the experience of developed countries such as Organizationof Economical Co-operation members. Little attention has been given to developingcountries’ experiment with different exchange rate systems due to their presumed rigidvolatility. However, nominal currencies of the developing nations might not producefixed and predictable exchange rate and its parity level might deviate, paving way forcurrency speculation moves. This explains why a currency crisis frequently takesplace in developing countries.

The exchange rate volatility faced by developing nations most likely will also varydespite of its pegging system due to the implicit weight of the currency that onecountry pegs. Though Asian economies generally prefer managed float exchange ratesystem, the exchange rate volatility of each currency is said to vary even in thepresence of a pegging system (Warner and Kreinin, 1983; Alba and Papell, 1998). Inview of this, the forces to exchange rate volatility for the four Asian economies thathave been selected for this study – Malaysia, Indonesia, Thailand and Singapore –deserve vigorous examination. The four countries, adopting semi-fixity exchange ratesystem, are small and open economies that have substantially liberalised their tradeand investment sectors since the 1980s. According to the classical problem ofmacroeconomic trilemma, exchange rate stability, capital mobility and autonomousmonetary policies all cannot be achieved simultaneously and one of the three must besacrificed. Therefore, the importance of identifying the factors of exchange ratevolatility of the four currencies must not to be underestimated or overlooked in order tomaintain their macroeconomic success. This paper, thus, intends to examine the causescontributing to the volatility of the Malaysian ringgit, the Indonesian rupiah, the Thaibaht and the Singapore dollar.

Given that the four economies experienced several switches in their exchange ratearrangements since the 1980s, this study only analyses the macroeconomic factors ofexchange rate volatility during the flexible exchange rate period. At the same time, the

Factors ofexchange rate

volatility

267

underlying macroeconomic factors of exchange rate volatility instead of the level ofexchange rate are investigated. In computing the exchange rate volatility, this studyuses a more sophisticated approach in measuring the volatility of exchange rate wheregeneralised autoregressive conditional heteroscedastic (GARCH) approach is selectedin view of its superior performance in providing a parsimonious stochastic process ofexchange rate return. In order to capture the asymmetric effect, exponential GARCH(E-GARCH), an extension of GARCH approach, is chosen.

The outline of this paper is as follows: Section 2 describes the exchange ratevolatility for the pre- and post-Asian crisis 1997 periods. Section 3 explains themethodology used while Section 4 delineates the results and discussions. Section 5presents a summary and the relevant concluding remarks.

2. Exchange rate volatility during the pre- and post-Asian crisis in 1997Figure 1 shows the volatility of Malaysian ringgit, Indonesian rupiah, Thai baht andSingapore dollar from January 1980 to December 2003 which these currencies arevis-a-vis US dollar. The volatility rates are computed based on the E-GARCHspecification where the lag order is selected based on the Schwartz criteria[1].

For most of the past two decades, a majority of the South-East Asian economies arein favour of a managed float system. The four countries of Malaysia, Indonesia,Thailand and Singapore attach great importance to exchange rates stability in theirpolicy making. This section looks at the 20-year exchange rates volatility in the hope tocast some light on the degree of exchange rate risk for the four economies bycomparing the volatility of their currencies. Overall, Indonesia is the country whichsuffered the most in terms of volatile exchange rate even for the past several years,when the effect of the Asian crisis began to subside for the others. Thailand’s exchangerate is the most volatile after Indonesia. Singapore had kept its exchange rate volatilityin the lowest compared with the other three countries examined since the 1980s.

Figure 1.Exchange rate volatilityfor the pre- and post Asiancrisis in 1997 from January1980 to December 2003

0

5

10

15

20

25

30

35

40

45

Jan-

80

Jan-

81

Jan-

82

Jan-

83

Jan-

84

Jan-

85

Jan-

86

Jan-

87

Jan-

88

Jan-

89

Jan-

90

Jan-

91

Jan-

92

Jan-

93

Jan-

94

Jan-

95

Jan-

96

Jan-

97

Jan-

98

Jan-

99

Jan-

00

Jan-

01

Jan-

02

Jan-

03

Ringgit S$ Rupiah Baht

SEF24,4

268

Malaysia had preserved considerably low exchange rate risk and its exchange ratevolatility generally was lower than that of Indonesia and Thailand prior to the pegsystem.

Over the years, Malaysia, Indonesia and Thailand have experienced substantialchanges in their measures taken in the form of monetary and fiscal policies in responseto the ever-changing environment. The rapid changes are particularly visible since theliberalisation of their trade and investment sectors in the 1980s and after the 1997Asian financial crisis. The exchange rate of these economies had depreciated sharplyas a result of currency speculation attacks that raised the curtain on the 1997 Asianfinancial crisis. In mitigating the negative impact of the crisis, Malaysia had taken adifferent path from that chosen by Indonesia and Thailand. While Malaysia hadpegged its currency to the US dollar in the year 1998, the other two had floated theircurrencies to arrest the sharp depreciation of their currencies. Singapore had weatheredits economy relatively well from the crisis and maintained a consistent exchange ratepolicy.

All the four countries are open economies that have generally implemented someform of managed exchange rate system since 1980 except for Indonesia and Thailand,which adopted the floating system for a few years following the 1997 Asian financialcrisis. All four economies have a high degree of similarities in their economicstructures, geographical location and stages of development but the measures theyeach took in response to the economic meltdown were somewhat different. Apparently,Singapore withstood the crisis with the least fluctuation in its currency given its soundmacroeconomic fundamentals but Indonesia suffered the most volatility in its rupiahmovement, partly because of the country’s political problem at that time. Malaysia, upto the point when the peg system was adopted, seemed to have lower volatility thanthat of Indonesia and Thailand.

3. MethodologyThis study covers the sample period for the four economies within the flexibleexchange rate period. Malaysia covers from January 1980 to September 1998, whenMalaysia ended its pegged system subsequently[2]. The sample periods for Indonesiaand Thailand when both embraced a flexible exchange rate span from August 1997 toJune 2001 and July 1997 to June 2001, respectively. The sample period for Singaporecovers from May 1985 to September 2003 due to its consistent exchange ratearrangement. The category of this exchange rate system is in accordance with IMF’sclassification and the data used are in monthly frequency. The exchange rate is quotedas domestic currency per foreign currency, namely the US dollar.

The exchange rate volatility is computed based on the E-GARCH specificationdeveloped by Nelson (1991) as it is able to account for the asymmetric effect. Itscanonical specification is written as follows:

logðhtÞ ¼ w0 þXq

i¼1

aigðzt2iÞ þXP

J¼1

gjlogðht2jÞ given

Rt ¼ mþ 1; 1tjVt21 , N ð0; htÞ


volatility

269

where w and m ¼ constant:

gðztÞ ¼ uzt þ g½jztj2 Ejztj� and zt ¼1tffiffiffiffiht

p

R ¼ logðet=et21Þ where e is an exchange rate of Malaysian ringgit, Indonesian rupiah,Thai baht and Singapore dollar vis-a-vis US dollar, p ¼ the order of the autoregressive(AR) process and q ¼ the order of the moving-average (MA) process. If Zt is positive,then gðztÞ is a linear function of the slope changes Zt with slope of ðuþ gÞ: If Zt isnegative, the slope will be ðu2 gÞ: The conditional variance ht reacts asymmetricallyto the magnitude of innovation, zt2i: The lag order is selected based on the Schwartzcriteria. The exponential GARCH estimation is used to compute the exchange ratevolatility in view of its superior to the standard GARCH model given its variance couldbe an asymmetric function of its past term. Several studies have also employed theexponential GARCH model in estimating exchange rate volatility. For instances, Kim(1998) and Bond and Najand (2002) had utilised the same method in computingexchange rate volatility.

In this study, the computed exchange rate volatility will be incorporated in vectorautoregressive (VAR) model in the context of an econometric modelling ofJohansen-Juselius cointegration and Granger causality tests. The procedure ofincorporating the conditional volatility into a VAR model is supported by Ramchanderand Sant (2002) who also did the same step. Both Johansen-Juselius cointegration testand autoregressive distributed lag (ARDL) model are used to measure the long-runrelations, with Granger causality test in the framework of vector-error correction model(VECM) is employed to gauge the short-run relations.

For the long-run relationship, a multivariate cointegration method developed byJohansen (1988) and Johansen and Juselius (1990) is employed. Johansen and Juselius(1990) employed a maximum likelihood procedure to examine cointegrated procedure.Defining a vector Yt of n potential endogenous variables, Yt is an unrestricted VARwith m lags as follows:

Yt ¼ A0 þ A1Yt21 þ Lþ AmYt2m þ 1t

where Yt is an ðn £ 1Þ vector of variables, A’s is an ðn £ nÞ matrices of parameters and1t is an ðn £ 1Þ vector of constant terms. The Johansen cointegration test can be doneby estimating the above equation in its first difference specification as follows:

DYt ¼ G1DYt21 þ Lþ Gm21DYt2mþ1 þPYt2m þ 1t

where Gi ¼ ðI 2 A1 2 · · · 2 AiÞ with i ¼ 1; 2; . . . ;m2 1 and P ¼ 2ðI 2 A1 2 · · · 2AmÞ: I ¼ the identity matrix and P ¼ an ðn £ nÞ matrix. This model can capture bothshort- and long-run adjustments to the changes in Yt by examining Gi and Prespectively. The Johansen method measures the rank of the matrix P and it can befurther specified as P ¼ ab0 with a indicates the speed of adjustment to disequilibriumand b is a matrix of long-run coefficients. b is an ðn £ rÞ matrix of cointegrating vectorswhile a is an ðn £ rÞ adjustment coefficients matrix which each cointegrating vectorenters the n equations of the VAR. r ¼ 0; 1; . . . ; n and it is the rank of matrix or thenumber of cointegrating independent cointegrating vectors. If the rank ofP is zero, thenthere is no linear combination of the variables in Yt and they are said no cointegrationamong the variables. If the rank of P is full, then they are also said to be stationary. If

SEF24,4

270

0 , r , n; there will be r cointegrating equations. The maximum likelihood estimate ofb is obtained as the eigenvectors corresponding to the r largest and significanteigenvalues. The significance of eigenvalues can be examined by two maximumlikelihood test statistics that are trace and maximum eigenvalues statistics. The nullhypothesis for trace statistics is that there are at most r cointegration vectors while thereare r cointegrating vectors against the r þ 1 cointegrating vectors for maximumeigenvalues statistics. Both test statistics are shown as below:

TraceðltraceÞ ¼ TXn

i¼rþ1

Lnð1 2 li Þ;

Maximum eigenvalue ðlmax Þ ¼ TLnð1 2 lrþ1Þ

where li are the eigenvalue values.Meanwhile, the ARDL model of Pesaran and Shin (1999) used the ordinary least

square estimation to explain long-run relationship. This method has the advantage ofgetting consistent estimation of long-run coefficients no matter the underlyingregressors are I(1) or I(0). In general, an ADRL model contains of lagged values ofdependent variable, current and lagged values of one or more independent variablesand our model is expressed as below:

Dervt ¼ a0 þXn

j¼1

bjDervt2j þXn

j¼1

cjDmt2j þXn

j¼1

djDyt2j þXn

j¼1

ejDit2j

þXn

j¼1

f jDpt2j þXn

j¼1

gjDtbt2j þXn

j

hjDcit2j þ d1ervt21 þ d2mt21

þ d3yt21 þ d4it21 þ d5pt21 þ d6tbt21 þ d7cit21 þ 1t

where erv ¼ exchange rate volatility; relative money supply, m ¼ (md 2 mf); relativeincome, y ¼ ( yd 2 yf); relative interest rate, i ¼ (id 2 if); relative inflation rate,p ¼ ðpd 2 pfÞ; relative trade balance, tb ¼ (tbd 2 tbf); relative stock index,ci ¼ (cid 2 cif); a ¼ constant; 1 ¼ error term; subscript d and f refer domestic andforeign countries, respectively. All variables are in natural logarithm except for interestrates. The F-statistics will be computed for the significance of the lagged levelvariables with the null hypothesis of no cointegration. The null hypothesis ofnon-existence of long-run relationship is defined by:

H 0 ¼ d1 ¼ d2 ¼ d3 ¼ d4 ¼ d5 ¼ d6 ¼ d7 ¼ 0

against its alternative hypothesis:

H 1 ¼ d1 – d2 – d3 – d4 – d5 – d6 – d7 – 0

The lag length, n, is selected by Akaike information criterion (AIC). Owing to theasymptotic distributions of the F-statistics are non-standard, Pesaran et al. (1996)provide two sets of asymptotic critical values which on one side, all variables are I(0)and on the other side, all variables are I(1). These two sets of critical values cover all the


volatility

271

possible categories of regressors into purely I(0), purely I(1) or mutually cointegrated. Ifthe computed F-statistic is greater than the upper bound (FU), the null hypothesis of nocointegration can be rejected whereas if the computed F-statistic falls below the lowerbound (FL), then the null hypothesis cannot be rejected. However, if the computedF-statistic falls between the upper and lower bound, the result is inconclusive and thestandard analysis for unit roots should be applied. Among others, Bahmani-Oskooeeand Kara (2000), Pesaran et al. (2001), Bahmani-Oskooee and Ng (2002), Piesse andHearn (2002) and Chou and Lee (2003) also utilised the ARDL technique in measuringlong-run relations.

On the other hand, causality test as proposed by Granger (1969, 1988) is included inthis study whereby error correction term(s) (ECM) is embraced in the causality test toavoid misspecification problem (Granger, 1988). The ECM(s) carries short-runcorrection to form long-run equilibrium and also captures short-run adjustment ofcointegration variables. Masih and Masih (1997a, b) indicated some important findingsthat if series are stationary after first differencing and if they are cointegrated of orderr, then r number of ECM(s) should be included in the VECM analysis. The vector timeseries can be written as Yt ¼ ð y1; y2; . . . ; ynÞ

0 while the general VECM formula is asbelow:

DYt ¼ d0 þPYt21 þXm

i¼1

uiDYt2i þ 1t

where Yt is an ðn £ nÞ vector of variables, d0 is an ðn £ 1Þ vector of constants, P and uare ðn £ nÞ matrices reflecting both short-and long-run effects, 1 is an ðn £ 1Þ vector ofwhite noise disturbances. If series are not cointegrated, then the P ¼ 0 and the VECMis an unrestricted VAR. However, if there is a cointegration relationship, the P can bedecomposed into two ðn £ rÞ matrices of a and b, for example P ¼ ab0.

On the other hand, the variance decomposition is a technique which allows fordynamic shocks among variables and it captures the forecast error of each of thevariables into components from the innovations by each endogenous variableincluding its own shock. The relative strength of the variable can be captured by usingthe forecast error of variance decomposition. This forecast error of variancedecomposition based on the Choleski decomposition is too sensitive to the order of thevariables. Therefore, the generalised variance decomposition based on Pesaran andShin (1998) is integrated to gauge the relative importance of the influence of eachmacroeconomic factor on exchange rate volatility.

The procedures of carrying out the analysis of macroeconomic factors of exchangerate volatility involve computing the conditional standard deviation of the exchangerates and then incorporate it into the setting as below:

erv ¼ a0 þ a1ðmd 2mfÞ þ a2ð yd 2 yfÞ þ a3ðid 2 ifÞ þ a4ðpd 2 pfÞ þ a5ðtbd 2 tbfÞ

þ a6ðcid 2 cifÞ þ 1

where erv ¼ conditional standard deviation of exchange rate[3]; m ¼ money supply(M2); y ¼ income[4]; i ¼ interest rate[5]; p ¼ inflation index[6]; tb ¼ ratio ofnominal exports to nominal imports; ci ¼ composite index[7]. Subscript d and f refer

SEF24,4

272

domestic and foreign countries, respectively. The use of relative terms is to capture theimportance of foreign and domestic influences on exchange rate volatility.

4. Results and discussionsTables I-V present the long-run relations of the macroeconomic factors of exchange ratevolatility for the four economies of Malaysia, Indonesia, Thailand and Singapore. In thissetting, the relative terms of interest rates (RI), money supplies (RM), consumer priceindices (RCPI), trade balances (RTB) and composite indices (RCI) are examined againstthe significance of their effect on the exchange rate volatility of the four countries. BothARDL and Johansen-Juselius methods indicate the presence of the long-run movementamong the variables for all the countries. However, the similar result is not found in thecase of Thailand. This suggests that exchange rate volatility and relativemacroeconomic factors are moving together to achieve their long-run equilibrium forthe three economies – Malaysia, Indonesia and Singapore. As a standard procedure, theunit root tests of augmented Dickey and Fuller (1979, 1981) and Kwiatkowski et al.(1992) techniques had been carried out prior to the cointegration test[8].

Both results of ARDL and Johansen-Juselius techniques seem to be consistent andthe following short-run factors of exchange rate volatility are, then, examined byemploying the Granger causality in the vector error-correction model. Tables VI-IXreport the short-run macroeconomic factors of exchange rate volatility for the foureconomies. For Malaysia, relative money supplies, trade balances and stock indices arethe factors which influence the volatility of the ringgit while for Indonesia, two relativemacroeconomic factors – money supplies and stock indices – might exert pressure tothe volatility of rupiah. There are two relative factors of consumer price indices andcomposite indices that might influence the volatility of Thai baht. For Singapore, tworelative macroeconomic factors of money supplies and composite indices mightmanifest their effect on the volatility of Singapore dollar.

To explain the relative forecast error variance of each macroeconomic factor forexchange rate volatility, Tables X-XIII present the results up to 48 months. TheMalaysian ringgit volatility is explained about 55 per cent by its own shocks andthen followed by the shocks in relative trade balance, money supplies and stockindices. On the other hand, the relative terms of interest rates, consumer priceindices, money supplies and stock indices contribute, in large proportion, toinnovations in Indonesian rupiah volatility for about 37, 26, 16 and 14 per cent,

Country F-statistics (lag selection based on AIC) T-statistics Results

Malaysia 3.2703 (1) * ECt21 ¼ 23.8772 * * CointegratedIndonesia 7.1261 (1) * * ECt21 ¼ 24.5485 * * CointegratedThailand 2.1031 (1) – No cointegrationSingapore 2.8977 (1) ECt21 ¼ 23.2809 * * Inconclusive

Notes: At the 5 per cent significance level, the critical values of the bound of the F-statistics are2.476-3.646 whereas at the 10 per cent significance level, the bounds are 2.141-3.250. Asterisks ( * *) and( *) indicate significance at 5 and 10 per cent levels, respectively, and figures in parentheses indicate thelag order. Kremers et al. (1992) have indicated that a significant lagged ECM is a relatively moreefficient way to suggest cointegration relationship. Hence, the error correction tern (EC) of Singapore issignificant and suggests that they are cointegrated

Table I.ARDL cointegration test

results for the underlyingfactors of exchange rate

volatility originated fromrelative macroeconomic

variables


volatility

273

Per

iod

:19

80:

M1-

1998

:M

8,E

RV

,R

I,R

M,

RC

PI,

RIP

,R

TB

and

RC

Isp

ecifi

cati

on(l

agor

der

¼1)

Max

imu

mei

gen

val

ues

test

Tra

cete

stN

ull

Hy

pot

hes

isA

lter

nat

ive

hy

pot

hes

isM

axim

um

eig

env

alu

esst

atis

tics

95p

erce

nt

crit

ical

val

ues

Alt

ern

ativ

eh

yp

oth

esis

Tra

cest

atis

tics

95p

erce

nt

crit

ical

val

ues

r¼

0r¼

135

.630

345

.28

r.

112

8.79

18*

124.

24r#

1r¼

234

.482

039

.37

r.

293

.161

594

.15

r#

2r¼

323

.759

533

.46

r.

358

.679

568

.52

r#

3r¼

420

.289

427

.07

r.

434

.920

147

.21

r#

4r¼

58.

4684

20.9

7r.

514

.630

629

.68

r#

5r¼

65.

2251

14.0

7r.

66.

1622

15.4

1r#

6r¼

70.

9371

3.76

r.

70.

9371

3.76

Notes:

Th

eV

AR

ord

eris

bas

edon

lik

elih

ood

rati

ote

st.

* in

dic

ate

sig

nifi

can

ceat

5p

erce

nt

lev

el.J

uk

s(2

003)

,Joh

anse

n(1

994)

and

Pan

tula

(198

9)su

gg

est

that

the

trac

ete

sth

asso

me

adv

anta

ges

over

the

max

imu

mei

gen

val

ue

test

Table II.Johansen-Juseliuscointegration test resultsfor the underlying factorsof exchange ratevolatility originated fromrelative macroeconomicvariables- Malaysia

SEF24,4

274

Per

iod

:19

97:M

8-20

01:M

6,E

RV

,R

I,R

M,

RC

PI,

RIP

,R

TB

and

RC

Isp

ecifi

cati

on(l

agor

der

¼1)

Max

imu

mei

gen

val

ues

test

Tra

cete

stN

ull

hy

pot

hes

isA

lter

nat

ive

hy

pot

hes

isM

axim

um

eig

env

alu

esst

atis

tics

a95

per

cen

tcr

itic

alv

alu

esA

lter

nat

ive

hy

pot

hes

isT

race

stat

isti

csa

95p

erce

nt

crit

ical

val

ues

r¼

0r¼

151

.048

0*

45.2

8r.

115

0.60

05*

124.

24r#

1r¼

235

.321

939

.37

r.

299

.552

6*

94.1

5r#

2r¼

327

.834

933

.46

r.

364

.230

668

.52

r#

3r¼

417

.164

927

.07

r.

436

.395

847

.21

r#

4r¼

511

.481

320

.97

r.

519

.230

929

.68

r#

5r¼

66.

0383

14.0

7r.

67.

7496

15.4

1r#

6r¼

71.

7113

3.76

r.

71.

7113

3.76

Notes:

Th

eV

AR

ord

eris

bas

edon

lik

elih

ood

rati

ote

st.

* in

dic

ate

sig

nifi

can

ceat

5p

erce

nt

lev

el.J

uk

s(2

003)

,Joh

anse

n(1

994)

and

Pan

tula

(198

9)su

gg

est

that

the

trac

ete

sth

asso

me

adv

anta

ges

over

the

max

imu

mei

gen

val

ues

test

.T

est

stat

isti

csis

adju

sted

acco

rdin

gto

Rei

nse

lan

dA

hn

(199

2)d

ue

toth

ep

rob

lem

ofsm

all

sam

ple

size

.Th

ete

stst

atis

tics

isad

just

edb

y(T

2nk)

/T.T

–to

tal

nu

mb

erof

obse

rvat

ion

s;n

–th

en

um

ber

ofse

ries

andk

–la

gle

ng

thor

der

ofth

eV

AR

syst

em.

aF

orM

alay

sia,

inco

me

(y)

refe

rsto

ind

ust

rial

pro

du

ctio

nin

dex

;fo

rIn

don

esia

,it

den

otes

gro

ssn

atio

nal

pro

du

ct(G

NI)

;fo

rT

hai

lan

d,

itre

pre

sen

tsm

anu

fact

uri

ng

pro

du

ctio

nin

dex

;fo

rS

ing

apor

e,it

ind

icat

esto

tal

man

ufa

ctu

rin

gin

dex

Table III.Johansen-Juselius

cointegration test resultsfor the underlying factors

of exchange ratevolatility originated from

relative macroeconomicvariables – Indonesia


volatility

275

Per

iod

:19

97:M

7-20

01:M

6,E

RV

,R

I,R

M,

RC

PI,

RIP

,R

TB

and

RC

Isp

ecifi

cati

on(l

agor

der

¼1)

Max

imu

mei

gen

val

ues

test

Tra

cete

stN

ull

hy

pot

hes

isA

lter

nat

ive

hy

pot

hes

isM

axim

um

eig

env

alu

esst

atis

tics

a95

per

cen

tcr

itic

alv

alu

esA

lter

nat

ive

hy

pot

hes

isT

race

stat

isti

csa

95p

erce

nt

crit

ical

val

ues

r¼

0r¼

142

.795

745

.28

r.

112

3.83

0212

4.24

r#

1r¼

234

.189

139

.37

r.

281

.034

594

.15

r#

2r¼

318

.353

133

.46

r.

346

.845

568

.52

r#

3r¼

416

.565

827

.07

r.

428

.492

447

.21

r#

4r¼

58.

2297

20.9

7r.

511

.926

629

.68

r#

5r¼

63.

3263

14.0

7r.

63.

6971

15.4

1r#

6r¼

70.

3707

3.76

r.

70.

3707

3.76

Notes:

Th

eV

AR

ord

eris

bas

edon

lik

elih

ood

rati

ote

st.

* in

dic

ates

sig

nifi

can

ceat

5p

erce

nt

lev

el.a

Tes

tst

atis

tics

isad

just

edac

cord

ing

toR

ein

sel

and

Ah

n(1

992)

du

eto

the

pro

ble

mof

smal

lsam

ple

size

.Th

ete

stst

atis

tics

isad

just

edb

y(T

2nk)

/T.T

–to

taln

um

ber

ofob

serv

atio

ns;n

–th

en

um

ber

ofse

ries

andk

–la

gle

ng

thor

der

ofth

eV

AR

syst

em.T

he

con

dit

ion

alst

and

ard

dev

iati

onof

exch

ang

era

teis

com

pu

ted

via

E-G

AR

CH

met

hod

.Th

ep

andq

ord

ers

for

the

fou

rec

onom

ies-

Mal

aysi

a,In

don

esia

,Th

aila

nd

and

Sin

gap

ore

are

(1,2

),(1

,2),

(2,2

)an

d(2

,2),

resp

ecti

vel

y.

Th

ese

lect

ion

ofp

andq

isb

ased

onth

eS

chw

artz

crit

eria

Table IV.Johansen-Juseliuscointegration test resultsfor the underlying factorsof exchange ratevolatility originated fromrelative macroeconomicvariables – Thailand

SEF24,4

276

Per

iod

:19

85:M

1-20

03:M

9,E

RV

,R

I,R

M,

RC

PI,

RIP

,R

Tan

dR

CI

spec

ifica

tion

(lag

ord

er¼

1)M

axim

um

eig

env

alu

este

stT

race

test

Nu

llh

yp

oth

esis

Alt

ern

ativ

eh

yp

oth

esis

Max

imu

mei

gen

val

ues

stat

isti

cs95

per

cen

tcr

itic

alv

alu

esA

lter

nat

ive

hy

pot

hes

isT

race

stat

isti

cs95

per

cen

tcr

itic

alv

alu

es

r¼

0r¼

155

.632

5*

45.2

8r.

114

3.64

41*

124.

24r#

1r¼

232

.769

939

.37

r.

288

.011

794

.15

r#

2r¼

323

.876

433

.46

r.

355

.241

868

.52

r#

3r¼

418

.133

827

.07

r.

431

.365

347

.21

r#

4r¼

57.

9812

20.9

7r.

513

.231

529

.68

r#

5r¼

64.

9929

14.0

7r.

65.

2504

15.4

1r#

6r¼

70.

2575

3.76

r.

70.

2575

3.76

Notes:

Th

eV

AR

ord

eris

bas

edon

lik

elih

ood

rati

ote

st.

* in

dic

ate

sig

nifi

can

ceat

5p

erce

nt

lev

el

Table V.Johansen-Juselius

cointegration test resultsfor the underlying factors

of exchange ratevolatility originated from

relative macroeconomicvariables – Singapore


volatility

277

Dep

end

ent

var

iab

les

Ind

epen

den

tv

aria

ble

s

DE

RV

DR

ID

RM

DR

CP

ID

RIP

DR

TB

DR

CI

EC

Tt2

1

Per

iod

:19

80:M

1-19

98:M

8F

-sta

tist

ics

T-s

tati

stic

s

DE

RV

–1.

5686

(0.2

118)

91.1

038

**

(0.0

000)

1.19

79(0

.275

0)0.

2359

(0.6

277)

6.84

73*

*

(0.0

095)

8.38

51*

*

(0.0

042)

24.

1460

**

DR

I5.

5872

**

(0.0

190)

–0.

0012

(0.9

722)

3.29

02*

(0.0

711)

0.08

10(0

.776

2)0.

8614

(0.3

544)

5.22

26*

*

(0.0

233)

21.

1547

DR

M2.

1858

(0.1

408)

1.14

25(0

.286

3)–

0.56

74(0

.452

1)0.

2457

(0.6

207)

0.13

15(0

.717

2)1.

4257

(0.2

338)

1.01

88D

RC

PI

4.78

55*

*

(0.0

298)

0.08

08(0

.776

6)0.

0276

(0.8

683)

–2.

0523

(0.1

534)

0.02

35(0

.878

3)0.

0050

(0.9

440)

20.

3782

DR

IP3.

5205

*

(0.0

620)

0.58

19(0

.446

4)0.

4335

(0.5

110)

4.49

26*

*

(0.0

352)

–1.

3185

(0.2

522)

0.02

07(0

.885

8)0.

7531

DR

TB

0.02

70(0

.869

7)0.

0132

(0.9

087)

3.09

43*

(0.0

800)

0.54

88(0

.459

6)0.

0212

(0.8

845)

–0.

7752

(0.3

796)

23.

4328

**

DR

CI

0.83

73(0

.361

2)0.

3019

(0.5

833)

0.24

39(0

.621

9)0.

0735

(0.7

866)

0.08

61(0

.769

5)4.

3792

**

(0.0

376)

–2

0.17

10

Notes:

Th

ev

alu

esin

par

enth

eses

are

thep-

val

ue.

** ,

*in

dic

ate

sig

nifi

can

ceat

5an

d10

per

cen

tle

vel

s,re

spec

tiv

ely

Table VI.Results of error correctionestimates for theunderlying factors ofexchange rate volatilityoriginated from therelative macroeconomicvariables – Malaysia

SEF24,4

278

Dep

end

ent

var

iab

les

Ind

epen

den

tv

aria

ble

s

DE

RV

DR

ID

RM

DR

CP

ID

RIP

DR

TB

DR

CI

EC

Tt2

1

Per

iod

:19

97:M

7-20

01:M

6F

-sta

tist

ics

T-s

tati

stic

s

DE

RV

–1.

1979

(0.2

812)

3.54

71*

(0.0

680)

0.37

69(0

.543

3)0.

2150

(0.6

458)

0.00

02(0

.988

6)7.

8313

**

(0.0

083)

26.

6747

**

3.60

96*

*

DR

I0.

9638

(0.3

330)

–0.

0108

(0.9

178)

0.05

50(0

.816

0)2.

8207

(0.1

020)

1.18

87(0

.283

0)0.

8344

(0.3

673)

20.

0843

22.

7460

**

DR

M2.

4887

(0.1

237)

0.02

84(0

.867

2)–

0.04

79(0

.828

1)0.

0669

(0.7

975)

0.06

25(0

.804

0)0.

6734

(0.4

174)

21.

2384

1.88

40*

*

DR

CP

I3.

5080

(0.0

694)

*1.

0703

(0.3

063)

25.8

235

**

(0.0

000)

–0.

4523

(0.5

056)

0.02

00(0

.888

4)3.

9335

*

(0.0

552)

1.13

140.

9821

DR

IP2.

6609

(0.1

118)

1.13

52(0

.294

0)8.

8702

**

(0.0

052)

7.61

83*

*

(0.0

091)

–5.

5206

**

(0.0

246)

2.19

52(0

.147

4)2

0.96

912.

4043

**

DR

TB

0.89

18(0

.351

5)5.

7833

**

(0.0

216)

0.14

56(0

.705

1)1.

6290

(0.2

102)

0.12

76(0

.723

1)–

2.32

46(0

.136

3)2

1.09

221.

1228

DR

CI

0.22

48(0

.638

4)0.

0191

(0.8

910)

0.00

94(0

.923

5)2.

6370

(0.1

134)

1.44

06(0

.238

1)0.

8865

(0.3

529)

–0.

3740

21.

2087

Notes:

Th

ev

alu

esin

par

enth

eses

are

thep-

val

ue.

** ,

* in

dic

ate

sig

nifi

can

ceat

5an

d10

per

cen

tle

vel

.Tw

oco

inte

gra

tin

gv

ecto

rsar

ein

corp

orat

edin

toth

eV

EC

Man

aly

sis

sin

ceth

etr

ace

test

has

som

ead

van

tag

esov

erth

em

axim

um

eig

env

alu

ete

st(J

uk

s,20

03;

Joh

anse

n,

1994

;P

antu

la,

1989

)

Table VII.Results of error correction

estimates for theunderlying factors of

exchange rate volatilityoriginated from relative

macroeconomic variables– Indonesia


volatility

279

Dep

end

ent

var

iab

les

Ind

epen

den

tv

aria

ble

s

DE

RV

DR

ID

RM

DR

CP

I

DR

IPD

RT

BD

RC

IP

erio

d:

1997

:M7-

2001

:M6

F-s

tati

stic

s

DE

RV

–1.

5778

(0.2

165)

0.93

12(0

.340

5)4.

9961

**

(0.0

312)

0.04

86(0

.826

6)0.

0190

(0.8

912)

6.07

24*

*(0

.018

2)D

RI

1.16

61(0

.286

8)–

0.01

75(0

.895

5)0.

1542

(0.6

968)

0.44

85(0

.507

0)0.

8558

(0.3

606)

0.02

14(0

.884

4)D

RM

1.87

77(0

.178

4)0.

7729

(0.3

847)

–0.

7096

(0.4

047)

2.54

01(0

.119

1)1.

3583

(0.2

509)

0.37

07(0

.542

6)D

RC

PI

0.70

31(0

.406

8)2.

6339

(0.1

127)

0.19

23(0

.663

4)–

0.00

77(0

.930

4)0.

0746

(0.7

861)

3.92

57*

(0.0

546)

DR

IP0.

6021

(0.4

425)

1.02

80(0

.316

9)0.

3567

(0.5

538)

1.41

87(0

.240

8)–

0.09

04(0

.765

3)0.

0673

(0.7

967)

DR

TB

0.37

33(0

.544

7)22

.400

8*

*(0

.000

0)0.

4737

(0.4

954)

13.3

617

**

(0.0

008)

4.09

44*

*(0

.049

9)–

0.59

69(0

.444

4)D

RC

I0.

8279

(0.3

685)

0.07

51(0

.785

5)21

.509

0*

*(0

.000

0)8.

1849

**

(0.0

068)

3.14

98*

(0.0

837)

0.08

95(0

.766

5)–

Notes:

Th

ev

alu

esin

par

enth

eses

are

thep-

val

ue.

** ,

* in

dic

ate

sig

nifi

can

ceat

5an

d10

per

cen

tle

vel

s,re

spec

tiv

ely

Table VIII.Results of error correctionestimates for theunderlying factors ofexchange rate volatilityoriginated from relativemacroeconomic variables– Thailand

SEF24,4

280

Dep

end

ent

var

iab

les

Ind

epen

den

tv

aria

ble

s

DE

RV

DR

ID

RM

DR

CP

ID

RIP

DR

TB

DR

CI

EC

Tt2

1

Per

iod

:19

85:M

1-20

03:M

9F

-sta

tist

ics

T-s

tati

stic

s

DE

RV

–2.

2542

(0.1

347)

2.95

12*

(0.0

873)

2.55

42(0

.111

5)1.

4069

(0.2

369)

1.23

25(0

.268

2)9.

8905

**

(0.0

019)

1.41

81D

RI

0.74

99(0

.387

5)–

1.44

98(0

.229

9)1.

6404

(0.2

017)

7.02

70*

*

(0.0

086)

0.20

20(0

.653

6)0.

1956

(0.6

587)

3.16

51*

*

DR

M0.

5942

(0.4

417)

4.66

52*

*

(0.0

319)

–0.

2447

(0.6

213)

3.23

77*

*

(0.0

734)

0.40

53(0

.525

0)0.

7049

(0.4

021)

21.

1492

DR

CP

I1.

8736

(0.1

725)

0.61

54(0

.433

6)0.

0175

(0.8

948)

–0.

7700

(0.3

812)

0.47

52(0

.491

3)2.

0867

(0.1

501)

20.

6482

DR

IP0.

4607

(0.4

981)

0.44

63(0

.504

8)1.

7216

(0.1

909)

5.66

99*

*

(0.0

181)

–0.

1006

(0.7

514)

0.84

96(0

.357

7)6.

6212

**

DR

TB

0.11

26(0

.737

5)0.

0702

(0.7

913)

3.95

58*

*

(0.0

480)

0.21

31(0

.644

8)2.

6289

(0.1

064)

–0.

4107

(0.5

223)

1.28

50D

RC

I0.

2335

(0.6

294)

0.00

29(0

.956

8)0.

3356

(0.5

630)

0.06

28(0

.802

4)0.

0424

(0.8

370)

4.30

09*

*

(0.0

393)

–2

0.64

47

Notes:

Th

ev

alu

esin

par

enth

eses

are

thep-

val

ue.

** ,

* in

dic

ate

sig

nifi

can

ceat

5an

d10

per

cen

tle

vel

s

Table IX.Results of error correction

estimates for theunderlying factors of

exchange rate volatilityoriginated from relative

macroeconomic variables– Singapore


volatility

281

respectively, wherein its own shock is only about 4 per cent. The innovations inThai baht volatility are explained mostly by its own shock for about 60 per cent.This is followed by the shocks of relative money supplies and consumer priceindices which are about 13 and 12 per cent, respectively. Meanwhile, theinnovations of the Singapore dollar volatility are explained best by its own shock,which is almost 97 per cent. It is only explained by the shock in relative tradebalances and money supplies for about 1.4 and 1.1 per cent. This implies that thevolatility of Singapore dollar has high-exogoneity compared with other variables.Overall, the innovations in the exchange rate volatility of the Malaysian ringgit,the Thai baht and the Singapore dollar are relatively exogenous, in which after

Relative forecast error variance for ERVOwing to innovation in (percentage of variation in)

Horizon (monthly) DERV DRI DRM DRCPI DRIP DRTB DRCI

1 87.6599 2.4460 4.1026 1.4266 0.3777 2.9078 1.079412 63.0010 2.1779 10.2907 3.0509 2.6492 15.6434 3.186824 57.9680 2.1343 11.4377 3.3990 3.0348 18.4781 3.548148 55.3590 2.1142 12.0466 3.5250 3.2333 19.9831 3.7389

Table X.Generalised variancedecompositions ofexchange rate volatilityfor Malaysia

Relative forecast error variance for ERV:Owing to innovation in (percentage of variation in)


1 47.8493 3.0859 5.1632 15.7791 10.4572 2.1192 15.546112 11.7248 25.7699 13.1205 24.7808 3.0599 1.8262 19.717924 6.8546 34.1483 15.2322 25.3422 1.7940 1.2771 15.351648 4.2647 37.5477 16.0735 25.9049 1.1281 0.9861 14.0951

Table XI.Generalised variancedecompositions ofexchange rate volatilityfor Indonesia



1 68.8558 2.3349 8.5605 11.1025 0.1999 0.9997 7.946612 62.5067 3.3825 12.7562 10.4582 0.3915 2.8698 7.635224 58.3759 6.1288 12.4696 10.5846 0.5328 4.2630 7.645448 60.3854 0.8919 12.5279 11.4639 0.6900 4.9899 9.0511

Table XII.Generalised variancedecompositions ofexchange rate volatilityfor Thailand



1 97.7934 0.2688 0.7074 0.2198 0.7143 0.2708 0.025612 97.0981 0.1310 1.0562 0.1153 0.3064 1.2089 0.084124 96.9881 0.1153 1.0979 0.1025 0.2730 1.3324 0.090948 96.9289 0.1065 1.1207 0.0956 0.2553 1.3987 0.0944

Table XIII.Generalised variancedecompositions ofexchange rate volatilityfor Singapore

SEF24,4

282

48 months, over 50 per cent of their forecast variances are explained by their ownshocks. As a comparison among the four economies, the exchange rate volatility ofIndonesia seems to be the most endogenous in which it is the most sensitive andits innovation is explained by most of other factors than its own shock.

5. Summary and concluding remarksThe empirical results are suggestive of the existence of long-run macroeconomicfactors of exchange rate volatility in all the economies except for Thailand. It impliesthat the exchange rate volatility and macroeconomic factors are moving together toachieve the long-run equilibrium for all the three countries of Malaysia, Indonesia andSingapore. The short-run results of the macroeconomic factors of exchange ratevolatility, however, show that a set of common macroeconomic factors seem toinfluence the exchange rate volatility for each of the four countries. The findingsindicate that the significant common set factor influences the exchange rate volatility isonly generated from stock market. Hence, capital market seems to play a significantinfluence to exchange rate volatility in all the economies examined.

As a comparison, the Singapore dollar volatility is mostly explained by its owninnovation whereas the volatility of Indonesian rupiah is greatly influenced bymacroeconomic variables rather than its own innovations. Since, both the long- andshort-run factors of exchange rate volatility from the macroeconomic perspective arefound, the exchange rate volatility is still a manifestation of the macroeconomicvariables. By understanding the impact of the imbalance of the macroeconomicfundamentals, one can, in a way, smooth the exchange rate variability and at the sametime, generate a greater flexibility in pursuing the economic policies given the greaterexchange rate stability. Particularly, the role of respective authorities and marketplayers in the four countries are imperative in managing a viable capital market so asto ensue exchange rate stability.

Notes

1. The orders of p and q are assigned as (2,2), (1,1), (1,1) and (2,2), respectively, for the currenciesexamined.

2. Malaysia had ended its peg system on 20 July 2005 and starting from 21 July 2005, itsexchange rate system was back to the managed float system.

3. The conditional standard deviation of exchange rate is computed via E-GARCH method. Thep and q orders for the four economies-Malaysia, Indonesia, Thailand and Singapore are (1,2),(1,2), (2,2) and (2,2), respectively. The selection of p and q is based on the Schwartz criteria.

4. For Malaysia, income ( y) refers to industrial production index; for Indonesia, it denotes grossnational product (GNI); for Thailand, it represents manufacturing production index; forSingapore, it indicates total manufacturing index.

5. For Malaysia, interest rate (i ) refers to Treasury bill rate; for both Indonesia and Thailand, itrefers to three-month discount rate; for Singapore, it denotes to three-monthTreasury-bill rate.

6. For all the four economies, inflation index ðpÞ refers to consumer price index.

7. For Malaysia, ci represents KLSE composite index; for Indonesia, it refers to Jakarta StockExchange Index; for Thailand, it is SET Stock Exchange Index; for Singapore, it is StraitTime Index.


volatility

283

8. The unit root results showed that all the series are generally integrated of order one.The results are available upon request.

References

Alba, J.D. and Papell, D.H. (1998), “Exchange rate determination and inflation in Southeast Asiancountries”, Journal of Development Economics, Vol. 55 No. 2, pp. 421-37.

Bahmani-Oskooee, M. and Kara, O. (2000), “Exchange rate overshooting in Turkey”, EconomicsLetters, Vol. 68, pp. 89-93.

Bahmani-Oskooee, M. and Ng, R.C.W. (2002), “Long-run demand for money in Hong Kong: anapplication of the ARDL model”, International Journal of Business and Economics, Vol. 1No. 2, pp. 147-55.

Bond, C.A. and Najand, M. (2002), “Volatility changes in European currency exchange rates dueto EMS announcements”, Global Finance Journal, Vol. 13, pp. 93-108.

Chou, W.L. and Lee, D.S.K. (2003), “Cointegration analysis of audit pricing model: a panel unitroot test approach”, Journal of Business Finance &Accounting, Vol. 30 Nos 7/8, pp. 1141-64.

Dickey, D.A. and Fuller, W.A. (1979), “Distribution of estimators for autocorrelated time serieswith a unit root”, Journal of the American Statistical Association, Vol. 74, pp. 427-31.

Dickey, D.A. and Fuller, W.A. (1981), “Likelihood ratio statistics for autoregressive time serieswith a unit root”, Econometrica, Vol. 49, pp. 1057-72.

Granger, C.W.J. (1969), “Investigating causal relations by error-correction models andcross-spectral methods”, Econometrica, Vol. 37 No. 3, pp. 424-38.

Granger, C.W.J. (1988), “Some recent developments in a concept of causality”, Journal ofEconometrics, Vol. 39, pp. 199-211.

Johansen, S. (1988), “Statistical analysis of cointegration vectors”, Journal of Economic Dynamicsand Control, Vol. 12, pp. 231-54.

Johansen, S. (1994), “The role of the constant and linear terms in cointegration analysis ofnonstationary variables”, Econometric Review, Vol. 13, pp. 205-30.

Johansen, S. and Juselius, K. (1990), “Maximum likelihood estimation and inference oncointegration with application to the demand for money”, Oxford Bulletin of Economicsand Statistical, Vol. 52 No. 2, pp. 169-210.

Juks, R. (2003), “The relationship between REER and trade flows in the context of the equilibriumexchange rate”, Working Papers of Eesti Pank, no 9.

Kim, S.J. (1998), “Do Australian and the US macroeconomic news announcements affect theUSD/AUD exchange rate? Some evidence from E-GARCH estimations”, Journal ofMultinational Financial Management, Vol. 8, pp. 233-48.

Kremers, J.J., Ericson, N.R. and Dolado, J.J. (1992), “The power of cointegration tests”, OxfordBulletin of Economics and Statistics, Vol. 54, pp. 325-47.

Kwiatkowski, D., Phillips, P.C.B., Schmidt, P. and Shin, Y.C. (1992), “Testing the null hypothesisof stationarity against the alternative of a unit root”, Journal of Econometrics, Vol. 54,pp. 159-78.

MacDonald, R. and Taylor, M.P. (1994), “The monetary model of the exchange rate: -runrelationships, short-run dynamic and how to beat a random walk”, Journal of InternationalMoney and Finance, Vol. 13 No. 3, pp. 276-90.

Masih, M.M.A. and Masih, R. (1997a), “Dynamic linkages and the propagation mechanismdriving major international stock markets: an analysis of the pre-and post-crash eras”, TheQuarterly Review of Economics and Finance, Vol. 37 No. 4, pp. 859-85.

SEF24,4

284

Masih, M.M.A. and Masih, R. (1997b), “A comparative analysis of the propagation of stockmarket fluctuations in alternative models of dynamic causal linkages”, Applied FinancialEconomics, Vol. 7, pp. 59-74.

Meese, R.A. (1990), “Currency fluctuations in the Post-Bretton Woods era”, Journal of EconomicPerspectives, Vol. 4 No. 1, pp. 117-34.

Nelson, D.B. (1991), “Conditional heteroscedasticity in asset returns: a new approach”,Econometrica, Vol. 59, pp. 347-70.

Pantula, S.G. (1989), “Testing for unit roots in time series data”, Econometric Theory, Vol. 5 No. 2,pp. 256-71.

Pesaran, M.H. and Shin, Y. (1998), “Generalised impulse response analysis in linear multivariatemodels”, Economic Letters, Vol. 58, pp. 17-29.

Pesaran, M.H. and Shin, Y. (1999), “An autoregressive distributed lag modelling approach tocointegration analysis”, in Strom, S. (Ed.) paper presented at Econometrics and EconomicsTheory in the 20th Century: The Ragnar Frisch Centennial Symposium, CambridgeUniversity Press, Cambridge.

Pesaran, M.H., Shin, Y. and Smith, R.J. (1996), “Testing for the existence of a long-runrelationship”, DAE Working Paper, No 9622, Department of Applied Economics,University of Cambridge.

Pesaran, M.H., Shin, Y. and Smith, R.J. (2001), “Bounds testing approaches to the analysis of levelrelationships”, Journal of Applied Econometrics, Vol. 16, pp. 289-326.

Piesse, J. and Hearn, B. (2002), “Equity market integration versus segmentation in threedominant markets of the Southern African Customs Union: cointegration and causalitytests”, Applied Economics, Vol. 14, pp. 1711-22.

Ramchander, S. and Sant, R.R. (2002), “The impact of federal reserve intervention on exchangerate volatility: evidence from the futures market”, Applied Financial Economics, Vol. 12,pp. 231-40.

Rapach, D.E. and Wohar, M.E. (2002), “Testing the monetary model of exchange ratedetermination: new evidence from a century of data”, Journal of International Economics,Vol. 58, pp. 359-85.

Reinsel, G.C. and Ahn, S.K. (1992), “Vector autoregressive models with unit roots and reducedrank structure: estimation, likelihood ratio test and forecasting”, Journal of Time SeriesAnalysis, Vol. 13, pp. 353-75.

Warner, D. and Kreinin, M.E. (1983), “Determinants of international trade flows”, Review ofEconomics and Statistics, Vol. 65 No. 1, pp. 96-104.

Further reading

IMF (n.d.a) IMF Annual Report on Exchange Arrangement and Exchange Restriction, variousseries.

IMF (n.d.b) IMF Annual Report on International Financial Statistics, various series.

Corresponding authorChong Lee-Lee can be contacted at: [email protected]

To purchase reprints of this article please e-mail: [email protected] visit our web site for further details: www.emeraldinsight.com/reprints


volatility

285

macroeconomic factors of exchange rate volatility

Documents