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Global Economic Review: Perspectives

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Is an Optimum Currency Area Feasible

in East and South East Asia?Chandan Sharma

a& Ritesh Kumar Mishra

b

a Department of Economics, National Institute of FinancialManagement (NIFM), Haryana, Indiab

Department of Economics, GGS Indraprastha University, New

Delhi, India

Version of record first published: 20 Aug 2012.

To cite this article: Chandan Sharma & Ritesh Kumar Mishra (2012): Is an Optimum Currency

Area Feasible in East and South East Asia?, Global Economic Review: Perspectives on East Asian

Economies and Industries, 41:3, 209-232

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Is an Optimum Currency Area Feasiblein East and South East Asia?

CHANDAN SHARMA* & RITESH KUMAR MISHRA***Department of Economics, National Institute of Financial Management (NIFM), Haryana, India &

**Department of Economics, GGS Indraprastha University, New Delhi, India

ABSTRACT In the backdrop of the recent economic crisis in the European Union, this studyattempts to assess the degree of regional integration and the suitability of a monetary union in the

East and South-East Asian (ESEA) region. For this purpose, we analyse the issue in a variety ofways. First, a long-run linkage of real output of the countries is tested using the cointegrationanalysis. Results suggest that real output of most of the countries in the region is cointegrated andmove together in the long-run. To analyse the issue in detail, we focus on the impact of threedifferent shocks, namely global, regional and country-specific, on real output of the countries.Results of impulse response and variance decomposition analysis reveal that regional shocks donot dominate in the sample countries, which is an indication of unfavourable condition to form anoptimal currency area (OCA) in the region. These results are further confirmed by the outcome ofcomputation of the modified Bayoumi and Eichengreens Indices. Finally, we employ the conceptof Generalized Purchasing Power Parity (G-PPP), which however reveals that the bilateral realexchange rate of ESEA countries move together in the long-run and share a common stochastictrend, which in turn provides some empirical support for an OCA in the region.

KEY WORDS: OCA; output shocks; G-PPP; East and South-East Asian; monetary union

JEL CLASSIFICATION: F36, F42, F33, C32

1. Introduction

Exchange rate management has been the core of economic policy debate since the

East Asian currency crisis. It is now widely recognized that the regime of soft-peg was

the prime cause of the financial debacle in the Asian economies during the latter part

of 1990s. In some sense, the crisis has exposed the inherent complications inmanaging the exchange rate individually and efficiently in a small open economy,

especially in the presence of massive international capital inflows and outflows (see

Wilson & Choy, 2007). The East and South-East Asian (ESEA) region is

characterized by diverse, uncoordinated exchange rate arrangements. For instance,

Japan and China, the two dominant countries in the region, have adopted an

exchange rate regime akin to a pure float and a tightly managed US dollar-based

regime, respectively. Most other economies in the region have adopted intermediate

Correspondence Address: Chandan Sharma, Department of Economics, National Institute of Financial

Management, Sector 48, Faridabad 121 001, Haryana, India. Email: [email protected]

Global Economic Review

Vol. 41, No. 3, 209232, September 2012

1226-508X Print/1744-3873 Online/12/03020924

# 2012 Institute of East and West Studies, Yonsei University, Seoul

http://dx.doi.org/10.1080/1226508X.2012.709991
http://dx.doi.org/10.1080/1226508X.2012.709991http://dx.doi.org/10.1080/1226508X.2012.709991


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regimes such as managed float. The presence of different exchange rate regimes and

strategies in the region has made it difficult to maintain intra-regional exchange rate

stability through the dollar pegging. Therefore, it has become increasingly important

for the countries to work in the direction of a similar exchange rate regime and ensure

the intra-regional exchange rate stability. Following this and taking lessons from the

Asian crisis, a group of researchers have proposed for the formation of an optimalcurrency area (OCA) in the East Asian region (e.g. Williamson, 1998; McKinnon,

2000; Mundell, 2003).1

Currency union, as discussed in the literature, has numerous real and monetary

effects on the trading and economic environment of member economies. For

example, with the formation of currency union the trading costs are expected to

decline, and therefore it leads to increase in both output and consumption. The loss

of independent stabilization policy has costs and benefits of different magnitude and

importance attached to it. For example, a country that sacrifices its currency actually

loses a stabilization device targeted to domestic shocks. But the country is also

expected to gain credibility, and in so doing reduces undesired inflation (see Alesina& Barro, 2002). Moreover, in a correctly defined OCA, the forward (intra-regional

currency) market premium will disappear, which in turn may raise the relative

advantage of intra-regional trade against cross-regional trade in the region.2

A number of studies have investigated the economic and political feasibility of

forming an OCA in the region in the post crisis period (19992008).3 Some studies

have concluded that the economic and political benefits of adopting a single currency

are far greater than the potential costs that its member economies are likely to incur.

There is no denying of the fact that the degree of economic and financial

integration among Asian countries has increased considerably, especially in the post-

crisis (19992010) period. Over time the response to global shocks and symmetry in

economic activities has also increased in the region. The recent American sub-prime

crisis (2008) has provided some further confirmation in this concern. Most of the

Asian countries experienced spillover effect of the crisis, and as a result they faced

similar challenges such as capital outflows, currency depreciation, plunge in stock

prices, credit crunch, and a sharp fall in export demand. The debate over formation

of currency union in the Asian region has intensified and has taken on a new

dimension, especially in the wake of the European Union debt crisis (20092010).

Now a group of researchers are of the opinion that the crisis of Europe (20092010) is

mainly the undesirable outcome of poor economic policies and heterogeneity of the

monetary union (see Eichengreen, 2010), and it seems that the crisis is sowing the

seeds of collapse of the union (Arghyrou & Tsoukalas, 2010). On the other side, agroup of researchers (e.g. Dolls et al., 2010) argue that the monetary union has

provided better mechanism and power to the European countries in handling the

crisis through automatic stabilisers. In this situation, the debate of the OCA in Asia

has taken a new turn, and it has become relevant and important to test the feasibility

from a different perspective using the most recent data-set.

Against this background, the present study aims to empirically assess the degree of

regional integration and examine the suitability of an OCA in the ESEA region. We

mainly focus on the period between post-Asian crises (1999) and the pre-American

crisis (2008). Our analysis covers South Korea, Singapore, Malaysia, Indonesia,

Thailand, the Philippines, China, Japan, and India.4

Most of the previous studies

210 C. Sharma & R. K. Mishra


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have followed any one empirical framework to test the feasibility. In the present study,

we implement three important but alternative analysis techniques simultaneously to

examine the feasibility of the OCA. Specifically, following Sato and Zhang (2006), we

first adopt the cointegration technique to test whether there is any evidence of long-

run co-movements of real output among the ESEA economies. If the real output of

the sample countries is not cointegrated, it indicates that each countrys outputmoves randomly over a period of time and follows a different growth path. This

scenario would indicate the existence of a high economic divergence among these

economies. In the next stage, following Chow and Kims (2003), we estimate the

output growth function subject to three different types of shocks. In other words, we

attempt to decompose external shocks into three different levels, namely global

shock, regional shock, and country-specific shock. The origin of these different

shocks has significant and relevant policy implications, and this may indicate towards

some degree of inter-linkage among the countries as well as the inter-relationship

with the rest of the world. Further, to check the robustness of the results, we have also

applied a modified version of Bayoumi and Eichengreens Indices. In the final stage,we apply Generalized Purchasing Power Parity (G-PPP), introduced by Enders and

Hurn (1994), which is expected to provide further evidence for OCA in the region by

analysing the behaviour of exchange rate.

Rest of the study is organized as follows: Section 2 reviews the related literature

whereas Section 3 contains discussion on data-related issues. Section 4 presents

empirical models, methodologies and their estimation results. Section 5 provides

conclusion of this study.

2. Review of the Related Literature

The theory of OCA asserts that some crucial conditions should be satisfied for the

formation and success of a common currency in a region. These conditions include:

factor mobility and symmetry of shocks across countries (Mundell, 1961), openness

of economies and trade integration (McKinnon, 1963) and well-diversified econo-

mies, and regional production pattern (Kenen, 1969). Therefore, from this viewpoint

it could be argued that countries of ESEA are in position to satisfy at least some

minimum standard criteria to form an OCA.5 Further, McKinnon (1963) emphasized

on international openness of the country as an important criterion for the OCA.

According to McKinnon, trade between two countries is an important channel of

interdependence of economic activities through which economic shocks of one

country may be transmitted to the others. Also, reduction in various transaction costsin the OCA region would further lead to more trade.6

Given that numerous economic merits of the OCA are theoretically well established

in the literature, recently, a number of studies have tested the empirical viability of

forming an OCA for various subsets of countries of the ESEA region using different

econometric methodologies. The broad picture which emerges from the available

literature is not very encouraging for the formation of an OCA in the near future in

the region. Nevertheless, the favourable findings of some studies have kept the debate

on scope for monetary cooperation among Asian countries still alive. Earlier studies

by Frankel (1991, 1993) and Frankel and Wei (1994) show that a yen block does not

exist in the East Asian region. They conclude that even though Asia has shown bias

Currency Area in East and South East Asia 211


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towards intra-regional trade, but the degree of this intra-regional bias has not

increased in the recent past. Similarly, Park and Park (1990) rejected the empirical

viability of OCA, and raised doubt over the formation of a yen block between Japan

and other East Asian economies. Chow and Kim (2003) investigated the feasibility of

a common currency peg in the East Asian region with the context of Western

European countries. Their findings suggest that domestic outputs of East Asiancountries are strongly influenced by country-specific shocks whereas in the case of

European countries regional shocks play a dominant role. Also, it appears that the

East Asian economies are structurally different from each other and thus they are

more likely to experience asymmetric shocks. Therefore, a common currency peg in

East Asia may not be economically advantageous and sustainable. Following the

same empirical methodology, Soo and Choong (2010) reached the conclusion that

most of the Asian economies look highly segmented especially in the pre-crisis

period. However, findings of the study suggest that the degree of segmentation

among these economies has declined and the influence of Japanese economy on the

performance of some Asian countries has increased in the recent past.On the other hand, some recent studies have reported encouraging evidence bygiving

some support to the proposition that a common currency union is feasible in Asia. For

example, Bayoumi and Eichengreen (1994) reached the conclusion that a subset of nine

East Asian countries satisfies the necessary economic criteria for the formation of an

OCA almost similar to Western Europe. Zhang et al. (2004) investigated the suitability

of East Asian economies for potential monetary integration. They find that empirical

results do not offer any strong support for forming an OCA in the entire East Asian

region. However, some small sub regions have the required qualities for becoming

potential candidates of the OCA. By using the cointegration and common cyclical

feature analysis, Sato and Zhang (2006) investigated the feasibility of a monetary union

in East Asia and found that some pair of countries share synchronous movement of real

output in both short- as well as long-run. Therefore, these countries are good

candidates for forming a monetary union because their short-run dynamics is

correlated and they share long-run output co-movements. Shirono (2008) focus on

trade-creating and welfare effects of various common currency arrangements in East

Asia. Interestingly, the study finds that formation of a single currency area in the region

will stimulate the scale of regional trade considerably and regional currency

arrangements that include Japan will create substantial welfare gains for the member

countries. In another study, Shirono (2009) assessed the role of Japan, along with China

and the USA, in the East Asian currency regime by estimating trade-creating effects

and accompanying welfare gains of different currency arrangements in the region.Findings of the study suggest that currency union with China will generate higher

average welfare gains for the East Asian countries than any other arrangement with

Japan and China. Further, the study shows that the welfare gains of currency union

associated with Japan are much higher than that of USA. Banik et al. (2009) investigate

the feasibility of an OCA for South Asian countries and find that a small cluster of

countries, namely Bangladesh, India, and Pakistan, appear to be good candidates for

forming an OCA. Recently, Lee and Azali (2010) assessed the dynamic relationship

between trade, finance, specialization, and business cycle synchronization for East

Asian economies and reached the conclusion that there is good scope for formation of a

monetary union. Similarly, on the basis of results of G-PPP Choudhry (2005) reached



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the conclusion that evidences are supportive of an OCA only in the post-Asian crisis

period. More recently, Mishra and Sharma (2010) also find some favourable evidence

for formation of an OCA in East Asia. Their findings suggest that still the degree of

economic integration among the countries needs to be enhanced further for formation

of a currency union in the region.

3. Data

To examine the long-run co-movements of real output of the sample countries, we use

real GDP series as a proxy for real outputs. The data on all variables are of quarterly

frequency, expressed in natural logarithms and seasonally adjusted using the Census

X-12 method. Nine countries, namely the South Korea, Singapore, Malaysia,

Indonesia, Thailand, the Philippines, China, Japan, and India, are included in this

study for the empirical investigation. Output series of the USA is also used for the

analysis purpose. The sample period spans from 1999Q1 to 2008Q4 for all economies.

To avoid the turbulent Asian crisis period (19971998) and the sub-prime crisis of

20082009 in the US economy and subsequent European Union debt crisis (2010

2011), we did not consider the period before 1999 and after 2008 for the analysis.

Further, to test the G-PPP, we utilize monthly data of nominal exchange rate (defined

as market rate per US dollar) and price level represented by consumer price index

(CPI). In this case, the sample period spans from 1999:01 to 2008:12. All series are

seasonally unadjusted and expressed in logarithms before any econometric analysis.

We consider USA as the base country to calculate the real exchange rate. All data

series are collected from the International Financial Statistics (IFS) database

provided by the International Monetary Fund.

4. Empirical Results

4.1. Testing Co-movements of Output: Bivariate Cointegration Test

To investigate whether there exists a stable linear steady-state relationship between

the real output of the sample countries, we conduct the cointegration test. Testing of

cointegration is important as it will indicate whether the real output series share

synchronous long-run movements. For testing cointegration, it is required to test the

stationarity of variables. If all variables in the system are non-stationary at the level

and stationary at their first difference, that is I(1), we can apply the Johansen

maximum likelihood (ML) method (Johansen & Juselius, 1990; Johansen, 1991) to

test whether these variables are cointegrated.We begin our analysis by providing the univariate properties of the variables of

interest using the standard Augmented DickeyFuller (ADF) and the Phillips

Perron (PP) unit root tests to establish the order of integration of all variables.

Both the ADF and PP tests fail to reject the null hypothesis of a unit root for all

variables at the levels. However, the null hypothesis is overwhelmingly rejected for all

the series at first-differences. As all the variables are integrated of the same order, this

allows us to conduct the JohansenJesulius cointegration test.7

We next investigate the bivariate relations of real output co-movements between

the ESEA economies. For this purpose, we conduct the Johansen cointegration test

for 35 pairs of the economies and their results are reported in Table 1. It is clearly



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Table 1. Co-movements of output: results of cointegration rank

Trace test lmax Trace test lmax

Country pair (country i&j) Deterministic components H0: r 00 H0: r51

ChinSing No trend 19.61556** 19.46461** 0.150958 0.150958 ChinThai No trend 12.66725 12.32568 0.341573 0.341573ChinIndi No trend 19.39314** 19.33577** 0.057369** 0.057369*ChinIndon Trend 9.900251 7.283894 2.616357 2.616357ChinKorea No trend 12.05739 11.15827 0.89912 0.89912 Chin-Malay No trend 14.20491** 14.17597** 0.028937 0.028937 ChinPhilip No trend 38.39309** 38.38100** 0.012088 0.012088 IndiKorea No trend 10.07799 7.459153 3.618835 3.618835IndiMalay No trend 17.49231** 15.82437** 1.667937 1.667937 IndiPhilip No trend 35.26262** 33.05627** 2.206353 2.206353 IndiSing No trend 22.39943** 21.19035** 1.20908 1.20908 IndiThai No trend 11.42961 11.25822 0.171394 0.171394Indi-indon Trend 14.3445 13.3062 0.038296 0.038296IndonKorea No trend 22.33580** 4.423444** 17.91236** 4.423444*IndonMalay Trend 19.42353** 17.82656** 1.596977 1.596977 IndonPhilip No trend 33.96150** 32.61858** 1.34292 1.34292 IndonSing No trend 23.35268** 21.77221** 1.580476 1.580476 IndonThai Trend 29.40074** 29.30018** 0.100557 0.100557 Jap-Chin No-trend 21.4246** 0.392390** 2.492234 0.063481 JapIndi No trend 27.45547** 24.931221** 2.516939 2.514089 JapIndon No trend 22.62306** 19.98112** 2.641933 2.641933 JapKorea Trend 15.79471** 14.26460** 2.828426* 2.828426*

JapMalay No trend 15.60576** 12.49700** 3.108751 3.108751 JapPhilip No trend 37.48481** 35.33011** 2.154704 2.154704 JapSing No trend 15.30830* 12.81346* 2.494838 2.494838 JapThai No trend 18.50924** 15.59674** 2.912503 2.912503 KoreaMalay No trend 13.72269** 11.153508** 5.569177** 5.569177*


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Table 1. (Continued)

Trace test lmax Trace test lmax

Country pair (country i&j) Deterministic components H0: r 00 H0: r51

KoreaPhilip No trend 40.45201** 35.81763** 4.634388** 4.634388*KoreaSing Trend 7.987452 5.589537 2.397915 2.397915KoreaThai Trend 17.01554** 16.23392** 0.781615 0.781615 MalaySing No trend 17.19921** 14.94187** 2.257342 2.257342 MalayThai No trend 18.4608** 16.31003** 2.150771 2.150771 PhilipMalay Trend 36.98675** 35.46495** 1.521797 1.521797 PhilipSing No trend 39.52308** 18.27456** 37.69562 1.827456 PhilipThai No trend 31.78751** 31.76814** 0.019365 0.019365 Thai -Sing No trend 21.35249** 23.313827** 3.03866 3.313827

Notes: (1) Asterisks (**) denote statistically significant at the 5% level.

(2) Critical values are taken from Osterwald-Lenum (1992).

(3) The following notations are applied: Jap, Japan; Indi, India; chin, China; Indon, Indonesia; Malay, Malaysia; Philip, Philippi

South Korea.


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observable that the hypothesis of no cointegration is rejected by either the trace or

maximum eigenvalue test at 5% level in 28 cases. Only in the case of seven country

pairs, no evidence of cointegrating relationship is observed at conventional level of

significance. Surprisingly, the output co-movements of China and India with other

economies are not found to be very strong, as countries such as Indonesia, South

Korea, and Thailand do not share a cointegrating relationship with both theeconomies. Nevertheless, both of the economies share a common trend with the rest

of the sample countries including cointegrating relation between themselves.

Furthermore, results also indicate that output of South Korea and Singapore does

not share a similar movement. The last column of the table reports the estimated

cointegrated coefficients, which suggest that most of the country pairs have

significant impact on each other, and affect each other positively. Therefore, on the

basis of these findings, we can argue that there is some evidence to conclude that

the ESEA economies have long-run co-movements in their output, and therefore

formation of an OCA in the region appears to be a possible economic event in the

future.

4.2. Analysis of the Symmetry of Response: Evidence from Impulse Response and

Variance Decompositions

After examining the bilateral cointegration relationship, it is now relevant to compare

the response of economies to different types of shocks in terms of the magnitude and

speed of adjustment. This can be done by analysing the impulse response functions

(IRFs). The larger the size of the shock, more disruptive will be its effects on the

economy. Similarly, if the adjustments after the disturbances are slow, larger will be

the cost of maintaining a single currency. Therefore, we have applied the IRF to

capture the response of different types of shocks for the analysis. In addition,the forecast error variance can be used to show the contribution of each shock to the

movements in the output of countries. This is important because difference in the

cause of variability in the countries could be an indicator of underlying difference in

transmission mechanism and policy strategies of the countries in the region, which

would be an obstacle to regional monetary integration. Keeping this issue in mind,

we have also applied the variance decomposition (VD) analysis.

Following Chow and Kim (2003) and Soo and Choong (2010), we estimate the

output growth DyDt function subject to three different types of shocks, namelyglobal uW , regional uR , and domestic uD -specific:

DyDt b1 b2LuW b3Lu

R b4LuD (1)

where bi L bi0 bi2 L bi3 L2 is a polynomial function of the lag

operator, L. Generally, global shocks can potentially influence economies both

inside and outside the regional boundary and regional shocks can affect economies

within a certain region. For example, a shock in the yen-dollar exchange rate could

synchronize and latter this could pass to other countries in the region (Kwan, 1994).

On the other hand, country-specific shocks are generated in a country and can affect

uniquely to that country only. Origin of these shocks could be from monetary or

fiscal policy changes, demand or supply shocks on productivity, or terms of trade

(Bayoumi & Eichengreen, 1993).



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In this context, if it is observed that country-specific shocks are dominating in a

country, and it is less correlated across the region, then losing the monetary

independence for an OCA could be a costly affair for the country. At the same time,

the occurrence of regional shocks or correlated local shocks may indicate for a

similar monetary policy within the region therefore the OCA could be a feasible

option in this scenario. In contrast, if it is detected that global shocks are dominatingand play a crucial role in determining the directions of movement of macro-economic

factors of various countries in the region, then a more global arrangement might be

more appropriate. Nevertheless, as long as shocks influence all economies in the

similar pattern, a global rather than regional policy arrangement may be a more

appropriate course of action in dealing with such shocks. Therefore, identification of

different shocks could be crucial, as this may provide information regarding the inter-

linkage among the countries.

For this purpose, we move further to identify and analyse the nature and role of

observed shocks in the countries. Results of our analysis can indicate for three

exhaustive and exclusive outcomes. First, if global shocks are dominating in theregion, then we can conclude in favour of the formation of a Dollar bloc. Second, if

the analysis observes that regional shocks are dominating in the region, then we can

recommend forming a Yen bloc. Finally, the outcome of dominance of domestic

shocks would compel us to recommend against any monetary arrangement among

the countries.

On the basis of these explanations, we apply the vector auto-regression (VAR)

model to estimate the impact of these shocks. This framework generates the IRFs and

VD, which helps in distinguishing the shocks on output (log of real GDP) of

the sample countries. Given the size and impact of USA and Japanese economy on

the sample countries, we consider the real GDP of the USA and Japan as proxiesfor the global shocks uW and regional shocks uR , respectively. Further, to test theimpact of regional shocks on the Japanese output, the Chinese GDP is used as a

proxy, given the fact that China is the next most important country in this region.

Figures 19 show the response of each of the nine countries real GDP growth over

eight periods to innovations (shocks) in global and regional output growth. We use

these results in recognizing the origin of shocks, which are observed by real output of

the countries. The results can be understood in the VAR framework. Specifically, we

are interested to know here that one standard deviation (SD) shock to the

innovations in current and future values of endogenous variables (global, regional,

and country-specific shocks) leads to what magnitude of change in output of thesample countries. The effect begins in t(quarter) 0 1, and it is observed until

Figure 1. Response of China to Cholesky, One SD Innovations.



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t(quarter) 0 8 in our analysis. Results of the analysis demonstrate that eight of the

nine countries, namely China, Indonesia, Japan, South Korea, Malaysia, the

Philippines, Singapore, and Thailand, have broadly symmetrical negative response.

Among the sample countries, only India shows a positive response to both global and

regional shocks. Response of the Chinese output growth is negative throughout the

study period to both global as well as regional shocks. Response of the Indonesian

output growth is slightly different, as it is sluggish in the initial period, however,

intensified in the latter period (after five quarters). Further, it shows positive response

to the regional shocks but negative response to global shocks. Response of South

Korea is interesting, it is slow-moving in the initial quarters but negative in the

medium period and stable in the long period to both types of external shocks. In the

case of Malaysia, it has positive response to global shocks in initial periods, but after

five quarters, it demonstrates steep negative response to both types of external

shocks. The Philippiness output growth appears to be inversely correlated to the

global as well as the regional shocks. Response of Singapores output is mixed

towards global and regional shocks. Specifically, the response of both types of shockis estimated to be negative in initial quarters; however, it turns out to be positive after

the third quarter. Thailands output growth shows a negative response throughout

the observation period to the global as well as regional shocks. In the case of

India the evidence is different. It has slow response in the initial quarters, however, it

picks up in latter periods. Nevertheless, it consistently demonstrates positive response

to regional as well as to global shocks. In the case of Japanese, response of global

shocks is positive; however, regional shocks, proxied by Chinese shocks, are identified

to be negative throughout the considered time horizon.

Next, we discuss the results of orthogonalized forecast error VD, which is based on

Choleski factorization with particular ordering, namely: global shock, regional

Figure 2. Response of India to Cholesky, One SD Innovations.

Figure 3. Response of Indonesia to Cholesky, One SD Innovations.



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shock, and country-specific shock. The analysis is done up to eight-quarter horizon.

The VDs are presented in Table 2. The results of the VD of global, regional, and

country-specific shocks for eight-quarter ahead forecast errors are produced by their

innovations. The table shows that, as expected, the variations in countries output in

first two quarters are mainly because of country-specific shocks except in the cases ofIndia and South Korea, where it is mainly driven by global and regional shocks,

respectively. In the latter quarters the impact of global and regional shocks has

generally dominated across the countries. Only the case of Indonesia and Malaysia is

identified to be different. In Indonesia the domestic effect is estimated to be above

90% throughout the time horizon, and the impact of both regional and global shocks

on the domestic output is found to be negligible. Similarly, in the case of Malaysia,

domestic shocks are found to be most crucial as even in the longer period (eighth

quarter) it has more than 50% impact. In the case of South Korea (56%), the

Philippines (55%), Japan (47%), India (46%), and Singapore (44%), the regional

effect is recognized to be most important in the long-term horizon. On the otherhand, Thailand (61%), China (59%), Singapore (41%), and India (37%) observe

sizable impact from the global shocks in the long period.

Overall, on the basis of these results, it can be inferred that the results do not offer

a strong support of the proposition that Asian countries are economically integrated

enough to form an OCA in the short-run. The main reason behind this inference is

that these countries are strongly affected by their own country-specific shocks. The

dominance of country-specific shocks may indicate towards the presence of different

aggregate demand shock due to either monetary or fiscal policy changes or supply

shocks on productivity or terms of trade (Bayoumi & Eichengreen, 1993).

Figure 4. Response of South Korea to Cholesky, One SD Innovations.

Figure 5. Response of Malaysia to Cholesky, One SD Innovations.



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4.3. Robustness Check

In order to test the feasibility of an OCA in the context of the European Union,

Bayoumi and Eichengreen (1993, 1997) followed by Kim and Chow (2003) have

introduced an index based on the degree of exchange rate variability, as measured bySD of the log of the bilateral exchange rate between a pair of countries. In this model,

a high (low) correlation of aggregate supply shocks between two countries suggests

that the economies are subject to symmetric (asymmetric) shocks and consequently

likely (unlikely) candidates for an OCA. In the present study, we have computed SD

of the log of the exchange rate and then constructed an index of their standard

deviation (XSD).8 on the basis of XSD, we have constructed an index of rank (XSD

Rank) of the sample countries. Further, the role of regional shocks as an important

indicator of regional integration by measuring the extent of symmetric shocks, and

thus suitability of countries in joining a currency area in the region, is well established

in the literature. We consider Japan as the anchor and the prime source of regionalshock. With this viewpoint, we first construct an index based on results of VDs of the

regional shock (RS Index). On the basis of RS Index, we rank (RS Rank) the sample

countries. Table 3 reports computed values of indices for each country and their

ranks. We have constructed both indices with India (panel A of the table) and without

India (panel B of the table). In terms of exchange rate variability, Singapore is ranked

1, as it has the lowest variability, while Indonesia has the largest variability in both

panels. Focusing on the regional shocks in domestic output (RS Index), both panels

suggest that South Korea has the largest value, while Indonesia has the lowest value.

Next we estimate the Spearman rank correlations among the rank of indices and the

values are reported at the bottom of Table 3. Confirming our previous results, the

Figure 6. Response of the Philippines to Cholesky, One SD Innovations.

Figure 7. Response of Singapore to Cholesky, One SD Innovations.



14/25

correlation coefficients appear to be negative, not sizable and statistically insignif-

icant at the conventional level. Therefore, it seems that these economies are not very

strong candidates for forming an OCA, at least in the present scenario. It is also

noteworthy that inclusion or exclusion of India in the analysis does not alter these

results considerably.

4.4. Concept of G-PPP and OCA

So far the analysis of output co-movement and their reaction to various shocks has

produced mixed results. In this section, therefore, we move further to examine the

issue in an alternative way by focusing on the movements of real exchange rates. To

this end, we utilize the concept of G-PPP proposed by Enders and Hurn (1994),

which is essentially an alternative way of evaluating exchange rate behaviour across

countries. According to the G-PPP theory, even though bilateral real exchange rates

are generally non-stationary, they might be cointegrated in the long-run, if the long-run fundamental macro-economic variables that determine real exchange rates are

highly associated. If this is true in a suitably defined currency area, then the real

exchange rates in the area may share common stochastic trends, and at least one

linear combination of various bilateral real exchange rates may exist that is stationary

(see Enders & Hurn, 1994). Now, in what follows, we discuss the G-PPP theory and

the related empirical methodology in brief (see Enders & Hurn, 1994 and Ahn et al.,

2006 for further details). Assume that a subset of m'1 countries in an n-country

world constitute a currency area. Given that there are only m independent real

Figure 8. Response of Thailand to Cholesky, One SD Innovations.

Figure 9. Response of Japan to Cholesky, One SD Innovations.



15/25

Table 2. Results of forecast error VDs of domestic output

Period SE Country-specific shock Regional shock Global shock

China2 98.15782 0.042825 1.7993524 47.20221 7.233278 45.56451

6 24.66158 10.43332 64.905108 27.77714 12.93339 59.28946

India2 37.20035 42.50908 20.290574 24.93906 44.55575 30.505196 18.92287 46.22821 34.848928 16.40275 46.51138 37.08587

Indonesia2 99.83353 0.030827 0.1356424 99.43193 0.364543 0.203525

6 97.36585 2.268160 0.3659888 92.40364 4.709245 2.887116

South Korea2 44.18149 54.92190 0.8966094 37.75420 61.18313 1.0626716 36.61644 60.98224 2.4013298 40.45731 56.83423 2.708456

Malaysia2 88.15653 10.89798 0.9454874 75.13501 19.36502 5.499974

6 64.60440 21.80383 13.591788 54.73248 21.18617 24.08135

The Philippines2 96.72099 2.423169 0.8558444 68.17624 29.42664 2.3971216 40.27880 49.29285 10.428368 27.51751 55.92816 16.55433

Singapore2 63.55096 26.54588 9.9031594 32.25324 36.84003 30.906746 19.08810 42.62098 38.290928 14.79469 44.11359 41.09171

Thailand2 57.08141 18.69839 24.220194 18.07195 35.62152 46.306546 8.039384 33.88931 58.071318 4.875078 33.23685 61.88807

Japan2 64.42786 25.32312 10.249014 38.92961 38.58159 22.488796 31.75629 44.77222 23.47149

8 29.09604 47.68553 23.21844



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Table 3. Results of Bayoumi and Eichengreens Indices

XSD XSD Rank RS Index RS Rank XSD XSD R

Panel A

China 0.023752 3 7.67 8 0.023752 3 India 0.025722 5 44.95 2Indonesia 0.053678 8 1.94 9 0.053678 7 Japan 0.030296 6 39.0875 3 0.030296 5

South Korea 0.035135 7 58.477 1 0.035135 6 The Philippines 0.059998 9 34.26325 5 0.059998 8 Malaysia 0.022505 2 18.3125 7 0.022505 2 Singapore 0.016597 1 37.5275 4 0.016597 1 Thailand 0.023858 4 30.355 6 0.023858 4

Spearman coefficients of rank correlationXSD Rank 1.000 (0.116 (0.765) 1.00RS Rank 1.000

Note: P-value in parentheses.


17/25

exchange rates within the subset of m'1 countries, we can write the reduced form

solution for the m independent real exchange rates as follows:

Qt

AXt

a11 a12 . . . a1m1. . . . . . . . . . . .

am1 am2. . .

amm1

2

664

3

775

x1tx2t

..

.

xm1t

2

6664

3

7775 (2)

where Qt is the m )1 vector of real exchange rates, A is m )(m' 1) parameter matrix

and Xt is the (m' 1) )1 vector of real fundamental variables such as output levels.

As a matter of fact, the real exchange rate will be stationary and the empirical validity

of PP will be confirmed, if all the elements of real fundamentals or Xt are stationary.

Given the fact that the elements of Xt represent real shocks, each of the element is

assumed to be non-stationary. Now, using the common trend representation

developed by Stock and Watson (1988), we can express Xt as follows:

Xt W/t; (3)

where C is the (m' 1) )(m'1) matrix of the parameters and ft is the (m' 1) )1

vectors of the non-stationary stochastic trends. Thus, the behaviour of the real

exchange rates Qt is determined using Equations (2) and (3) as follows:

Qt AW/t (4)

The behaviour of real macro-economic shocks and therefore that of real exchange

rates depend on the rank of the matrix C. As long as the rank (C)Bm, it is always

possible to pre-multiply Qt by m)m matrix b to obtain at least one cointegrating

vector of the real exchange rates as follows:

bAW 0: (5)

Equations (3) and (4) imply bQt00. If the rank (C) 0 1, all the elements of Xt share

a single common trend and hence there exist m (1 linear combinations of the real

exchange rates, which are stationary. Further, bQt00 can be rewritten as follows:

b2q12t b3q13t b4q14t . . . bm1q1m1t 0: (6)

where qt is the real exchange rate defined as qt et pt pt (where et is the natural

logarithms of the national currency price of foreign currency, pt and pt are the naturallogarithms of the foreign domestic price levels, respectively). Equation (6) shows the

long-run equilibrium relationship between the m bilateral real exchange rates within

the group ofm'1 countries. In the next step, we apply the multivariate cointegration

technique to test and estimate the cointegrating relations.

Now we advance our analysis further to test the potential of an OCA in the ESEA

region by examining the empirical validity of G-PPP. As a matter of fact, for the G-

PPP to hold all the bilateral real exchange rates must be non-stationary individually

and there should be at least one linear combination of all non-stationary real

exchange rate which is stationary, that is I(0). As a prerequisite to cointegration

analysis, we first test the order of integration of all nine real exchange rates. For this



18/25

purpose, we conduct the ADF and PP unit root tests and the results are reported in

Table 4 which shows that all the real exchange rates are integrated of order one.

After establishing that all variables are I(1), in order to assess whether the sample

ESEA countries constitute an OCA using the concept of G-PPP, we perform the

Johansen multivariate cointegration test. We have examined the cointegration test for

all the sample countries with and without India and results are reported in panels Aand B of Table 5. At the group level, the results of cointegration test confirm the

presence of cointegrating relationship among the real exchange rate of the countries,

as the results reveal that the null hypothesis of no cointegration is strongly rejected in

favour of significant cointegrating relation among Asian real exchange rates. Trace

statistics confirm the presence of 7 and 4 cointegrating vectors for full sample and the

sample excluding India, respectively. This is a supportive evidence for the validity of

G-PPP and OCA in the region. The presence of cointegration among real exchange

rates of the ESEA countries implies that macro-economic fundamentals that drive

real exchange rates are sufficiently interrelated, and hence bilateral real exchange

rates of these countries share common stochastic trends in the long-run.Table 6 presents the result of normalized coefficients (panel A) and speed of

adjustment parameters (panel B). We use Chinese currency (Renminbi) to obtain the

normalized equations in the model. It is, however, noteworthy that there is no specific

reason for the choice of Renminbi to create the normalized equations of real

exchange rates, and any bilateral real exchange rate could have been utilized for the

purpose. In our case the normalized vectors provide information on the interrelation

among real exchange rates included in the study. These normalized coefficients can be

interpreted as long-run elasticities between the real exchange rates. There seems to be

some asymmetries in exchange rate adjustment process in response to any

disequilibrium in the system. For full sample countries (row 1), while consideringthe US dollar-based real exchange rates a 1% rise in the Renminbi (real depreciation)

leads to a real depreciation of around 4% in the Indian Rupees, 3% in Indonesian

Rupiah, 1% in Korean Won, 14% in Singapore dollar, and 1.3% in Thai Bhat.

Japanese Yen, Malaysian Ringgit, and the Philippines Peso have opposite movement,

Table 4. Results of unit root test

US dollar-based real exchange rates

Countries ADF (Level) PP (Level) ADF (1st Diff.) PP (1st Diff.)

China 0.008020 3.833434 (3.252968t' (5.684161t*India (2.610610 (2.366308 (8.207679* (8.143852*Indonesia (2.793979 (2.730168 (9.307610* (13.98435*Japan (1.327378 (1.327378 (10.03211* (10.03031*Malaysia 0.325310 (0.360682 (4.650030* (7.439881*The Philippines (0.750142 (0.881662 (10.01815* (10.01150*Singapore (1.200614 (1.061091 (4.512639* (10.85103*South Korea (1.450274 (1.474180 (4.194101* (8.799987*Thailand (1.594813 (1.624233 (7.333349* (10.72266*

Notes: (1) Asterisks (*) denote rejection of the null hypothesis at 5% significance level.

(2) For the ADF and PP the null hypotheses are series contain unit root.

(3) The optimal lag of respective model is determined based on modified SBC.

(4) t donates inclusion of trend.



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which implies depreciation (appreciation) in Renminbi lead to appreciation

(depreciation) in these currencies. It is noteworthy that results for other exchange

rate do not change considerably when we exclude Indian exchange rate from the

analysis, which reflects neutrality of India in the framework.

Now we shift our attention on the results of speed of adjustment (panel B, Table 6).

We utilize this result to explain how quickly a change in the real exchange rates in the

system is inclined to correct itself in the VAR framework. For the US-based real

exchange rate system, the largest coefficients are found in the case of Korea and the

Philippines. The estimated coefficients for Korea ( (0.143) and the Philippines

( (0.093) imply that US dollar-based real exchange rate adjusts at the rate of 14.3%and 9.2% per month towards the long-run equilibrium (see row 3, Table 6). The

adjustment coefficients in the case of Indian Rupees, Singapore Dollar, and Thai

Bhat are moderate as they vary from 1% to 5%, whereas adjustment movement in

Renminbi and Ringgit is estimated to be very small (below 1%). We also report

results of speed of adjustment for the sample excluding India. The exclusion has

affected the speed of adjustment of most of the currencies, nevertheless, their signs

remain unaffected (see row 4, Table 6). On the basis of these results, we can conclude

that the speed of adjustment is relatively high. However, some of the adjustment

coefficients are small, which indicate that these currencies are weakly exogenous in

the system. To some extent our results are in agreement with the results reported byChoudhry (2005) and Wilson and Choy (2007).

Now we focus on two major emerging economies in the region China and India,

and make an attempt to examine their suitability to become members in the possible

OCA in the ESEA. For this purpose, we test the effect of other countries real

exchange rate on the movement of Renminbi/US dollar and Rupees/US dollar real

exchange rate in the cointegration framework. For this purpose, we utilize the fully

modified OLS (FMOLS) estimator of Phillips and Hansen (1990). The technique is

appropriate in the present case as it eliminates the problems caused by the long-run

correlation between the cointegrating equation and stochastic regressor innovations,

which is likely the case here. The FMOLS estimates are asymptotically unbiased and

Table 5. Test of G-PPP: results of cointegration rank

Eigenvalue Trace stat. Eigenvalue Trace stat.

Rank All sample countries (Panel A)All sample countries excluding

India (Panel B)

r 00 0.495810 316.4463** 0.439109 231.7385**r51 0.410439 238.3788** 0.360521 165.8203**r52 0.381687 178.1437** 0.287150 114.8508**r53 0.273358 123.3370** 0.224714 76.26354**r54 0.228662 86.93440** 0.151357 47.24789r55 0.175822 57.33681** 0.117148 28.53864r56 0.139892 35.29280** 0.088564 14.33443r57 0.091288 18.11325 0.032468 3.762756r58 0.061208 7.200388

Notes: (1) Asterisks (**) denote statistically significant at the 5% level.

(2) Critical values are taken from Osterwald-Lenum (1992).



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Table 6. Test of G-PPP: results of normalized equations and speed of adjustmen

China Indonesia Korea MalaysiaThe

Philippines Singapor

Normalized coefficients (A)All sample

countries (1)1 2.8394

(0.446)1.0301

(0.518)(6.3869

(1.548)(8.1868

(0.997)14.20126(2.66796

All sample countriesexcluding India (2)

1 3.259031(0.63932)

2.735173(0.73620)

(4.360977(1.75860)

(8.614644(1.29675)

14.40280(3.43588

Speed of adjustment parameters (B)All sample

countries (3)0.000564

(0.01115)(0.014747

(0.01447)(0.143135

(0.03275)(0.001630

(0.01886)(0.092515

(0.01643)0.00231

(0.01104All sample countries

excluding India (4)(0.008

(0.00813)(0.11731

(0.02375)(0.06444

(0.01237)(0.00044

(0.00816)(0.02407

(0.01171)(0.02325

(0.00697

Note: Standard errors are in parentheses.


21/25

has fully efficient mixture of normal asymptotics allowing for standard Wald tests

using asymptotic x2 statistical inference.

Results of the estimation are reported in Table 7. Column 1 reports results

regarding India, which suggests that the real exchange rates of China, Indonesia,

Japan, and the Philippines have significant impact on the Indian Rupee. However, the

impacts of the first three countries exchange rate are observed to be negative, whichindicate for their inverse relationship with India. Only the Peso and Ringgit have

positive and significant impact on the movement of Indian Rupees. Therefore, the

case of India as a member of possible OCA in the ESEA region appears to be

somewhat weak on empirical grounds. Column 2 of the table reports results of China,

which suggest that except Thailand, all other sample countries real exchange rate

have significant impact on Chinas exchange rate movement. However, only

Singapore and the Philippines currency have positive impact on the Renminbi.

Therefore, we can conclude that results of the FMOLS analysis indicate that both

China and India are not very likely candidates for the possible OCA in the region.

5. Conclusion

The present article empirically assesses the level of regional integration and suitability

of a monetary union in the ESEA region. For this purpose, we performed three

alternative analyses to provide empirical evidence for an OCA. Estimation results of

the bivariate relations of real output co-movements between the Asian economies

suggest that out of 35 pairs of the sample economies the hypothesis of no

cointegration is rejected for 28 cases, and only 7 country pairs do not show any

cointegration relationship. Furthermore, the estimated cointegration coefficients

suggest that most of the pairs have significant impact on each other, and therefore

affect each other positively. On the basis of these results, it appears that the realoutput of these Asian countries move together in the long-run which in turn provides

some support for the feasibility of monetary union in the region. Subsequently, to

analyse the issue in more detail, we focus on the impact of three different shocks,

namely global, regional and country-specific, on real output of the countries. To this

end we employ techniques of impulse response and VD in the VAR framework.

Table 7. Determinants of India and Chinas real exchange rate: results of FMOLS regression

Variable India (1) China (2)

Constant 1.498443** (0.451162) 13.66282 (1.513921)China (0.069086** (0.027344)India (1.880656** (0.762671)Indonesia (0.2265** (0.068643) (1.767560** (0.287743)Japan (0.0002** (0.096359) (1.316032** (0.415085)Korea 0.059222 (0.066442) (1.073086** (0.294884)Malaysia 0.157144 (0.205031) (2.914759** (0.877313)The Philippines 0.324965** (0.135986) 3.151909** (0.593309)Singapore 0.039597 (0.317782) 3.197531** (1.450594)Thailand 0.230701* (0.176189) (0.180981 (0.880054)

Notes: (1) Standard error is in parenthesis.

(2) Asterisks (**) and (*) denote statistically significant at the 5% and 10% level.

(3) R2

00.972386.



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Results of the analysis reveal that regional shocks do not have dominant roles in the

sample countries, which is an indication of unfavourable condition to form an OCA

in the region. These results are further confirmed by the outcome of computation of

the modified Bayoumi and Eichengreens Indices. Nevertheless, results based on G-

PPP are found to be somewhat supportive for the validity of G-PPP and OCA in the

region. The existence of cointegration among real exchange rates of ESEA countriesimplies that macro-economic fundamentals that drive real exchange rates are

sufficiently interrelated, and therefore, bilateral real exchange rates of these countries

share common stochastic trends in the long-run. Finally, to investigate the

candidature of India and China in the proposed OCA, we have tested the effect of

other real exchange rates on the movement of both countries real exchange rate using

FMOLS estimator. Results of the analysis indicate that there is no strong evidence to

support the view that both India and China are likely candidates of the monetary

union in the ESEA region.

Overall results suggest that the degree of economic integration has increased

among the ESEA countries in the post-Asian crisis period. However, considering themixed results of this study and the recent European experience, it appears that the

right time for the OCA has not come yet. And a still higher degree of economic

integration is required to achieve to build a sound economic platform for the OCA.

Thus, in the presence of a poor level of integration, presently these countries can

pursue only a limited monetary cooperation. In addition, for structural convergence,

which is extremely critical for the OCA, these countries can initiate further reforms in

important areas, such as industry, financial sector development, capital account

openness, and institutional and regulation. These measures may bring them a little

closer in near future than now. Our results are somewhat not very favourable for

China and India in the analysis. Perhaps the de facto exchange rate policy of China isthe reason behind this scenario, and it is becoming one of the biggest hurdles in the

process of integration in the region. Therefore, for a coordinated process to begin in

the region, China needs to increase its exchange rate flexibility, and accepts the

market-driven appreciation of its currency and abandon its existing stabilization

policy. On the other hand, the policy suggestion for India is straightforward. If it is

looking forward to be a possible candidate of the OCA, then it should consider

integrating itself more intensely with other economies in the region in the near future.

In this concern, the central bank of the country needs to abandon some its excess risk

averse strategies and initiate long waiting reforms in capital account convertibility

and financial market.

Acknowledgements

We thank two anonymous referees of this journal for their useful comments and helpful suggestions on the

previous version of this article. Any errors or omission are solely ours.

Notes1 OCA and monetary union is used interchangeably in this study.2 It is also argued that with greater regional monetary and exchange rate stability, the region may become

more attractive for foreign investment (Benassy-Quere, 1999). However, the formation of OCA would

lead to the loss of monetary autonomy. This will restrict countries to follow export-led catch-up strategy,

which worked effectively before the 1990s (see Fabella, 2000). Another problem is the great diversity (in



23/25

terms of culture and religion, etc.) among countries in the region which could potentially create some

serious problems in the formation of the OCA (see Kawai & Takagi, 2000). The historical facts also

suggest that the pattern of use of stabilization policies in the region varies significantly.3 See, for example, Park and Park (1990), Frankel (1991, 1993), Bayoumi and Eichengreen (1994), Taguchi

(1994), Kwan (1998), Chow and Kim (2003) and Shirono (2007), just to name a few in the long list of

recent studies.4

In the last decade ASEAN countries have increased their trade and financial relationship with China,Japan, and India. Many agreements have been signed between these economies in direction of free trade

zone and monetary cooperation, for instance, ASEAN-China free trade zone, Tokyo Declaration,

ASEAN-India Trade in Goods Agreement (TIG) and other AIFTA-related Agreements.5 Further, in a relatively narrow sense some other important conditions for the formation of an OCA are

(1) a large market size, (2) high degree of openness in trade, (3) high degree of intra-regional economic

interdependence, (4) symmetry between shocks across countries, and (5) less dependence on exchange

rate as an instrument for correcting macro-economic imbalances (Kwan, 1998). Further, some authors

have also argued for a supra-national government body able to conduct interregional transfers (see De

Grauwe, 1997).6 Although the idea is theoretically appealing, empirical literature on trade and international finance has

not reached a consensus as to whether a large degree of trade relationship between countries will result in

correlated business cycles (see Hallett & Richter, 2006). In this regard, evidence presented by Kose et al.(2003) reveals that international trade relationship does not necessarily lead to the synchronization of

business cycles.7 To conserve space, we do not report results of unit root test here but would be made available upon

request.8 It is noteworthy that Bayoumi and Eichengreen (1993, 1997) have consider four factors the SD of

relative output growths, the dissimilarity of the composition of exports, the extent of bilateral trade, and

the average size of the economy for constructing the index of exchange rate variation. We have broadly

followed Kim and Chow (2003) and have taken the variation of exchange rates.

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