Grant Colthup Thesis

The University of Queensland Faculty of Business, Economics & Law

School of Business

Market Linkage in the Pacific-Basin: A VAR and Cointegration Analysis

A thesis submitted to the School of Business, the

University of Queensland, in partial fulfillment for the requirements for the degree of Bachelor of Commerce

with Honours

By Grant Colthup

02 November 2004

Supervisor: Dr. Maosen Zhong

DISCLAIMER STATEMENT

The work presented in this thesis is, to the best of my knowledge and belief, original, except as acknowledged in the text. The material has not been submitted, in either whole or in part, for a degree at this or any other university. Signed: Date: 02 November 2004 Grant Colthup

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ABSTRACT

The equity markets of the Pacific-Basin tend to display comovements with one

another. Thus giving rise to the possibility that these markets are actually linked to

each other. This thesis uses cointegration analysis, persistent profile analysis,

generalised variance decomposition and impulse response functions and block

exogeneity tests, to establish the fact that the exhibit some level of market linkage.

The same procedures will also be employed to determine whether the relationship

fluctuates through time. Lastly, this thesis attempts to identify a set of variables that

can possibly explain the level of market linkage.

The results tend to suggest that the markets of the Pacific-Basin are closely linked to

one another in both the short and long run. In addition, the evidence also seems to

support the notion that the relationships are not constant over time. Certain variables

were found to help describe the level of market linkage is the result from the final

section.

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ACKNOWLEDGMENTS

First and foremost, I would like to thank my supervisor Dr. Maosen Zhong for his

invaluable advice, guidance and encouragement through out the year. Without his

enthusiasm and commitment and I am certain this thesis would not have been

possible. I am also appreciative to Bin who was an immense help with the statistical

programming.

To everyone in honours this year I am thankful for making such a difficult year

incredibly enjoyable. To Brent it has been a pleasure to work on numerous

assignments and other activities together. I would also like to extend my sincere

thanks to Tim for his great advice pertaining to job applications; Michael for his good

natured sense of humour; Matt for our engaging conversations about wrestling; Jono

for thinking I have an idea about econometrics; Vince for his unique views on the

world and Pat and Ed for always being there to share a beer or feed. To all the other

friends I have made during my five year trek through the academic wilderness that is

university I am thankful. Special mention must go to Patty, a person whom I am lucky

to call a close friend.

To my family and friends thank you for all your support and encouragement. To my

mum and dad, this is as much my achievement as it is yours. Without your support

(both emotional and financial), none of this would have been possible. For that, I am

ever grateful

.

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Table of Contents List of Figures..................................................................................................................... iv List of Tables ........................................................................................................................v Chapter 1: Introduction......................................................................................................1

1.1 Research Question and Objective...................................................................... 1 1.2 Motivation .............................................................................................................. 4 1.3 Contribution........................................................................................................... 6 1.4 Thesis Structure ................................................................................................... 7

Chapter 2: Literature Review............................................................................................ 9 2.1 Literature on the use of long term measures of market linkage................... 9 2. Literature on the use of short run measures .................................................... 15 2.3 Literature regarding the time evolution of market linkage.......................... 18 2.4 Literature regarding the determinants of market linkage............................. 24

Chapter 3: Hypothesis and Theory Development...................................................... 26 3.1 Introduction ......................................................................................................... 26 3.2 Description of the Pacific-Basin Economies................................................. 26 3.3 Linkage Hypothesis ............................................................................................ 37 3.3.1 Cointegration Analysis .................................................................................... 38 3.3.2 Persistent Profile Analysis .............................................................................. 41 3.3.3 Generalized Variance Decomposition......................................................... 43 3.3.4 Generalized Impulse Response Function ................................................... 44 3.3.5 Block Exogeneity ............................................................................................. 44 3.4 Time Varying Hypothesis .................................................................................. 47 3.5 Factors Responsible for explaining market linkage ...................................... 49

Chapter 4: Sample Data................................................................................................... 59 4.1 Introduction ......................................................................................................... 59 4.2 Data........................................................................................................................ 59 4.3 Descriptive Statistics........................................................................................... 67

Chapter 5: Empirical Results and Findings.................................................................. 68 5.1 Introduction ......................................................................................................... 68 5.2 Cointegration Results ......................................................................................... 63 5.2.1 Bivariate and Multivariate Cointegration Analysis for the full period.... 63 5.2.2 Bivariate Recursive........................................................................................... 76 5.3 Persistent Profile Analysis ................................................................................. 79 5.4 Generalised Impulse Response Functions Results ....................................... 80 5.5 Generalised Variance Decomposition Results .............................................. 84 5.6 Block Exogeneity ................................................................................................ 90 5.7 Probit and Regression Analysis Results .......................................................... 92

Chapter 6: Conclusion ..................................................................................................... 95 6.1 Contribution......................................................................................................... 95 6.2 Limitations............................................................................................................ 96 6.3 Future Research................................................................................................... 97

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Reference List .................................................................................................................... 98 Appendix A: Weekly Recursive Bivariate cointegration tests........................................ Appendix B: Persistent Profiles .......................................................................................... Appendix C: Generalised Impulse response functions .................................................. Appendix D: Generalised Variance Decomposition ...................................................... Appendix E: Block Exogeneity Tests ................................................................................ Appendix F :Univariate Regression Results...................................................................... Appendix G : Probit regression reults ............................................................................... ...................................................................................................................................................

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List of figures 3.1 Log Stock Indexes of Australia, New Zealand and the US.....35 3.2. Log Stock Indexes of Hong Kong, Japan, Korea and Taiwan ............................................................................................36 3.3 Log Stock Indexes of Indonesia, Malaysia, the Philippines, Singapore and Thailand.......................................................................37 3.4 Recursive Cointegration Test between Australia and Hong Kong....................................................................................48 6.2a The impact of the East Asia crisis on market linkage ............72 6.2b The impact of the September 11 on market linkage..............72 6.2c The impact of both the East Asia crisis and September 11 on market linkage........................................................73

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List of tables 3.5 Expected Relationship between market linkage and the ............ factors proposed...................................................................................49 4.1 Augmented Dickey-Fuler and Phillips-Perron unit root ............ tests (p-values) ................................................................................59-60 4.2 Augmented Dickey-Fuler and Phillips-Perron unit root ............ tests for interest rates ...........................................................................61 4.3 Unit root tests for industrial production statistics in log............. levels .......................................................................................................64 4.4 Unit root tests for the log differenced industrial .......................... production statistics .............................................................................64 4.5 Turnover Ratios for all 12 markets .............................................66 4.6 Descriptive Statistics:Stock index returns for the full period..67 5.3.1 Cointegration Tests for the presence of long run...................... market linkage between the nations of the Pacific-Basin...............73 5.3.2 Cointegration Test Results for the presence .............................. of Long-Run Equity Market Linkage in the Pacific-Basin on........... the full period (January 5, 1998 - September 7, 2004) ..................74 5.3 Cointegration Test Results for the presence.................................. of Long-Run Equity Market Linakge in the Pacific-Basin.............79

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CHAPTER 1: Introduction 1.1 Research Objectives and Question The introduction of the theory of portfolio diversification heralded a new era in investment

management. Diversification pundits expounded the benefits obtained through its application.

Since then controversy has surrounded the true benefits generated through diversification.

The advantage of diversification diminishes in the presence of market linkage. In this study,

market linkage refers to common comovements between markets in either the short of long

run. The past several decades has played witness to immense change in the worlds financial

markets. Changes have stemmed from the removal of capital restrictions, the floating of

currencies, improvements in information technology, the superior level of investor education,

the increased mobility of labour, the escalation and participation in trade organisations and a

general shift towards a global economy and community. These structural changes have led to

markets displaying a greater level of linkage. Therefore, potential rewards garnered through

diversification have subsequently have been reduced.

The effect of market linkage is not an issue only concerning diversification. It has broad

consequences for both the academic and practical worlds. From an academic standpoint, it

invalidates many of the assumptions underpinning theories related to finance. For a

practitioner, it affects the pricing of securities across countries, investment strategies of

international asset portfolio managers and global hedging opportunities (Darrat and Zhong

2004). Bekaert and Harvey (1995) point to a number of papers from the developmental

economics literature that highlights the importance of financial market integration as a

conduit of economic growth. Cooper and Kaplanis (2000) even argue that the level of

financial market linkage influences optimal corporate capital structure through differing costs

of capital. The issue of market linkage has wide-ranging consequences in several different

fields and is of some importance in the modern world.

During the last fifteen years, a vast array of literature regarding short and long-term market

linkage has emerged. Early papers by Eun and Shim (1989) and Kasa (1992) supported the

assertion of market linkage over a long-term time horizon. The advancement of econometric

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techniques in the early 1990s saw a number of pieces surface that contradicted the original

findings. Chan, Gup and Pan (1992) and DeFusco, Geppert and Tsetsekos (1996) find no

evidence to support long run market linkage. More recently papers by Leong and Felmingham

(2003), Manning (2002), Masih and Masih (2000), Phylaktis and Ravazzolo (2002) and

Phylaktis (1999) all seem to point to an increase in the level of long run financial market

linkage in the Pacific-Basin region (PBC herein).

Several authors have also examined the potential short run dynamics in the Pacific-Basin.

Papers by Forbes and Rigobon (2002), Phylaktis (1999), Dekker, Sen and Young (2001) and

Johnson and Soenen (2002) all seem to point to considerable linkage over the short run.

Considering the spate of mixed empirical evidence and importance of the subject, the first

research question proposed in this thesis is one of market linkage between the markets of the

PB over short and long time horizons. It is important to distinguish between the short and

long relationships as they have vastly different consequences for market participants and

interested parties.

Papers by Bekaert and Harvey (1995), Lognin and Solnik (1995) and Karolyi and Stulz

(1996) all suggest that short run market linkage has a time determinant characteristic. Silkos

and Ng (2001), Leong and Felmingham (2003) and Yang, Kolari and Sutanto (2004) reach a

similar conclusion regarding long run market linkage. By employing cointegration

techniques, generalised variance decomposition, generalised impulse response analysis,

persistent profile analysis, and block exogeneity tests, this thesis attempts to address the

questions surrounding the strength, direction and development of the market linkage in the

Pacific-Basin region. In this analysis, there will also be an examination of how large

unanticipated shocks such as the East Asian currency crisis (EA crisis henceforth) and the

September 11 2001 terrorist attacks (September 11 henceforth) have affected market linkage.

This thesis based upon prior empirical evidence and finance theory proposes a third research

question that the short and long-term relationship intensifies in the period immediately

preceding market disturbances1.

1 This phenomenon is commonly referred to as contagion and has been widely discussed in both finance and economic literature.

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The last section of this study will attempt to identify the factors responsible for the increase in

market linkage. This section is the most important and breaks new ground in several areas. To

estimate the level of market linkage, tests of cointegration are completed and three test

statistics extracted. The use of these test statistics represents a contribution unique to this

study. Darrat and Zhong (2004), Kouparitsas (1997), Roll (1992) and Bracker, Docking and

Koch (1999) all highlight the importance of integration in the goods market when considering

equity market linkage.

Ng (2000) highlights that many countries in the PB region are effected by volatility spillovers

caused by the US and Japan. Furthermore, Ng finds evidence to support the proposition that

market linkage intensifies during times of high volatility, this supporting market contagion.

This study hypothesizes that a common volatility process acts as a transmission mechanism

through which market linkage can develop.

The use of three unique measures ensures the accurate estimation of the effect of market

development on market linkage. Market development is likely to have a positive effect on

market linkage is the proposition argued in this thesis.

Cheung and Hung (1995), Silkos and Ng (2001) and other researchers suggest that exchange

rates impact upon the level of market linkage. Bilateral exchange rate volatility assists in

examining the proposition. Ammer and Mei (1996), Bracker et al (1999) and Dekker, Sen and

Young (2001) all suggest that geographical location is an important factor in determining

market interactions. The use of multiple proxies fro this variable ensures the robustness of the

results.

Lastly, capital market integration is considered. Countries that have capital markets linked to

one another are likely to share significant capital flows and exhibit monetary policy

dependence. The link between capital flows, monetary policy and economic performance has

been widely discussed in the economic literature. Therefore, this thesis supposes that capital

market integration influences the prevailing level of market linkage.

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1.2 Motivation The motivation of this thesis arises from several issues. The question of market linkage has

not adequately been addressed in the existing literature appears to be valid. Many prior

studies have used inappropriate sample periods and incomplete econometric procedures.

Therefore, motivation arises from the desire to use more robust and complete econometric

testing.

This thesis draws motivation from examining the subject of market linkage from both a short

and long run perspective. Several previous studies have conducted a complete analysis, but

never in the same context as of this thesis. Continuing along this line, there will be an attempt

to identify which markets are dominant in the Pacific-Basin region. That is, does the US or

Japan dictate market behaviour, is there a strong link between Singapore and Malaysia? The

role individual markets play in the region is an interesting goal of this thesis.

As discussed in section 1.1 there is legitimate interest from the practical and academic world

surrounding the existence of cointegration between equity markets. Consequently, motivation

for this thesis derives, in part by this interest. Furthermore, by adding evidence to the existing

body of literature the true relationship between the equity markets of the PB is more likely to

reveal itself.

There is a relative lack of literature examining market linkage from an Australian perspective.

The majority of the literature focuses upon the linkage between the US or Japanese markets

and the developing markets of South East Asia or the US and Latin American markets or

between the established European markets. The inclusion of markets that are important from

the Australian perspective helps to differentiate this thesis from prior works.

Thirdly, the fifteen year plus time horizon provides a unique opportunity to examine the East

Asian crisis and events of September 11, 2001. These events are likely to have a considerable

effect upon the relationship between world markets. In times of market disturbances, it is

likely that investors and markets behave differently. Evidence from the 1987 crash suggests

markets exhibited higher levels of linkage in the post-crash period. These studies fail to

control for the fact that in the late 1980s that a number of other events occurred that

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dramatically shaped financial markets. This thesis attempts to isolate the effect of market

crashes and corrections by examining multiple events across developed and developing

financial markets. The question pertaining to the level of linkage prior to market disturbances

is one that requires some investigation. Through analysing markets both pre and post market

shocks it provides a complete picture about market behaviour. During the 1990s the US

equity market experienced abnormal growth and returns, while the Japanese market was

relatively stagnant. The vast difference in performance of the two largest markets in the PB

region is likely to have considerable ramifications for market linkage, thus making for an

interesting comparison.

Many pieces point to a time varying relationship between markets. Earlier pieces examined

the changes in contemporaneous correlations or other short run measures to establish a time

deterministic trend. More recently some literature has emerged which observes the change in

long run market interactions. However, most of the literature measures evolution through

examining the differences between two sub periods of the sample employed. The result of this

is there is a noteworthy motivation in mapping the evolution of the market linkage in the PB

region using higher frequency testing.

Following on from the discussion above, recent events such as regulatory reforms, improved

financial infrastructure and floating of currencies in the Pacific-Basin are likely to have

significantly affected the way in which markets interact. This provides justifiable interest for

policy makers to see how reforms affect market integration.

The motivation behind determining factors driving financial market linkage arises from two

sources. The first is that in the current literature less than a handful of papers have attempted

to identify a full set of deterministic factors. Furthermore, those that have used a full set of

factors have estimated the level of market linkage using short run proxies as opposed to the

long run orientation of this thesis. Secondly, there exists considerable interest from both the

academic and practical worlds. In the academic world knowing which variables are

responsible for intensifying market integration is desirable for helping to formulate and

further theories in international finance and trade. For practitioners it could help to identify

which markets offer the best opportunities for diversification, situations when the domestic

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version of CAPM is invalid for multinational firms computing their cost of capital, optimal

hedging policy and the pricing of securities.

1.3 Contribution This thesis aims to add to the current academic literature in three aspects:

1. Are the equity markets of the Pacific-Basin region linked to one another in either the

short or long run;

2. If so, has this relationship varied over time; and

3. Which variables have been responsible for markets becoming more integrated with

one another

Regarding the first proposition, this study contributes in several ways, through the utilization

of robust, comprehensive and unique econometric testing. Innovations include the use of a

recursive Johansen-Juselius test to establish market cointegration, which is absent from the

existing literature. To examine the differing interaction affects, the test for both the bivariate

and multivariate systems requires completion. Generalised variance decomposition and

impulse response functions and persistent profile analysis are all procedures that sparingly

employed in the existing literature regarding the PB. The sample employed in this thesis is

longer and more diverse than that employed in prior studies, thus giving greater power to the

statistical procedures presented in this study. Finally, this thesis attempts to consolidate the

findings of past studies to provide a definitive answer regarding the question of market

linkage.

The contribution by this thesis to the possibility of a time varying relationship arises from

several issues. Following the procedure outlined in Darrat and Zhong (2004), cointegration

tests are re-run with dummy variables included in the cointegrating vector representing events

such as September 11 and the EA crisis. This methodology has rarely been used in previous

studies and never applied in a study considering the Pacific-Basin.

The use of a recursive JJ test for cointegration is a new method proposed in this area. This

thesis expects that a more complete picture of market linkage will be the result. The

combination of testing methods provides a unique and substantial contribution to the existing

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literature considering the comprehensive nature of the methods employed and analysis

conducted.

A long sample period allows the examination of several large and unanticipated shocks. Most

studies tend to focus on the impact of only one event. This study contributes in the fact that it

compares and contrasts the effects of a major shock originating from a developing market

(e.g. EA crisis) to that of a developed (e.g. September 11) market. Again, in opposition to

other studies the usage of both long and short measures to establish the time variation is a

contribution made by this thesis. Examining both is necessary as they have distinct

implications for financial markets.

The contribution of this study in relation to identifying which variables are responsible for

causing markets becoming linked is quite extensive and represents a true contribution to the

existing material. This is largely based on the lack of research conducted in this area. The

contribution made to this area is considerable. Secondly, the use of a new approach to analyse

the linking relationship adds a considerable contribution. The first contribution is the new

approach taken to analyse the relationship. This study proposes to employ a Probit and OLS

model to inspect the cross sectional relationship. The use of multiple measures to evaluate the

level of market linkage ensures the results from this thesis are robust. The set of variables

presented in this thesis are unique, thus making a significant contribution. The use of a new

methodology and set of variables ensures the contribution made by this thesis to the current

academic literature is noticeable.

1.4 Thesis structure The structure of this thesis is as follows: Chapter 2 provides a review of the literature relevant

to long and short run linkage, time varying linkage and the factors possibly responsible for

causing market linkage. The first section of Chapter 3 provides a detailed examination of the

major events and policies that have affected markets in the PB over the last sixteen years. The

second part of the chapter expands upon the theory presented in section one and presents the

market linkage hypothesis. This is followed by a discussion on the methods and techniques

required to examine the relationship. The third section of chapter 3 covers the time variation

hypothesis, and details how this study plans to attempt to answer this question. The last

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section of chapter 3 develops the model used to try to determine which factors are responsible

for driving market linkage in the PB.

Chapter 4 offers a description of the data and its stochastic properties. This section also

contains a comprehensive discussion about how several variables were computed. The last

section of chapter 4 displays the descriptive statistics for the sample employed. The empirical

findings and conclusions are examined in chapter 5. Last of all chapter 6 presents some

thoughts on the contribution of the study to the current literature, the limitations and further

areas for research and the conclusions that can be taken from this study.

CHAPTER 2–LITERATURE REVIEW

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CHAPTER 2: Literature Review 2.1 Literature on the use of long term measures of market linkage The extension of Engle and Grangers work in the field of cointegration by Johansen and

Juselius (JJ) heralded the beginning of numerous pieces of literature incorporating

cointegration analysis. The majority of these papers attempted to discern whether or not

international equity markets were cointegrated with one another.

Kasa (1992) represents the most important and controversial contribution to the early

literature on cointegration. Kasa (1992) uses the procedure outlined by Johansen (1990) to

test for the presence of a common stochastic trend between the equity markets of the US,

Japan, Canada, Germany and England. Monthly and quarterly data from January 1974

through August 1990 on Morgan Stanley's Capital International indices are used to compute

Johansen (1990) tests for cointegration. The results indicate the presence of a single common

trend driving these countries' stock markets. Estimates of the factor loadings suggest that this

trend is most important in the Japanese market and least important in the Canadian market.

This result sparked great debate among academics and spawned countless papers either

verifying or disproving Kasas work.

Chan, Gup and Pan (1992) are one of the first groups of researchers to reject the findings

purported by Kasa (1992). Using daily and weekly data a period from February 1 1983 to

May 18, 1987 is sampled for the equity markets of the US, Japan, South Korea, Taiwan, Hong

Kong and Singapore. Chan et al find that prices are non-stationary but when differenced (i.e.

transformed into returns) the series become stationary, thus satisfying the first order condition

for cointegration. Pairwise and higher-order cointegration tests are performed for these

markets. No evidence of cointegration is found. Neither the stock price of a single country nor

that of a group of countries can be used to predict the future stock price of another country.

The conclusions are robust for both daily and weekly data. The conclusion reached by the

authors is no longer persuasive for a number of reasons. The first being the absence of the

Johansen-Juselius test for cointegration. Secondly, considering the period and countries

selected the result is not surprising due to the level of capital market restrictions that were in


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place at the time of the study. In the time since the study, markets in the Pacific-Basin region

have significantly relaxed capital market restrictions and are highly likely to exhibit market

linkage. Lastly, it is a well-known fact that cointegrating relationships are hard to find over

such a short time horizon as in the sample used by Chan et al.

Blackman, Holden and, Thomas (1994) use monthly data on 17 different equity indices

obtained from Morgan Stanley's Capital International to conduct tests for cointegration using

Johansens (1988) procedure. Blackman et al test two different sets of markets. The first set

contains ten national stock markets2. For the period 1970-1979, there is no evidence of

cointegration, while for the period 1984-1989 two cointegrating vectors are discovered. The

second set also comprises ten markets3. Blackman et al find two cointegrating vectors for the

1970-79 period and four vectors in the 1984-89 period. While this study includes only four

markets common to this thesis it provides evidence that certain equity markets were

cointegrated as early as 1979.

Cotsomitis, Kwan and Sim (1995) use Engle-Grangers cointegration technique to examine a

series of nine equity market indices from January 1982 to February 1991. Monthly index

levels are obtained from the DX Database for nine stock markets4. Cotsomitis et al find that

when the series of markets are tested as a whole that a cointegrating relationship is present.

Furthermore, it is found that the Four Little Tigers and the US are cointegrated as a series as

is Japan, Hong Kong, South Korea and Taiwan. . Probably the most surprising result is that

when the four major G-7 countries and Four Little Tigers are tested as separate systems there

is no evidence of cointegration. Cotsomitis et al provides some useful evidence on which to

base this thesis. The usefulness of the results is limited considering only the Engle-Granger

cointegration test is employed and the study is dated.

Richards (1995) provides the some of the most compelling and robust evidence in favour

against the possibility of cointegration. Richards essentially reviews the paper by Kasa (1992)

and shows that the results obtained are misleading. Richards employs Monte Carlo simulation 2 First set of markets examined includes US, UK, Japan, Australia, Austria, Belgium, Canada, Denmark, France and Germany. 3 The second set is comprised of the US, Hong Kong, Italy, Japan, UK, Netherlands, Norway, Spain, Sweden and Switzerland 4 Markets examined include Hong Kong, Japan, Australia, Singapore, South Korea, Taiwan, UK, US and West Germany


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techniques to demonstrate that Kasas findings are the result of failing to account for the small

number of degrees of freedom that remain in the Johansen multivariate cointegration

procedure. Richards proposes methods by Cheung and Lai (1993) and Ahn and Reinsel

(1988) to correct the asymptotic critical values for the limited degrees of freedom. Using the

procedure detailed by Ahn and Reinsel, Richards finds that the critical values should be

scaled up by a factor of 8.14. Cheung and Lai maintain that Ahn and Reinsel procedure results

in an overestimated scaling factor, while Richards asserts that the Ahn and Reinsel factor is

actually understated in his analysis.

Quarterly data from Morgan Stanley's Capital International indices denominated in US

dollars is collected. Unlike most other studies Richards (1995) uses indices for total returns

(capital gains and dividends) instead of price levels. Richards (1995) sample includes

seventeen stock indices5 spanning a period from the end-December 1969 to end-December

1994. Richards tests the system as a whole and finds little evidence to support the presence of

cointegration. Based on the Engle-Granger test, Richards then conducts one hundred and

twenty pairwise cointegration tests. Six pairs are found to be cointegrated at the five percent

level and eighteen at the ten percent level. The results, which can be taken from Richards, are

that cointegration tests are biased when small samples are employed and that Kasas findings

are likely to have been overstated6. This thesis will employ the small sample correction factor

developed by Cheung and Lai (1993) to maintain the robustness of the results.

Cheung and Hung (1995) contend the result obtained from Chan et al (1992) by noting that

they denote returns in domestic currency terms and therefore ignores currency risk. Secondly,

Chan et al used pairwise cointegration tests, such limited testing disregards the possibility of a

cointegrating relationship between multiple equity markets. Cheung and Hung also note that

the use of daily data can lead to erroneous conclusions due to nonsynchronous trading and

various other phenomena. As a result, Cheung and Hung use the Johansen-Juselius (1990)

procedure on weekly data ranging from January 1981 to December 1991 to examine the

relationship between Hong Kong, South Korea, Malaysia, Singapore and Taiwan. When

denominated in domestic currencies, Cheung and Hung fail to find any cointegrating 5 Markets included are the US, UK, Japan, Australia, Austria, Belgium, Canada, Denmark, France, Germany, Hong Kong, Italy, Netherlands, Norway, Spain, Sweden and Switzerland 6 As will be discussed in Chapter 5 the critical values for cointegration used in this thesis require no for small sample size adjustment


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relationship over the whole period, or the two sub periods (1981-87 and 1987-1991). When

returns are denoted in US dollar terms, Cheung and Hung find a strong cointegrating

relationship for the whole period (1981-1991) and the period 1987-1991 reporting the

presence of three cointegrating vectors. After obtaining this result, the authors suppose that

exchange rate fluctuations rather than equity price movements are driving the cointegrating

relationship. Cheung and Hung then test the five Asian currencies against the US dollar for

the presence of cointegration. The results proved Cheung and Hung hypothesis to be true

finding four cointegrating vectors for the entire period and three vectors for 1987-1991

period.

DeFusco, Geppert and Tsetsekos (1996) use weekly data obtained from the International

Finance Corporation for thirteen emerging markets. The sample ranges from January 1989 to

May 1993, with price indices given in US dollar terms. The 13 markets are then split into

three groups. The only group of interest to this thesis is the Pacific-Basin group comprised of

the US, South Korea, the Philippines, Malaysia and Thailand. DeFusco et al (1996) use the JJ

(1990) procedure to assess the possibility of cointegration between the five markets. DeFusco

et al find no evidence of cointegration but also very low levels of correlation with any average

of 0.216 among the four Asian markets. The only real caveat in the work from DeFusco et al

is the relative short time horizon of sample employed and its timing. Both these issues could

be a major reason as to why no cointegrating relationship and low correlation was discovered.

Using end of month interest rates instead of stock market indices, Phylaktis (1999) employs

bivariate and multivariate JJ (1990) cointegration tests. Phylaktis examines capital market

integration among six Pacific-Basin countries7 and the US. Significant cointegration is found

for all bivariate tests8, except between Japan and the US for the period January 1974

December 1980. The trivariate tests (i.e. PBC, US and Japan) for the February 1982-

September 1993 period find the presence of two cointegrating vectors for all tests except for

when Korea is included, in which case only one vector is found. The final cointegration test

completed is a multivariate test with all markets included except Korea. The author cannot 7The six Pacific-Basin countries include; Hong Kong (January 1976September 1993), Singapore (August 1973September 1993), Malaysia (January 1982September 1993), Taiwan and Korea (February 1972September 1993) and Japan (January 1974September 1993). The bracketed dates represents the periods for which data could be collected. 8 Phylaktis conducts a series of bivariate cointegration tests between the US and a Pacific-Basin market and Japan and a Pacific-Basin market.


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reject the hypothesis that five stochastic trends are present, an alternative interpretation is that

there is one single stochastic common trend. Therefore, it can be concluded that integration is

present amongst the capital markets of the Pacific-Basin economies, Japan and the US. It is

important to note that, Phylaktis uses a correction factor for small sample bias developed by

Reimers (1992). The use of this procedure makes the results even more robust and persuasive.

Masih and Masih (2001) examine the stock market indices for nine countries9 for a period

ranging from January 1982-June 1994. They use monthly averaged stock-price indexes

obtained from International Financial Statistics published by the IMF. The authors employ

the JJ (1993) procedure to conduct a series of multivariate cointegration tests. Masih and

Masih cite Gonzalo (1994) as providing empirical evidence to highlight the relative

superiority of the JJ method when testing for the order of the cointegration rank compared to

other procedures available. Masih and Masih find the presence of a single cointegrating

vector among the markets included in the system. The authors go further to note that Cheung

and Lais (1993) correction for small sample bias does not affect their finding.

Manning (2002) examines weekly and quarterly data for the returns of ten10 equity indices

denominated in domestic and US dollar terms sourced from DataStream. When a lag length

of one week is, selected Manning (2002) detects the presence of a single cointegrating vector

and eight common trends and finds no difference in the results between the domestic and US

dollar series. When the lag length is extended to twenty-six weeks, a significantly stronger

relationship is unearthed. For both the domestic and US dollar series, seven vectors and two

common trends are evident. Manning recomputes the cointegration tests using quarterly data.

Similar results to the weekly tests with a lag of twenty-six weeks are achieved. After several

tests, the author concludes that the result is even stronger when the quarterly data is used.

Furthermore, the results are slightly altered when the tests are conducted using domestic and

US dollar series. Manning found that Japan, Hong Kong and the US were the dominant

markets in one trend11, while Malaysia and Singapore lead the other. The author notes that the

pairwise tests yield no evidence of cointegration except for two pairs12.

9 The countries include the UK, Germany, the US, Japan, Hong Kong, Taiwan, Australia, Korea and Singapore. 10 The sample contains the US, Hong Kong, Indonesia, Japan, Korea, Malaysia, Philippines, Singapore, Taiwan and Thailand. 11 It must be noted that the US plays a significantly larger part in this trend. 12 The two pairs found to be cointegrated were Malaysia and Indonesia and the US and Indonesia.


14

Using the framework proposed by Ammer and Mei (1996), Phylaktis and Ravazzolo (2002)

decompose excess stock return innovations between different countries into news about

excess returns, dividend growth rates, interest rates and exchange rates using Campbell and

Schillers (1988) approximate present value model.

The results relevant to this study are those for the period 1990-1998 covering nine markets

(the same as those outlined in footnote 9) in the Pacific-Basin region plus the US.

Comovements between dividend growth rates is taken as an indicator of real economic

integration, while excess return movements represent the level of financial integration. The

authors find that the covariances pertaining to future dividend news were significant (at the

five percent level) in forty-one out of forty-five cases, while for excess returns they were all

found to be significant. It was found that there was significant correlation in thirty-nine out of

forty-five pairs regarding future dividend news and in thirty-eight cases for future excess

returns news13. Phylaktis and Ravazzolo found that the EA crisis reduced global economic

integration but increased regional economic and financial integration. The main result that can

be taken away from this study is the high level of financial and economic integration in the

Pacific-Basin region.

Using daily data denoted in domestic currency terms for five Asian stock price indices14

obtained from DataStream, Leong and Felmingham (2003) conduct a series of pairwise and

multivariate cointegration tests under the procedure described by Gregory and Hansen (1996).

The methodology proposed by Gregory and Hansen differs from the standard JJ test in that it

allows for structural breaks in the data. When structural breaks are not allowed Leong and

Felmingham find no evidence of bivariate cointegration at the five percent level, while at the

ten percent level ten pairs are identified. A multivariate test for cointegration on the system as

a whole provides evidence supporting the existence of a single cointegrating vector between

the five markets. When the Gregory and Hansen test is reapplied, results that are more robust

are obtained. The authors find three pairs to be significant at five percent and a further two at

ten percent.

13 It must be noted that in all cases when an insignificant relationship was discovered it was between a Pacific-Basin economy and the United States. 14 The five stock price indices include Japan, Singapore, Taiwan, Hong Kong and South Korea.


15

The main conclusions that can be drawn from the literature concerning long-term measures of

market linkage:

1. There is a need for a large sample period (five years or longer) to be able to accurately

discover cointegration

2. The presence of cointegration does not seem to be sensitive to what currency prices

are denominated in

3. The use of high frequency data (e.g. weekly rather than monthly) in general allows for

a cointegrating relationship to be identified more easily

4. Capital and exchange rate controls do not necessarily segment markets from the world

economy

5. The majority of the recent evidence tends to support the hypothesis of market

cointegration and linkage in the Pacific-Basin region since the early 1990s.

Furthermore, it appears that there is significant links between Japan, the US and Hong

Kong.

6. The US and Japan are independent of all other markets in the Pacific-Basin region.

7. Economic integration seems to be a major contributing factor driving financial market

linkage.

2.2 Literature on the use of short run measures

Stretching back to seminal pieces by Levy and Sarnat (1970), Grubel and Fadner (1971),

Lessard (1973) and Solnik (1974) it has been well accepted that contemporaneous correlations

between markets are relatively low. In the last decade, a myriad of papers has addressed this

issue. Traditional correlation analysis and a variety of other measures including Geweke

feedback and Granger causality tests have been employed.

Using cross correlation Forbes and Rigobon (2002) addresses the issue of contagion

following the Asian crisis. Forbes and Rigobon define contagion as a significant increase in

cross-market linkages after a shock to one or more countries. The first major point made by

Forbes and Rigobon is that during crises, markets are more volatile and consequently

correlation coefficients are upwardly biased. The authors show that once the correlation

coefficients are corrected for heteroscedasticity that any evidence of contagion post Asian


16

crisis disappears. They note that high cross-market correlations during the sample period are a

continuation of strong linkages that exists in all states of the world rather than an increase in

these linkages (contagion). This result provides considerable weight to the hypothesis

presented in this thesis that markets are linked to one another in the short run.

To measure the extent of financial market integration (defined as greater comovement on the

same day or a stronger lead/lag relationship across days) Bracker, Docking and Koch (1999)

use Geweke measures of feedback. Bracker et al compute a series measures for nine national

equity indexes15 using daily returns for a period 1972 to 1993 based on data provided by

Morgan Stanley Capital International. Bracker et al finds that the US significantly affects all

other eight markets contemporaneously for the entire sample period. Bracker et al even finds

some evidence of Australia, Hong Kong, Singapore, Germany, the UK and Canada

significantly affecting the US in a contemporaneous manner16.

Hon, Strauss and Yong (2004) examine the impact of the September 11 tragedy upon

international equity markets. Building on the work of Forbes and Rigobon (2002), Hon et al

estimate a series of cross-market correlations that have been adjusted for heteroscedasticity.

Hon et al obtain daily returns for 25 economies from the CEIC Data Company Limited

database from September 11 2000 to March 11 2002. The first step taken by Hon et al is a

series of Granger causality tests from which they find the US exerts significant causality over

eighteen economies prior September 11 and twenty-one of the twenty-four markets post

September 11.

After adjusting for heteroscedasticity twelve markets exhibit significant increases in market

comovements for the three and six months post September 11. Findings pertinent to this study

is the relatively low (less than 0.17) level of correlation between the US and Australia, Hong

Kong, Indonesia, Japan, Korea, Malaysia, New Zealand, Philippines, Singapore, Taiwan and

Thailand for the twelve months pre September 11. Post September 11, Hong Kong, Japan and

Korea become significantly correlated with the US with coefficients in excess of 0.6.

Australia, Singapore, Malaysia and Taiwan range from 0.2 0.3 while New Zealand, 15 The nine markets examined include Japan, Australia, Hong Kong, Singapore, Switzerland, Germany, the UK, US and Canada. 16 Significant results are found for Australia in 1980 and 1991, 1985 for Hong Kong and 1980 and 1982 for Singapore.


17

Indonesia and the Philippines all remain below 0.13. The results of Hon et al presents

evidence that the September 11 attacks caused a dramatic shift in market linkage in the short

run.

To examine the level of equity market comovements between thirteen economies17 Johnson

and Soenen (2002) obtain daily returns from Morgan Stanley Capital International from 1988

through 1998. Similar to the procedure employed by Bracker et al (1999), the authors

calculate a series Geweke measures between Japan and the twelve other markets for each of

the ten years. For Australia, China, Hong Kong, Malaysia, New Zealand and Singapore

significant (5% level) contemporaneous comovement was found for at least 82% of the years

1988 to 1998 and an average Geweke measure of 26.318. Indonesia (36%), Korea (18%), the

Philippines (55%) Taiwan (45%) and Thailand (55%) were linked with Japan less often at the

five percent level and had an average Geweke measure of 5.9. Johnson and Soenen add

weight to the notion of time varying linkage through plotting the average Geweke measures

for all twelve markets over the ten years. The authors find that the relationship peaks in 1991

and by 1993 has declined to its lowest point and by 1998 had reached the levels present in

1991.

The long run methods employed by Phylaktis (1999) have already been discussed in an earlier

section of this chapter. To establish capital market integration the author also uses two

methods to examine the short run dynamics. The impulse response analysis shows that for the

January 1974 December 1980 period both Singapore and Korea are closely integrated with

both the US and Japan, while Taiwan is only related to the US. In the second period (February

1982-September 1993), all five markets are more closely linked to Japan compared to the US.

The author conducts several multivariate Granger causality tests between the US, Japan and

each Pacific-Basin country. The US and Japan Granger cause real interest rates in Singapore,

Korea, Malaysia and Hong Kong, while only Japan exerts causality over Taiwan.

Furthermore, reverse causality is found to exist between Japan and Malaysia and Japan and

Hong Kong.

17 Australia, China, Hong Kong, India, Indonesia, Japan, Korea, Malaysia, New Zealand, the Philippines, Singapore, Taiwan and Thailand are the thirteen markets examined 18 The higher the Geweke measure the greater the contemporaneous relationship between the two markets


18

The conclusions that can be drawn from the literature pertaining to the use of short run

measures include:

1. Correlation coefficients are biased in the presence of heteroscedasticity and therefore

need to be corrected. This situation is likely to occur when examining periods of

market distress, where volatility is higher than usual.

2. The events of September 11 resulted in an increase in correlation between the US and

the select markets in the Pacific-Basin region for up to six months after the event.

However, at the same time the results show the relatively low level of correlation

between the US and markets in the Pacific-Basin

3. The US displays considerable contemporaneous influence over Australia, Hong Kong

and Japan and to a lesser extent Singapore and Malaysia. The other less developed

markets of the Pacific-Basin seem to be only weakly linked to the US in the short run.

By contrast, Japan exhibits considerable effect over Hong Kong, Malaysia and

Singapore and greater influence over the developing markets compared to the US.

There also is more evidence to support the existence of a block between the US, Hong

Kong and Japan.

2.3 Literature regarding the time evolution of market linkage

In an effort to explain why expected returns vary across global equity markets, Bekaert and

Harvey (1995) address the issue of market integration. Asset pricing studies and models can

be divided into three broad groups. The first assumes markets are completely segmented, the

second supposes full integration and lastly that markets are somewhat segmented (and fixed at

that level forever). Using a regime-shifting model, the authors construct a measure that allows

the level of market integration to fluctuate through out time. In the study, twelve emerging

markets are examined, only the results for Korea, Malaysia, Taiwan and Thailand are relevant

to this thesis. For the sample period (1977-1992) Korea averages over a 95% chance of being

integrated, Malaysia in excess of 75%, Taiwan over 80% and from the beginning of 1986

Thailand obtains a score of 100%. Bekaert and Harvey present one of the first and most

influential pieces regarding time varying market integration. This study provides much of the

impetus towards the third hypothesis presented in this thesis and shows that the relationship is

likely to vary across time.


19

Longin and Solnik (1995) present one of the most widely cited pieces within the last ten years

on the evolution of correlation among international equity returns. Using the monthly excess

returns for seven major stock indices19over the period 1960-90, Longin and Solnik find that

correlations between markets have strengthened. The authors attempt to describe the

evolution of the conditional covariance structure through employing a multivariate GARCH

(1, 1) model with constant conditional correlation. Variance is found to have changed over

time and the hypothesis of constant conditional correlation is rejected. This allows Longin

and Solnik to conclude that over the last thirty years the correlation between international

markets has intensified. Another major finding in the paper is that correlation rises during

periods of high conditional volatility. The findings of this paper as well as the Bekaert and

Harvey study act as a cornerstone for this thesis, concerning the hypothesis of time varying

relationship between international equity markets.

Several papers examined in section 2.2 also identified a time varying relationship in the short

run. Johnson and Soenen (2002) using Geweke measures of contemporaneous feedback and

Phylaktis (1999) with impulse response functions both find evidence of a change in the

linking relationship over time. Using cross correlations Forbes and Rigobon (2002) and Hon

et al (2004) examine the months immediately preceding the events of the EA crisis and

September 11 respectively. As noted earlier Forbes and Rigobon find no evidence of

increased correlation post crisis, while Hon et al found considerable evidence to suggest that

post September 11 that the short run linkage between markets strengthened.

There exists a lack of literature that has attempted to examine the time varying relationship in

a long run setting. Silkos and Ng (2001) collect monthly stock market data for six Pacific-

Basin countries20 and the US from the World Stock Exchange Fact Book for the period

January 1976 to August 1995. The authors conduct a number of JJ tests for cointegration over

five year rolling samples. The most pertinent results to this thesis are those from the 1988 to

1993 and 1991 to 1995 periods. With all seven countries included in the test, six cointegrating

vectors are found in period one and five in the second. Five vectors are found in period one

and four in period two when the US is excluded from the test. Similar results are found when

19 The seven included in the sample are the US, Germany, Switzerland, Japan, France, Canada and the UK. 20 The six countries examined include Hong Kong, Japan, Korea, Singapore, Taiwan and Thailand


20

the US is reinstated and Japan excluded. Finally, when both the US and Japan are excluded

from the sample four vectors are discovered in both periods.

To further study the evolution of market integration Ng and Silkos, apply the rank constancy

test developed by Qunitos (1993, 1997). The rank constancy test is conducted by testing the

hypothesis that the number of cointegrating vectors in sub-sample one is equal to the number

from the whole sample, which is also equal to the number from sub-sample two. The authors

find that in almost every case that the null hypothesis of rank constancy is rejected. In

addition, it is found that the market crash of 1987 and the Gulf War (1990) significantly

affected the long term relationships among the developing Asian markets and those of the US

and Japan.

Similar to Ng and Silkos (2001), Kolari, Sutanto and Yang (2004) conduct a number of rank

constancy tests based upon the procedure devised by Hansen and Johansen (1993, 1999). The

authors conduct a number of bivariate cointegration tests between the US and developing

markets. Pairings relevant to this study are those including Korea, Malaysia, Taiwan and

Thailand. For Korea the relationship strengthens from the early nineties to late 2000 and then

declines in early 2001, however a significant relationship is never discovered. Both Taiwan

and Thailand exhibit a similar pattern to that of Korea, except for part of 1998 when a

significant relationship is found to exist between Thailand and the US. Malaysia displays a far

more volatile relationship when compared to the other three markets. From 1990 to 1997, the

relationship strengthens slowly, in mid 1997 it rapidly declines before quickly rebounding and

displaying significance multiple times in 1998 and 1999. Similar to the other markets from

the beginning of 2000 the linkage begins to weaken. Lastly, Kolari et al conduct a constancy

test on the Asian group (includes India plus the other markets discussed above) of countries as

a whole over the seventeen year sample period. The cointegrating relationship is found to be

significant for times in 1990, 1991 and 1992 after which it begins to deteriorate until it

reaches a minimum in late 1996/early 1997. From mid 1997 to early 1998, a significant link is

present, followed by a quick decline and lack of significance for the remainder of the period

except for a brief moment in 1999.

Bracker et al (1999) tackle the issue of time variation by including a time trend variable in

their regression analysis. In four out of six cases, the trend is found to be significant at the one


21

percent level. The main conclusion that can be drawn from the literature presented in this

section is that the relationships between markets exhibit considerable variation in both the

short and long run.

2.4 Literature regarding the determinants of market linkage

The issue of what causes market linkage has not been widely addressed in the current

literature. The most commonly proposed factor is economic/good market integration.

Roll (1992) hypothesizes that the industrial structure of a countries economy significantly

affects stock price. Starting in April 1988 and finishing in March 1991 Roll, collects daily

returns for 24 countries from the FT Actuaries/Goldman Sachs International Indexes

published daily in the London Financial Times. Through a series of time series regressions

Roll establishes that industrial composition is an important factor in determining stock prices.

Roll also proposes that exchange rates play a considerable role in explaining the volatility in

stock market returns. After a series of tests, Roll discovers that industrial composition and

exchange rates combined explain almost fifty percent of the volatility in returns and

individually account for forty percent and twenty-three percent respectively21.

Phylaktis and Ravazzolo (2002) using return decomposition find significant evidence to

suggest that the economies of the Pacific-Basin region are integrated in an economic sense

between themselves and with Japan and the US. Furthermore, the authors find that variations

in news about dividends (proxy for economic integration) are the main source of stock return

variance in all of the markets included in the sample. Overwhelming evidence is found to

suggest that economic integration acts as a channel through which financial integration can

travel.

Using more traditional measures of economic integration Bracker et al (1999) finds evidence

to suggest its importance in explaining contemporaneous market linkage. Four measures are

employed in an effort to capture economic integration22. In the three regressions the measures

21 The adjusted R2 figures are unable to be added together because exchange rates are correlated with foreign industry indices. 22 The four measures used are:


22

are included in, significance is found in eight out of twelve cases. Using similar measures to

Bracker et al, Johnson and Soenen (2002) finds that bilateral import dependence to have a

negative significant affect upon contemporaneous feedback. In addition, Johnson and Soenen

include the differential in GDP growth finding it exerts substantial influence over the level of

short run market linkage. Dekker, Sen and Young (2001) support the findings of the other

papers noting that markets with close economic ties account for a greater proportion of

forecasted error variance.

Ammer and Mei (1996), Dekker et al (2001) and Bracker et al (1999) all find evidence to

support the importance of geographical location in determining market integration. For

example Ammer and Mei find significant financial integration between Switzerland and

Germany and the US and Canada, while Dekker et al discover that markets like Australia and

New Zealand and Singapore and Malaysia exert considerable influence over one another.

Bracker et al considers the importance of geographical location through two different proxies

in their regression analysis. The first proxy uses the distance between markets and as expected

finds a significant negative relationship. For their second proxy several dummy variables are

employed (one for the Pacific Rim, Europe, UK-US and US-Canada), all coefficients are

found to be positive and significant.

There exists a substantial amount of literature documenting the effect of returns volatility

upon market linkage. Papers by Longin and Solnik (1995) and Karolyi and Stulz (1996) find

that the correlation between markets increases during periods of high volatility. Building on

these findings and using the methodology proposed by Bekaert and Harvey (1997), Ng (2000)

presents the most relevant paper to this thesis. Ng observes the effects of high volatility

periods in the US and Japan upon the correlations with markets in the Pacific-Basin region.

Weekly data is compiled for the S&P 500, Tokyo Stock Price Index, Hang Seng, Korean

Composite stock price index, Kuala Lumpur Stock Exchange, the Stock Exchange of

Singapore All Share Index, the Taiwan Stock Exchange Weighted Price Index and the Stock

Exchange of Thailand in US dollar terms for the period 1975 to the end of 1996. Ng finds that

the correlations between the US and Japan and the Pacific-Basin markets increases

1. exports from country i to country j as a percent of is total exports, during year t 2. exports from country j to country i as a percent of js total exports, during year t 3. imports from country i to country j as a percent of is total imports, during year t 4. imports from country j to country i as a percent of js total imports, during year t


23

considerably in times of high volatility. The only peculiar result was between the US and

Korea. When the volatility in the US rose, the correlation weakened and even displayed low

levels of negative correlation during the most volatile periods. Ng then employs a volatility

spillover model to fully investigate the magnitude and changing nature of spillovers

originating from the US and Japan upon the Pacific-Basin. Both world (the US) and regional

(Japan) factors are found to be important in explaining volatility in the Pacific-Basin.

The effect of exchange rates upon market linkage has been addressed in several publications.

Silkos and Ng (2001) find that exchange rate volatility has a significant influence over the

number of cointegrating vectors found. As discussed above Roll (1992) found that exchange

rates accounted for twenty-three percent of variation in stock return volatility. By contrast

Bracker et al (1999) find that neither exchange rate volatility nor the percentage change

significantly explain contemporaneous market movements. Johnson and Soenen (2002) who

find that neither variable (exchange rate volatility or the percentage change) influences the

intensity of market linkage reinforce these results.

Market development in relation to stock market linkage is a variable that has been discussed

in a number of studies in the current literature. Many papers have used market development to

proxy for market integration. That is, poorly developed markets23 are not likely to be linked

with other markets. However, Bekaert (1995) and Bekaert and Harvey (1995), find that many

developing markets are not segmented from the world. Therefore, this raises the question as to

how the level of development affects the level of market linkage. Bracker et al (1999) use the

absolute size differential between two markets to proxy for market development. Holding

everything else constant, a larger differential indicates the greater the likelihood that the

markets are not linked with one another. In all six regressions conducted, the variable as

expected is found to be negative and significant at the five percent level. Again, Johnson and

Soenen (2002) utilize a similar metric in their analysis and find it to be both economically and

statistically insignificant.

Hon et al (2004) find evidence to support the hypothesis that better developed and more

liquid markets have closer links to the US post September 11. They find that for the quarter

23 Development in this sense refers to a poorly developed legal system, investment barriers and markets that are not deep and liquid.


24

post September 11, the US on average explained seventeen percent of the variation in returns

in the European markets as opposed to three percent prior to the crisis. Furthermore, it is

discovered that Malaysia, Indonesia, the Philippines and Thailand are not closely linked to the

US.

Karolyi and Stulz (1996) attempt to explain market comovements by investigating return

comovements between Japan and the US. Intraday and overnight return correlations between

a portfolio of Japanese ADRs and US stock portfolio are computed for various sub samples

related to different information variables24. Fisher Z tests are then employed to determine if

there has been a significant change in correlation. The authors find that the higher the absolute

overnight and daytime return of the S&P 500 and Nikkei the higher the level of correlation

between the two portfolios. Connolly and Wang (2003) report similar results.

Connolly and Wang (2003) use linear and non-linear news models to examine the causes of

equity comovements between the US, the UK and Japan. The authors find that

macroeconomic news announcements do not economically or statistically affect intraday or

overnight comovements in a significant manner. By contrast, the previous day return from

foreign markets exerts significant influence on the subsequent day return in the domestic

market. That is, the prior day returns appear to contain unique information not captured in

economic fundamentals.

The effect of interest rates upon international capital flows has been well documented in both

the finance and economic literature. Three papers that have been discussed above examine the

effects of interest rates on market linkage. Bracker et al (1999) and Johnson and Soenen

(2002) include the absolute difference between real interest rates of the markets included in

their sample. A negative relationship is expected as the larger the difference then the greater

the deviation away from interest rate parity. As expected Johnson and Soenen find a negative

relationship that is statistically and economically significant. The evidence from Bracker et al

is a little more muddied. In five out of six regressions, the variable is found not to be

significant and displays little economic significance in any of them.

24 Examples include the Yen/Dollar exchange rate, the US Treasury bill futures or the Nikkei and S&P 500 index returns.


25

Forbes and Rigobon (2002) extract conditional correlations from a VAR with lagged returns

and interest rates as the explanatory variables. The authors rationalise the inclusion of interest

rates by stating that they control for any aggregate shocks and monetary policy coordination.

The results are found to be insensitive to whether or not interest rates are controlled.

There are several major conclusions that be drawn from the literature reviewed in this section:

1. Economic integration appears to be an important factor when considering market

linkage. A number of variables can be used to proxy for economic integration

ranging from import/export independence, industrial production statistics and future

excess returns.

2. Geographical location appears to influence the level of market comovements. That

is, markets that are closer to one another are more likely to move in the same

direction.

3. Periods of high volatility appear to be linked to periods of high correlation.

Furthermore, volatility spillovers from the US and Japan significantly affects the

volatility processes of markets in the Pacific-Basin.

4. There is mixed evidence with regard to the influence of exchange rates over market

linkage. It is interesting to note that the two papers in which exchange rates do not

explain comovements are those that employ short run measures. By contrast, the

strongest evidence to support the effect of exchange rates is when a long run

measure is used.

5. The level of market development is an important factor in explaining market

linkage. However, there appears to be conjecture as to what is an appropriate proxy

to accurately capture the relationship.

6. The empirical evidence regarding the effect of interest rates on market linkage odes

not provide convincing evidence regarding the true relationship.

CHAPTER 3–HYPOTHESIS AND THEROY DEVELOPMENT

26

Chapter 3: Hypothesis and Theory Development

3.1 Introduction

The focus of this chapter is threefold; the first section provides a brief history of the

economies and stock markets of the Pacific-Basin countries. The second section building on

the facts presented in section one presents why Pacific-Basin markets are likely to display

strong linkage with one another and why it is probable that the relationship has varied over

time. The third section develops the theory behind the factors proposed that are responsible

for driving market comovements.

3.2 Description of the Pacific-Basin Economies

During the last several decades, the Pacific-Basin region was one of the most dynamic regions

in the world. Starting in the 1960s and 70s new economic policies, cultural and social

change and a shift in political regimes saw the region blossom. Discussed in the section below

is the background of the financial markets and economic performance of Hong Kong,

Indonesia, Korea, Malaysia, the Philippines, Singapore, Taiwan and Thailand. Bekaert and

Harvey (1998), the Economist, the CIA World Fact book 2003 and the IMF deserve special

mention for the information presented below, while Silkos and Ng (2001) provided some

further insights.

A. Hong Kong

Hong Kong is a market that has enjoyed a far greater level of economic and financial market

development compared to some of its Asian counterparts due to its close links to the Western

economies. In the wake of the 1973-74 crash, the Office of the Commissioner for the

Securities was established. During the 1980s the economy of Hong Kong flourished with an

average GDP growth rate of 6.83%. The level of international activity in the Hong Kong

market mirrored that of the growth in the economy.

In May 1989, a new body formed in light of the crash of 1987. The Securities and Futures

commission consisted of full-time professional regulators whom had wider reaching powers

to investigate and enforce breaches. The EA crisis did not adversely affect Hong Kong until

the middle of October 1997. The government intervened in August 1998, to fend off


27

manipulators in the currency, futures and stock markets. However, throughout this time the

government was still able to maintain a strong banking and financial sector. Post crisis, Hong

Kong quickly rebounded before taking another hit in 2001 and once again currently. Since

2001, the economy has displayed strong growth. The lack of restrictions pertaining to foreign

ownership, strong regulatory presence, prudent economic policy and the level openness in the

Hong Kong economy provides sufficient evidence to support the hypothesis that it would

exhibit significant links with other financial markets.

B. Indonesia

During the thirty years of president Suharto GDP per capita grew from 70 USD to 1000 in

1996, inflation ranged between 5-10% and the rupiah was generally relatively stable. Several

policies introduced in the 1980s served to attract foreign investment into the financial and

external sectors. With the relaxation of regulations, investors from the US, Japan, Korea,

Hong Kong and Taiwan all contributed significant amounts of capital to the Indonesian

economy. The Indonesian stock market was officially liberalised in September 1989 and the

First country Fund was launched in February of the same year. Foreigners were able to own

up to 49% of all company listing shares on the domestic exchange25. In 1992, more changes

took place, making Indonesia even more receptive to foreign investment. These changes

included the launching of the first American Depository Receipt (ADR herein), the allowance

of foreign investors to own 49% of banks, and the creation of a foreign board for trading

stocks by foreign investors.

The road towards integration continued throughout the mid-1990s with the reduction of

import tariffs, increases in foreign investment approvals, the listing of an Indonesian firm on

the NYSE and most importantly growing investor confidence in the Indonesian economy.

However, the rapid development in the Indonesian economy did not match improvements in

the practices and regulations of the banking sector or legal framework. When the Thai Baht

suffered a sharp decline in July of 1997, the rupiah lost close to 7% against the USD. The

following month the rupiah depreciated by a massive 39.8%. The Indonesian government

desperately tried to stabilize the economy, but failed to do so as foreign capital flew out of the

25 This was excluding financial firms which was capped at 25%


28

country with great velocity. Real GDP growth declined by 13.7% in 1998 and late 1999 the

economy begun its recovery.

During this time the IMF employed a number of reforms aimed at stabilizing the macro

economy and the rupiah, and improving the banking and legal frameworks. Since 2000,

economic growth has hovered around 4%, interest rates have dropped and the rupiah

stabilized. Foreign investment has improved but never returned to the levels prior to 1997.

Political turmoil has continued to stifle attempts to fully complete required reforms. However,

considerable progress has resulted in a substantial improvement in investor confidence. The

US and Japan continue to be the largest trading partners and sources of capital to Indonesia,

while Hong Kong, Korea and Singapore also remain key parties. Recently, China has

emerged as an important player by contributing sizeable amounts of foreign direct investment

to Indonesia.

C. South Korea

Described by the World Bank as one of the most outstanding success stories of international

development (Harrison, 1991, p. 141), the South Korean economy has developed immensely

over the last 30 years to currently become the 11th largest economy in the world. In 1977, The

Securities and Exchange Commission and Securities Supervisory Board came into existence.

The Korea Fund launched on the NYSE in 1984, followed by the abolition of exchange rate

controls in 1989 and the first ADR being announced in late 1990. The announcement of major

reforms to the South Korean stock exchange took place in early 1991. The reforms made

foreign participation easier and removed barriers concerning the repatriation of capital. When

the stock market opened to foreign investors in January 1992, a 10% ownership limit was

enforced and over 500 investors registered with the Securities Supervisory Board. Foreign

ownership continued to grow and by the end of 1993, it accounted for 9.8% of market

capitalisation. In addition, most firms had reached their 10% threshold. By May 1997, the

foreign investors could hold up to 23% of South Korean companies.

As the EA crisis swept across from Indonesia and Thailand, the South Korean economy took

a sizeable hit. In November 1997 the Seoul stock exchange depreciated by more than 20%.

From 1998 to 2002, the Korean economy averaged real GDP growth of 4.2% thanks in part to

assistance from the IMF. The most encouraging signal from the Korean government has been


29

the extensive banking reform that took place in the post crisis period. Ailing banks were

recapitalised and a number of mergers occurred eliminating the weaker institutions.

Furthermore, the index derivatives market has evolved to become the second most liquid

behind that of the US. South Korea maintains strong trade ties with the US, Japan, China,

Hong Kong, Taiwan and Australia. South Korea is likely to pursue further reductions

regarding foreign ownership in the near future. Therefore, greater integration with the world

capital and financial markets is expected.

D. Malaysia

Similar to South Korea, Malaysia has proven to be a marvel of modern economic planning,

going from a one dimensional commodity producer to a developing multi-sector industrial

economy. In 1970, foreign investment accounted for around 55% of the Malaysian economy.

The following year a new reform program aimed to reduce the level of ownership to 30% by

1990. During the early 1980s the fixed exchange rate was abandoned and other controls

regrading foreign exchange loosened. The Malaysia Fund began trading on the NYSE in the

December of 1987 and followed by the first ADR in August 1992.

The creation of the Kuala Lumpur Options and Financial Futures Exchange, changes in listing

requirements (in an effort to improve transparency and regulation) and the issuance of

guidelines pertaining to the lending and borrowing of securities, during 1995, all helped

strengthen Malaysian financial markets. From the period 1992-1996, the market capitalisation

of the KLSE tripled, to represent 325% of GDP. Until the onset of the EA crisis in mid-late

1997, the Malaysian economy had averaged real GDP growth of around 8% for the last

decade and its financial markets had been relatively open and advanced compared to its peers.

With the onset of the EA crisis, market confidence plummeted and large portfolio flows took

place. In addition, foreign exchange speculators exerted considerable pressure on the ringgit

leading to subsequent devaluations of the ringgit. Late in 1997, the KLSE dropped 856 points

in a single days trading, reaching its lowest point since 1993. Initially the Malaysian

government raised interest rates and tightened fiscal policy. These policies proved to be

ineffectual and during 1998, the KLSE and economic growth continued on a downward

spiral. The lack of stabilisation prompted further action by the Malaysian government. In

September 1998 several policies were introduced, these included; the ringgit becoming


30

pegged to the USD coupled with other exchange rate and capital restrictions and a fiscal

stimulus package to increase the level of capital expenditure. The initiatives were successful

and interest rates could be reduced, and reforms in the financial and corporate sectors

implemented26. 1999-2000 saw the Malaysian economy and stock market quickly rebound as

investor confidence recovered in the face of reductions in capital restrictions, strong economic

growth and decreased vulnerability of the financial sector.

The major problem facing Malaysia in the post crisis period has been the inability to regain

the lost foreign direct investment. FDI equalled 43.1% of nominal GDP in 1997, while in

2002 it laid around 23%. Recently the Malaysian government signed a new bilateral trade and

investment agreement with the US in an effort to reignite once strong bonds. The cumulative

effect of reforms over the last six years has made Malaysia more open to foreign investment

and has actively promoted foreign participation. Japan, Singapore, China, Taiwan, Thailand

and Hong Kong retain close economic and trade ties with Malaysia.

E. The Philippines

The Philippines economy is largely based on agricultural and some natural resource

production. Unlike many of its Asian neighbours, the Philippines did not enjoy high single

digit growth rates in the 1980s. This was largely due to poor economic policy in the late

1970s and early 80s. To rectify the problem a number of reforms took place in the late 80s

and early 90s to improve the economic situation. One of these policies allowed for 100%

foreign ownership in certain businesses. For most of the early 90s low growth was achieved,

however for the four years 1994 to1997 growth averaged 5%.

During the late 1980s and early 90s several events occurred making the Philippine economy

and stock exchange readily accessible to foreign investment. These reforms included the

lifting of import restrictions, the launch of the first large scale Country Fund, and the

announcement of the first ADR. More importantly, restrictions pertaining to capital and

dividend repatriation were removed and foreign investors only had to register with the

Securities and Exchange Commission to be eligible to own 100% in most sectors of the

economy.

26 The reforms included a bank consolidation program and improvements in prudential regulation to bring it inline with best practice and procedure.


31

When the Asian Flu reached, the Philippines its impact was relatively mild compared to that

of its neighbours. In mid-late 1997, interest rates were increased in order to defend the peso

from being devaluated. When the Thai Baht was floated, the pressure became unbearable and

the peso followed suit on July 11. Fiscal, monetary and structural reforms accompanied the

float in an effort to stabilise the economy. Moving into the 1998 the effects of the EA crisis

coupled with poor weather conditions saw the Philippines economy contract by 0.5% and the

stock exchange drop to below 120027. By 1999 the Philippines was back on track on the back

of strong import demand from the US and the policies of 1997 bearing fruit. Subsequently,

the economy grew by 3.4%, the peso appreciated, the stock market stabilised, and prices rose

twofold.

Since the EA crisis, the deficiencies in the Filipino economy have been bought to light. A

number of inherent weaknesses plague the public sector, resulting in the failure to collect

considerable quantities of tax revenue. The level of domestic savings and investment remain

critically low and the legal system requires extensive reform. Despite this, the economy has

grown at around 4% for the last four years, and several reforms are in place to address the

issues mentioned above. There still exist only few restrictions on foreign investment into the

Philippines either directly or through the stock exchange. This makes it incredibly easy for

international investors to participate in the market. The US and Japan continue to be dominant

trading partners, while Taiwan and Singapore also contribute significantly.

F. Singapore

The growth and development of the Singaporean economy has been unrivalled over the last

40 years. After gaining independence in 1965 the government actively pursued a pro-

business, pro-foreign investment, export orientated economy with government control of

select industries. The results of the policy were a resounding success, with average real GDP

growth of 8% from 1960 to 1999. The development of the banking and financial sectors has

been focal point of government policy since the 1970s.

27 This was a considerable decline considering in January 1997 that it had been around the 3400 level.


32

The EA crisis of 1997/98 was largely ignored by the economy of Singapore. During 1998, the

reduced level of trade and loss in investor confidence saw GDP drop sharply from 8% in 1997

to 1.5% in 1998. The stock market also contracted but it was only mild compared to the other

Asian economies. By mid 1999 any fears of a long-term economic slowdown had been

alleviated by strong demand in the US28 and in 2000 GDP growth of close to 10% was

observed.

To further the development of the financial sector the Stock Exchange of Singapore (SES)

and Singapore International Monetary Exchange (SIMEX) were demutualised and merged in

1999 to form the Singapore Exchange (SGX). Additional policies were implemented to

improve the transparency and disclosure of companies and promote the investment fund

industry and bond market. The Singapore market has been made even more accessible to

international investors through alliances and cooperation with the American, Australian and

Indian stock exchanges.

During 2001, Singapore was adversely affected as the slowing of the US and Japan and the

continuation of the global slump in the electronics market. Consequently, the economy

contracted by 2.37%. The resiliency and strong structure of the Singapore economy ensured

the downturn was short lived and as expected growth was observed in 2002. The economy

has continued to expand and high industrial production so far this year indicates the

likelihood of high single digit growth. Currently Singapore is one of the most open markets in

the world, with over some 3000 multinational corporations operating within its borders.

Significant levels of FDI continue to flow in from the Western economies, while over the last

five years Singaporean companies have made sizeable investments into China, Malaysia,

Hong Kong and Indonesia. From a trade perspective the US and Malaysia continue to be its

main partners, while Japan, Taiwan and Thailand also warrant mention.

G. Taiwan

The economy of Taiwan has performed in line with that of South Korea and Singapore over

the last thirty years. Aggressively pursuing a policy of trade and openness the Taiwan

economy was transformed from a labour intensive agricultural producer to a sophisticated

28 Singapores total level of trade rose by 21% between 1999 and 2000.


33

manufacturer of high-tech items. During the 1980s wide sweeping reforms saw a

considerable reduction in the level of tariffs, the removal of foreign exchange controls, the

launch of the Taipei fund on the London Stock Exchange (LSE herein) and the ability for

foreign stockbrokers to open offices in Taiwan.

The theme of reform continued into the early 1990s. Textile and agricultural production

shifted to mainland China as diplomatic relations between the two countries improved and

was replaced by a expanding manufacturing industry, the first ADRs were announced and

new legislation made it possible for foreign investors to invest directly in Taiwan securities29.

Taiwanese capital also began to flow into South-East Asia and mainland China. In 1996, the

Taiwan stock exchange (TSE henceforth) further relaxed restrictions and allowed individual

investors into the market, with holdings limited to 50 million USD. The EA crisis of 1997/98

only slightly slowed the economy of Taiwan. Its close links to the US and Japan coupled with

a well developed stock market and banking sector allowed Taiwan to avoid the troubles

suffered by the other Asian economies and in contrast real GDP growth of 4.5% was

obtained.

Taiwan continued to display robust performance throughout 1999 and 2000 with growth rates

of 5.42% and 5.86% respectively. The global slowdown in 2001 and the decline in demand

for technological goods saw the economy contract by 2.18%. This was the first period a

negative growth since records had been kept. Even in the face of reduced consumer

confidence and the downturn in the US, growth returned in 2002. During the last five years,

Taiwan has shifted its focus away from the US and to its peers in Asia. As a result, more and

more capital has flowed into the region from Taiwan, with the bulk of it going to mainland

China. The TSE has recently eradicated the investment quota placed on foreign institutional

investors and made foreign participation simpler by only requiring investors to register with

the exchange. The US and Japan still remain the major trading partners with Taiwan, while

Hong Kong, South Korea, China and the EU are of growing importance.

H. Thailand

29 Participants had to have SEC approval as a Qualified Foreign Institutional Investor. Furthermore, repatriation was not allowed within the first three months, while ownership was limited to 5% of listed companies, with total foreign investment not able to exceed 10% of anyone company.


34

Thailand like most of its neighbours has enjoyed significant growth during the last thirty

years because of trade and willingness to accept foreign investment. From 1985 to 1995,

economic growth averaged over 9% and was the highest throughout the world for this period.

The Securities Exchange of Thailand commenced trading in April 1975 as a not for profit

organisation, that was controlled by the Ministry of Finance (Park, 1993). During the 1980s

several reforms made the Thai economy and stock market more open to foreign investment.

These policies included the decision to abandon the fixed exchange rate against the USD

(instead a pegged regime was instigated), the launch of the Bangkok Fund on the LSE and the

creation of the Alien Board on the stock exchange30.

The production of high-tech components31 was a strategy pursued in earnest by the Thai

government in the early and mid 90s. The strategy was a success as the Thai economy

continued to experience record growth. The stock exchange continued to be reformed with the

removal of restrictions pertaining to companies paying dividends to foreigners and the

lessening of controls and reporting requirements in relation to the repatriation of dividends,

capital gains, foreign currency and share certificates. However, like its Asian counterparts the

rapid expansion masked deep underlying weaknesses in regulation and best practice. As the

US dollar begun to appreciate in the middle of 1996, Thai exports lost competitiveness in the

global market. An overvalued Baht also led to excessive external borrowing and considerable

exposure to foreign exchange risk in the corporate and financial sectors. The increased

borrowing and declining exports caused the Current Account Deficit (CAD) to widen. The

combined result of this situation saw massive speculative attacks launched against the Baht in

the May of 1997. In the face of mounting pressure, the government was forced to float the

Baht in July 1997. The Baht continued on a downward spiral through the remainder of 1997

and hit rock bottom in 1998, while the stock market depreciated by 75% in 1997.

Consequently, the economy contracted by 1.4% in 1997 and a massive 10.5% in 1998,

leading to the IMF launching a bail out package in excess of 19 billion US dollars.

The Thai government also reacted by restructuring distressed financial institutions, tightening

fiscal policy to improve the CAD, encouraging private sector involvement in the economy

30 The Alien Board allows foreign investors to trade stocks on those companies that have reached their foreign investment limits. 31 These included computer accessories and motor vehicle parts


35

and employing new reforms to attract foreign investment. By September 1999, the policies

outlined above had been successful in reducing interest rates to pre-crisis levels, the Baht had

been stabilised and growth in the order of 4% was returned. Furthermore, significant and

meaningful policy reform in areas pertaining to bankruptcy, foreclosure procedures and

foreign investment restrictions strengthened the institutional framework.

Since 1999, the Thai economy has experienced a period of steady growth. In 2001, the

economy slowed as international export demand declined and the US economy begun to

contract. During 2002, the economy recovered and still experiences steady growth.

Consequently, the Thai stock market has appreciated considerably in the post crisis period

and is readily accessible to foreign investment. Like most of the Pacific-Basin, the US and

Japan are Thailands major trading partners, while Malaysia and Singapore are also worthy of

mention.

I. Australia, Japan, New Zealand and the US

Figure 3.1 Log Stock Indexes of Australia, New Zealand and the US

4

5

6

7

8

5/01/88 13/06/89 20/11/90 28/04/92 5/10/93 14/03/95 20/08/96 27/01/98 6/07/99 12/12/00 21/05/02 28/10/03date

Log

Stoc

k In

dexe

s

AUSP NZSP USSP Asian Crisis Sep-11 All four are open markets in which trade and investment is encouraged. Japan and the US

represent the two largest economies in the region with both importing and exporting large

quantities of goods in the Pacific-Basin. They are also the main source of FDI and provide


36

substantial amounts of aid. In the last decade, the influence of Australia in the Pacific-Basin

has expanded. Australia plays an important role in promoting trade, investment, economic

cooperation and political stability. There are few foreign ownership restrictions, well-

developed legal and prudential regulatory bodies, strong banking sectors, floating exchange

rates, political stability, low levels of corruption and wastage and advanced stock exchanges

in all four economies.

Figure 3.2 - Log Stock Indexes of Hong Kong, Japan, Korea and Taiwan

5.5

6.5

7.5

8.5

9.5

10.5

5/01/88 13/06/89 20/11/90 28/04/92 5/10/93 14/03/95 20/08/96 27/01/98 6/07/99 12/12/00 21/05/02 28/10/03date

log

of s

tock

inde

x

HKSP JPSP KOSP TASP Asian Crisis Sep-11 For the majority of the 1990s and early this decade Australia, New Zealand and the US all

enjoyed steady and stable growth. This saw a considerable appreciation in the stock markets

of all countries. The EA crisis provided little concern for the three markets discussed above.

While there was a downturn in the economies and stock markets, it was only slight and

quickly passed through. The economic slowdown and events of September 11 saw markets

decline by a noticeable amount. However, recent robust growth has seen markets approach

previous record levels.

The Japanese economy did not fair so well and suffered a prolonged period of low growth in

the 1990s averaging only 1.7%. The Japanese stock market peaked early in 1990 and has

contracted since. Substantial reforms put into place, have yet to bear fruit. Over the last year,


37

the Japanese market has experienced a minor resurgence and appears to have freed itself from

the static that has restricted growth for the over the last decade.

3.3 Linkage hypothesis

Building on the previous section there is a significant amount of evidence to support the

proposition of market linkage. Figures 3.1-3.332 help support the notion of linkage.

Figure 3.3 Log Stock Indexes of Indonesia, Malaysia, Singapore and Thailand

4

5

6

7

8

9

5/01/88 13/06/89 20/11/90 28/04/92 5/10/93 14/03/95 20/08/96 27/01/98 6/07/99 12/12/00 21/05/02 28/10/03date

Log

Stoc

k In

dexe

s

IDSP MYSP PPSP SGSP THSP Asian Crisis Sep-11 From the plots it is obvious that over the last sixteen years that there appears to be some

common influence among the markets of the Pacific-Basin. From Figure 3.1 it obvious that

Australia, New Zealand and the US have shared an upward sloping trend over the last sixteen

years33. Furthermore, around nine months after the East Asian crisis all three markets display

a noteworthy depreciation. Lastly, since mid-2003 Australia and New Zealand have closely

traced the US in light of the global recovery.

There are several interesting points from Figure 3.2. Prior to the East Asian crisis, Hong Kong

and Taiwan had been on an upward trend while, Japan and Korea were much more stagnant.

32 The two vertical lines in Figure 3.1, 3.2 and 3.3 represent the beginning of the East Asian Crisis ( 21/10/1997) and September 11 2001 respectively. 33 It will be discussed in the following chapter, but the stock indexes used for Australia and New Zealand are not their national indexes. Indexes constructed by DataStream were employed in place of unavailable data. .


38

In the six to nine months post the EA crisis, all four markets display a certain level of

comovement between each other. It appears that Hong Kong led the recovery followed by

Korea. Japan and Taiwan experienced only a minor adjustment due to the lack of impact the

crisis had upon them. In the post September 11 period, there appears to be considerable

covariance between the four markets.

Figure 3.3 represents the Southeast Asian block. Up until late 1996, Indonesia, Malaysia, the

Philippines and Thailand exhibit noticeable comovement between one another while sharing

an upward sloping relationship. During this period similar to Korea and Japan, Singapore is

static. Following the East Asian crisis and right up to September 11 all five markets display

substantial comovements. This trend continues after the events of September 11, as the series

of markets appear to be sharing a common long run path.

Previous empirical evidence based with the facts presented above leads to the possibility that

market linkage exists. However, the question of importance is, whether or not the relationship

is a spurious coincidence or there is an actual statistically and economically significant

relationship binding these markets to each other. To this end, the following hypotheses are

presented:

horizon time run- shorta over another one to linked are Basin-Pacific the of marketsequity TheH

run long the in another one to linked are Basin-Pacific the of marketsequity TheH

2 :

:1

3.3.1 Cointegration Analysis

To examine the possibility of market linkage over a long run time horizon, the use of

cointegration analysis is the most appropriate methodology. The use of cointegration to proxy

for a long run relationship is extensively used through out the current literature. Therefore, no

questions as to the credibility of the results are raised. The concept underlying cointegration is

that there is a long equilibrium relationship between a series of variables. Significant

cointegration indicates that markets will display considerable linkage over a long run time

horizon. The first condition required for cointegration to exist between a system of variables

is that they are integrated of the same order. The methodology used to establish that the stock

price indexes are of the same order is outlined in Chapter 4.2.


39

Once it has been established that the respective markets are integrated of the same order, the

test for cointegration can be completed. Engle-Granger (EG henceforth) (1987) developed the

original test for cointegration. The usefulness of EG's procedure is severely limited by the fact

that it could only determine cointegration but not the number of cointegrating vectors.

Secondly the EG method employs a two stage estimation process, therefore any error made in

step one is carried over into the second stage. Lastly, the EG methodology cannot be used to

test for cointegration in a multivariate setting. Johansen (1988) developed a new procedure

based upon maximum likelihood estimation that can discern the number of cointegrating

vectors between multiple variables. Developments by Johansen and Juselius (1990, 1992),

Johansen (1991, 1992 and 1995), and Hansen and Johansen (1993, 1999) have made the

procedure even more robust.

Enders (1995) provides the procedure used to conduct a JJ test. The first step in completing a

JJ test for cointegration is to determine an appropriate lag length. An undifferenced VAR

should be estimated and then a Sims log likelihood test conducted to determine the optimal

lag length. Alternatively the AIC or SBC can be used to determine lag length. This step is

important, as the results can be sensitive to lag length. In order to ensure the robustness of the

results a number of different lag lengths are employed and the test re-conducted.

The next step is to estimate the model and determine the rank of the coefficient matrix.

Consider a VAR of order p:

t

p

iitit xAAx ε++= ∑

=−

10 (1)

where tx is a 1×n vector of nonstationary I (1) dependent variables, 0A is a 1×n vector of

intercepts, iA is a nn × coefficient matrix and tε a 1×n vector of disturbance terms.

Equation (3) can be written in its Vector Error Correction (VEC herein) representation:

tt

p

iitt xxAx ε+∆Γ+∏+=∆ −

−

=− ∑ 1

1

110 (2)

where


40

∑=

−=∏p

ii IA

1

, ∑+=

−=Γp

ijji A

1

(3)

Grangers original theory proposed that if the coefficient matrix ∏ has a reduced rank, such

that r < n then there is nr × matrices α and β each with rank r. Furthermore, βα ′=∏ and

txβ ′ is I (0) where each column of the β matrix is an error correction term and α the speed

of adjustment. Johansens technique allows the ∏ matrix to be estimated from an unrestricted

VAR. If the rank equals 0 then a VAR in the differences is the correct specification. When the

rank equals n (number of equations contained in the VAR) then the VAR is stationary and no

correction is required. A rank equal to 1 indicates a single cointegrating vector, when the 1 <

rank < n there are multiple cointegrating vectors. Assuming 0 < rank < n, a test for the

number of cointegrating vectors is required. To conduct the test two test statistics are

computed:

∑+=

−−=n

riitrace Tr

1)1ln()( λλ (4)

)1ln()1,( 1max +−−=+ rTrr λλ (5)

where iλ is the estimated values of the characteristic roots (referred to as eigenvalues)

obtained from the estimated coefficient matrix, T is the number of usable observations, and r

is the number of cointegrating investors contained in the null hypothesis. The traceλ statistic

tests that there is r or less cointegrating vectors against a general alternative

(e.g. 1:,1:0 >≤ rH rH A ). The maxλ statistic tests that there is r cointegrating vectors against

the alternative of r + 1 cointegrating vectors (e.g. 2:,1:0 == rH rH A ). The test statistics are

compared against the critical values developed by Osterwald-Lenum (1992). The small

sample correction factor discussed in chapter 2 is not necessary when using the Osterwald-

Lenum critical values. Monte Carlo analysis conducted by Cheung and Lai (1993) found

evidence to support the claim that the trace statistic is more robust compared to the max

statistic and will only be used to establish significance. To ensure the results presented in this

thesis are of the highest quality, the small sample correction factor proposed by Cheung and


41

Lai will be applied to all test statistics generated. The correction factor adjusts the tests

statistics using the following computation:

−×=

TnpT unadjusted Adjusted tracetrace λλ (6)

where T is the total number of observations, n is the number of equations in the VAR and p is

the lag length. As discussed above the first step in conducting any test for cointegration is to

select an appropriate lag length using selection criteria. The lags must also be long enough to

remove any traces of serial correlation in the residuals of the VAR. All the cointegration tests

have been completed with differing lag structures in order to maintain the robustness of the

results. The first series of tests completed are bivariate tests between the sixty-six unique pairs

of markets34 based on the full sample period (5/01/1988 to 7/09/2004).

The second set of cointegration tests are weekly recursive bivariate tests based on a 416-week

moving window. For week 0, all sixty-six tests of cointegration are completed based on data

from week -415 to week 0. The window then moves one week and the tests are re-computed

based on the data from week -414 to week 1. The procedure repeats until it reaches week 454.

3.3.2 Persistent Profile Analysis

Persistent profile analysis is a methodology developed by Pesaran and Shin (1996) that can be

used to examine the speed of convergence in response to a system-wide shock in the price

cointegrating relation. Darrat and Zhong (2004) note that the estimated profiles are unique

and do not require the prior orthogonalisation of shocks. As the time horizon increases, the

profile will approach zero as the effect of a shock on the cointegrating vectors is transitory by

nature and dissipates as the system returns back to equilibrium. The procedure used by Darrat

and Zhong and designed by Pesaran and Shin is outlined below. The first step is to estimate

the VECM as seen in equation (2):

tt

p

iitt xxAx εβα +∆Γ+′+=∆ −

−

=− ∑ 1

1

110 (7)

where tx is a 112 × vector of stock price indices, tx∆ is a 112 × vector of continuously

compounded returns, 0A is a 112 × vector of intercepts, α and Γ are 1212× coefficient

34 There is only sixty-six unique pairs, because the test for cointegration between the US and Korea is the same as the test between Korea and the US.


42

matrices and β is the r×12 long run coefficient matrix. Equation (2) is estimated using the JJ

method to derive an approximation of the coefficient matrix β and the covariance matrix of

the residuals, Ω .

The Beveridge and Nelson (1981) procedure is used to decompose the price vector tx , such

that:

tttx ϖτ += (8)

where

∑∞

=−=

0lltlt C εϖ , and ∑

∞

=+−=

0llll BAC , and ∑

=

=l

jjl AB

0

(9)

1101 −− +++= tttt AA εετµτ (10)

The price cointegrating relation can be written as:

∑∞

=−′+′+′==′

010 )(

ltltt Btx εβµβτβξβ (11)

With r cointegrating vectors, the unscaled ( ji, ) elements of the persistence profile matrix tξ

are given by:

jnniij BBH ββ ′Ω′= , where ji ≠ , and for rji ,.....,2,1, = ,....2,1,0=n (12)

where iβ′ , jβ′ and Ω are obtained from (8), and nB is the cumulative effect matrix defined

in (10) and is computed from the following recursive relation:

pnpnnn BBBB −−− Φ++Φ+Φ= ...2211 , n = 1, 2 and p is the order of the VAR (13)

where 0,12120 == × nBIB for 0≤n and the si 'Φ come from the matrices α and β such that:

pI Φ−−Φ−Φ−=′ × ...211212βα (14)

Therefore, the scaled measure of the persistent profile matrix of tξ is obtained as the elements

of:

),()()( nhGnGHnh ijZZ == where )0(,)0(,)0( 2/12/122

2/111

−−−= rrHHHdiagG (15)

Following a unit composite shock to the cointegrating vectors in a stationary system,

convergence to zero will occur.

.

Using a variety of measures, the short run linkage hypothesis is studied. The most obvious

choice would be to measure the level of correlation between the returns in the twelve markets.


43

However, as discussed in Chapter 2 the presence of heteroscedasticity causes correlation

coefficients to become upwardly biased. The present solution to this problem is not

acceptable, requiring the imposition of a number of assumptions by the user. This thesis

proposes three alternative measures of short run linkage in place of using correlation analysis.

3.3.3 Generalised Variance Decomposition

Generalised forecast error variance decomposition (GVD henceforth) developed by Pesaran

and Shin (1998) is used to further examine the behaviour of market linkage. When markets

are not linked, a shock in one market will not be transmitted to others. Conversely, as the

level of market linkage intensifies, return innovations in one market explained by their

domestic variation should decline, while shocks from other markets assume greater

importance. The traditional method to conduct forecast error variance decomposition is the

Choleski procedure. The major weakness of the Choleski methodology is that the results are

sensitive to the ordering of the VAR. Furthermore, there is no firm theoretical or empirical

evidence to suggest how the variables should be ordered. The generalised form of variance

decomposition is unaffected by the order of the variables and produces unique results that

fully take account of the historical patterns of correlations observed amongst the different

shocks (Pesaran and Shin, 1998, p.4).

Another estimation issue needs to be addressed at this stage. The issue is whether or not to

conduct the GVD using a Vector Error Correction model (if needed) or an unrestricted VAR

in differences. Dekker et al (2001) provide an excellent discussion on the topic. They cite a

paper by Naka and Tufte (1997) who find that imposing the cointegrating vector does not

necessarily improve performance over short time horizons. In fact, Clements and Hendry

(1995), Engle and Yoo (1987) and Hoffman and Rasche (1996) found that an unrestricted

VAR is superior (in terms of forecast variance) to a restricted VECM over short time

horizons. Since the GVD is conducted over a 20-day horizon this thesis will use employ the

unrestricted differenced VAR. This also applies to the Generalised Impulse Response

functions outlined below.

Following the procedure from Darrat and Zhong (2004), the first step is to obtain estimates of

expected returns. The estimates are generated through using the VAR to forecast observed (or


44

actual) returns. The forecast errors are taken to represent the unexpected returns (shocks), the

VAR model of returns is as follows:

∑=

−++ ++=k

ltltlkt rr

11 εβα (16)

where tr is a vector of continuously compounded stock returns of the twelve equity markets in

period t. α is a vector of intercepts, lβ is a matrix of autoregressive parameters and tε is a

white noise vector of disturbance terms. The n-period forecast error of returns is represented

by the following equation:

∑−

=−+++ =−

1

0

n

llntlnttnt DrEr ε (17)

The GVD of the n-step forecast error of return i explained by innovation, j is derived from the

following computation:

′Σ′

Σ′=

∑

∑

=

=n

lilliij

n

ljli

ij

eDDe

eDenGVD

0

0

2

)(

)()(

σ (18)

where ie is an 112 × selection vector with unity as its ith element and zeros elsewhere, Σ is the

sum of squared residuals, )( tt vvE ′ and ijσ is the residual variance in the ith equation in the

VAR.

3.3.4 Generalised Impulse Response Functions

An alternative measure of market linkage is to examine the speed at which a market adjusts to

an innovation in another market. The argument put forward is that the greater the level of

market linkage then the impact of an innovation in a foreign market will quickly decay in the

domestic market. Bivariate generalised impulse response analysis will be considered. For the

bivariate analysis, the effects of innovations in the Japanese and US markets upon each other

and the PB countries will be examined. Like variance decomposition, Choleski decomposition

has traditionally been used to orthogonalise the innovations. As discussed above there are

several inherent deficiencies related to Choleski decomposition. Building on the work by

Koop, Pesaran and Potter (1996), Pesaran and Shin (1998) provide the procedure required to

conduct the generalised form of impulse response (GIR herein) analysis in the linear setting35.

35 Koop et al (1996) originally developed GIR analysis for non-linear multivariate models.


45

Consider the following VAR of order p:

Tt for xAx t

p

iitit ,...,2,1

1

=++= ∑=

− εα (19)

where tx is a 1×n vector of dependent variables, α is a 1×n vector of intercepts and iA is a

nn × coefficient matrix. Pesaran and Shin make the following assumptions:

1. tt all for E( matrix definite positive nn

an is nji wheret, all for EE

tt

ijttt

′==′×

==ΣΣ=′=

′ 0)

,...,2,1,,)(,0)(

εεσεεε

(20a)

2. circle unit the outside fall zAI of roots the Allp

i

iin 0

1=−∑

= (20b)

3. collinearperfectly not are T1,2,...,t for xxx pttt =−−− ,,...,, 21 (20c)

Assumption 2 ensures that tx is a covariance stationary process. Equation (19) can be re-

written as an infinite moving average (VMA) series:

∑∞

=− ==

0,

iitit T1,2,...,t x εβ (21)

where iβ is a nn × coefficient matrix estimated from the following recursive estimation

process:

1,2,...,i AAA pipiii =+++= −−− ,...2211 ββββ (22)

with nI=0β and 0=iβ for 0<i , and ψβiiG = .

The GIR function of tx at horizon k as stated in Koop et al (1996) is defined as;

)|(),|(),,( 111 −+−+− Ω−Ω==Ω tktttkttX xExEkGI δεδ (23)

where δ is a 1×n vector of shocks and 1−Ωt is a non-decreasing information set that captures

the known history of the series up to time 1−t . Assume a shock is then applied to the jth

element of the error vector tε . The effects are integrated out using an assumed or historically

observed distribution of the errors. A more specific form of equation (25) is the result:

)|(),|(),,( 111 −+−+− Ω−Ω==Ω tkttjjtkttjX xExEkGI δεδ (24)

If tε is assumed multivariate normal, then Koop et al show that:

jjjjjjjnjjjjjtt eE δσδσσσσδεε 1121 ),...,,()|( −− Σ=′== (25)


46

where je is an 1×n selection vector with unity as its jth element and zeros elsewhere, Σ is the

sum of squared residuals, )( tt vvE ′ , as seen in equation (19). The 1×n vector of the unscaled

GIR of the effect of a shock in the jth equation at time t on ktx + is as follows:

0,1,2,...k eA

jj

j

jj

jk =

Σ,

σδ

σ (26)

The scaled GIR function is derived by setting jjj δσ = :

0,1,2,...,k eAk jkjjgj =Σ= − ,)( 2/1σψ (27)

Due to the stationarity of the system, there is no concern over the responses converging to 0.

3.3.5 Block Exogeneity

The concept of block exogeneity is based upon Granger causality. X is said to cause Y if Y can

be more accurately forecast based on past values of X and Y compared to Y alone. If

substantial market linkage is discovered, then causality is expected. Furthermore, endogenous

markets like Japan and the US are likely to cause other markets, but no other markets are

likely to exert causality over them. In certain cases of strong market linkage causality could

possibly exist in both directions (i.e. market A causes market B and market B causes market

A). Consider the following VAR:

+

+

+

=

−

−

−

−

USt

AUt

USt

AUt

USt

AUt

US

AU

USt

AUt

r

r

BB

BB

r

r

AA

AA

a

a

r

r

ε

εMM

L

MOM

K

M

L

MOM

K

MM

2

2

12,121,12

12,11,1

1

1

12,121,12

12,11,1

(28)

To test the null hypothesis that returns in the US does not causes Australian returns, a

standard Wald test imposing the restriction 012,112,1 == BA is no longer valid36. The

extension of the test for Granger causality to a multivariate setting is referred to as a block

exogeneity test.

36 Testing the restriction is only valid in a bivariate VAR model. This is the traditional Granger test.


47

For Australia, the block exogeneity test essentially imposes a zero restriction on the lags of AU

tr in the eleven remaining equations. The test is formally conducted using a log likelihood

test as seen below37:

|)|log||)(log( urcT Σ−Σ− (29)

where || uΣ is the determinant from the covariance matrix from the unrestricted model, || rΣ

is the determinant from the covariance matrix from the restricted model, T is the number of

observations c is the number of parameters estimated in each equation of the unrestricted

system38. If the null hypothesis is rejected it implies that the variable adds significant

information to the model.

The use of three measures to examine the level of short-term market linkage in the PB, will

present a broad and complete picture of the true relationship. All three measures have been

used in the previous literature and are seen as legitimate methods to observe the dynamics of

short run associations.

3.4 The time variation of market linkage

Based on the discussed in section 3.2 it would be likely that the level of market linkage varies

over time for such reasons as changes in trade and investment restrictions, the level of

investor confidence, legal and prudential regulation, composition of the economy and trading

partners and political and social attitudes. Furthermore, the EA crisis and events of September

11 are likely to have a significant enough affect to cause either a temporary or a permanent

change to the relationship between markets. Based upon this line of reasoning the following

hypotheses are proposed:

time over altered has Basin-Pacific the of markets stockthe between iprelationsh TheH A :3

isturbancea market dfollowing nsifies Basin intee Pacific-ets of thstock marktween the ionship be:The relatH3B

37 The testing procedure is provided by Enders (1995) 38 The unrestricted model is estimated by regressing HK

tr , IDtr , JP

tr , KOtr , MY

tr , NZtr , PP

tr , SGtr , TA

tr , THtr , US

tr

against p lagged values of AUtr , HK

tr , IDtr , JP

tr , KOtr , MY

tr , NZtr , PP

tr , SGtr , TA

tr , THtr , US

tr . The restricted

model is the same as the unrestricted model with the lagged values of AUtr excluded from all eleven equations.


48

This hypothesis requires the use of several methods to get the full picture. Weekly recursive

cointegration tests establish the fact that the cointegrating relationship has changed through

time. To enable easy interpretation of the volumes of data provided by the sixty-six pairs of

weekly tests all the trace statistics are graphed. An example can be seen below in Figure 3.4.

Figure 3.4 Recursive cointegration test between Australia and Hong Kong

0

0.5

1

1.5

2/01

/96

2/05

/96

2/09

/96

2/01

/97

2/05

/97

2/09

/97

2/01

/98

2/05

/98

2/09

/98

2/01

/99

2/05

/99

2/09

/99

2/01

/00

2/05

/00

2/09

/00

2/01

/01

2/05

/01

2/09

/01

2/01

/02

2/05

/02

2/09

/02

2/01

/03

2/05

/03

2/09

/03

2/01

/04

2/05

/04

2/09

/04

Date

Nor

mal

ised

Tra

ce S

tatis

tic

Normalised Trace Statistic Normalised Critical Value Asian Crisis Sep-11 The design of the graph is such that when the line is above one, significant cointegration

exists between the two markets. Again, all trace statistics reported in this thesis have been

adjusted for small sample bias.

A secondary technique used to determine the effect of the EA crisis and September 11 on the

level of market linkage is to include an intercept dummy variable for the East Asian crisis and

September 11, in the test for cointegration. The use of this technique represents a substantial

improvement in accurately detecting changes in the long run relationship and is only

employed for the bivariate and multivariate tests on the full period (5/01/1988 to 7/09/2004).

To determine if the level of short run market linkage fluctuates over time, the sample is

broken into three sub-samples and all the tests re-computed. The first sub-sample starts from

the beginning of the sample (1/05/1988) and runs to the 31 October 1997. The second period

runs from the 1 November 1997 to September 11 2001 and the last period extends from


49

September 12 2001 to 7 September 2004. This provides significant evidence as to how the

degree of short-run market linkage varies over time.

3.5 Factors responsible for market linkages

This thesis, postulates that several variables influence the level of market linkage. Table 3.5

summarizes the expected affect of the variables on market linkage. The following discussion

highlights the theoretical underpinnings as to how these variables influence the level of

market linkage.

Table 3.5 Expected relationship between market linkage and the factors proposed

Variable Proposed

Effect Goods Market Integration + Trade Dummy + Market Development

Turnover ratio - Size Difference - Size Dummy +

Exchange rate volatility - Returns volatility + Money Market

Money Market integration + Interest rate differential +

Location -

A. Goods Market Integration and Trade

Goods market integration is likely to be a dominant factor in explaining market linkage.

Referring back to economic theory, the goods market is defined as the IS curve. The IS and

LM curves combine to form the aggregate demand function (AD), AD is equivalent to the

growth of the economy. The inclusion of this variable also helps to control for any shocks in

aggregate demand. It also is a good proxy for the level of economic growth in that market.

The effect of economic growth on equities markets is obvious, that is significant appreciations

will occur during times of good growth and depreciations during contractions.

Consider two economies that are characterised by large natural resource and primary

production sectors, that is, they have a similar industry composition. If commodity prices rise,

then those companies in both economies will have higher earnings without incurring any extra

costs and therefore increase profit and cash flows to the firm. The market would interpret this


50

news favourably and subsequently the stock markets in both economies will exhibit an

appreciable increase.

Now let us extend the model further by including two other economies that produce durable

consumer and capital goods. Neither economy has any significant natural resource or primary

production. As a result, they import all their necessary production inputs from the two

economies mentioned above. The effect of an increase in commodity prices will make

production more expensive. The companies of these two economies then have two options

available to them. They can keep prices fixed, remain competitive in the global market,

absorb the cost themselves, and earn lower profits and cash flows. Alternatively, they could

increase price to retain their profit margin but lose competitiveness and again suffer a

reduction in profit and cash flows. Either option will result in a decline in profit and cash

flows. Investors in these economies will view this news with displeasure and the price of

these firms will depreciate along with the respective national stock markets. Obviously if the

price of capital goods increased, the scenario would occur the other way around but the result

would still be the same. That is, the two small open natural resource and primary producers

would move together and the industrial production economies move together.

Finally, consider the effect of a positive (negative) shock to the industrial production

economies. Production in the economy rises (declines); therefore, more (less) inputs are

required from the other two economies. In this case, the stock markets in all four economies

will experience an appreciation (depreciation).

The scenarios described above all rely upon the possibility of trade between markets. If trade

is not possible then good market integration is unlikely. The lack of international trade is an

indication of market segmentation. In segmented markets, domestic factors are the only

providers of information. As discussed in the previous section the Pacific-Basin region has a

high level of trade between each other and the world. Therefore, trade barriers do not limit the

probability of goods market integration. Most nations in the Pacific-Basin are net exporters

and therefore rely heavily upon trade to promote growth and innovation. Thus, trade is of vital

importance to these economies.


51

In this thesis, the level of goods market integration is captured by running bivariate

cointegration tests on the industrial production statistics of each economy. Since industrial

production statistics are used to measure the level of goods market production, the trace

statistic from the cointegration test must provide an insight into the long run relationship

between the goods markets of two economies. Sixty-six tests in total are conducted based on

monthly data from 15/01/1989 to 15/06/2004. Darrat and Zhong (2004) use a similar metric to

indicate the level of goods market integration between the two markets.

Two proxies are presented in this study to describe the effect of trade upon market linkage. As

per the discussion above it is theorised that trade will be a vital role in determining market

connections. The two measures employed in this thesis are dummy variables. The first

dummy considers the effect of trade organisations upon market linkage. If a pair of markets

both belongs to ASEAN then the dummy takes a value of one and zero otherwise.

B. Location

The geographical location of markets is likely to affect the level of linkage for two reasons.

The first is that, markets that are close to each other are more likely to experience economic

integration. This is because it has been shown through out the world that markets that are

close to each other display a higher than average level of trade. As discussed above, trade

provides a transmission channel between economies to transmit stock market comovements.

Secondly, markets close in geographical proximity are likely to share similar factor

endowments. That is, markets that are close to one another have a similar environment and

climate. Therefore, it is probable that they have similar deposits of natural resources and

ability to cultivate certain primary products. As argued above, markets of similar industry

composition and factor endowments are going to comove in the same direction.

The second theory as to why markets of a close geographical location are likely to comove

with each other is the home bias puzzle. Expanding this theory to an international level,

investors are more likely to invest in countries that are close because they believe they 'know'

them better39. Therefore, if market A performs well and investors from market B have an

investment in market A, they will have higher wealth. Investors from market B may then

39 For example investors from Singapore are likely to invest in Malaysia instead of New Zealand


52

consume this additional wealth in country B, consequently firms in market B perform better

and the markets display comovement.

The manner in which this variable is operationalised in this thesis is just to measure the

distance between two markets in kilometres. As seen in table 3.5 a negative relationship is

expected, however the effect of the US could significantly influence the results and a positive

relation discovered because the US is so open, but also so far away.

C. Market Development

The effect of market development upon market linkage would appear to be obvious. The

logical conclusion would be to assume developed markets are display higher levels of market

linkage. However, as markets become further developed they also become more exogenous.

Therefore, the determining factor becomes not the absolute level of development, but the

relative level of development. In the literature to date there have been numerous suggested to

describe this variable but no solid findings. Bekaert and Harvey (1997) argue that market

capitalisation is a good proxy for the level of development in a market. Expanding on this

proposition, this thesis presents two variables as proxies for size. The first is the large market

group dummy variable that takes a value of one when Australia, Hong Kong, Japan, Korea,

Taiwan or the US are in the pair and a zero otherwise. The small market group dummy

variable takes a value of one when the pair contains Indonesia, Malaysia, New Zealand, the

Philippines, Singapore or Thailand40. Another variable computed is also based on the size of

the relevant markets. Section 4.2 provides an in depth description of this size difference

variable. Lastly, a concept new to this thesis is the use of a turnover ratio to proxy for market

liquidity and ultimately market development. The effect of deep and liquid markets and their

relation to market development has been well established in the previous literature. Again,

section 4.2 provides a comprehensive discussion surrounding the properties of this variable.

D. Money Market Linkage

This thesis proposes that the level of money market linkage affects the level of market

linkage. As seen in section 3.2 the benefits and costs from capital flows are considerable and

can be long lasting. While much of the growth in the East Asian economies can be linked to

40 This thesis realizes that these markets are not necessarily small or unimportant. The large dummy variable was created by finding the markets with the six highest market capitalizations over the 16-year period.


53

massive capital flows from Japan and the US during the 1980s, the EA crisis just as readily

highlights the pitfalls associated with unrestricted capital movements. The probability of

market linkage increases significantly in the presence of capital market integration for several

reasons.

In economics, the LM curve represents capital markets. The stance of the monetary policy

adopted by the respective monetary authority dictates the position of the LM curve.

Consequently, capital market integration has significant ramifications for the breadth and

depth of the conduct of monetary policy. Assume a country adopts a contractionary monetary

policy stance. The increased interest rate raises the cost of capital and subsequently

investment in the economy falls along with economic growth. According to the theory of

interest rate parity, the rise in interest rates will also cause the domestic currency to

appreciate. In turn, exports become more expensive and imports cheaper, leading to a

worsening of the balance of trade and ultimately a decline in GDP. The decline in aggregate

output can potentially have significant consequences for other economies.

Another way for the money market to influence the level of the linkage between markets is

through the Present Value Model (PVM herein). Essentially under PVM the value of a firm is

its future dividends discounted back to today, where the discount rate is a function of the risk

free rate. Therefore, if the risk free rate rises, so will the appropriate discount rate for that

firm. Ceteris paribus, if the discount rate increases then the price of the firm will fall. If two

markets have display money market linkage then it is highly likely that their interest rates will

move in a similar direction, thus both stock indexes will appreciate.

To examine the effect of money market linkage, two variables have been put forward. The

first is the absolute weekly interest rate differential between the two markets averaged over

the period 5 January 1993 to 7 September 2004. Under the concept of interest rate parity, it

would be assumed that any deviations away would cause substantial flows in capital. The

discussion above supposes that capital flows can cause significant linkage between markets.

However there has been some literature that has found that uncovered interest rate parity (UIP

henceforth) does not hold. If, a negative relation is found between the proxy for monry market

linkage and equity market linkage then it is proof toward the PVM. If a positive relationship

is uncovered then it is proof in the favour UIP holding.


54

The second measure used to proxy for money market linkage is the trace statistic from a test

for cointegration conducted on the interest rates of the respective markets. The use of this

measure largely extends from the work of Phylaktis (1999) that uses cointegration tests to

proxy for capital market integration.

E. Market Volatility

Extensive research has established the importance of volatility in stock markets. Therefore,

the volatility of markets will influence the level of market linkage. The market contagion

hypothesis provides the theoretical underpinning for this expectation. Market contagion

dictates that markets tend to exhibit higher linkage when they display higher volatility.

Therefore, it would be expected that volatility is positively related to market linkage if the

contagion theory is credible. However, emerging literature led by Forbes and Rigobon (2002)

have found that the EA crisis did not result in contagion between the markets of the PB. The

result of this is that either situation is legitimate considering that both are based on sound

economic theory and robust empirical evidence.

F. Exchange Rate Volatility

Exchange rates play an important part of any economy. This study supposes two ways for

exchange rate volatility to influence the level of market linkage. The first is that when

investors are making their decisions, they compute returns denominated in their own domestic

currency. Therefore, volatility in exchange rates can raises the systematic risk of that nation.

Therefore risk averse investors, are less likely to invest into the market, thus driving the two

markets apart.

Furthermore, fluctuations in exchange rates affect the level of trade in an economy through

deviations away from purchasing power parity (PPP). PPP dictates that a countries level of

exports and imports will vary according to the prevailing real exchange rate. As discussed

above trade plays a crucial role in promoting market linkage and goods market integration.

Therefore the greater the volatility in the exchange rates, the higher the probability of

deviations away from PPP and subsequently a higher level of trade However, it is important

to note that PPP has found not to hold in empirical testing. Therefore, if a positive relation is

found then it is likely that PPP is holding, as opposed to a negative relationship which


55

provides evidence toward the first argument presented above.. The two theories put forward

to explain the effect of exchange rate volatility on market linkage actually seem to oppose one

another. However, both are economically sound and valid arguments, thus making it difficult

to predict the direction of the relationship. This actually provides for an interesting

comparison to see if it is investors decision making or wider economic variables that

influence market linkage.

Based upon the above theoretical underpinnings, a cross-sectional regression model is

proposed to determine which factors significantly affect market linkage.

iSMALLLARGEOECDASEANjirate X

ji

returnsjijiji,ji,jiji,ji

DDDDTO

rTrace IR SdiffLocationTrace IPTrace

εβββββσβ

σββββββα

+++++++

+++++++=∆

1211109,8,7

,6,543,21,

where,

jiTrace , is the trace test statistic extracted from a bivariate cointegration test between the

stock markets indices of country i and j. Sixty-six tests in total are conducted based on data

from the full sample (5/01/1988 to 7/09/2004). Since over 760 observations are included for

the tests of cointegration, the results obtained are highly robust and persuasive. jiTrace , is an

indication of the strength of the relationship between the two stock markets. A value in excess

of 19.96 indicates the presence of cointegration at the 95% confidence level. In general, high

trace statistics are an indication of stronger market linkage. To ensure the robustness of the

result obtained, the maximum test statistic and eigenvalues from the cointegration test will be

taken as further proxies for market linkage. Masih and Masih (2001) note that eigenvalues are

an indication of the intensity of the cointegrating relationship.

ji,Trace IP is the trace statistic extracted from a bivariate cointegration between the industrial

production indexes of markets i and j. Sixty-six tests in total are conducted based on monthly

data from 15/01/1989 to 15/06/2004. Darrat and Zhong (2004) use a similar metric to indicate

the level of goods market integration between the two markets. A value in excess of 19.96

indicates the presence of cointegration at the 95% confidence level. Similar to jiTrace , , a high

statistic represents greater goods market integration.


56

jiLocation , is the distance measured in kilometres between the cities in which the stock

exchanges are situated.

ji,Sdiff is the average weekly logged market capitalisation of market i deflated by the average

weekly logged market capitalisation of market j. For further clarification, refer to section 4.2.

jiTO , is the bilateral turnover ratio between markets i and j41 . The manner in which the

statistics were computed a low ratio, indicates high liquidity.

ji,Trace IR is the trace statistic extracted from a bivariate cointegration test between the

interest rates of markets i and j. Sixty-six tests in total are conducted based on weekly data for

the period 5th January 1993 to 7th September 2004. ji,Trace IR is an indication of the level of

capital market integration between the two markets. A value in excess of 19.96 indicates the

presence of cointegration at the 95% confidence level.

returns

ji ,σ is the equally weighted standard deviations of market i and j averaged over the full

period. The individual standard deviations are extracted from a bivariate GARCH (1, 1)

model. All sixty-six standard deviations are derived from returns starting the 12th January

1988 and ending the 7th September 2004.

rate

,∆Xjiσ is the volatility between the exchange rates of markets i and j extracted from a

univariate GARCH (1, 1) model. Sixty-six tests in total are conducted based on weekly

exchange rate returns starting on the 2nd of January 1988 and finishing the 7th of September

2004.

jir , is the equally weighted mean return of market i and j for the period 12th January 1988 to

the 7th September 2004.

41 The method used to compute the weekly turnover ratios is discussed in Section 4.2


57

ASEAND is a dummy variable that takes a value of 1 when markets i and j are ASEAN member

nations, and 0 otherwise

OECDD is a dummy variable that takes a value of 1 when markets i and j are OECD member

nations, and 0 otherwise

LARGED is a dummy variable that takes a value of 1 when markets i and j are a pair containing

Australia, Hong Kong, Japan, Singapore, Taiwan or the US and 0 otherwise.

SMALLD is a dummy variable that takes a value of 1 when markets i and j are a pair containing

Indonesia, Korea, Malaysia, New Zealand, the Philippines or Thailand and 0 otherwise.

Not all twelve variables will be regressed against the dependent variable in a single equation.

Several variables proxy for the same effect, therefore multiple models will be estimated with

variables certain factors omitted. Equations including slope dummy variables (interaction

terms) are also employed. To ensure the robustness of the regression several diagnostic tests

are completed. All regressions are estimated with robust standard errors using the Newey-

West procedure. Furthermore, the correlation matrix of independent ensures multicollinearity

is not an issue. A Probit model, where the dependent variable takes a value of 1 if significant

bivariate cointegration is found42 and 0 otherwise. The Probit model will have the same set of

independent variables as the OLS equation. For both models, a series of univariate and

multivariate equations will be estimated.

iSMALLLARGEOECDASEANjirate X

ji

returnsjijiji,ji,jiji,ji

DDDDTO

rTrace IR SdiffLocationTrace IP

εβββββσβ

σββββββα

+++++++

+++++++=∆

1211109,8,7

,6,543,21,Pr

The reasons behind using two different estimation techniques for are:

1. The OLS equation allows linkage to be measured in a continuous rather than discrete

setting. This is important distinction because there are likely to be situations where

strong linkage is discovered but is just not strong enough to qualify as significant.

2. An interesting feature of Probit models is the following relation:

iiprobabilty σβ *=∆ (30)

42 At the 5% significance level


58

where iβ is the coefficient for variable i and iσ is the standard deviation of variable i. The

probabilty∆ measures the increase (decrease) in the probability of markets being linked due

to a one standard change in i with all other coefficients held constant. This allows the

economic significance of variable i upon market linkage to be examined.

CHAPTER 4–SAMPLE DATA

59

Chapter 4: Sample Data

4.1 Introduction

Considering the importance of statistical testing procedures in this study, it is imperative that

the data is of the highest quality. Section 2 of this chapter outlines the data collected, any

adjustments required to ensure the integrity of the data and the methods used to compute

certain variables. Lastly, section 3 contains the descriptive statistics for the returns series of

each market.

4.2 Data and its stochastic properties

Many prior studies have shown that a long sample period is necessary to be able to discover a

cointegrating relationship. Weekly price levels were collected for the following stock

markets; the S&P500 (the U.S.), Nikkei 225 (Japan), Hang Seng (Hong Kong), Jakarta

Composite (Indonesia), KLSE Composite (Malaysia), PSE Composite (Philippines), Straits

Times (Singapore), Seoul Composite (South Korea), SET (Thailand) and the Taiwan

Weighted (Taiwan). For Australia and New Zealand indexes constructed by DataStream were

employed because of missing data, the methodology used to construct the indexes is outlined

in Appendix 1. For all markets, the sample starts on the 5th of January 1988 and finishes on

the 7th of September 2004, giving 871 observations per country. The week runs from Tuesday

to Tuesday in an attempt to avoid the bias often times associated with data from Monday or

Friday. . The returns series were generated using continuous compounding in the following

calculation:

)ln()ln( 1−−= ititit PPr (1)

where itr is the continuously compounded return on index i at time t and itP is the price level

of index i at time t. Table 4.1 panel A and B show that in all four periods that stock prices are

found to be non-stationary, while returns are stationary at the 1% significance level. To proxy

for the market contagion effect, the equally weighted standard deviation of the pair i and j is

computed:


60

2,ji

ji

σσσ

+= (2)

Table 4.1. Augmented Dickey-Fuller and Phillips-Perron Unit Root Tests (p-values)

Panel A: Log Stock Price Levels

Country Full Period Pre- East Asian

Period Post - East Asian

Period Post - September

11 Period Australia

ADF 0.564 0.789 0.519 0.700 PP 0.617 0.783 0.491 0.739

Hong Kong ADF 0.405 0.816 0.488 0.758

PP 0.407 0.819 0.488 0.756 Indonesia

ADF 0.941 0.249a 0.259 0.942 PP 0.930 0.248a 0.146 0.924

Japan ADF 0.773 0.708 0.909 0.482

PP 0.765 0.641 0.930 0.532 South Korea

ADF 0.194a 0.194 0.577 0.282 PP 0.124a 0.195 0.569 0.265

Malaysia ADF 0.377a 0.173 0.426 0.766

PP 0.298a 0.173 0.365 0.741 New Zealand

ADF 0.503 0.785 0.470 0.922 PP 0.541 0.801 0.454 0.923

Philippines ADF 0.398 0.661 0.534 0.870

PP 0.315 0.629 0.417 0.797 Singapore

ADF 0.191a 0.575a 0.633 0.869 PP 0.124a 0.572a 0.616 0.885

Taiwan ADF 0.060a 0.248a 0.932 0.361

PP 0.060a 0.158a 0.922 0.361 Thailand

ADF 0.455 0.190 0.179 0.868 PP 0.371 0.185 0.108 0.860

US ADF 0.946 0.994 0.318 0.449

PP 0.927 0.999 0.369 0.543 Panel B: Log Returns


61

Country Full Period Pre- East Asian

Period Post - East Asian

Period Post - September

11 Period Australia

ADF 0.000** 0.000** 0.000** 0.000** PP 0.000** 0.000** 0.000** 0.000**

Hong Kong ADF 0.000** 0.000** 0.000** 0.000**

PP 0.000** 0.000** 0.000** 0.000** Indonesia

ADF 0.000** 0.000** 0.000** 0.000** PP 0.000** 0.000** 0.000** 0.000**

Japan ADF 0.000** 0.000** 0.000** 0.000**

PP 0.000** 0.000** 0.000** 0.000** South Korea

ADF 0.000** 0.000** 0.000** 0.000** PP 0.000** 0.000** 0.000** 0.000**

Malaysia ADF 0.000** 0.000** 0.000** 0.000**

PP 0.000** 0.000** 0.000** 0.000** New Zealand

ADF 0.000** 0.000** 0.000** 0.000** PP 0.000** 0.000** 0.000** 0.000**

Philippines ADF 0.000** 0.000** 0.000** 0.000**

PP 0.000** 0.000** 0.000** 0.000** Singapore

ADF 0.000** 0.000** 0.000** 0.000** PP 0.000** 0.000** 0.000** 0.000**

Taiwan ADF 0.000** 0.000** 0.000** 0.000**

PP 0.000** 0.000** 0.000** 0.000** Thailand

ADF 0.000** 0.000** 0.000** 0.000** PP 0.000** 0.000** 0.000** 0.000**

US ADF 0.000** 0.000** 0.000** 0.000**

PP 0.000** 0.000** 0.000** 0.000** Notes: This table reports the unit root tests for the log price levels and log returns of the twelve national stock indexes. The full period is January 5, 1988 to September 7, 2004, the pre- East Asian period is January 5, 1988 to October 21, 1997, the post East Asian period is October, 22, 1998 to September, 11, 2001 and the post September 11 period is September 12, 2001 to September 7, 2004. The numbers reported are the p-values of the ADF and PP tests statistics for unit roots. If not otherwise specified a constant was included in the unit root test. a indicates a trend term was included in the test regression. An ** indicates the rejection of the null hypothesis that the series contains a unit root at the 5% significance level.


62

where iσ and jσ is the standard deviation of markets i and j respectively. A GARCH (1, 1)

model provided the estimates for the weekly conditional variance. The sixteen-year average

for each of the two elements was determined and the square root taken to obtain the standard

deviation.

The city distance tool hosted on http://www.geobytes.com/CityDistanceTool.htm gave the

distance (in kilometres) between the markets i and j. To determine capital market integration,

suitable interest data was required. The most appropriate rate to use would be the cash or

overnight rate set by the relevant monetary authorities. However, due to the limitations of

available data over the testing period other short-term rates had to be employed. Following

the example of Bracker et al (1999), various three-month interest rates were employed. For all

interest rates, the data was available from the 5th January 1993 to the 7th of September 2004.

Three-month T-bill rates were used for the U.S., Singapore and the Philippines. For

Indonesia, Malaysia and Thailand 3-month deposit rates were obtained, while for Australia,

Hong Kong and New Zealand three-month interbank rates were collected.

Table 4.2 Augmented Dickey-Fuller and Phillips-Perron Unit Root Tests (p-values) for Interest Rates

Log Levels Log Differences ADF PP ADF PP

Country Australia 0.645 0.449 0.000*** 0.000*** Hong Kong 0.488 0.224 0.000*** 0.000*** Indonesia 0.592 0.322 0.000*** 0.000*** Japan 0.247a 0.166a 0.000*** 0.000*** South Korea 0.461 0.538 0.000*** 0.000*** Malaysia 0.553 0.563 0.000*** 0.000*** New Zealand 0.372 0.315 0.000*** 0.000*** Philippines 0.115 0.147 0.000*** 0.000*** Singapore 0.108 0.113 0.000*** 0.000*** Taiwan 0.663 0.659 0.000*** 0.000*** Thailand 0.959 0.933 0.000*** 0.000*** US 0.946 0.927 0.000*** 0.000*** Notes: This table reports the unit root tests for the log interest rates and the differenced log interest rates. The full period is 5th January 1993 to the 7th of September 2004. The numbers reported are the p-values of the ADF and PP tests statistics for unit roots. An intercept was included in the test regression. a indicates a trend term was included in the test regression. An *** indicates the rejection of the null hypothesis that the series contains a unit root at the 1% significance level, while ** and * indicate rejection at the 5% and 1% levels respectively.


63

The Japanese three-month certificate of deposit rate, South Korean 91-day certificate of

deposit and the Taiwanese 90 day money market rate were also collected. These rates still

have a short enough time horizon to accurately reflect the monetary policy goals of the

respective authorities. Table 4.2 shows that interest rates are non-stationary in their levels, but

stationary in the first differences. Therefore, it can be concluded that interest rates are I (1)

and therefore a cointegration test can be conducted in order to extract the trace statistics

required to proxy for money market linkage. The use of the trace statistics generated from the

cointegration tests on interest rates to explain money market linkage is unique to this study.

The exchange rates for the different countries were collected from DataStream. Due to the

lack of data for the period being examined, the rates were computed using cross rates. For all

thirteen markets, the bilateral exchange rate with the US was available for the entire period.

The cross rates were computed as follows:

HKDUSD

USDAUD

HKDAUD *= (2)

The use of cross rates is not ideal, but the level of error is extremely small and still allows for

strong comparisons. Furthermore, in practice virtually all currency traders convert their

domestic currency into USD in order to purchase other foreign currency and implicitly use

cross rates anyway.

The bilateral exchange rates were required to compute the volatility of the bilateral exchange

rate. The first step was to generate the weekly continuously compounded returns using the

procedure outlined in equation (1) for the period 2nd January 1990 to 7th September 2004. A

GARCH (1, 1) model was employed to extract the weekly conditional variance. The variance

was averaged over the sixteen years and the square root taken to derive the standard deviation.

Industrial production statistics were collected from DataStream on a monthly basis for the

twelve markets being examined, for the period 15/01/1989 to 15/06/2004. Industrial

production statistics are a good proxy for both industry composition and output of the

economy. Similar to capital market integration, the proxy for goods market integration will be

the trace statistic from a bivariate test for cointegration. The motivation for this choice of


64

statistic stems from Darrat and Zhong (2004) whom employ a similar methodology. As per

the discussion pertaining to capital market integration, cointegration tests require the system

of variables to be integrated of the same order. Table 4.3 and 4.4contains the results for the

unit root test for the industrial production statistics.

Table 4.3 Unit Root tests for Industrial Production Statistics in log levels Critical Values Critical Values

Country Augmented

Dickey-Fuller test statistic 90% 95% 99%

Phillips-Perron test Statistic 90% 95% 99%

AU -1.4247 -2.575 -2.877 -3.466 -1.406 -2.575 -2.877 -3.466 HK -1.2303 -2.575 -2.877 -3.466 -1.585 -2.575 -2.877 -3.466 ID -0.2092 -2.575 -2.877 -3.466 0.047 -2.575 -2.877 -3.466 JP -1.9762 -2.575 -2.878 -3.467 -11.383*** -2.575 -2.877 -3.466 KO -0.3180 -2.575 -2.877 -3.466 -0.293 -2.575 -2.877 -3.466 MY -0.5969 -2.575 -2.877 -3.466 -0.529 -2.575 -2.877 -3.466 NZ 0.3147 -2.575 -2.877 -3.466 0.308 -2.575 -2.877 -3.466 PH -1.1725 -2.575 -2.877 -3.466 -1.580 -2.575 -2.877 -3.466 SG -2.0599 -2.575 -2.877 -3.466 -2.272 -2.575 -2.877 -3.466 TA -2.0521 -2.575 -2.877 -3.466 -2.373 -2.575 -2.877 -3.466 TH -1.1342 -2.575 -2.877 -3.466 -1.116 -2.575 -2.877 -3.466 US -0.4265 -2.575 -2.877 -3.466 -0.534 -2.575 -2.877 -3.466

Table 4.4 Unit Root tests for the log differenced Industrial Production Statistics Critical Values Critical Values

Country

Augmented Dickey-Fuller test statistic 90% 95% 99%

Phillips-Perron test Statistic 90% 95% 99%

AU -9.49609*** -2.575 -2.877 -3.466 -13.30209*** -2.575 -2.877 -3.466 HK -16.8483*** -2.575 -2.877 -3.466 -19.25698*** -2.575 -2.877 -3.466 ID -7.20959*** -2.575 -2.877 -3.466 -7.52741*** -2.575 -2.877 -3.466 JP -25.39081*** -2.575 -2.877 -3.466 -20.60083*** -2.575 -2.877 -3.466 KO -9.46027*** -2.575 -2.877 -3.466 -15.66235*** -2.575 -2.877 -3.466 MY -12.94404*** -2.575 -2.877 -3.466 -21.19589*** -2.575 -2.877 -3.466 NZ -9.76598*** -2.575 -2.877 -3.466 -13.82546*** -2.575 -2.877 -3.466 PH -15.60689*** -2.575 -2.877 -3.466 -20.9483*** -2.575 -2.877 -3.466 SG -22.29304*** -2.575 -2.877 -3.466 -28.08833*** -2.575 -2.877 -3.466 TA -15.63103*** -2.575 -2.877 -3.466 -24.01909*** -2.575 -2.877 -3.466 TH -11.66317*** -2.575 -2.877 -3.466 -21.33933*** -2.575 -2.877 -3.466 US -14.73194*** -2.575 -2.877 -3.466 -22.61475*** -2.575 -2.877 -3.466

Notes: This table reports the unit root tests for the log price levels and log returns for the industrial production statistics for the twelve countries. Monthly data is used. The full period covers 15/01/1989 to 15/06/2004. AU ! Australia, HK ! Hong Kong, ID ! Indonesia, JP ! Japan, KO ! Korea, MY ! Malaysia, NZ ! New Zealand, PP ! The Philippines, SG ! Singapore, TA ! Taiwan, TH ! Thailand and US ! The United States. An intercept is included in all test regressions. An *** indicates the rejection of the null hypothesis that the series contains a unit root at the 1% significance level, while ** and * indicate rejection at the 5% and 1% levels respectively.


65

In order to calculate the size differentials between markets, the weekly market capitalisations

(denominated in US dollars) of the twelve markets was collected for the period between

3/04/1990 to 7/09/2004. This data was also required to compute turnover ratios. In this thesis,

size differentials were computed in the following fashion:

j

iji SIln Avg

SIln Avgsdiff =, (3)

where i SIln Avg is the natural log of stock market capitalisation i computed at weekly

intervals. The average of the logged stock index i is then calculated for the full period. The

same procedure is repeated for market j. The impetus behind this proxy derives from Bracker

et al (1999) who utilize a similar metric.

Before, continuing some preliminary notes regarding this statistic are required. The first is

that the US never appears in the numerator because of the ordering. The consequence of this

is that, for the eleven cases when the US is in the denominator an average difference of

around 0.6 is returned (excluding Japan). This thesis proposes that most markets display

strong linkage the US, therefore when is smaller jisdiff , higher market linkage is expected.

Size differences of over one likely represent cases when Australia, Japan, South Korea and

Taiwan appear in the numerator and the less developed economies of SE Asian in the

denominator. Based on past empirical evidence, economic links and information presented in

chapter 3, the proposition that Australia, South Korea, Taiwan and especially Japan do not

display significant market linkage with the less developed markets in SE Asia is reasonable.

The combination of facts leads to the conclusion that, when jisdiff , ,is high market linkage is

expected to decline.

When the two markets are of the same size it is expected that market linkage will higher..

Values of one are expected in two situations. The first is when two large markets are

included in the computation and the second when two small markets are compared. When

considering the large markets group (Australia, Hong Kong, Japan and Taiwan and the US),

this thesis predicts that the level of linkage between these markets to exceed the average.


66

Turnover ratios serve, as a proxy for market development is a proposition presented in this

thesis. Market capitalisation and trading volume figures were obtained from DataStream.

Turnover ratios are traditionally estimated as the value of the volume traded deflated by the

market capitalisation. Data restrictions made this not possible for this thesis. In an attempt to

mitigate the effects of exchange rates and market composition, all market capitalisations

figures are denominated in USD terms. The trading volume (measured in the number of

shares not value) was also collected for the twelve markets. The major problem that arises

from using the physical number of shares traded is that in some markets many relatively

cheap stocks might be traded quite often, while in other markets more expensive stocks are

traded at a lower frequency. Thus, it would appear that the first market is liquid when in fact it

may not be. Therefore, by collecting all market capitalisation figures in USD and using

domestic trading volumes allows for greater comparability. For example, the turnover ratio

for Korea would be completed as follows:

USD) (in Korea of tionCapitalisa Market Korea) (in traded sharesof NumberTOKO = (3)

All turnover ratios are computed on a weekly basis. These ratios are then averaged over the

period 23/04/1991 to 7/09/2004, such that one ratio is obtained for each market.

Table 4.5 - Turnover ratios for all 12 markets

Country Turnover Australia 2.992405Hong Kong 3.012569Indonesia 15.26897Japan 0.8147Korea 2.922903Malaysia 3.253496New Zealand 4.123634Philippines 14.34273Singapore 4.195414Taiwan 3.985235Thailand 13.52558US 0.575817

The bilateral turnover ratio is the equally weighted turnover ratio between market i and j. The

lower the ratio the more advanced the market; Japan and the US have the two lowest, while


67

Indonesia, the Philippines and Thailand have the three highest. As can be seen the results

obtained appear to be fairy accurate proxies of market development.

4.3 Descriptive Statistics

Table 4.6 displays the descriptive statistics for the returns series of the twelve stock markets.

The table highlights several issues. The first is the outstanding performance of Australia and

the US over the sample period. Australia had a mean weekly return of 0.151% (4th highest)

and the lowest variance, while the US had a weekly mean return of 0.169% (3rd highest) and

the second lowest variance. By contrast, the poor performance of the Japanese market is

evident, as it is the only market to have a negative return. As expected, all markets strongly

rejected the null hypothesis of normality and kurtosis.

Table 4.6 - Descriptive Statistics: Stock Index Returns for the full period Country Mean Variance Skewness Kurtosis Jarque-Bera Australia 0.00151** 0.00041 -0.21166** 3.38445** 421.72246** Hong Kong 0.00195 0.00152 -0.96946** 8.45726** 2729.07022** Indonesia 0.00259 0.00278 5.61822** 99.47763** 363299.578** Japan -0.00074 0.00090 0.10695 1.35944** 68.65168** South Korea 0.00050 0.00193 -0.00426 1.92855** 134.82717** Malaysia 0.00129 0.00140 0.17016** 8.18368** 2431.95431** New Zealand 0.00094 0.00065 0.27354** 7.4037** 1997.88645** Philippines 0.00083 0.00173 0.11595 1.69941** 106.63966** Singapore 0.00094 0.00079 -0.16393** 2.53159** 236.22112** Taiwan 0.00105 0.00231 -0.5785** 1.92253** 182.51053** Thailand 0.00085 0.00198 0.20676** 3.5089** 452.52362** US 0.00169** 0.00050 -0.18182** 2.99853** 330.72471** Notes: The full period begins 1st January 1988 and finishes 7th September 2004. The returns are weekly continuously compounded returns calculated as per (1). ** denotes significance at the 5% level.

Significant skewness was found in all cases except Japan, South Korea and the Philippines.

The uncharacteristic performance of the Japanese market during the sample period compared

to the other countries examined is likely to reduce the possibility of it being linked with other

markets

CHAPTER 5–EMPIRICAL RESULTS AND FINDINGS

68

CHAPTER 5: EMPIRICAL RESUTS AND FINDINGS

5.1 Introduction

This chapter outlines the major empirical results of this thesis. Conclusions are made from the

results in reference to the hypotheses and propositions outlined in Chapter 3. The chapter is

divided into several major sections. Section 5.2 address the results from the cointegration

analysis, while Section 5.3 presents the findings obtained from the persistent profile analysis.

5.4 and 5.5 cover the findings regarding the generalized impulse response functions, and

block exogeneity tests. Lastly, section 5.7 contains the results from the regression analysis

concerning the factors driving market linkage.

5.2 Cointegration Analysis

This section is broken into; Section 5.2.1 examines the bivariate and multivariate tests for

cointegration on the full sample period. 5.2.2 contains, the results pertaining to the recursive

bivariate cointegration analysis.

5.2.1 Bivariate and Multivariate Tests for Cointegration for the full sample period

The tests for stationarity are contained in panel A and B of Table 4.1. For the full sample

period, all twelve markets are found to be I (1), thus satisfying the first condition required for

cointegration. Following the procedure outlined in section 3.3.1, the first step is to choose an

appropriate lag length (p). The Sims likelihood ratio test, AIC or SBC are be used to

determine the optimal lag length. Based on a mixture of evidence from the selection criteria

and previous empirical findings, the bivariate cointegration tests are completed with 10, 15,

20, 30 and 52 lags in the test VAR to ensure robustness. The results from all five-lag

structures are displayed in Table 5.1. All trace statistics were deflated by the small sample

bias correction factor as per the discussion in section 3.3.1. Due to the test VAR containing

twelve equations for the multivariate tests, the degrees of freedom rapidly decline. Therefore,

lag lengths of 5, 8, 10, 15 and 20 are employed. The use of longer lag lengths in cointegration

tests is preferred for two reasons. The first reason as mentioned above and in Chapter 5, serial


69

correlation between variables can significantly affect the performance of the JJ procedure.

Therefore, the order of the VAR should be increased until serial correlation is removed from

the residual series. Secondly, because cointegration tests are used to identify the possibility of

long run relationships, an excessively short lag length is inappropriate. Furthermore, uses of

relatively short lag lengths decrease the probability of significant cointegration being

discovered. As discussed in Chapter 2, the drawback of over-parameterization is the loss of

discriminatory power in the JJ procedure. This issue is not relevant to this thesis, due to the

full sample containing 871 observations for each series. Therefore, even with a lag length of

fifty-two weeks, some 819 observations per series are still available for testing.

For Australia, some interesting results were obtained. The first was that under all five-lag

structures no strong linkage was found to exist with Hong Kong, Malaysia, New Zealand, the

Philippines, Thailand or the US43. Significant cointegration with Indonesia was found in all

cases except when 52 lags were included. The conclusion that could be drawn from this result

is that Australia affects the short-term return in the Indonesian market over a long run time

horizon. When p = 30 and 52, Australia, exhibits significant linkage with Japan. It would be

thought that movements in the Japanese market affect Australia in the long run. At a lag,

length of 10 and 52 significant cointegration is discovered between Taiwan and Australia.

While at the 52-week lag, Korea and Singapore were found to be connected to Australia. This

thesis proposes the role of Australia in the Pacific-Basin is one of a market clearer, that is it

returns the system to equilibrium. Movements in Japan, Hong Kong and the US are

transmitted by Korea, Singapore and Taiwan into Australia.

Cointegration is found at p = 10, 15 and 30 between Hong Kong and Indonesia. It can be

safely assumed that Indonesia is influenced by Hong Kong in the long run. South Korea and

Singapore display marginal linkage44 with Hong Kong. Indonesia is found to take its lead

from multiple markets. Indonesia shows strong linkage with Japan, Korea, New Zealand,

Singapore, Taiwan and the US at lengths, 10, 15, 20 and 30. Fro all situations besides the

relationship with New Zealand it is supposed that the other five markets exert influence of

43 With 10 lags, a cointegrating relationship between Australia and the US was found to exist at the 10% significance level. While a cointegrating relationship was found between New Zealand and Australia at the 10% significance level, when p = 52. 44 Marginal linkage refers to significance at the 10% level.


70

Indonesia. There also appears to be weak linkage with Malaysia. Besides the results discussed

above, Japan also demonstrates weak significance with New Zealand and Singapore.

From the results obtained, South Korea appears to be one the most exogenous markets in the

Pacific-Basin. There is substantial support for the notion that Australia, Indonesia, Hong

Kong, Malaysia, New Zealand, Singapore, Taiwan, and the US are all linked with Korea in

the long run. A believable story is that shocks from Hong Kong, Singapore and the US flow

into Korea and it then transmits them to Australia, Indonesia and New Zealand. The relation

between Taiwan and Korea is not so clear. Even looking at the evidence from the GVD, both

markets seem to be important to one another.

The Malaysian market was found to have a robust relationship with Korea and Taiwan and a

less well developed relations with the US and Indonesia. Disturbances in Hong Kong, Japan

and Singapore are likely to flow through Korea and Taiwan into Malaysia then to Indonesia.

Malaysias dependence on investment from the US is likely to influence the relationship. The

behaviour of New Zealand in the Pacific-Basin is not unexpected. As discussed above New

Zealand is affected by the Indonesian and Japanese market, while the New Zealand does not

influence any other market in the Pacific-Basin.

The Philippines is likely to be endogenous because of the relative lack of development. The

bivariate cointegration tests support this proposition. Only in one case with Taiwan do the

Philippines display market linkage with any other markets in the PB. Like South Korea,

Singapore proves to be an exogenous market. As previously, discussed Singapore is proposed

to affect the Australian and Indonesian markets, while it is influenced by Hong Kong and

Japan. This thesis suggests that Singapore as a conduit to link Hong Kong and Japan with

Korea. Furthermore, Taiwan and the US appear to be linked in a less significant fashion.

Taiwan assumes a similar position to that of South Korea as a transmitter in the Pacific-

Basin. As discussed in the section above, Taiwan is found to be linked to Australia, Indonesia,

Korea, Malaysia, the Philippines and Singapore. However, the other interesting characteristic

of Taiwan is its strong relationship with the US. From the evidence presented above, Taiwan

stands as the only market in the PB that exhibits robust ties with the US. This is likely to have

considerable consequences on the importance of Taiwan in the PB.


71

Following the procedure developed by Husted (1992) and outlined in Darrat and Zhong

(2004), the tests of cointegration are recomputed with the inclusion of two dummy variables.

The EA crisis dummy takes a value of 1 for the period October 17, 1997 to 31 December

1998 and 0 otherwise45. The choice of this period is based on the work of Forbes and Rigobon

(2002) whom use a similar time horizon. The second dummy takes a value of 1 for the four

weeks following September 11 2001 and 0 otherwise46. The choice of twenty-six weeks

derives from the findings of Hon et al (2004) whom find that correlation between the US and

Hong Kong, Japan, Korea, Australia, Singapore, Malaysia and Taiwan significantly increased

post-September 11 for the next six months. Three different specifications are estimated. The

first includes both the dummies, while the other two excludes one dummy and re-tests, for all

tests p = 52. Panel B of Table 5.2 contains the results. Caution must be employed when

interpreting the result of these tests. The inclusion of a stationary variable (i.e. the dummy

variables) is likely to lead to the true cointegrating relationship being overstated. Furthermore,

the Osterwald-Lenum critical values may not be applicable. This thesis takes a conservative

stance and only concludes that the event has caused an increase in linkage if the increase is

considerable.

The results show that the inclusion of the EA crisis dummy appreciably intensifies the

cointegrating relationship47. Figure 5.2a plots the trace statistics from the bivariate tests with

and without the dummy variable. The trace statistics have been normalized by the 95%

Osterwald-Lenum critical values, such that a value greater than 1 indicates significance at the

95% level. The increase in the strength of the cointegrating relationship produced after

including the dummy for the EA crisis period is so intense that even from a conservative

standpoint it can be concluded that the EA crisis caused a once off structural change in the

equity markets of the PB.

45Dummy variables for four and twelve weeks post October 17 were also tested. The results did not display any material difference and therefore are not reported. However, they are available upon request. 46 Dummy variables for twenty-six and twelve weeks post September 11 were also tested. The results did not display any material difference and therefore are not reported. However, they are available upon request. 47 All trace statistics were adjusted using the small sample bias correction factor.


72

Figure 6.2a The impact of the East Asian crisis on market linkage

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65

Trac

e st

atis

tic

Trace with dummy Trace without dummy Normalised Critical Value Out of the sixty-six pairs, the cointegration relationship increased for fifty-two. An interesting

result is that in seven of the cases when the relationship did not intensify Australia was one of

the markets included. This indicates the relatively small impact the EA crisis had upon the

Australian market and its standing in the PB. These findings provide further support for

hypothesis 3A and 3B. The effect of September 11 is presented in Figure 5.2b.

Figure 6.2b The impact of September 11 on market linkage

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65

Trac

e St

atis

tic

Trace with dummy Trace without dummy Normalised Critical Value Considering the caution with which the results should be interpreted, the effect of September

11 on the PB is still note worthy. This graph though highlights the relatively mild impact of


73

September 11 on the PB compared to the EA. Finally, both dummy variables were

simultaneously included and the tests recomputed. Figure 5.3c can be seen below.

Figure 6.3c The impact of both the EA crisis and September 11 on market linkage

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65

Pair

Trac

e St

atis

tic

Both dummies No Dummy Asian dummy Normalised Critical Value The most obvious result from the graph above is that the increase in the intensity of the

cointegrating relationship is mainly attributable to the EA crisis and not September 11. The

results of the multivariate results can be viewed in table 5.3.1.

Table 5.3.1 Cointegration Tests for the presence of long run market linkage between the nations of the Pacific-Basin

lag =5 lag = 8 lag =10

Null Unadjusted Trace

Adjusted Trace

Unadjusted Trace

Adjusted Trace

Unadjusted Trace

Adjusted Trace

r = 0 399.58 372.05 403.81 375.99 421.97 392.90 r ≥ 1 324.83 302.45 324.23 301.89 327.20 304.66 r ≥ 2 257.35 239.62 252.38 234.99 263.95 245.77 r ≥ 3 194.22 180.84 198.79 185.10 212.08 197.47 r ≥ 4 145.04 135.05 151.83 141.37 166.87 155.37 r ≥ 5 112.58 104.82 114.84 106.93 121.96 113.56 r ≥ 6 81.31 75.71 80.00 74.49 87.93 81.87 r ≥ 7 58.06 54.06 57.21 53.27 61.10 56.89 r ≥ 8 39.49 36.77 41.74 38.86 42.32 39.40 r ≥ 9 25.05 23.32 26.65 24.81 26.63 24.80 r ≥ 10 12.98 12.09 13.91 12.95 14.84 13.82 r ≥ 11 6.16 5.74 6.57 6.12 6.76 6.29 lag =15 lag = 20 Critical Values

Null Unadjusted Trace

Adjusted Trace

Unadjusted Trace

Adjusted Trace 95% 90%


74

r = 0 410.17 381.91 439.14 408.89 340.39 333.26 r ≥ 1 319.27 297.28 349.47 325.40 291.40 281.63 r ≥ 2 249.57 232.38 272.91 254.11 244.15 237.35 r ≥ 3 199.99 186.21 213.97 199.23 202.92 196.66 r ≥ 4 155.08 144.40 171.05 159.27 165.58 159.74 r ≥ 5 117.28 109.20 130.02 121.06 131.70 126.71 r ≥ 6 87.70 81.66 100.60 93.67 102.14 97.17 r ≥ 7 63.67 59.28 74.87 69.71 76.07 71.66 r ≥ 8 43.15 40.18 52.15 48.56 53.12 49.91 r ≥ 9 29.39 27.37 35.88 33.41 34.91 31.88 r ≥ 10 17.36 16.16 21.85 20.34 19.96 17.79 r ≥ 11 8.41 7.83 9.54 8.88 9.24 7.50 Notes: This table reports the Johansen-Juselius (1992) trace statistics for the multivariate cointegration tests conducted on the log of stock market indexes of all twelve markets included in the system. Weekly data is used. The full period covers 5/01/1988 to 7/09/2004. Intercepts are included in the estimated cointegration vectors. The small sample correction factor suggested by Cheung and Lai (1993) is applied to all the trace statistics. A lag length of ten was used for all tests. Other lag lengths were tested. The differences between the results were immaterial. The results are available upon request. The 95% and 90% critical values are obtained from Osterwald-Lenum (1992). An * indicates rejection of the null of no cointegration at the 10% level, while an ** indicates rejection at the 5% significance level.

The tests seem to indicate the presence of two to three cointegrating vectors depending on

which lag length is believed. To determine whether Japan or the US is responsible for driving

cointegration in the PB, the tests were re-computed with Japan excluded, then the US

excluded. As can be seen from table 5.3.2 panel A, the trace statistic from when Japan is

excluded is considerably higher than when the US is excluded.

Table 5.3.2 Cointegration Test Results for the presence of Long-Run Equity Market Linkage in the Pacific-Basin on the full period (January 5, 1998 - September 7, 2004)

Panel A. The effect of Japan and the US on market linkage

Null Trace

statistic for all markets

Trace statistic for when Japan is

excluded

Trace statistic for when the US is excluded Critical Values

95% 90% r = 0 392.90** NA NA 340.39 333.26r ≥ 1 304.66** 310.6807** 278.2973 291.40 281.63r ≥ 2 245.77** 230.937 218.098 244.15 237.35r ≥ 3 197.47* 176.3436 167.0039 202.92 196.66r ≥ 4 155.37 137.2844 129.8755 165.58 159.74r ≥ 5 113.56 102.1726 95.92551 131.70 126.71r ≥ 6 81.87 72.35816 69.53845 102.14 97.17 r ≥ 7 56.89 52.9183 49.07055 76.07 71.66 r ≥ 8 39.40 35.3941 33.44207 53.12 49.91 r ≥ 9 24.80 21.32065 20.73436 34.91 31.88


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r ≥ 10 13.82 10.2989 9.658377 19.96 17.79 r ≥ 11 6.29 4.455487 3.871029 9.24 7.50 Notes: This table reports the Johansen-Juselius (1992) trace statistics for the multivariate cointegration tests conducted on the log of stock market indexes of all twelve markets included in the system. Weekly data is used. The full period covers 5/01/1988 to 7/09/2004. Intercepts are included in the estimated cointegration vectors. The small sample correction factor suggested by Cheung and Lai (1993) is applied to all the trace statistics. A lag length of ten was used for all tests. Other lag lengths were tested. The differences between the results were immaterial. The results are available upon request. The 95% and 90% critical values are obtained from Osterwald-Lenum (1992). An * indicates rejection of the null of no cointegration at the 10% level, while an ** indicates rejection at the 5% significance level.

It must be noted that this does not provide concrete evidence to support the proposition that

the US influences the cointegrating relation more than Japan; however, it does provide

persuasive proof to suggest it does. The effect of the EA crisis and September 11 are also

considered in the context of the multivariate setting. The effect of the EA crisis is found to

have a far greater impact upon the level of cointegration than did September 11. It can be seen

that the inclusion of the Asian dummy leads to the identification of two extra cointegrating

vectors compared to the result from the September 11 dummy. This evidence provides

enough proof is from a conservative standpoint to conclude that the EA crisis affected the

long run market linkage between the countries of the PB. Such compelling findings are not

seen when only the September 11 dummy is included.

Panel B. The impact of the East Asian crisis and September 11 on market linkage

Null

Trace statistic for no dummy

variables

Trace statistic for when the EA crisis dummy is included

Trace statistic for when the

September 11 dummy is included

Trace statistic for when both dummy

variables are included

Critical Values

95% 90% r = 0 392.90** 386.23** 371.42** 387.54** 340.39 333.26r ≥ 1 304.66** 307.92** 289.44* 308.20** 291.4 281.63r ≥ 2 245.77** 253.04** 233.67 253.14** 244.15 237.35r ≥ 3 197.47* 199.28* 183.62 199.44* 202.92 196.66r ≥ 4 155.37 153.86 139.82 154.01 165.58 159.74r ≥ 5 113.56 116.00 106.82 116.04 131.7 126.71r ≥ 6 81.87 83.77 76.81 84.88 102.14 97.17 r ≥ 7 56.89 57.51 54.95 57.79 76.07 71.66 r ≥ 8 39.4 39.68 36.99 39.82 53.12 49.91 r ≥ 9 24.8 22.41 22.65 22.54 34.91 31.88 r ≥ 10 13.82 12.08 11.77 12.18 19.96 17.79 r ≥ 11 6.29 5.69 5.43 5.88 9.24 7.5 Notes: This table reports the Johansen-Juselius (1992) trace statistics for the multivariate cointegration tests conducted on the log of stock market indexes of all twelve markets included in the system. Weekly data is used. The full period covers 5/01/1988 to 7/09/2004. Intercepts are included in the estimated cointegration vectors. The small sample correction factor suggested by Cheung and Lai (1993) is applied to all the trace


76

statistics .A lag length of ten was used for all tests. Other lag lengths were tested. The differences between the results were immaterial. The results are available upon request. The 95% and 90% critical values are obtained from Osterwald-Lenum (1992). An * indicates rejection of the null of no cointegration at the 10% level, while an ** indicates rejection at the 5% significance level.

5.2.2 Bivariate Recursive Cointegration Analysis

The methodology for the recursive tests is outlined in section 3.3.1. Because there are 416

observations available per series, to ensure the credibility of the results, lags of 10, 30 and 52

were tested. Even when p = 52, there are still some 260 observations on which the test can be

completed, thus the test still maintains significant discriminatory power. The results from the

30 lag tests will be focused upon in the following discussion and all sixty-six graphs can be

seen in Figure 5.2.2 of the Appendix. Some quick explanatory notes about the graphs

presented, allow for easier interpretation. The first observation corresponds to the 2/01/1996

and finishes on the 7/09/2004, the EA crisis occurs around the 90 observation, while

September 11 is observation 298. The trace statistics have been adjusted for small sample bias

and have been standardized by the 95% critical value provided by Osterwald-Lenum (1992),

such that a valued in excess of 1 indicates significance at the 5% level. The abbreviations

used for the different markets are as follows: AU ! Australia, HK ! Hong Kong, ID !

Indonesia, JP ! Japan, KO ! Korea, MY ! Malaysia, NZ ! New Zealand, PP ! The

Philippines, SG ! Singapore, TA ! Taiwan, TH ! Thailand and US ! The United States.

Therefore, the graph TATH is the weekly recursive cointegration test between Taiwan and

Thailand.

Due to the intricate explanation required to properly describe the findings, only those

containing Australia will be discussed in detail, while other important results also deserve

attention. The overall result that can be taken away from all sixty-six graphs is that, the level

of market linkage is extremely volatile over time and can intensify or decline quickly.

Furthermore, without any doubt it can be concluded that the EA crisis and September 11 to a

lesser extent led to an increase in the level of market linkage. Additionally for some markets,

the fear concerning the Y2K bug caused an increase in market segmentation leading up to the

year 2000; however, the evidence seems to suggest that post 2000 markets quickly reverted to

displaying higher levels of market linkage. Lastly, some evidence emerges supporting the

notion that in the build up to the EA crisis, September 11 and the Y2K bug that the level of


77

linkage was frequently below its average levels. These conclusions opens up a completely

new question to be addressed by the literature and that is, are the effects of market

disturbances more long lived and heightened due to the transmission channels between

markets not operating at full efficiency. This question is far to complex and broad to be

covered in this study, but provides and interesting platform for further study to be completed

upon.

The results pertaining to the tests including Australia and the other eleven markets illustrate

some interesting findings. Australias relationship with Hong Kong is only significant at very

few times over the eight-year period. In the build up to the EA crisis, the level of linkage

increased substantially to its highest before rapidly declining prior to the EA crisis. Following

the crisis, the relationship increased dramatically and attained significance several times in the

following months. In the lead up to the year 2000, the markets displayed a visible lack of

linkage. However, almost immediately into the new year the relationship experienced a

considerable upsurge. This evidence tends to suggest that the fear surrounding the Y2K bug

had a noticeable impact upon markets behaviour. The events of September 11 had an almost

identical effect upon the level of market linkage as the EA crisis did. Pre- September 11,

linkage was relatively low; however almost immediately following the attacks the strength of

the relationship nearly doubled and was significant for an extended period of time (2-3

months). Post- September 11 the intensity declined dramatically, but in the last year has once

again experienced a sizeable increase and a return to significance.

The results for Australia and Indonesia and Australia and Japan, both demonstrate an upward

trend. Prior to the year 2000, Indonesia displayed no significant long run market linkage with

Australia. In the post 2000 period, there has been considerably strong linkage between the

two markets, with a significant relationship being evident constantly for almost the last two

and a half years. For Japan, a similar relationship is found, instead though it appears that

September 11 acted as a catalyst behind increasing the level of association. This result is

mirrored by the GVD results that finds the amount of variance explained by Japan

considerable increase post- September 11. Significant linkage can be seen to have been

present for around the last 130 weeks between Australia and Japan.


78

The importance of the Korean market in regard to Australia can be seen to have increased

gradually over the last eight years. The three events (EA crisis, y2k and September 11) all

seem to result in considerable spikes in the cointegrating relationship. The relationship

displays brief significance post y2k, but quickly deteriorated, before the events of September

11 sparked a marked increase in the level of linkage. The connection continued to be

significant to the end of the sample period, with only small periods of insignificance. The

GVD results exhibit a comparable pattern, showing an increase of 7.5% from the pre- EA

period to the post- September 11 sample.

Malaysia and Australia do not demonstrate any long periods of significant linkage over the

past eight years besides the last couple of months. Again, the three events discussed above all

produce noticeable spikes in the cointegrating relationship, at times to such an extent to cause

brief significance. The connection between Australia and New Zealand exhibits an

unexpected amount of volatility. For the first 250 observations, there is an obvious upward

trend in the relationship and shows sporadic significance over a 60-week period. However,

the decline was swift and by week 300, the intensity had more than halved. Since the decline,

September 11 caused a sharp rise but no significance, while in the last year there has been a

considerable reduction in the strength of the relationship. The GVD produces a result that

describes this relationship almost exactly. In the post- EA/pre September 11 period

Australia accounts for 13.19% of the forecast error variance in New Zealand, while New

Zealand explains 12.71% of the Australian variance. In the post September period Australia

accounts for only 7% in new Zealand and New Zealand a considerably smaller 4.43%.

Immediately preceding the EA crisis the level of association between Australia and the

Philippines is almost negligible. Like other results, the EA crisis caused the relationship to

undergo a considerable change. Even though the spike was only temporary, the underlying

level of linkage remained quite high. Almost simultaneously, following the new year in 2000,

the level of linkage tripled and fir the first time displayed significance. From the time since,

the relationship has continued to expand and a considerably high concentration of linkage is

apparent. On the back of extensive and robust growth, it is not surprising that Australia and

Singapore have become linked to one another. The relationship can best be described as

spasmodic expansion followed by an immediate decline then a period of static followed by

another jump in the intensity and such the cycle continues. The driving forces behind the


79

spasmodic increases are the three major events as described above. However, only in the past

year has a significant relationship been realized. The GVD results provide further

corroborating evidence to support the gradual increase in linkage between the two markets.

Australia and Taiwan have been significantly linked to one another briefly at several times

over the sample examined. The first period followed the EA crisis, while the other two

periods of significance can be directly attributed to the passing of the y2k bug and September

11. Thailand and Australia displays an almost identical relationship as to Australia and

Taiwan, that is, the three periods over significance are driven by the three events already

mentioned. In the last year, the relationship for both markets and Australia has remained

robust, but still insignificant.

Lastly, the result between Australia and the US as per expectations has been remarkably

strong during the sample period. The strong association with the US began in early 2000 as

the fears surrounding the Y2K bug subsided. Almost immediately, the relationship

experienced a dramatic appreciation. For the next three and a half years the US and Australia

were significantly linked to one another. It has only been since September 2003 that

significance has been lost. An interesting observation is the relatively small affect of

September 11 compared to the EA crisis and Y2K bug.

5.3 Persistent Profile Analysis

Section 5.4 outlines the methodology required to estimate persistent profiles. The first

condition is to ensure the variables are integrated of the same order. The result of ADF and

PP tests for stationarity can be seen in Table 4.1. For all twelve markets, the null hypothesis

of non-stationarity for stock prices could not be rejected for the full sample period as well as

the three sub periods. The null hypothesis of non-stationarity is rejected in all cases for all

periods for the differenced data. Thus, the condition of being integrated of the same order is

satisfied.

As outlined in section 3.3.1, the JJ test for cointegration is performed. Table 5.3 displays the

results from the multivariate JJ cointegration tests conducted on the full sample. The presence


80

Table 5.3 Cointegration Test Results for the presence of Long-Run Equity Market Linakge in the Pacific-Basin

Null Adjusted Trace Statistic Critical Value

full

period pre EA post EA 95% 90% r = 0 392.90** 360.71** 388.22** 340.39 333.26 r ≥ 1 304.66** 292.6** 304.63** 291.4 281.63 r ≥ 2 245.77** 229.7932 236.956 244.15 237.35 r ≥ 3 197.47* 182.4899 184.5456 202.92 196.66 r ≥ 4 155.37 144.3046 133.9664 165.58 159.74 r ≥ 5 113.56 108.9757 100.4413 131.7 126.71 r ≥ 6 81.87 79.86194 69.35261 102.14 97.17 r ≥ 7 56.89 56.06952 43.93166 76.07 71.66 r ≥ 8 39.4 35.89299 29.65278 53.12 49.91 r ≥ 9 24.8 20.8248 18.73039 34.91 31.88 r ≥ 10 13.82 12.37812 19.50358 19.96 17.79 r ≥ 11 6.29 4.494546 3.973298 9.24 7.5

of three cointegrating vectors48 can be identified. Similar results are found for the pre and post

EA crisis period. Unlike the short run measures, the multivariate tests for cointegration

consume a considerable number of degrees of freedom. As a result, it makes the sample too

small to accurately conduct the tests on the three sub samples.

The results from the PP for the full period are contained in the Appendix in Figure 5.3a, 5.3d

and 5.3e. The cointegration tests identify three cointegrating vectors. An alternative

interpretation is that there are nine different stochastic processes in the Pacific-Basin. The

results show that near convergence is achieved after thirty weeks for vectors 1, 2 and 3. The

rate of convergence is very stable, implying a low level of volatility between markets.

Looking at Figure 5.3b, 5.3d and 5.3e the PP for the pre-East Asian crisis period can be seen.

The results show that cointegrating vectors obtains near convergence after 20 weeks.

Cointegrating vector two is faster to revert to equilibrium with over 95% of convergence

completed after thirty fifteen weeks. Like the results for the full period, convergence for the

pre-EA crisis period occurs steadily, thus indicating low volatility is the system.

Figures 5.3c, 5.3d and 5.3e show the PP for the post-EA period. The notable difference is the

in the speed of the convergence. Both vector 1 and 2 achieve over 95% of convergence within

48 Assuming a lag length of 10 is being employed


81

ten weeks. The increased speed in convergence indicates that the markets of the PB became

more efficient and linked to one another following the EA crisis.

5.4 Generalized Impulse Response Functions

Figures 5.4a 5.4g display the results from the GIR functions. As mentioned in earlier

sections generalized impulse functions show how quickly a market reacts and reverts to

equilibrium following a one standard deviation shock in another market. Fast convergence is

an indicator of strong market linkage. A ± two standard error bound can be observed on all

graphs of the GIR functions. This bound should be interpreted in the following manner. When

the both the bounds are in the positive or negative section of the graph, then it can be

concluded that the response by market A to a shock in market B is significant. The situation

when both bounds are not in the same region indicates an insignificant response. This can

indicate two vastly different situations. The first is that market A is not linked to market B

and therefore does not respond to the disturbance. The alternate interpretation is that the

response is so fast that it cannot be discerned using weekly returns. For the full period and

the three sub-samples, Japan and the US were shocked and the responses observed.

Figures 5.4a and 5.4b show the GIR functions for the full sample period. Looking at the effect

of a shock in the US, most markets exhibit significant reversion after the two-week mark.

Australia, Hong Kong, Japan, Korea, Malaysia, New Zealand and Taiwan display strong

linkage with the US. The conclusion arises from the speed at which the markets converge to

equilibrium. The markets are highly efficient with most of the convergence completed by the

second week and only an immaterial level of noise remaining. These results provide

supporting evidence for the bivariate cointegration tests. From table 5.2, significant

cointegration with the US is evident between Australia, Japan, Korea, Malaysia and Taiwan at

shorter lag lengths.

Singapore and Thailand seem to respond slower with significant adjustments still present after

two weeks. However, this result still reinforces the existence of a noticeable relationship. As

postulated in this thesis, both the Philippines and Indonesia take the longest to return to

equilibrium. The Philippines takes over three weeks, while Indonesia displays significant

responses in the fourth week following the initial shock.


82

Markets in the PB appear to react differently to a shock originating from the Japanese market.

For all markets, the response to a shock originating form Japan is more quickly incorporated

than a shock from the US. The US displays the fastest convergence and the lowest volatility

with full convergence achieved in less than two weeks. . The results pertaining to Australia,

Hong Kong, Korea, Malaysia and New Zealand are not materially different from the

responses to a shock in the US. This finding provides evidence to support the proposition that

more developed markets are linked with one another. It might can argued that the Malaysian

economy is not as advanced as the other four markets discussed above, therefore how can this

finding be explained. The linkage between Malaysia and the US arises from the enormous

amount of capital investment US companies have contributed over the last thirty years. The

relationship with Japan extends from the dominance of the Japanese market in the Pacific-

Basin.

Singapore and Thailand exhibit faster reversion to a Japanese shock, considering all

significant adjustment occurs in less than two weeks. Both the Philippines and Indonesia,

display substantially faster convergence considering no significant corrections take place after

two weeks have passed. This evidence reveals that the markets of the Pacific-Basin

demonstrate a greater level of linkage with Japan than with the US.

The results for the pre-EA crisis period displayed in Figures 5.4c and 5.4d, uncovers some

intriguing results. Figure 5.4d shows the effect of a one standard deviation shock to the US

market. The major conclusions that can be reached from the impulse response functions are

that Hong Kong and Japan completely adjust in less than a week. Australia, Malaysia, New

Zealand, Singapore and Thailand also rank highly considering no significant reaction takes

place after week two. This result is an improvement over the full period result for Singapore

and Thailand. The Philippines displays markedly faster reversion, with only residual noise

present after week 3. No significant convergence takes place by Indonesia, Korea or Taiwan.

This finding is somewhat unexpected considering the strong cointegration results obtained for

Korea and Taiwan, but not for Indonesia that appears to be segmented from the US.

The impulse response functions generated by a shock to the Japanese market are presented in

Figure 5.4c. The results further provide evidence toward the suggestion that the Pacific-Basin


83

has a different relationship with Japan than it does with the US. Hong Kong, Malaysia, New

Zealand, Singapore, Thailand and the US all demonstrate an extremely high level of market

linkage with Japan considering no significant adjustment occurs past week one. Australia,

Korea and Taiwan follow suit and complete any necessary reversion by the end of week two.

In almost all cases, this marks a considerable improvement in the level of linkage compared to

the full period result. Indonesia and the Philippines do not display any significant adjustment

over all time horizons.

In the post-ea/pre-September 11 period the level of the speed of convergence for all markets

improves dramatically. The impulse response functions in relation to a shock in the US are

exhibited in Figure 5.4f. All markets besides Indonesia achieve full convergence in or less

than two weeks. Korea, Malaysia and Taiwan require special mention considering they show

no significant adjustments past week one. This is a noticeable improvement for Korea and

Taiwan whom in the pre- EA period did not display any significant response. Thus, it can be

argued that following the EA crisis, either US investors shifted their attention toward Korea

and Taiwan or vice versa. This result provides convincing evidence towards hypothesis 3A

and 3B, showing that in the period following the EA crisis the level and nature of market

linkage underwent an appreciable change. In opposition to this Indonesia does not

significantly respond to the shock in the US. That is, it appears that following the EA crisis

that Indonesia became even further segmented from the US.

The result of a shock to the Japanese market during this time is similar to that of the US

except for two differences. The first is that the speed of convergence to the shock in Japan is

slightly faster than that originating from the US. Secondly, unlike in the US case, Indonesia

responds quickly and significantly to a deviation in the Japanese market. Phylaktis and

Ravazzolo (2002), who found that the EA crisis reduced the level of global integration but

increased regional (i.e. the Pacific-Basin) integration, support this result.

Finally, the post-September 11 findings are outlined in Figures 5.4g and 5.4h. The obvious

result is the distinct increase in the level of market linkage concerning both markets. When

the US is shocked most markets experience significant reversion in week 1 and only some

noise after. The only other result worthy of mention is the lack of meaningful response by

Indonesia following a disturbance in the US. Hon et al (2004) find that post September 11


84

Indonesia has one of the lowest level of correlation (below 0.13) with the US, while on

average correlation increased between the US and all other Pacific-Basin markets.

The GIR functions for a shock to the Japanese market in the post-ea/pre-September 11period

can be viewed in Figure 5.4g. New Zealand and Taiwan demonstrate the highest level of

market linkage with Japan. The continuing speed at which New Zealand reverts to

equilibrium following a disturbance provides considerable evident to support the proposition

that New Zealand takes its lead from Japan and not necessarily Australia. Australia, Hong

Kong, Korea, Malaysia, Singapore and the US also quickly converge back to equilibrium

following the shock to the Japanese market. The interesting fact to note is that both Indonesia

and the Philippines display significant adjustment for up to two weeks. This finding is in stark

contrast from the result obtained from when the US is shocked and indicates that Indonesia

shifted away from being linked with world markets and became more reliant upon nations in

the Pacific-Basin. Therefore, it can be concluded that following September 11 the level of

market linkage experienced a substantial improvement.

These results provide overwhelming evidence to support hypothesis 2 that is the markets of

the Pacific-Basin are linked to one another in a short run fashion. In addition, the results

obtained form the generalized impulse response functions help to validate the findings of the

bivariate cointegration tests, often finding a significant relationship in both the short and long-

run. Furthermore, it provides evidence as to the structure of market linkage both pre and post

market disturbances. The evidence clearly supports hypothesis 3A and 3B. That is, not only

has the relationship between markets in the PB altered over time, but also following market

turmoil the level and structure of market linkage undergoes an evolution. Building on the

point further it is possible that the increase in market linkage in the post-ea/pre-September 11

period led to the events of September 11 not having such a significant impact because markets

had become more efficient and therefore the shock passed through faster. Possibly a more

credible argument is that, as suggested by the evidence and past empirical work that the PB is

not as strongly linked with the US as it is with itself.


85

5.5 Generalized Variance Decomposition

the generalized variance decomposition was completed using TSP. As discussed in section

3.3.3 the computations were completed for the full period and three sub-samples. The results

of the analysis can be observed in Panel A D of Table 5.5.

The results from all four periods provide some interesting findings. The results from the full

period show that Hong Kong, New Zealand, Singapore and the US strongly influence

Australia. Furthermore, at the 20-week horizon foreign markets explain 63% of the variance

in Australia. This evidence clearly displays the high level of market linkage between Australia

and the Pacific-Basin in the short run. Hong Kong is found to be even more exogenous than

Australia, with over 68% of its variance explained by other Pacific-Basin economies. As per

expectations, Australia is found to account for about 9% of the variance in Hong Kong, while

Singapore contributes around 12.75%. Malaysia, the Philippines and Thailand are also worthy

of mention, explaining 7.52%, 6.84% and 7.23% (at the 20-week horizon) respectively.

Indonesia is found to be highly endogenous accounting for 58% of its own variance. This

result complements the findings from the impulse response functions. Additional evidence to

support this conclusion comes from the fact that the Philippines contribute 40.5% to its own

variance.

On the other hand, Japan and the US are not as endogenous as would have been thought. At

the 20-week horizon, all other markets explain 54% of the variance for Japan and 55% for the

US. Several markets make material contributions to the variance of Japan; they include

Australia (6.63%), Hong Kong (6.89%), Korea (5.89%), Singapore (8.16%) and the US

(6.89%). For the US, Australia (10.84%), Hong Kong (7.47%), Japan (6.6%) and Singapore

(6.7%) are all important markets.

Malaysia and Singapore, like Australia and Hong Kong are highly exogenous, only

accounting for 37.75% and 31.19% of their own variances. Furthermore, the well-documented

relationship between Singapore and Malaysia is highlighted by the results. Singapore explains

10.94% of the variance in Malaysia, while Malaysia accounts for 8.67% of variance in


86

Singapore. In both markets, Hong Kong plays a critical role, while the contribution from

Thailand should not be underestimated49.

Korea and Taiwan display considerable endogeneity over the full period. Korea is responsible

for 45.59% of its variance, while the contribution of Taiwan to itself is an incredible 61.25%

after week 1 and 55.84% at week 20. Again, Hong Kong and Singapore are vital to both

markets, explaining some 9.34% and 8.43% in Korea and 5.89% and 6.04% in Taiwan

respectively.

The market of New Zealand is somewhat endogenous, while Thailand for a developing

market is quite exogenous. The only note worthy result pertaining to New Zealand is that

Australia is responsible for over 14% of the forecasted error variance. Hong Kong (9.21%),

Malaysia (8.92%), Singapore (8.13%) and Taiwan (11.19%) all considerably influence

Thailand.

The pre- EA results are similar to the generalized impulse response functions, that is, markets

are far more endogenous and not linked during this time. In terms of the GVD, it essentially

means that markets explain more of their own variance and less of each others. Australia

explains material levels of variance in Hong Kong (6.61%), Japan (5%), New Zealand

(13.14%) and the US (9.41%), while Hong Kong, Malaysia, new Zealand, Singapore and the

us are discussion all these levels are down compared to the full period results. Not

surprisingly, Australia accounts for more than 13% of the error variance for New Zealand.

Besides Australia, the only other country to explain any other material amount in New

Zealand is the US. The conclusion that can be drawn from these results is the effect of close

economic relationships on the level of market linkage. The four countries mentioned above

represent the major trading partners of one another in the pre-ea period.

The endogeneity of Indonesia is even further exacerbated in the pre EA sample. Even after

20 weeks, Indonesia still accounts for 69.26% of its own variance, while no other market

accounts for more than 5% and it explains no more than 3% in any other market. Similar to

Indonesia, Japan also demonstrates considerable independence. However, it is essential to

49 For Malaysia, Hong Kong explains 9.49%, while Thailand accounts for 9.22%. In the case of Singapore, Hong Kong contributes a staggering 12.93% and Thailand a considerable 8.86%.


87

note that during the period being examined that Japan at times was the largest equity market

in the world and therefore is expected to display a certain level of segmentation. This same

argument is also applicable to the US. However, unlike Japan significant contributions are

made by individual countries including Australia (9.41%), New Zealand (5.61%) and

Singapore (6.25%).

Both Korea and Taiwan display a distinct lack of integration, with each market contributing

over 59% of their own variance. The only country worthy of mention is Singapore, which has

the greatest level of explanatory power in both markets. The level of fragmentation restricts

the contributions made by Korea and Taiwan to other markets in the Pacific-Basin to below

5%.

Hong Kong is found to exert considerable influence over Malaysia, the Philippines, Singapore

and Thailand, while all four markets explain more than 5% of the forecasted error variance in

Hong Kong. In addition, it must be noted that the other four other markets are the largest

contributors to the variance decomposition of one another beside that explained by the

domestic market. The evidence supports the notion that these five markets form a bloc within

the PB.

As has been noted in the previous sections, the EA crisis caused a significant transformation

of relationships in the PB. This conclusion is further supported by the results outlined in table

5.5 panel C. The overriding finding is that markets explain considerably less of their own

variance and are explained noticeably more by foreign markets.

Some of the most compelling results from the post ea crisis include, the incredible change in

the Korean and Taiwan markets from being endogenous to being more exogenous than

several other markets. Korea established strong links with Australia, Hong Kong, Japan, the

Philippines, Singapore and most importantly the US. This result almost exactly mirrors the

relationship found to exist between Korea and the US from the impulse response functions

presented in section 5.4. Taiwan on the other hand demonstrates considerable influence over

Korea and Japan, while Hong Kong, Japan, Korea, the Philippines and Singapore all make

sizeable contributions towards the forecasted error variance of Taiwan.


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The proposed bloc between Hong Kong, Malaysia, the Philippines, Singapore and Thailand

seems to be weakened in the post EA period. The main change seems to be related to

Malaysia whose orientation moves away from the four markets towards Australia, Korea, new

Zealand and the US. However, the other four markets still display above average levels of

market linkage.

The level of independence shown by Japan and the US continued to decline, to such an extent

that they became more exogenous than other smaller markets in the PB. It is also interesting

to see that in the case of Japan that the US is the most important market, while for the US,

Japan ranks second behind Australia. Meanwhile, the strong relationship between Australia

and New Zealand continued to be evident.

The level of segmentation of Indonesia and the Philippines between the markets in the PB

decreased substantially. The level of integration of the Philippines increased to such an extent

that it is responsible for explaining more than 7% of the variation in 10 out of 11 other

markets. Indonesia does not display the same increase in market integration, but still

demonstrates significant improvement.

The other standout discovery is the reaction of Malaysia to the EA crisis. The amount of

variance explained by itself for the 1, 5 and 10-week horizon after a shock on average is

greater than that in the pre EA sample. Furthermore, as discussed above the Malaysian

market appeared to shift away from the group of markets including Hong Kong, the

Philippines, Singapore and Thailand and toward Korea and New Zealand. This could have

been the result of Malaysia introducing capital restrictions in the wake of the EA crisis, or a

structural shift in the economy or trading partners.

The post September 11 period highlights how the markets of the PB have become

increasingly dependent upon each other. For all 12 markets at the 20-week horizon the level

of forecast error variance in the domestic market, explained by the domestic market does not

exceed 24%. The credibility and significance of this result should not be underestimated. It

shows that the level of market linkage in the PB is not the result of a spurious correlation.

Instead, the increase in intensity can be traced back to a systematic reduction in barriers that

has led to immeasurable changes in the region. The results from this sample are extremely


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interesting considering that this thesis uses data for up to September 7, 2004, and therefore is

likely to highly relevant to the current market environment. All the figures referred to in the

analysis can be observed in table 5.5 Panel D.

The composition of markets affecting Australia post September 11 is markedly different

from that from previous periods. The importance of Hong Kong, New Zealand, and the US

have declined,50 while Japan, Korea, Singapore and Taiwan have assumed greater meaning51.

Hong Kong experienced a comparable shift in composition. Japan, Korea, Singapore and

Taiwan consolidated their positions as being markets of considerable importance to Hong

Kong, while Australia and the Philippines suffered a reduction in their level of influence52.

Following the events of September 11, a valid argument supposing that Indonesia is one of

the most exogenous markets in the PB. This conclusion appears to be in direct opposition

with the results from the GIR. However, a more careful inspection finds that both of the

results actually complement one another. The critical point to remember is that in the impulse

response functions only the Japanese and US markets were shocked. After examining the

figures from table 5.5 panel D, it is clear to see that the involvement of the US in Indonesia is

considerably lesser than that of Japan. This result supports the findings from the GIR

function, which found Indonesia to be linked with Japan, but not the US. Nearly identical

proof is found for the Philippines, that is, the variance decomposition shows a stronger

relationship with the US than with Japan, as per the GIR functions.

In the post-September 11 world, the effect of the US over markets in the PB is not as

influential as it once was. For all 11 cases, the level of forecast error variance explained by

the US does not surpass 10%, and only averages 5.94%. By contrast, the Japanese market

becomes highly influential, it describes over 10% of the forecast error variance in Australia,

Hong Kong, Korea, Malaysia, Singapore and Taiwan and averages over 7.5% for all markets.

50 The decrease in the amount of forecasted error variance explained by New Zealand in the post September 11 sample is around 10%, while for the US and Hong Kong around 1% depreciation is recorded. 51 The increase in the contribution attributable to Japan is around 4.5%, Korea 1.5%, Singapore 5.2% and Taiwan a substantial 6%. 52 The Philippines declined by more than 7%, while the decrease in Australias explanatory power was only marginal


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The markets of Korea and Taiwan continued to expand their influence over the PB region.

They both contain substantial explanatory for several markets and display a high level of

linkage between themselves. It is also interesting to note the connections concerning Taiwan

and Australia and the US. In the post-EA period, neither market exerted material influence

over Taiwan. However, post- September 11 and the relationship dramatically changed with

Australia accounting for 11.31% and the US 8.16% respectively. The combination of Korea

and Taiwan represents one of if not the most influential pairing in the PB following

September 11. this is extremely intriguing considering their beginnings as closed endogenous

markets. The findings that have been discovered for Taiwan supports the decision by the

Taiwan government to focus its attention toward the PB53.

Malaysias position in the PB following September 11, is one of a market that is considerably

influenced by the markets of Australia, Hong Kong, Japan, Singapore and Taiwan, while

displaying noteworthy explanatory power in the forecast error variance of Japan, Singapore,

Taiwan and the US. For New Zealand, September 11 greatly affected its relationships with

markets in the PB. The major role filled by Australia in the past was replaced by a

combination of Hong Kong, the Philippines, Singapore, Taiwan and the US.

Since the events of September 11, Singapore has continued to mature in its role as a market

leader in the PB. Singapore is found to be the second most exogenous market (only

marginally trailing Hong Kong) and one that accounts for substantial proportions of the

forecast error variance in nearly all markets, averaging of 9.5% of the variance for all 11 other

markets. Unexpectedly, the lowest level variance provided by Singapore is for Malaysia.

However, this result should not be that surprising for the following reason. Prior to the EA

crisis, the linkage between the markets was the strongest between any of the other 66 pairs.

Through the early-mid 1990s, a number of factors changed that drove a wedge between the

two. As the Malaysian economy became more sophisticated, it depended less upon Singapore,

subsequently their linking relationship diminished. The EA crisis then swept into Malaysia

and forced numerous reforms. The reforms attempted to restrict the level of integration of

Malaysia with the world. In addition, the level of market linkage has never reached the levels

prior to the EA crisis.

53 This is actually discussed in section 3.2


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When the results from the GVD are examined in an overall context, a credible and convincing

set of conclusions can be drawn. The first refers to H2, which postulates the existence of

market linkage in the short run. After conducting the four sets of tests, it can clearly be

observed that the degree of market linkage in the PB over the short-run is exceedingly high

(especially in the post September 11 period). Further evidence from the findings allow the

conclusion that the linking relationship has altered through time and once more, has

experienced an appreciable increase following a period of market turmoil.

5.6 Block Exogeneity Tests

Four sets of block exogeneity tests were completed, the results of which can be seen in Panel

A D of table 5.6. As discussed in previous sections, block exogeneity tests are just the

extension of bivariate Granger causality tests to a multivariate system. The presence of

Granger causality can be interpreted two different ways. The first is that if market A causes

market B it can be said that market A leads market B. The alternative explanation is that if

market A causes market B, then it can be concluded that market B is efficient as it reacts to all

available market information, including that of market A. The first option is likely to apply

when if the US is found to cause Thailand, while the second explanation is more likely if

Thailand is found to cause the US.

The main results that come from the exogeneity tests are that:

1. In the full period the smaller less developed markets of Indonesia, Malaysia, New

Zealand, the Philippines and Thailand all have returns caused by several different

markets. Also for all the countries mentioned above when all markets are excluded

from the equation the evidence suggests that the Pacific-Basin as a whole holds

information for these markets. It is also interesting to note that Japan, Korea and

Taiwan are caused by some markets. However, considering the size of the Japanese

market it is likely that these countries arent causing returns in Japan, rather than

Japan responding to the news from those markets.

2. In the pre EA period the very few cases of causality does not warrant discussion,

except for one surprising result that finds the US is significantly affected by the


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Pacific-Basin as a whole. This is likely to mean that in this period the US was

sensitive to information originating from the Pacific-Basin

3. Japan again is affected by several markets in the post EA similar to how it was in

the full period. Except this time Australia and the US are found to cause returns in

Japan. Even though Australia is a large developed market it would still is thought

that it does not cause returns in Japan. However, it would be argued that the US

does lead Japan in the post EA period. The other interesting point to make is that

seems that Indonesia is significantly influenced by Australia, Korea, Malaysia and

New Zealand.

4. The most interesting results from the block exogeneity results come from the post

September 11 period. Australia, Hong Kong, Japan and the US are all caused by

much smaller markets that just arent big enough to have a material effect over these

four markets. However, what it dies indicate is that Australia, Hong Kong, Japan

and the US have become more efficient post September 11 such that they react to

sources from information even from the smaller markets.

5.7 Regression Analysis

The cross sectional analysis from the OLS model is contained in panel A D of table 5.7.

Four different dependent variables are tested in the OLS model. Trace1 is the average trace

statistic from the weekly recursive tests with 10 lags, while Trace2 is from the test with 30

lags. Eigen1 and Eigen2 are the eigen values from the respective tests. For the probit model,

the values are restricted to take a value of either 1 or 0. From the tests for when p = 10,

Australia and Hong Kong was found to be cointegrated 0 times. The average number of

cointegrating vectors discovered over the 66 pairs was 58.3. Therefore, in this situation the

pair between Australia and Hong Kong would get a value of 0. That is, a value of 1 is given

when the number of cointegrating vectors for a pair exceeds the average number of

cointegrating vectors over the 66 pairs. This method is used to compute Pr1 and Pr2 (is based

on tests when there are 30 lags).

In three out of four cases, the ASEAN dummy is positive and significant, while in two cases

the interest rate trace is found to be negative and significant. A negative coefficient for the

interest rate trace statistic is unexpected. Location is found to be highly significant in all four


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tests, and displays positive correlation with market linkage instead of the predicted negative

relation. This result can be explained quite easily, after examining the data the longest

distances between markets always is the distance between themselves and the US. Since the

US is the largest market in the world, and as discovered in the previous section is open to

other markets, then it would be expected that there is high market linkage between the

countries of the PB and the US, thus biasing this variable upward.

The variable sdiff is found to be strongly negative in all the regressions. The discussion

regarding the construction of this variable in chapter 4, predicted that a negative relationship

should be discovered. The turnover ratio is also found to be significant and negative as

predicted in an earlier section. The size dummies, display mixed results. From a theoretical

perspective the dummies both are as expected. That is, the large dummy has a positive

(although not significant) coefficient while the small group is negative. The results from these

variables demonstrate the importance of market development regarding market linkage. The

OECD dummy was found not to be significant in any model and appears to not be fitting with

the other variables. Therefore, in the multivariate tests it was not included.

Unexpectedly the industrial production trace is found to be negative and only significant in

one regression. This result is likely for the inability of the proxy to properly capture the

information it was supposed to. Keeping in mind that this section is highly experimental then

any results serve as an interesting platform for further work to launch from. In contrast to the

contagion hypothesis the volatility between markets is likely to cause a decline in the level of

market linkage volatility gets higher. This indicates that investors will try to stay away from

markets that are characterized by high volatility. Only the probit results for the multivariate

model is discussed, because of the immaterial difference with the OLS results54. Six

regressions were conducted for pr1 and pr2, an example of the output is provided below:

Extract of table 5.7 pr1 intercept aseand exvol iptrace irtrace location vol ldum sdum

coefficient -3.010 1.956 -4.423 -0.044 0.059 0.000 885.111 0.857 -0.198 t-stat -2.124 2.763** -0.009 -1.504 1.509 2.940** 1.135 1.716 -0.390

std err 1.417 0.708 516.633 0.029 0.039 0.000 779.679 0.500 0.507 std dev 0.361 0.001 9.023 7.790 4670.089 0.001 0.422 0.422

% ∆ 0.707 -0.002 -0.395 0.459 1.214 0.474 0.362 -0.084 R² 0.432

54 The results are available upon request.


94

pr1 intercept aseand exvol iptrace irdiff Location vol ldum sdum coefficient -1.230 2.011 691.121 -0.015 -0.075 0.000 106.723 0.680 0.184

t-stat -1.199 2.843** 0.897 -0.606 -0.756 2.674** 0.156 1.458 0.400 std err 1.026 0.707 770.853 0.025 0.100 0.000 685.361 0.466 0.460 std dev 0.361 0.001 9.023 7.790 4670.089 0.001 0.422 0.422

% ∆ 0.726 0.387 -0.136 -0.586 0.782 0.057 0.287 0.078 R² 0.408

Notes: The results above are produced from a probit regression where pr1 is the dependent variable and the independent variables are the same as those described in section 3.5. The t-stats have 55 degrees of freedom and at the 95% level the critical value is 2.0040 and 1.673 at the 90% level.

The full results are contained in table 5.7. In both the OLS and probit models the ASEAN

dummy was found to be postivie and significant. This result is credible as trade and economic

integration is likely to substantially affect the level of market linkage. Excahnge rate volatility

is found to be significant in eight of the OLS regressions, but not in the probit model. The

sign is almost always found to be positive. This indicates that the level of linkage increases in

the face of higher exchange rate volatility. As argued in chapter 3, greater exchange rate

volatility is likely to result in greater levels of trade and capital flowing between countries.

The trace statistic from the cointegration tests on the industrial production statistics is found

not to be significant in any of the cases. Again, this relationship can be attributed to the proxy

for goods market linkage, that is, the trace statistic appears not to be an appropriate proxy.

From the OLS regression the interest differential was significant in the majority of cases and

negative in all cases. This result provides evidence to support the PVM proposition, that is, a

higher differential will lead to a greater differential in prices and therefore reduce the level of

market linkage between markets. Location like the ASEAN dummy is one of the most

significant variables. Again, the slope is always positive. However, as discussed above this

result can be easily accounted for. The large market dummy was significant and positive in a

number of cases. This result is as per the predictions of this thesis. That is, the larger markets

are linked to one another, but display lower levels of linkage with the smaller markets. The

small market dummy again was found to be significant in the bulk of cases and the coefficient

was negative. This result was also not surprising as it would be thought that the small markets

might be linked with a medium to large country, but not another developing market.

While it appears that the regression results are not persuasive, the adjusted R squared provides

evidence to the contrary. In most regressions (both the OLS and probit) the R squared


95

exceeded 40%, indicating that the factors may not be significant individually, they still have

considerable explanatory power. Lastly, examining the %∆ statistic gives an indication of the

economic significance of the variable. Location is found to be the most economically

significant variable. The ASEAN dummy also was economically significant, while the

interest rate trace and interest rate differential were alos found to be economically significant.

The remainder of the results is too erratic to be able to accurately interpret. However, it must

be noted that the testing procedures and proxies employed in this section were completely

original and experimental. The magnitude of the R squared statistics does still indicate that

the model is capturing a considerable amount of variation in market linkage.

CHAPTER 6–CONCLUSION

96

CHAPTER 6: Conclusion

6.1 Contribution

As per the discussion in section 1.3, this thesis aimed to add to the current academic literature

in three aspects:

1. Are the equity markets of the Pacific-Basin region linked to one another in either the

short or long run;

2. If so, has this relationship varied over time; and

3. Which variables have been responsible for markets becoming more integrated with

one another

The actual contribution made regarding the first proposition is the undeniable level of

evidence produced that unequivocally establishes that the equity markets of the Pacific-Basin

demonstrate a certain level of interaction in both the short and long run. The use of recursive

bivariate cointegration provides the most accurate and complete picture of the structure of the

market linkage in the Pacific-Basin constructed to date. Furthermore, the results from this

thesis are made even more compelling due to all test statistics being adjusted by a small

sample bias correction. Therefore, the results generated by this thesis are extremely robust

and allows credible conclusions to be drawn from them. . The use of three methods to

establish the short run market linkage ensures a complete analysis of the subject.

Like the first proposition, this thesis provides overwhelming evidence that the level of market

linkage varies through time. The recursive bivariate tests provides some of the most

persuasive evidence in regard to the current literature. The use of the dummy variable analysis

in regard to the Pacific-Basin markets has not previously been employed. The inclusion of

this methodology was validated by the results produced from it. The persistent profile

analysis showed that the speed at which prices in the Pacific-Basin converge back to

equilibrium is significantly higher in the post- EA crisis period. All three short run measures

for substantial evidence to suggest the relationship has altered over time and in fact grown

stronger.

The evidence presented in relation to the final proposition was not as strong as that provided

for propositions 1 or 2. However, it still did make several contributions that could be used to


97

guide future research. First off it must be noted that the testing procedure implemented in this

thesis is completely original and experimental. That is, no previous works have used trace

staitistics or eigenvalues to proxy for the level of market linkage. Secondly, the applicaiton of

a probit model has not previously been used or propsed in the context of marklet linkage.

While some of the independent variables in this study were influenced by other pieces fo

literature, ultimately they too are unique to this study. Considering, the level of

experimentation surrounding this final proposition the fact that outstanding results were not

obtained should not come as a surprise.

6.2 Limitations

While the section above outlined the strengths of the results obtained in this thesis, there still

exists ample room for improvement in this study. The first and probably most obvious

limitation of this study was using weekly prices over a sixteen-year period. After completing

this thesis, it has been made obvious that when considering cointegration weekly data is

acceptable, but daily data is preferred. The use of daily data would have enabled an even more

robust examination of market linkage in the Pacific-Basin. The effect of having a small

sample severely limited the use of multivariate cointegration tests.

While this thesis presented a number of propositions regarding the direction of the

cointegration between markets it is not formally tested by imposing restrictions upon the

cointegrating vector. A detailed examination on the cointegrating vectors would be quite

interesting.

This thesis did not explicitly consider the impact of other major market disturbances, such as

the Russian default, both Gulf Wars, or effect of the Y2K bug. It is possible that it was one

of these events driving the results rather than those presented in this thesis. Furthermore, the

results pertaining to September 11 could be underestimated because there is only three years

of data available.

There are a number of other variables that could be employed in the final analysis that could

be more important than the variables proposed in this study. Other variables that could be


98

included are trade and capital flow statistics, sovereign debt ratings, level of unemployment or

inflation. These are also many others could be included.

6.3 Further Research

The area of market linkage has been extensively studied in the current literature; however

there still exist a number of areas that require further examination. The first is the inability of

this thesis and the current literature to explain the level of market linkage between national

equity indices. The question as to what causes market linkage is an important question and

deserves further attention.

Some of the limitations discussed above also open up new avenues for future research. The

identification of the cointegrating vectors would constitute a significant development in the

field. While the use of OLS and probit is adequate, there are other more robust methods such

as GMM that could be employed.

There exists a unique chance at the moment to be able to study the integration of the Chinese

market into the world economy. Considering, the size and importance of China, it would be

interesting to see how it merges into the Pacific-Basin.

99

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Figure 5.2.2 All sixty six graphs of the weekly recursive bivariate cointegration tests

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100 200 300 400

HKID

105

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

50 100 150 200 250 300 350 400 450

HKJP

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

50 100 150 200 250 300 350 400 450

HKKO

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

50 100 150 200 250 300 350 400 450

HKMY

0.0

0.4

0.8

1.2

1.6

2.0

2.4

50 100 150 200 250 300 350 400 450

HKNZ

0.2

0.4

0.6

0.8

1.0

1.2

1.4

50 100 150 200 250 300 350 400 450

HKPP

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

50 100 150 200 250 300 350 400 450

HKSG

106

0.0

0.4

0.8

1.2

1.6

2.0

50 100 150 200 250 300 350 400 450

HKTA

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

50 100 150 200 250 300 350 400 450

HKTH

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

50 100 150 200 250 300 350 400 450

HKUS

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

50 100 150 200 250 300 350 400 450

IDJP

0.0

0.2

0.4

0.6

0.8

1.0

1.2

50 100 150 200 250 300 350 400 450

IDKO

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

50 100 150 200 250 300 350 400 450

IDMY

107

0.0

0.4

0.8

1.2

1.6

2.0

50 100 150 200 250 300 350 400 450

IDNZ

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

50 100 150 200 250 300 350 400 450

IDPP

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

50 100 150 200 250 300 350 400 450

IDTA

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

50 100 150 200 250 300 350 400 450

IDSG

0.2

0.4

0.6

0.8

1.0

1.2

1.4

50 100 150 200 250 300 350 400 450

IDTH

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

50 100 150 200 250 300 350 400 450

IDUS

108

.2

.3

.4

.5

.6

.7

.8

.9

50 100 150 200 250 300 350 400 450

JPKO

0.0

0.2

0.4

0.6

0.8

1.0

1.2

50 100 150 200 250 300 350 400 450

JPMY

0.2

0.4

0.6

0.8

1.0

1.2

1.4

50 100 150 200 250 300 350 400 450

JPNZ

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

50 100 150 200 250 300 350 400 450

JPPP

0.2

0.4

0.6

0.8

1.0

1.2

1.4

50 100 150 200 250 300 350 400 450

JPSG

0.2

0.4

0.6

0.8

1.0

1.2

1.4

50 100 150 200 250 300 350 400 450

JPTA

109

0.2

0.4

0.6

0.8

1.0

1.2

50 100 150 200 250 300 350 400 450

JPTH

0.4

0.6

0.8

1.0

1.2

1.4

1.6

50 100 150 200 250 300 350 400 450

JPUS

0.0

0.4

0.8

1.2

1.6

2.0

50 100 150 200 250 300 350 400 450

KOMY

0.0

0.2

0.4

0.6

0.8

1.0

1.2

50 100 150 200 250 300 350 400 450

KONZ

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

50 100 150 200 250 300 350 400 450

KOPP

.2

.3

.4

.5

.6

.7

50 100 150 200 250 300 350 400 450

KOSG

110

.2

.3

.4

.5

.6

.7

.8

50 100 150 200 250 300 350 400 450

KOTA

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

50 100 150 200 250 300 350 400 450

KOTH

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

50 100 150 200 250 300 350 400 450

KOUS

0.2

0.4

0.6

0.8

1.0

1.2

1.4

50 100 150 200 250 300 350 400 450

MYNZ

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

50 100 150 200 250 300 350 400 450

MYPP

0.4

0.6

0.8

1.0

1.2

1.4

1.6

50 100 150 200 250 300 350 400 450

MYSG

111

0.0

0.4

0.8

1.2

1.6

2.0

2.4

50 100 150 200 250 300 350 400 450

MYTA

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

50 100 150 200 250 300 350 400 450

MYTH

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

50 100 150 200 250 300 350 400 450

MYUS

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

50 100 150 200 250 300 350 400 450

NZPP

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

50 100 150 200 250 300 350 400 450

NZSG

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

50 100 150 200 250 300 350 400 450

NZTA

112

0.0

0.4

0.8

1.2

1.6

2.0

50 100 150 200 250 300 350 400 450

NZTH

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

50 100 150 200 250 300 350 400 450

NZUS

0.0

0.4

0.8

1.2

1.6

2.0

2.4

50 100 150 200 250 300 350 400 450

PPSG

0.0

0.4

0.8

1.2

1.6

2.0

50 100 150 200 250 300 350 400 450

PPTA

0.2

0.4

0.6

0.8

1.0

1.2

1.4

50 100 150 200 250 300 350 400 450

PPTH

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

50 100 150 200 250 300 350 400 450

PPUS

113

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

50 100 150 200 250 300 350 400 450

SGTA

0.2

0.4

0.6

0.8

1.0

1.2

50 100 150 200 250 300 350 400 450

SGTH

0.2

0.4

0.6

0.8

1.0

1.2

1.4

50 100 150 200 250 300 350 400 450

SGUS

0.2

0.4

0.6

0.8

1.0

1.2

50 100 150 200 250 300 350 400 450

TATH

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

50 100 150 200 250 300 350 400 450

TAUS

0.2

0.4

0.6

0.8

1.0

1.2

1.4

50 100 150 200 250 300 350 400 450

THUS

114

Figure 5.3a Persistence Profile for the Full period

0

0.2

0.4

0.6

0.8

1

1.2

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101

Weekly Horizons

Pers

iste

nce

Prof

ile

Full Period CV1 Full Period CV2 Full Period CV3

Figure 5.3bPersistence Profile for the Pre-East Asian crisis preiod

0

0.2

0.4

0.6

0.8

1

1.2

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101

Weekly Horizons

Pers

iste

nce

Prof

ile

Pre-Asian Crisis CV1 Pre-Asian Crisis CV2

115

Figure 5.3c - Persistence Profile for the Post East Asian crisis period

0

0.2

0.4

0.6

0.8

1

1.2

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101

Weekly Horizons

Pers

iste

nce

Prof

ile

Post-Asian Crisis CV1 Post-Asian Crisis CV2

Figure 5.3d Persistence Profile for Cointegrating Vector 1 for all periods

0

0.2

0.4

0.6

0.8

1

1.2

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101

Weekly Horizons

Pers

iste

nce

Prof

ile

Full Period CV1 Pre-Asian Crisis CV1 Post-Asian Crisis CV1

116

Figure 5.3e Persistence Profile for Cointegrating Vector 2 for all periods

0

0.2

0.4

0.6

0.8

1

1.2

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101

Weekly Horizons

Pers

iste

nce

Prof

ile

Full Period CV2 Pre-Asian Crisis CV2 Post-Asian Crisis CV2

117

-.004

-.002

.000

.002

.004

.006

.008

.010

2 4 6 8 10 12 14 16 18 20

Response of Australia

-.008

-.004

.000

.004

.008

.012

.016

.020

2 4 6 8 10 12 14 16 18 20

Response of Hong Kong

-.004

.000

.004

.008

.012

2 4 6 8 10 12 14 16 18 20

Response of Indonesia

-.01

.00

.01

.02

.03

.04

2 4 6 8 10 12 14 16 18 20

Response of Japan

-.010

-.005

.000

.005

.010

.015

.020

2 4 6 8 10 12 14 16 18 20

Response of Korea

-.004

.000

.004

.008

.012

.016

2 4 6 8 10 12 14 16 18 20

Response of Malaysia

-.004

-.002

.000

.002

.004

.006

.008

.010

2 4 6 8 10 12 14 16 18 20

Response of New Zealand

-.004

.000

.004

.008

.012

2 4 6 8 10 12 14 16 18 20

Response of the Philippines

-.004

.000

.004

.008

.012

.016

2 4 6 8 10 12 14 16 18 20

Response of Singapore

-.004

.000

.004

.008

.012

.016

2 4 6 8 10 12 14 16 18 20

Response of Taiwan

-.008

-.004

.000

.004

.008

.012

.016

2 4 6 8 10 12 14 16 18 20

Response of Thailand

-.004

-.002

.000

.002

.004

.006

.008

.010

2 4 6 8 10 12 14 16 18 20

Response of the US

Figure 5.4a :Generalized impulse response functions to a one standard deviation shock in the Japanese market based on the full sample period

118

-.004

.000

.004

.008

.012

2 4 6 8 10 12 14 16 18 20


-.005

.000

.005

.010

.015

.020

2 4 6 8 10 12 14 16 18 20


-.008

-.004

.000

.004

.008

.012

2 4 6 8 10 12 14 16 18 20


-.004

.000

.004

.008

.012

.016

2 4 6 8 10 12 14 16 18 20

Response of Japan

-.008

-.004

.000

.004

.008

.012

.016

2 4 6 8 10 12 14 16 18 20

Response of Korea

-.005

.000

.005

.010

.015

2 4 6 8 10 12 14 16 18 20


-.004

-.002

.000

.002

.004

.006

.008

.010

2 4 6 8 10 12 14 16 18 20


-.005

.000

.005

.010

.015

2 4 6 8 10 12 14 16 18 20


-.004

.000

.004

.008

.012

.016

2 4 6 8 10 12 14 16 18 20


-.008

-.004

.000

.004

.008

.012

.016

2 4 6 8 10 12 14 16 18 20

Response of Taiwan

-.008

-.004

.000

.004

.008

.012

.016

2 4 6 8 10 12 14 16 18 20


-.010

-.005

.000

.005

.010

.015

.020

.025

2 4 6 8 10 12 14 16 18 20

Response of the US

Figure 5.4b : Generalized impulse response functions to a one standard deviation shock in the US market based on the full sample period

119

-.004

-.002

.000

.002

.004

.006

.008

2 4 6 8 10 12 14 16 18 20


-.008

-.004

.000

.004

.008

.012

2 4 6 8 10 12 14 16 18 20


-.008

-.004

.000

.004

.008

.012

2 4 6 8 10 12 14 16 18 20


-.01

.00

.01

.02

.03

.04

2 4 6 8 10 12 14 16 18 20

Response of Japan

-.008

-.004

.000

.004

.008

.012

2 4 6 8 10 12 14 16 18 20

Response of Korea

-.008

-.004

.000

.004

.008

.012

2 4 6 8 10 12 14 16 18 20


-.008

-.004

.000

.004

.008

.012

2 4 6 8 10 12 14 16 18 20


-.008

-.004

.000

.004

.008

.012

2 4 6 8 10 12 14 16 18 20


-.004

-.002

.000

.002

.004

.006

.008

.010

2 4 6 8 10 12 14 16 18 20


-.012

-.008

-.004

.000

.004

.008

.012

2 4 6 8 10 12 14 16 18 20

Response of Taiwan

-.010

-.005

.000

.005

.010

2 4 6 8 10 12 14 16 18 20


-.004

-.002

.000

.002

.004

.006

2 4 6 8 10 12 14 16 18 20

Response of the US

Figure 5.4c :Generalized impulse response functions to a one standard deviation shock in the Japanese market based on the pre-EA sample period

120

-.004

.000

.004

.008

2 4 6 8 10 12 14 16 18 20


-.008

-.004

.000

.004

.008

.012

2 4 6 8 10 12 14 16 18 20


-.012

-.008

-.004

.000

.004

.008

.012

2 4 6 8 10 12 14 16 18 20


-.004

.000

.004

.008

2 4 6 8 10 12 14 16 18 20

Response of Japan

-.008

-.004

.000

.004

.008

2 4 6 8 10 12 14 16 18 20

Response of Korea

-.008

-.004

.000

.004

.008

.012

2 4 6 8 10 12 14 16 18 20


-.004

.000

.004

.008

.012

2 4 6 8 10 12 14 16 18 20


-.008

-.004

.000

.004

.008

.012

.016

2 4 6 8 10 12 14 16 18 20


-.004

.000

.004

.008

2 4 6 8 10 12 14 16 18 20


-.012

-.008

-.004

.000

.004

.008

.012

.016

2 4 6 8 10 12 14 16 18 20

Response of Taiwan

-.008

-.004

.000

.004

.008

.012

.016

2 4 6 8 10 12 14 16 18 20


-.004

.000

.004

.008

.012

.016

.020

2 4 6 8 10 12 14 16 18 20

Response of the US

Figure 5.4d :Generalized impulse response functions to a one standard deviation shock in the US market based on the pre-EA sample period

121

-.012

-.008

-.004

.000

.004

.008

.012

.016

2 4 6 8 10 12 14 16 18 20


-.03

-.02

-.01

.00

.01

.02

.03

.04

2 4 6 8 10 12 14 16 18 20


-.03

-.02

-.01

.00

.01

.02

.03

2 4 6 8 10 12 14 16 18 20


-.02

-.01

.00

.01

.02

.03

.04

2 4 6 8 10 12 14 16 18 20

Response of Japan

-.03

-.02

-.01

.00

.01

.02

.03

.04

2 4 6 8 10 12 14 16 18 20

Response of Korea

-.02

-.01

.00

.01

.02

.03

2 4 6 8 10 12 14 16 18 20


-.012

-.008

-.004

.000

.004

.008

.012

2 4 6 8 10 12 14 16 18 20


-.02

-.01

.00

.01

.02

.03

2 4 6 8 10 12 14 16 18 20


-.01

.00

.01

.02

2 4 6 8 10 12 14 16 18 20


-.02

-.01

.00

.01

.02

.03

2 4 6 8 10 12 14 16 18 20

Response of Taiwan

-.03

-.02

-.01

.00

.01

.02

.03

2 4 6 8 10 12 14 16 18 20


-.012

-.008

-.004

.000

.004

.008

.012

.016

.020

2 4 6 8 10 12 14 16 18 20

Response of the US

Figure 5.4e :Generalized impulse response functions to a one standard deviation shock in the Japanese market based on the post-EA sample period

122

-.010

-.005

.000

.005

.010

.015

.020

2 4 6 8 10 12 14 16 18 20


-.03

-.02

-.01

.00

.01

.02

.03

.04

2 4 6 8 10 12 14 16 18 20


-.03

-.02

-.01

.00

.01

.02

.03

2 4 6 8 10 12 14 16 18 20


-.01

.00

.01

.02

2 4 6 8 10 12 14 16 18 20

Response of Japan

-.04

-.03

-.02

-.01

.00

.01

.02

.03

.04

2 4 6 8 10 12 14 16 18 20

Response of Korea

-.03

-.02

-.01

.00

.01

.02

.03

2 4 6 8 10 12 14 16 18 20


-.015

-.010

-.005

.000

.005

.010

.015

2 4 6 8 10 12 14 16 18 20


-.03

-.02

-.01

.00

.01

.02

.03

2 4 6 8 10 12 14 16 18 20


-.02

-.01

.00

.01

.02

.03

2 4 6 8 10 12 14 16 18 20


-.02

-.01

.00

.01

.02

2 4 6 8 10 12 14 16 18 20

Response of Taiwan

-.03

-.02

-.01

.00

.01

.02

.03

2 4 6 8 10 12 14 16 18 20


-.02

-.01

.00

.01

.02

.03

.04

2 4 6 8 10 12 14 16 18 20

Response of the US

Figure 5.4f :Generalized impulse response functions to a one standard deviation shock in the US market based on the post-EA sample period

123

-.016

-.012

-.008

-.004

.000

.004

.008

.012

.016

2 4 6 8 10 12 14 16 18 20


-.03

-.02

-.01

.00

.01

.02

.03

2 4 6 8 10 12 14 16 18 20


-.04

-.03

-.02

-.01

.00

.01

.02

.03

.04

2 4 6 8 10 12 14 16 18 20


-.04

-.03

-.02

-.01

.00

.01

.02

.03

.04

2 4 6 8 10 12 14 16 18 20

Response of Japan

-.06

-.04

-.02

.00

.02

.04

.06

2 4 6 8 10 12 14 16 18 20

Response of Korea

-.02

-.01

.00

.01

.02

.03

2 4 6 8 10 12 14 16 18 20


-.016

-.012

-.008

-.004

.000

.004

.008

.012

.016

2 4 6 8 10 12 14 16 18 20


-.04

-.03

-.02

-.01

.00

.01

.02

.03

.04

2 4 6 8 10 12 14 16 18 20


-.03

-.02

-.01

.00

.01

.02

.03

2 4 6 8 10 12 14 16 18 20


-.06

-.04

-.02

.00

.02

.04

.06

2 4 6 8 10 12 14 16 18 20

Response of Taiwan

-.05

-.04

-.03

-.02

-.01

.00

.01

.02

.03

.04

2 4 6 8 10 12 14 16 18 20


-.04

-.03

-.02

-.01

.00

.01

.02

.03

2 4 6 8 10 12 14 16 18 20

Response of the US

Figure 5.4g :Generalized impulse response functions to a one standard deviation shock in the Japanese market based on the post-September 11 sample period

124

-.020

-.015

-.010

-.005

.000

.005

.010

.015

2 4 6 8 10 12 14 16 18 20


-.03

-.02

-.01

.00

.01

.02

.03

2 4 6 8 10 12 14 16 18 20


-.04

-.03

-.02

-.01

.00

.01

.02

.03

.04

2 4 6 8 10 12 14 16 18 20


-.04

-.03

-.02

-.01

.00

.01

.02

.03

.04

2 4 6 8 10 12 14 16 18 20

Response of Japan

-.04

-.03

-.02

-.01

.00

.01

.02

.03

.04

2 4 6 8 10 12 14 16 18 20

Response of Korea

-.03

-.02

-.01

.00

.01

.02

.03

2 4 6 8 10 12 14 16 18 20


-.015

-.010

-.005

.000

.005

.010

.015

2 4 6 8 10 12 14 16 18 20


-.04

-.03

-.02

-.01

.00

.01

.02

.03

2 4 6 8 10 12 14 16 18 20


-.03

-.02

-.01

.00

.01

.02

.03

2 4 6 8 10 12 14 16 18 20


-.05

-.04

-.03

-.02

-.01

.00

.01

.02

.03

.04

2 4 6 8 10 12 14 16 18 20

Response of Taiwan

-.04

-.03

-.02

-.01

.00

.01

.02

.03

.04

2 4 6 8 10 12 14 16 18 20


-.04

-.03

-.02

-.01

.00

.01

.02

.03

.04

2 4 6 8 10 12 14 16 18 20

Response of the US

Figure 5.4h :Generalized impulse response functions to a one standard deviation shock in the US market based on the post-September 11 sample period

Grant Colthup Thesis

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