Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
Investigating Financial Contagion as a Prelude to its
Mitigation: Australia and its Key Trading Partners
Sandra Mukulu*, Samanthala Hettihewa*† and Christopher S. Wright***
The incidence and virulence of global and regional financial
crises has encouraged analysts to re-examine extant
economic policies and tools. An issue of concern is how
beneficial financial links can turn malignant and transmit a
financial crisis across borders. This study explores financialcontagion processes in financial markets with an emphasis on
Australia. Monthly data from Jul/97-Jun/14 was obtained from
Yahoo Finance for the composite-stock-market indices of
Australia and its key trading partners. Eviews7 software was
used to explore the relationship between stock indices of
those countries via co-integration and pair-wise Granger
Causality (GC) tests. This study found that stock returns in
Australia are co-integrated with returns of the other countries.
However, when appropriate lagged variables were used, only
past values of returns in the Chinese, Singapore and US
stock-indices have predictive value in terms of future
Australian-stock-index values. This study should be of interest
to policy makers, especially in terms of its suggestion of the
flow-on effects of sharp discontinuities in the Chinese,
Singapore and US stock markets on the Australian stock
market. Bilateral trade links appear to be a key linkage in the
transmission of financial contagion. However, more research
is needed to identify and understand other factors that
contribute to the spread of financial crises.
Introduction
The recent global financial crisis (GFC) enhanced interest among financial
researchers on the spread of financial crisis across borders and on finding
appropriate policy stances to mitigate such contagion. In particular, there is interest
as to how and why some nations are vulnerable to financial contagion, whereas,
others appear to be more immune. Kaminsky and Reinhart (2000, p.51) define
contagion as the process by which ―…financial difficulties spread from one economy
to another in the same region and beyond‖ via trade and financial linkages. However,
Caramazza et al. (2004) note that, because countries tend to concurrently establish
regional trade agreements and the interbank linkages, it is difficult to determine if
financial contagion is mostly due mostly to financial or to trade links. However,
empirical studies on linkages may help develop early-warning indicators to facilitate
_________________
† Contact Author, Faculty of Business, Federation University, PO Box 663, Ballarat, Victoria
3353, Australia; Email: s.hettihewa@ballarat.edu.au; Tel: +61 3 5327 9158
* Faculty of Business, Federation University, Ballarat, Victoria
** Higher Education Faculty, Holmes Institute, Melbourne, Victoria
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Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
timely intervention to ameliorate future crises. This empirical study seeks to examine
the role that inter-country linkages play in the spread of financial crises by focusing
on the notion that an understanding of financial linkages can explain why and how
financial crises spread across borders. Further, this study hypothesises that a
country is more likely to experience financial contagion from a country with which it
has extensive trade links. This study investigates Australian economy and its top
trading partners. The Department of Foreign Affairs and Trade (DFAT, 2012),
asserts that Australia’s top six bi-lateral trading partners in 2011 were (in order of
importance) China, Japan, US, Korea, Singapore and UK. Minerals are Australia’s
key export.
The rest of the paper is organised as follows: A literature review considering the role
of policy makers during a financial crisis is given first; an outline of the data selection,
research method and analysis and findings are discussed next; and The paper is
drawn to a close with limitations of the study and conclusion.
Literature Review
The Role of Policy Makers during a Financial Crisis
In the aftermath of a financial crisis, researchers often seek to understand what
cause a crisis, and, in the case of financial contagion 9 the 2007-09 ―Global Financial
Crisis‖ (GFC)), the contributing factors of how it spread. A common view is that
timely intervention might have avoided or minimized the resulting losses.
Information asymmetry
Generally, some level of information asymmetry is present in all financial markets
and limits the ability of investor to differentiate between profitable and non-profitable
securities (Mishkin, 1999). Edgar (2009) argues that a lack of accountability on the
part of the regulatory authorities may have created a toxic environment where
regulators could make decisions aimed at self-preservation as opposed to the
common good of society.
Systemic weakness
Dabrowski (2010) notes that, during the GFC, even though policy response was
delayed and poorly co-ordinated, systemic weaknesses were prevalent in European
banks that were over-leveraged. Regulators could address future vulnerability of the
European financial sector to contagion of a financial crisis by improving financialinstitution regulation and via timely policy implementation.
In the absence of systemic weaknesses, adaptive policies may minimise the effects
of a crisis on an economy (e.g. strict supervisory practices by the Australian
Prudential Regulation Authority’s (APRA) is a major reason why Australian banks
fared well during the GFC; Pais and Stork, 2011; Edwards (2010)). Also since 2003
Australia enjoyed a mining boom driven by China’s demand (Sykes, 2010). There is,
however, speculation that this mining boom has ended and that Australia may be
less immune to future financial crises or contagion. Thus, it may be prudent for
Australia to consider the potential for future contagion from its key trading partners.
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Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
Research Questions
This research aims to answer the following research questions:
a) Does the risk of financial contagion in equity markets reflect the trade linkages
that exist between Australia and its key trading partners?
b) Can past values of composite stock indexes of Australia’s key bilateral partners
be used to use to predict movements of the Australian composite stock index?
Data Selection, Research Methods, Analysis and Findings
Data Selection
Monthly data (Jul/97-Jan/14) for closing prices of the All Ordinaries (Australia), Hang
Seng (China), S&P500 (United States), Kospi (South Korea), Nikkei 225 (Japan),
Straits Time (Singapore) and FTSE 100 (United Kingdom) stock indexes were
obtained from the Yahoo finance database. All stock time series are transformed to
natural logarithms and the summary statistics of the transformed indexes are in
Table 1.
INSERT TABLE 1 HERE
This study also considers a subset of the entire dataset ranging from Jan/00-Jan/14
(see Table 2). The second data set excludes the 1997-1999 Asian crisis that
originated in Thailand and subsequently affected neighbouring countries. Statistical
analysis is performed using the Eviews7 econometric package.
INSERT TABLE 2 HERE
Research Methods
The main objective of this study is to explore causal relationships between stock
indexes of Australia and its key trading partners. This section discusses the Granger
causality procedure and briefly highlights the empirical tests required prior to
conducting Granger causality tests.
Granger causality was first introduced by Granger (1969) who argued that it occurs
when past values of one series ( ) can be used to predict the current value of
another series ( ).
is said to Granger cause
if it contains information that
predicts series
and vice versa. The nature of causality may be unidirectional or
bidirectional. Granger causality relationships between economic or financial
variables can form the basis for risk management (e.g. a study on causal
relationships between world oil and agricultural commodity prices could reveal that
causal relationships exist; Nazlioglu and Soytas, 2012).
Granger-causality tests check for existence of short-run causal relationships
between two series using bivariate vector autoregressive (VAR) models. The
structure of bivariate VAR models is formulated based on the properties of the
individual series and the co-integrative relationship between the two series of
interest. A stationary series has mean reverting tendencies and contains no unit root
while a non-stationary series follows a random walk and contains a unit root. This
study uses the standard Augmented Dickey Fuller (ADF) tests developed by Dickey
and Fuller (1979) to check for the presence of a unit root (Engle and Granger, 1987).
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Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
The second step of Granger causality testing involves checking whether variables
are co-integrated. The use of non-stationary variables that are integrated of the
same order and yet are not co-integrated could result in spurious causality
regressions (Dakurah and Sampath, 2001) Granger and Newbold (1974). We
discuss two well accepted co-integration tests—the Engle and Granger (1987)
technique and the Johansen framework (Johansen, 1988; Johansen, 1991)—and
explain why we opted for the use of the latter
Granger (1969) recommends that when two series are level stationary (meaning they
are integrated of order zero I(0)), the Granger causality relationship can be tested
using the bivariate vector autoregressive (VAR) model in equation (1) and (2).
∑
∑
∑
∑
Eqn (1)
Eqn (2)
Where
and
represent logged stock indexes at the level,
and
are the ith lagged coefficients of stock index
and
respectively,
and
are the
constant terms and
and
are the error terms of the estimated VAR models. It
was found that that
Granger causes
if any
is not equal to zero and
Granger causes
if any
is not equal to zero. Thus, if all
and
are equal to
zero no causal relationship exists between
and .
According to Granger et al. (2000), if two series are not stationary and are not cointegrated, the bivariate VAR model for Granger causality test should be specified
using the differenced form of the series as shown in equations (3) and (4), where is
the first difference operator for the logged time series.
∑
∑
∑
∑
Eqn (3)
Eqn (4)
Furthermore, if two variables are non-stationary at the level yet co-integrated, Engle
and Granger (1987) recommend the inclusion of an error correction term (ECT) to
equation (3) and (4) to avoid model misspecification. The resultant error correction
model (ECM) is as shown in equation (5) and (6), where
and
are the
coefficients for the error correction term for series
and
respectively.
and
are the lagged error correction terms for equation (5) and (6)
respectively.
∑
∑
∑
∑
Eqn (5)
Eqn (6)
Results
Unit Root Tests
Graphical representations of the logged stock indexes in Figure 1 and 2 indicate that
the series are non-stationary.
INSERT FIGURE 1 HERE
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Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
INSERT FIGURE 2 HERE
This study checked each series stationary properties using the standard Augmented
Dickey Fuller (ADF) tests developed by Dickey and Fuller (1979). The ADF tests the
null hypothesis that a series contains a unit root. The mathematical expressions for
testing the null hypothesis as show in equation (7) and (8). Equation (7) has a
constant and no trend while equation (8) has a constant and a trend term.
Eqn (7)
Eqn (8)
Where
is the first difference of the stock index, α is a constant term, β is the
coefficient of the trend term, t is the trend term and is the correlation coefficient of
the lagged stock index. is coefficient of the first difference of the first lag of the
stock index, is the coefficient of the 1st difference of the pth lag of the stock index
and
is the error term.
Ng and Perron (2001) recommend the used of the Modified Akaike Criterion (MAC)
to select the number of lags (
to include in equations (7) and (8). The null
hypothesis of a unit root is rejected if
, and it will be concluded that a series is
stationary; conversely, if
, the null hypothesis of existence of a unit root cannot
be rejected and a conclusion should be made that the series is non-stationary. Table
3 show the results of ADF unit root tests for the first and second dataset. As
expected, the tests confirm that all stock indexes contain a unit root and all series
are non-stationary at 5% level of significance.
INSERT TABLE 3 HERE
In order to determine the order of integration we use the first difference of all series.
There is no evidence of a deterministic trend component in the differenced series as
shown in Figure 3. Consequently, the ADF test for a unit root is performed using the
equation (7), which excludes the trend component. The results in table 4 show that
the first difference of the stock indexes (the stock returns) is stationary since the null
hypothesis for no unit root is rejected at all levels of significance. Consequently, the
stock returns of the indexes are used for the subsequent analysis of Granger
Causality.
INSERT FIGURE 3 HERE
INSERT TABLE 4 HERE
Co-integration Tests
The results of the unit root tests show that all stock index series are integrated of
order one (I(1)) and are stationary after taking the first difference.
The Engle and Granger (1987) technique we use involves a two-step-testing process
that tests for a co-integrating relationship between two variables by estimating an
ordinary least squares regression. Thereafter, the residuals of the regression are
examined in order to establish whether the two series are co-integrated (i.e. a linear
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Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
combination of the variables has residuals ( ) that are stationary (
). As a
group of M variables can have up to M-1 co-integrating relationships (Koop, 2006),
we can have up to six co-integrating relationships in this study. The Johansen
framework was used to resolve this issue (Johansen, 1988; Johansen, 1991).
Johansen (1988) developed a framework using a vector-autoregressive (VAR)
model. The VAR of order p is expressed in matrix notation as shown in equation (9)
(Johansen, 1995; Johansen, 1991). As the Johansen technique is sensitive to the
variation of lags, the Akaike Information Criterion (AIC) is used to specify the
appropriate lag length (p) for performing a Johansen tests. The AIC recommends the
use of VAR model with two lags for both datasets.
∑
Eqn (9)
∑
are n x n matrices of unknown parameters where
∑
.
is the first difference of a k-vector of I(1) variables,
vector of deterministic variables and is the vector of innovations.
and
, and
is a d-
If the coefficient matrix Π has reduced rank r < p, then there exist p × r matrices α
and β such that Π = α β΄ and β΄ is stationary, even if
is nonstationary
(Johansen, 1988: 170). The rank r shows the number of cointegrating relations, α
indicates the speeds of adjustment and each column of β contains the long-run cointegrating vectors. Two likelihood-ratio tests are recommended for successive
hypothesis tests (i.e. the trace test and maximum-eigenvalue test). The trace test
statistic and maximum-eigenvalue test statistic are calculated where is the sample
size,
is the ith largest eigenvalue of the
matrix and is the total number of
endogenous variables under consideration).
∑
Eqn (10)
Eqn (11)
In the null hypothesis, the trace tests is
and, in alternative hypothesis,
. The maximum eigenvalue tests
against
where k = 0
through 6. The sequential hypothesis testing starts with the test for zero or no cointegrating relationships versus the hypothesis of one or more co-integrating
relationships. Subsequent hypothesis tests are performed by successively increasing
the value of k by one unit. See Tables 5 and 7.
INSERT TABLE 5 HERE
From table 5, the null of no co-integration is rejected by the trace test, at a 5% level
of significance, since the trace test statistic of 125.9897 is greater than the critical
value of 125.6154. The null hypothesis of the subsequent hypothesis tests is
rejected because the calculated trace statistics are less than the corresponding
critical values. Consequently, the trace test shows that one co-integrating
relationship exists among seven variables.
Turning to the maximum eigenvalue test results (Table 5), the maximum eigenvaluetest statistic for the null of no co-integration of 43.8964 is smaller than the critical
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Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
value of 46.2314. Thus, the null of no co-integrating relationships is accepted at a
5% level of significance. Similar results are obtained from subsequent tests where
findings fail to reject the null hypotheses because the calculated maximum
eigenvalue test statistics are smaller than the critical values. Based on the results of
the maximum eigenvalue test, there is no co-integration relationship between the
seven stock indexes.
The two likelihood-ratio tests for the whole period (Jul/97-Jan/14) yield conflicting
results, with the trace test indicating that there is one co-integrating relationship and
the maximum-eigenvalue test indicating that no co-integrating relationship exists.
However, Enders (1995) prefers the use of the maximum-eigenvalue test due to the
―sharper alternative hypothesis‖ (p.393). Similarly, Kennedy (2008) considers the
maximum-eigenvalue test a superior test for co-integration compared to the trace
test. The resultant co-integration equation can be expressed as shown in equation
(12) and the long-run coefficients of the resultant co-integrated equation are shown
in table 6.
Eqn (12)
INSERT TABLE 6 HERE
Table 7 shows the Johansen-co-integration test results for the second dataset
(Jan/00-Jan/14). For the null hypothesis of no co-integration, the test statistics are
135.1233 for the trace test and 51.6265 for the maximum-eigenvalue test. As both
test statistics exceed the critical values at the 5% significance level, the null
hypothesis (no co-integration) is rejected. Since subsequent trace and maximumeigenvalue tests statistics fall below the 5% critical value, there is no statistical
evidence to support the existence of more than one co-integrating relationship. The
co-integrating relationship can be expressed as shown in equation (12) and the
estimated co-integration coefficients are reported in Table 8.
INSERT TABLE 7 HERE
INSERT TABLE 8 HERE
Given that the series are non-stationary and are co-integrated an error correction
term must be included in the Granger-causality tests. The error correction model
(ECM) applicable in this instance is as specified in equations (5) and (6). We specify
the appropriate lag-length for conducting Granger-causality tests based on the
recommendations for the Johansen-co-integration test. Consequently, the ECM is
estimated using two lags and one co-integrating relationship. The resultant VECM’s
for the 2 datasets are shown in Tables 9 and 10.
INSERT TABLE 9 HERE
INSERT TABLE 10 HERE
Pairwise Granger causality tests were performed using the estimated error correction
terms for VECMs with two lags. Eviews tests for the null hypothesis of Granger noncausation, whereby a rejection of the null hypothesis means that a causal
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Proceedings of Global Business and Finance Research Conference
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relationship exists. The results of the Granger causality tests for both data sets are
contained in Table 11.
For the period from Jul/97-Jan/14, the null hypothesis that the Singapore stock index
does not Granger cause South Korea’s stock index is rejected, at a 1% significance
level. Similarly, the null hypothesis for non-causality is rejected at a 5% significance
level revealing six Granger-causality relationships. Specifically, a bidirectional
causation exists between Singapore and Japan (Singapore causes Japan and vice
versa) and unidirectional-Granger-causation relationships exist such that China
causes Australia, Singapore causes Australia, China causes Singapore and South
Korea causes Singapore. At a 10% significance level, US Granger causes Australia,
Australia Granger causes Singapore and US Granger causes Singapore. For the
period from Jan/00-Jan/14, there are fewer causality relationships with Singapore
Granger causing South Korea (1% level of significance), Japan Granger causing
Singapore (5% significance level) and US granger causing Australia and Japan
Granger causing the US (10% significance level). No other Granger causality
relationships are identified.
INSERT TABLE 11 HERE
Discussion of Findings and Conclusions
The objective of this study is to examine whether movements in the stock markets of
Australia’s key trading partners can explain movements in Australia’s equity markets.
The Johansen co-integration test revealed one long-run equilibrium relationship.
When the Jul/97-Jan/14 period was examined, more causality relationships were
observed as compared to Jan/00-Jan/14. Thus, in a period in which more crises
have occurred, there appears to be more causality relationships (i.e. perceived risk
of contagion). Thus, episodes of crisis appear to nurture more perceived risk of
contagion than would be observed in less turbulent times.
Concerning Australia, in the short-run, past values of the Chinese, Singaporean and
American stock index could help explain the current value of the Australia Stock
index when considering the first period (1997-2014). However, there is no evidence
of reverse causality from Australia to China or Australia to the US stock markets.
Despite the presence of extensive trade links and economic partnerships, only three
significant short-run relationships were found in the first period. Empirical results
indicate stronger Granger causation from China to Australia and Singapore to
Australia equity markets. We also find Granger Causation from the US to Australia
but the significance is lower at a 10% level compared to the 5% level of the two
aforementioned relationships. This means that Singapore has more impact on the
Australian equity market than vice versa. Furthermore, movements in the Chinese
and the US equity markets can affect the Australian equity markets but not vice
versa. This study found that certain trade links appear to play a more of a role in
contagion risks to the Australian economy than others. It is interesting that
Singapore, has more influence on the movements in the Australia equity market.
Accordingly, Australian policy makers should consider the degree of the impact of
the Chinese, Singaporean and the US stock market on the Australian stock market.
Nonetheless, while impact of trade links are important, more research is needed to
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Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
identify and understand other factors that contribute to financial crises and the
complex nature of how those factors interact with each other.
References
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Dabrowski M. 2010. The global financial crisis: lessons for European integration
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Kennedy P. 2008. A Guide to Econometrics, 6th edition, Cambridge, MA: WileyBlackwell.
Koop G. 2006. Analysis of Financial Data, Chichester: John Wiley & Sons, Ltd.
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Mishkin FS. 1999. Lessons from the Asian crisis. Journal of International Money and
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Crows Nest, NSW: Allen & Unwin.
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Appendices
Table 1: Descriptive Statistics for Stock Indexes (Jul/97-Jan/14)
Australi
Singapor
China
Japan
Korea
UK
a
e
Mean
Median
Maximum
Minimum
Std. Dev.
JarqueProbability
Bera
8.274
8.307
8.822
7.810
0.254
10.578
0.005
9.658
9.651
10.35
8.892
3
0.321
9.236
0.010
9.420
9.373
9.920
8.932
0.255
6.974
7.013
7.693
5.697
0.519
0.001
13.060
0.002
12.102
7.698
7.702
8.244
6.753
0.318
8.724
0.013
8.597
8.637
8.844
8.180
0.153
0.000
18.151
Table 2: Descriptive Statistics for logged Stock Indexes (Jan/00-Jan/14)
Australi China
Japan
Korea
Singapor
UK
Mean
8.335
9.717
9.371
7.088
7.757
8.586
a
e
Median
Maximum
Minimum
Std. Dev.
JarqueProbability
Bera
8.375
8.822
7.930
0.225
8.107
0.017
9.751
10.35
9.064
3
0.300
8.981
0.011
9.314
9.920
8.932
0.242
7.222
7.693
6.173
0.452
0.006
10.217
0.000
15.607
7.798
8.244
7.145
0.289
11.638
0.003
8.625
8.817
8.180
0.159
0.001
13.575
US
7.09
7.11
7
7.52
4
6.60
2
0.17
0
2.32
6
0.31
6
3
US
7.10
7.12
7
7.52
8
6.60
2
0.18
0
2.80
0
0.24
6
6
Table 3: Augmented Dickey Fuller Unit Root Test Results of Stock Indexes
Jul/97-Jan/14
Jan/00-Jan/14
Country
Variable
Constant Constant Trend Constant Constant Trend
Australia
LN(Aus)
-1.514325
-1.929803
-1.452244
-1.772412
China
LN(China)
-1.645075
-3.183113*
-1.468013
-2.353573
UK
LN(UK)
-1.848818
-1.886797
-1.636531
-2.051673
Singapore LN(Singapore) -1.724273
-3.015349
-1.383193
-2.538849
US
LN(US)
-1.895666
-2.065813
-1.400415
-1.900108
Japan
LN(Japan)
-2.111976
-1.69135
-2.490175
-2.186308
Korea
LN(Korea)
-1.283361
-3.181857*
-0.797688
-3.062941
Note: *, **, *** are statistically significant at, respectively, the 10 %, 5 % and 1 % level.
Table 4: Augmented Dickey Fuller Unit Root Test Results of Stock Indexes
Constant
Country
Variable
Jul/97-Jan/14
Jan/00-Jan/14
Australia
China
UK
Singapore
US
Japan
Korea
∆LN(Aus)
∆LN(China)
∆LN(UK)
∆LN(Singapore)
∆LN(US)
∆LN(Japan)
∆LN(Korea)
-5.856639***
-6.055578***
-4.582995***
-4.587288***
-5.399556***
-5.957932***
-6.763743***
-5.119305***
-5.798779***
-3.619911***
-7.606968***
-5.224065***
-6.138804***
-6.213806***
11
Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
Table 5: Seven-country Johansen co-integration test results (Jul/97-Jan/14)
Trace Rank test
Number of co-integrating
Eigenvalu
Trace Statistic
5% critical
relationships
e
value
None *
At most 1
At most 2
At most 3
At most 4
At most 5
At most 6
Maximum Eigenvalue Rank test
Number of co-integrating
relationships
None
At most 1
At most 2
At most 3
At most 4
At most 5
At most 6
* Hypothesis rejected at the 5% level
0.2007
0.1504
0.1402
0.0318
0.0307
0.0261
0.0149
Eigenvalu
e
0.2007
0.1504
0.1402
0.0318
0.0307
0.0261
0.0149
125.9897
82.0933
50.1547
20.5530
14.2209
8.1167
2.9411
Max-Eigen
Statistic
43.8964
31.9387
29.6017
6.3321
6.1042
5.1756
2.9411
125.6154
95.7537
69.8189
47.8561
29.7971
15.4947
3.8415
5% critical
value
46.2314
40.0776
33.8769
27.5843
21.1316
14.2646
3.8415
Table 6: Co-integrating equation for the seven countries
(Dependent variable: Australia, Time period: Jul/97-Jan/14)
Independent variables Normalized co-integrating coefficients Standard error
China
Japan
Korea
Singapore
UK
US
0.2264
0.7223
0.4716
0.1879
-0.9485
-0.1010
(0.2906)
(0.1415)
(0.1745)
(0.4195)
(0.3001)
(0.3606)
12
Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
Table 7: Seven-country Johansen co-integration test results (Jan/00-Jan/14)
Trace Rank test
Number of co-integrating
Eigenvalu Trace Statistic
5% critical
relationships
e
value
None *
At most 1
At most 2
At most 3
At most 4
At most 5
At most 6
Maximum Eigenvalue Rank test
Number of co-integrating
relationships
None *
At most 1
At most 2
At most 3
At most 4
At most 5
At most 6
* Hypothesis rejected at the 5% level
0.2673
0.1511
0.1271
0.0938
0.0572
0.0242
0.0211
Eigenvalu
e
0.2673
0.1511
0.1271
0.0938
0.0572
0.0242
0.0211
135.1233
83.4968
56.2990
33.7404
17.3866
7.6096
3.5349
Max-Eigen
Statistic
51.6265
27.1978
22.5586
16.3538
9.7769
4.0748
3.5349
125.6154
95.7537
69.8189
47.8561
29.7971
15.4947
3.8415
5% critical
value
46.2314
40.0776
33.8769
27.5843
21.1316
14.2646
3.8415
Table 8: Co-integrating equation for the seven countries
(Dependent variable: Australia, Time Period: Jan/00-Jan/14)
Independent variables Normalized co-integrating coefficients Standard error
China
Japan
Korea
Singapore
UK
US
-0.2392
0.5936
0.0789
1.0439
-0.5854
-0.2578
(0.1485)
(0.0696)
(0.0891)
(0.2299)
(0.2082)
(0.1709)
13
Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
Table 9: Vector Error Correction Estimates for Australia and Top Six Bilateral Traders (Jul/97Jan/14)
∆ AUSTRALIA
0.010556
(0.02074)
[ 0.50909]
∆ AUSTRALIA (-1) -0.049056
(0.12784)
[-0.38374]
∆ AUSTRALIA(-2)
0.166846
(0.12824)
[ 1.30102]
∆ CHINA(-1)
0.142287
(0.06665)
[ 2.13481]
∆ CHINA (-2)
-0.066014
(0.06618)
[-0.99752]
∆ JAPAN (-1)
0.113894
(0.06431)
[ 1.77089]
∆ JAPAN(-2)
-0.053445
(0.06393)
[-0.83600]
∆KOREA (-1)
-0.036421
(0.04156)
[-0.87632]
∆KOREA (-2)
0.013035
(0.04417)
[ 0.29509]
∆ SINGAPORE(1)
-0.095219
(0.07114)
[-1.33848]
∆ SINGAPORE(2)
0.157205
(0.06729)
[ 2.33618]
∆ US(-1)
0.196730
(0.12496)
[ 1.57429]
∆ US(-2)
-0.158183
(0.12282)
[-1.28795]
∆ UK(-1)
-0.135641
(0.13241)
[-1.02436]
∆ UK(-2)
-0.046302
(0.13302)
[-0.34809]
Constant
0.002710
(0.00277)
[ 0.97749]
Error Correction
Term
∆ CHINA
0.005225
(0.04127)
[ 0.12658]
0.007185
(0.25446)
[ 0.02823]
0.418073
(0.25527)
[ 1.63775]
0.104586
(0.13267)
[ 0.78830]
0.034475
(0.13173)
[ 0.26171]
0.208632
(0.12802)
[ 1.62967]
-0.082835
(0.12726)
[-0.65093]
0.158585
(0.08273)
[ 1.91693]
0.070965
(0.08793)
[ 0.80706]
∆ JAPAN
0.026595
(0.03200)
[ 0.83096]
-0.110370
(0.19732)
[-0.55935]
0.347417
(0.19794)
[ 1.75514]
0.150406
(0.10288)
[ 1.46201]
-0.092329
(0.10215)
[-0.90388]
0.129494
(0.09927)
[ 1.30446]
-0.030752
(0.09868)
[-0.31165]
-0.059715
(0.06415)
[-0.93087]
0.062607
(0.06818)
[ 0.91823]
∆KOREA ∆ SINGAPORE
0.193251
0.028438
(0.04553)
(0.03718)
[ 4.24402]
[ 0.76496]
-0.020419
-0.299923
(0.28073)
(0.22920)
[-0.07274]
[-1.30859]
-0.302565
0.413405
(0.28162)
(0.22992)
[-1.07437]
[ 1.79801]
0.265244
0.280558
(0.14637)
(0.11950)
[ 1.81219]
[ 2.34781]
0.038032
0.116788
(0.14533)
(0.11865)
[ 0.26170]
[ 0.98430]
0.076651
0.243952
(0.14124)
(0.11531)
[ 0.54272]
[ 2.11565]
0.170783
-0.175243
(0.14039)
(0.11462)
[ 1.21648]
[-1.52892]
0.069198
0.184020
(0.09127)
(0.07451)
[ 0.75819]
[ 2.46961]
-0.068616
0.111169
(0.09701)
(0.07920)
[-0.70734]
[ 1.40368]
∆ US
-0.023069
(0.02521)
[-0.91513]
0.105079
(0.15542)
[ 0.67611]
0.301573
(0.15591)
[ 1.93429]
0.096440
(0.08103)
[ 1.19018]
-0.102959
(0.08046)
[-1.27969]
0.135860
(0.07819)
[ 1.73757]
-0.094352
(0.07772)
[-1.21396]
-0.075159
(0.05053)
[-1.48750]
0.040236
(0.05370)
[ 0.74923]
∆ UK
-0.022342
(0.02346)
[-0.95233]
0.155500
(0.14464)
[ 1.07510]
0.219978
(0.14510)
[ 1.51607]
0.028965
(0.07541)
[ 0.38410]
-0.106618
(0.07488)
[-1.42391]
0.089854
(0.07277)
[ 1.23482]
-0.121554
(0.07233)
[-1.68049]
-0.035226
(0.04702)
[-0.74912]
-0.001159
(0.04998)
[-0.02320]
-0.132444 -0.128588 -0.145281
(0.14161) (0.10980) (0.15622)
[-0.93530] [-1.17107] [-0.92996]
-0.247293
(0.12755)
[-1.93887]
-0.063938
(0.08649)
[-0.73928]
0.009161
(0.08049)
[ 0.11382]
-0.059988
(0.13395)
[-0.44785]
0.067097
(0.24875)
[ 0.26974]
-0.317365
(0.24447)
[-1.29815]
-0.412735
(0.26358)
[-1.56589]
-0.081088
(0.26478)
[-0.30625]
0.001103
(0.00552)
[ 0.19976]
0.052129
(0.12065)
[ 0.43208]
0.306318
(0.22405)
[ 1.36720]
-0.388598
(0.22020)
[-1.76476]
-0.375263
(0.23740)
[-1.58069]
-0.135832
(0.23849)
[-0.56956]
0.000910
(0.00497)
[ 0.18299]
0.132869
(0.08181)
[ 1.62414]
0.036968
(0.15192)
[ 0.24333]
-0.236421
(0.14931)
[-1.58338]
-0.043719
(0.16098)
[-0.27158]
-0.020815
(0.16171)
[-0.12872]
0.002509
(0.00337)
[ 0.74437]
0.110115
(0.07614)
[ 1.44629]
0.186943
(0.14139)
[ 1.32219]
-0.085248
(0.13896)
[-0.61347]
-0.306797
(0.14982)
[-2.04779]
-0.066942
(0.15050)
[-0.44479]
-0.00007
(0.00314)
[-0.02375]
0.224815
(0.10386)
[ 2.16449]
0.066356
(0.19288)
[ 0.34402]
-0.266551
(0.18957)
[-1.40608]
0.160189
(0.20438)
[ 0.78377]
-0.106576
(0.20531)
[-0.51909]
-0.001548
(0.00428)
[-0.36167]
0.451984
(0.14777)
[ 3.05864]
-0.035590
(0.27442)
[-0.12969]
-0.306883
(0.26971)
[-1.13783]
0.048388
(0.29078)
[ 0.16640]
-0.322892
(0.29211)
[-1.10538]
0.007077
(0.00609)
[ 1.16236]
Note: Standard errors are shown in ( ) and t-statistics in [ ]
14
Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
Table 10: Vector Error Correction Estimates for Australia and Top Six Bilateral Traders (Jan/00Jan/14)
Error Correction Term
∆ AUSTRALIA (-1)
∆ AUSTRALIA(-2)
∆ CHINA(-1)
∆ CHINA (-2)
∆ JAPAN (-1)
∆ JAPAN(-2)
∆KOREA (-1)
∆KOREA (-2)
∆ SINGAPORE(-1)
∆ SINGAPORE(-2)
∆ UK(-1)
∆ UK(-2)
∆ US(-1)
∆ US(-2)
Constant
∆
∆ CHINA
AUSTRALIA
-0.089661 -0.056403
(0.04046) (0.07175)
[-2.21591] [-0.78613]
0.041350 0.206665
(0.13789) (0.24450)
[ 0.29988] [ 0.84525]
0.163209 0.264817
(0.13641) (0.24188)
[ 1.19645] [ 1.09482]
0.118460 0.093179
(0.08135) (0.14425)
[ 1.45616] [ 0.64595]
-0.033651 -0.133118
(0.07897) (0.14004)
[-0.42610] [-0.95060]
0.083056 0.054679
(0.06874) (0.12189)
[ 1.20827] [ 0.44860]
-0.118840 -0.136173
(0.07085) (0.12562)
[-1.67743] [-1.08397]
-0.065950 -0.015450
(0.06359) (0.11276)
[-1.03706] [-0.13701]
0.114634 0.186229
(0.06287) (0.11148)
[ 1.82333] [ 1.67049]
-0.078151 -0.019571
(0.09411) (0.16688)
[-0.83041] [-0.11727]
0.117561 0.046193
(0.08881) (0.15748)
[ 1.32374] [ 0.29333]
-0.043982 -0.055108
(0.15671) (0.27787)
[-0.28067] [-0.19832]
0.013387 0.074799
(0.15510) (0.27502)
[ 0.08632] [ 0.27198]
0.147900 -0.037612
(0.15002) (0.26602)
[ 0.98585] [-0.14139]
-0.288646 -0.306704
(0.14642) (0.25963)
[-1.97133] [-1.18130]
0.002159 -0.000338
(0.00293) (0.00519)
[ 0.73705] [-0.06514]
∆ JAPAN
∆KOREA
0.111687
(0.06470)
[ 1.72617]
-0.054856
(0.22049)
[-0.24879]
0.221216
(0.21813)
[ 1.01416]
0.062660
(0.13008)
[ 0.48169]
-0.124222
(0.12628)
[-0.98367]
0.135365
(0.10992)
[ 1.23151]
-0.040229
(0.11329)
[-0.35511]
-0.112247
(0.10169)
[-1.10382]
0.149972
(0.10053)
[ 1.49176]
0.079797
(0.15049)
[ 0.53024]
0.280785
(0.14201)
[ 1.97719]
0.184727
(0.25058)
[ 0.73719]
0.003493
(0.24801)
[ 0.01408]
-0.026630
(0.23989)
[-0.11101]
-0.384441
(0.23414)
[-1.64195]
-0.002425
(0.00468)
[-0.51773]
0.169021
(0.07477)
[ 2.26044]
0.343576
(0.25481)
[ 1.34835]
-0.089269
(0.25208)
[-0.35413]
-0.043817
(0.15033)
[-0.29147]
-0.203081
(0.14594)
[-1.39152]
0.043284
(0.12703)
[ 0.34074]
-0.025407
(0.13092)
[-0.19407]
-0.127809
(0.11752)
[-1.08757]
0.116820
(0.11618)
[ 1.00548]
0.108275
(0.17392)
[ 0.62257]
0.512227
(0.16412)
[ 3.12109]
0.101048
(0.28959)
[ 0.34894]
-0.015037
(0.28661)
[-0.05246]
-0.040864
(0.27724)
[-0.14740]
-0.544854
(0.27058)
[-2.01364]
0.004373
(0.00541)
[ 0.80780]
∆
SINGAPORE ∆ UK
∆ US
0.041327 -0.060584 -0.035163
(0.06229) (0.04572) (0.04921)
[ 0.66346] [-1.32521] [-0.71457]
0.086999 0.158200 0.164885
(0.21227) (0.15579) (0.16769)
[ 0.40985] [ 1.01545] [ 0.98325]
0.231662 0.167470 0.239477
(0.20999) (0.15412) (0.16590)
[ 1.10318] [ 1.08660] [ 1.44354]
0.220996 -0.006256 0.077536
(0.12523) (0.09191) (0.09894)
[ 1.76467] [-0.06806] [ 0.78371]
0.007788 -0.112747 -0.137879
(0.12158) (0.08923) (0.09604)
[ 0.06406] [-1.26358] [-1.43557]
0.228977 0.097834 0.163256
(0.10582) (0.07766) (0.08360)
[ 2.16385] [ 1.25970] [ 1.95289]
-0.189422 -0.139073 -0.088056
(0.10906) (0.08005) (0.08616)
[-1.73682] [-1.73742] [-1.02200]
-0.042528 -0.092164 -0.130860
(0.09790) (0.07185) (0.07734)
[-0.43442] [-1.28271] [-1.69202]
0.129042 0.079947 0.076033
(0.09678) (0.07103) (0.07646)
[ 1.33329] [ 1.12547] [ 0.99441]
-0.175216 0.079463 0.006075
(0.14488) (0.10633) (0.11445)
[-1.20939] [ 0.74731] [ 0.05307]
0.193892 0.123473 0.108515
(0.13672) (0.10034) (0.10801)
[ 1.41820] [ 1.23052] [ 1.00471]
-0.260303 -0.325737 -0.093477
(0.24124) (0.17705) (0.19058)
[-1.07903] [-1.83976] [-0.49049]
-0.231480 0.051256 -0.034868
(0.23876) (0.17524) (0.18862)
[-0.96950] [ 0.29250] [-0.18486]
0.230654 0.216012 0.058875
(0.23095) (0.16950) (0.18245)
[ 0.99873] [ 1.27439] [ 0.32269]
-0.149733 -0.248211 -0.189749
(0.22541) (0.16543) (0.17807)
[-0.66428] [-1.50037] [-1.06558]
0.000334 -0.001048 0.000377
(0.00451) (0.00331) (0.00356)
[ 0.07411] [-0.31657] [ 0.10573]
Note: Standard errors are shown in ( ) and t-statistics in [ ]
15
Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
Table 11: Granger Causality tests for seven countries
Null hypothesis
∆China /→∆Australia
∆Japan /→∆Australia
∆Korea /→∆Australia
∆Singapore /→∆Australia
∆US /→∆Australia
∆UK /→∆Australia
∆Australia /→∆China
∆Japan /→∆China
∆Korea /→∆China
∆Singapore /→∆China
∆US /→∆China
∆UK /→∆China
∆Australia /→∆Japan
∆China /→∆Japan
∆Korea /→∆Japan
∆Singapore /→∆Japan
∆US /→∆Japan
∆UK /→∆Japan
∆Australia /→∆Korea
∆China /→∆Korea
∆Japan /→∆Korea
∆Singapore /→∆Korea
∆US /→∆Korea
∆UK /→∆Korea
∆Australia /→∆Singapore
∆China /→∆Singapore
∆Japan /→∆Singapore
∆Korea /→∆Singapore
∆US /→∆Singapore
∆UK /→∆Singapore
∆Australia /→∆US
∆China /→∆US
∆Japan /→∆US
∆Korea /→∆US
∆Singapore /→∆US
∆UK /→∆US
∆Australia /→∆UK
∆China /→∆UK
∆Japan /→∆UK
∆Korea /→∆UK
∆Singapore /→∆UK
∆US /→∆UK
Jul/97 to Jan/14
Chi-squared stat.
6.148606**
3.846562
0.845459
7.897418**
4.810567*
1.069674
2.703734
3.087865
4.386563
1.006291
1.895752
2.452175
3.643162
3.317314
1.677681
6.583385**
2.278909
1.094597
1.156003
3.286126
1.769241
10.84112***
1.295698
1.381592
5.507432*
6.015007**
6.83891**
8.204443**
5.786174*
2.562723
3.965457
3.46726
4.5094
2.732502
3.436728
0.079384
3.143417
2.339001
4.36508
0.562589
2.092181
2.403572
Probability
0.0462
0.1461
0.6553
0.0193
0.0902
0.5858
0.2588
0.2135
0.1116
0.6046
0.3876
0.2934
0.1618
0.1904
0.4322
0.0372
0.3200
0.5785
0.5610
0.1934
0.4129
0.0044
0.5232
0.5012
0.0637
0.0494
0.0327
0.0165
0.0554
0.2777
0.1377
0.1766
0.1049
0.2551
0.1794
0.9611
0.2077
0.3105
0.1128
0.7548
0.3513
0.3007
Jan/00 to Jan/14
Chi-squared stat.
2.398278
4.259484
4.60438
3.091037
5.31076*
0.10512
1.789301
1.372824
2.83995
0.121352
1.39589
0.150036
1.132524
1.270545
3.620714
3.917147
2.699275
0.574953
2.022524
1.974963
0.153304
9.747623***
4.058175
0.142817
1.32686
3.117297
7.672407**
2.03058
1.590946
1.68379
2.861353
2.842628
4.844266*
4.03347
1.040747
0.244582
2.063913
1.596974
4.590128
3.066883
1.747912
4.311002
Probability
0.3015
0.1189
0.1000
0.2132
0.0703
0.9488
0.4088
0.5034
0.2417
0.9411
0.4976
0.9277
0.5676
0.5298
0.1636
0.1411
0.2593
0.7502
0.3638
0.3725
0.9262
0.0076
0.1315
0.9311
0.5151
0.2104
0.0216
0.3623
0.4514
0.4309
0.2391
0.2414
0.0887
0.1331
0.5943
0.8849
0.3563
0.4500
0.1008
0.2158
0.4173
0.1158
Notes: The null hypothesis for Granger causality tests for non-causation and is stated in the form
, where ― /→‖ stands for ―does not Granger cause‖.
*** statistically significant at 1% , ** statistically significant at 5%, *statistically significant at 10%
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