Dykstra, K 2016, Cross-market correlation coefficient analysis of contagion during the Global Financial Crises.

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Cross-market correlation coefficient analysis of
contagion during the Global Financial Crises
Evidence from Australia, Canada, Japan and the United Kingdom
Kim Louise Dykstra

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Executive summary
This report applies the cross-market correlation coefficient method to assess the presence
and impact of contagion upon Australia, Canada, Japan and the United Kingdom (UK) from
the United States (US) during the 2008 Global Financial Crisis (GFC).
The extensive literature review noted evidence of contagion in the majority of cases during
the GFC, irrespective of the definition or method adopted by researchers. A gap in the
literature was found pertaining to the application of the cross-market correlation coefficient
method on the previously mentioned nations during the GFC. The report was said to impact
the decision-making process of global governments and monetary systems, and investors
and financial institutions, as well as contribute to existing literature.
The research methodology outlined the data collection process, which included obtaining
data from 2005-2015 of each nation’s stock mark indices. The data was then statistically
analysed using the cross-market correlation coefficient method. The research found that
correlation increased between the US and each case nation during the GFC, relative to
tranquil times. Japan and the UK were impacted the most, while Australia and Canada
reported relatively subtle changes. Despite correlation increasing during the financial crisis,
the effects were short-lived. The findings suggest, therefore, that the contagion effect does
not decrease the overall effectiveness of international diversification.
This report supported extant literature in noting the presence of contagion between the
case nations and the US during the GFC. Recommendations were put forward to extend on
the current report by applying alternative methods and datasets. Governments and
monetary systems, and investors and financial institutions were recommended to use these
findings when making decisions regarding policies, procedures, and diversification to reduce
the negative impact of contagion in future circumstances. Concluding remarks made by the
author resolved the research problem and noted unresolved issues.

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Table of Contents
Executive summary ........................................................................................................................... 2
1 Introduction .............................................................................................................................. 5
2 Orientation................................................................................................................................ 7
2.1 Literature review................................................................................................................ 7
2.2 Case nations..................................................................................................................... 10
2.3 Research question............................................................................................................ 11
2.4 Significance of research.................................................................................................... 11
3 Research methodology ............................................................................................................ 12
3.1 Method............................................................................................................................ 12
3.2 Data collection ................................................................................................................. 12
3.3 Statistical analysis ............................................................................................................ 13
3.4 Ethical consideration........................................................................................................ 14
4 Presentation of findings........................................................................................................... 14
4.1 Analysis of data................................................................................................................ 14
4.2 Answering the research questions.................................................................................... 17
5 Implications and recommendations......................................................................................... 21
5.1 Literature and further research ........................................................................................ 21
5.2 Government and monetary systems................................................................................. 22
5.3 Investors and financial institutions................................................................................... 23
6 Conclusion............................................................................................................................... 24
Reference........................................................................................................................................ 25
Appendix A – Correlation data......................................................................................................... 29

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Table 1 Descriptive statistics of sample data set: 2005-2015............................................................ 14
Table 2 Correlation data .................................................................................................................. 16
Figure 1 Case nations share market index........................................................................................ 13
Figure 2 Data breakdown: tranquil versus crisis............................................................................... 13
Figure 3 Correlation scale................................................................................................................ 14
Figure 4 Correlation between Australia and United States (2005-2015) ........................................... 15
Figure 5 Correlation between Canada and United States (2005-2015) ............................................. 15
Figure 6 Correlation between Japan and United States (2005-2015) ................................................ 15
Figure 7 Correlation between the UK and United States (2005- 2015).............................................. 15
Figure 8 Correlation between US and case nations before, during, and after GFC ............................ 18
Figure 9 Stock market returns in 2013 ............................................................................................. 18
Figure 10 The US versus Japanese stock market index (2005-2015) ................................................. 19
Figure 11 The US versus the UK stock market index (2005-2015) ..................................................... 20
Figure 12 Stock market returns in 2015 ........................................................................................... 20
Figure 13 Correlation computation between US and host nations in 2005 ....................................... 29

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1 Introduction
International stock markets are becoming increasing interconnected through the
advancement in technology, the liberalisation of trade barriers, and the deregulation of
national financial markets (Anghelache & Ciobanu 2012; Barunik & Vacha 2013; Cheung,
Fung & Tsai 2010). This level of interconnectedness, or correlation, is seen to increase
significantly during times of crisis. This phenomenon is known as contagion. Forbes and
Rigobon (2002, p. 2223) define contagion as ‘a significant increase in cross-market linkages
after a shock to one or a group of countries’. During the Global Financial Crisis (GFC) in 2008,
markets, which usually operated independently, experienced a high level of cross-market
correlation (Chakrabarti 2011).
The following report sets out to determine the presence of contagion during the GFC.
Previous research has highlighted the difficulty faced by researchers with regards to the
definition and methodology used to measure contagion. Forbes and Rigobon’s (2002)
narrow definition of contagion has gained the most popularity in recent times due to its
clear and precise nature. Four alternative methods to measure contagion were investigated:
cross-market correlation coefficients (CMCC), autoregressive conditional heteroscedasticity
(ARCH) family of models, cointegration analysis and direct assessment of transmission
mechanisms. The extant literature found various advantages, assumptions, and
disadvantages for each method. However, irrespective of the method applied, there
appeared to be evidence of contagion during the GFC in the majority of cases (Cheung, Fund
& Tsai 2010; Min & Hwang 2012; Baumohl, Lyocsa & Vyrost 2011; Lupu & Lupu 2009;
Chakrabarti 2011).
A gap in the literature was found pertaining to contagion and Australia, Canada, Japan and
the United Kingdom (UK) from the United States (US) during the GFC. No previous research
had implemented the CMCC method to test for contagion in this situation. This report will,
therefore, seek to determine the presence and impact of contagion on the case nations
during the GFC. This report will also determine if contagion impacts the effectiveness of
international diversification.

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The purpose of this report is to contribute to the decision-making process of global
governments and monetary systems, and investors and financial institutions, surrounding
the impact of contagion. Financial crises and their spread are very expensive for
governments and investors and have the ability to unhinge a nation’s economy (Hunt and
Terry 2015). Therefore, governments must make decisions to reduce the spread of
contagion to their nation and investors must decide how to minimize the negative impact of
contagion upon their portfolio. In addition, this research will contribute to the extant
literature surrounding contagion, its definition and the methods used to measure it.
This report found that cross-market correlation increased between the US and each case
nation during the GFC, relative to tranquil times. Japan and the UK were impacted the most
with the strongest increase in correlations. Australia and Canada were also impacted but to
a lesser extent. The result also found that the impact of contagion is short-lived and that it,
therefore, does not decrease the effectiveness of international diversification overall.
It was recommended that future research is undertaken to broaden the scope of this study.
Alternative methods and datasets were suggested to increase the statistical validity of the
study. Recommendations were put forward to the case nation’s government and monetary
system to be aware of and monitor the correlation between said nation and the US. Policy
and procedures were suggested to decrease the spread of contagion in future financial
crises. It was also recommended that investors and financial institutions be aware of the
temporary nature of contagion and to base decisions on their investment horizons.
Furthermore, investors should prepare their portfolios accordingly when evidence of
contagion is present.
Section II provides an orientation of the forthcoming report, which includes a review of
extant literature and case nations, as well as the proposed researched questions and the
significance of the research. Section III outlines the methodology which was applied,
including how the data was collected and analysed. Section IV presents the study’s findings
and answers the research questions. Section V discusses the implication of the report on
future studies, governments and monetary systems, and investors and financial systems.
Recommendations are put forward in each case. Finally, Section VI provides concluding
remarks resolving the research problem and noting unresolved issues.

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2 Orientation
2.1 Literature review
The 2008 GFC is considered the most significant financial crisis since the 1929 Great
Depression (Anghelache & Viobanu 2012; Chakrabarti 2011). Unlike other financial crises,
the GFC was the first to originate from the largest and most influential economy - the
United States (Cheung, Fung & Tsai 2010). The prelude to the GFC is attributed to the US
housing price bubble, the sub-prime mortgage crisis and subsequent liquidity crunch in the
global credit market (Chakrabarti 2011). The situation became critical with the collapse of
the shadow banking system. The shadow banking system differs from traditional banks in
that it is not subjected to the same level of regulatory oversight (Allen & Faff 2012). This lack
of regulation contributed to the systems downfall and the consequent decline in household
wealth, stock market wealth, consumption and lending capacity (Chakrabarti 2011). The US
Government and Central Bank, along with other international governments and monetary
systems attempted to reduce the impact of the crisis by implementing unparalleled fiscal
stimulus, monetary expansion and institutional bailouts (Sorkin 2009; Chakrabarti 2011).
However, the GFC phenomena reverberated globally, severely impacting many countries
when confidence could not be regained (Su & Yip 2014). The global financial sentiment
weakened, resulting in the Global Financial Crisis (Min & Hwang 2012).
This phenomenon is known as the contagion effect. Forbes and Rigobon (2002, p. 2223)
narrowly define contagion as ‘a significant increase in cross-market linkages after a shock to
one or a group of countries’. Other academics opt for a broader definition to include events
in which no significant changes in cross-market relationships need occur (Lupu & Lupu 2009;
Barunik & Vacha 2013). Forbes and Rigobon’s (2002) definition will be adopted in this study,
as it clearly defines contagion and is most commonly used in contemporaneous literature
(Colins & Garvon 2005; Dajcman 2013; Barunik & Vacha 2013).
The contagion effect stems from Sharpe (1964) and Grubel and Fadner (1971), who first
researched the global cross-market dependence and correlation of stock markets during
historical financial crises. Since the seminal study by Sharpe, a plethora of research, with
varying contagion definitions, methodologies, and findings, has emerged (Peng & Ng 2012;
Lupu & Lupu 2009). The four most commonly applied methods to measure contagion are

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the CMCC method, ARCH family of models, cointegration analysis and direct assessment of
transmission mechanisms (Forbes & Rigobon 2002). Each come with advantages,
assumptions, and disadvantages.
The cross-market correlation coefficient method - pioneered by Sharpe (1964) and Grubel
and Fadner (1971), and extended by King and Wadhwani (1990) - compares the correlation,
or covariance, between two markets during a period of relative stability coupled with a
period of turmoil (Lupu & Lupu 2009). The advantage of this method is that it is
straightforward, uses basic correlation, and is the most commonly applied method in extant
literature (Lee & Kim 1993; Forbes & Rigobon 2002). This allows for easy and accurate cross-
examination of academic articles. King and Wadhwani (1990) wrote the seminal paper using
this approach and found a significant increase in stock market correlation (from 0.23 to
0.75) between the US, the UK, and Japan during the US stock market crash of 1987. Lee and
Kim (1993) elaborated on King and Wadhwani’s study by observing twelve major markets
during the same period and found further evidence for contagion through increased
correlation (from 0.23 to 0.39). The correlation method has also been applied to currency
prices, interest rates, and sovereign spreads during various financial crises (Baig & Goldfajn
1999). Each applied correlation method reached the same general conclusion: correlation
between nations usually increased significantly after a relevant crisis, and therefore
contagion occurred (Calvo & Reinhardt 1995; Baig & Goldfajn 1998; Claessens & Forbes
2001).
There are opposing academic studies by Forbes and Rigobon (2002), Forbes (2012) and
Dungey et al. (2003) who argue that the correlation method is biased and produces
inaccurate results due to heteroscedasticity in market returns. They state that it is
impossible to deduce if an increase in correlation is due to an unconditional correlation or
simply an increase in market volatility (Forbes & Rigobon 2002). Forbes and Rigobon (2002)
recommended their adjusted formula to account for this bias. However, Forbes and
Rigobon’s method has since received scrutiny. Corsetti, Pericoli and Sbracia (2005) state the
assumptions made regarding omitted variables, endogeneity, and feedback between
markets, limit the accuracy of results. This is supported by Dajcman (2013), who states that
these assumptions lead to biased null hypotheses of contagion.

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The ARCH model and its extension (GARCH and M-GARCH) is another widely used method
to detect for the contagion effect (Baumohl, Lyocsa & Vyrost 2011; Chakrabarti 2011; Min &
Hwang 2012; Suilman 2011). The ARCH model was first developed by economist Engle
(1982) and is now commonly used to describe and forecast changes in the volatility of
financial time series (Bauswens, Laurent & Rombouts 2006; Bodie, Kane & Marcus 2014).
Within the ARCH model, Engle (1982) set out to overcome the long-standing assumption of
traditional econometric models that a constant one-period forecast variance must be
assumed. The ARCH model was then expanded to include error variance (GARCH) and
multiple variables (M-GARCH) (Bollerslev 1986; Bauswens, Laurent & Rombouts 2006).
Contemporaneous literature most commonly applies the GARCH and M-GARCH methods.
Baumohl, Lyocsa and Vyrost (2011), Lupu and Lupu (2009), Chakrabarti (2011) and Min and
Hwang (2012) found statistically significant evidence of contagion in Europe, the UK, Asia,
Australia and North America during the GFC. The previous authors all adopted Forbes and
Rigobon’s (2002) definition of contagion and similar methodologies. Min and Hwang (2012)
were the only researchers to find a nation – Japan – that was not affected by contagion
during the GFC, but failed to draw conclusions on as to why.
While the GARCH model is considered too simplistic by some, others consider the M-GARCH
to be too flexible, requiring too many parameters over too many time series (Chakrabarti
2011; Baumohl, Lyoscsa & Vyrost 2011; Min & Jwang 2012, Bauswens, Laurent & Rombouts
2006). Forbes and Rigobon (2002) also argue that the ARCH model and its extensions do not
explicitly test for contagion under their definition of contagion. Although most research
surrounding contagion using these models find that market volatility is transmitted across
countries during crises, they do not test if these changes are significantly higher after the
crisis (Forbes & Rigobon 2002).
The third method used to measure contagion, known as cointegration analysis, focuses on
the changes over the long term relationship between markets; as opposed to short-term
changes after a shock (Claessens & Forbes 2001). This method uses a similar procedure to
the ARCH models, except it tests for changes in the co-integrating vector, instead of in the
variance-covariance matrix (Claessens & Forbes 2001). Longin and Solnik (1995) found that
on average, the correlation between the US and seven other nations increased by about
0.36 over a 30-year period from 1960 to 1990. The opposing view of Spring (2001), opines

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that cointegration analysis is not an accurate test for contagion due to the long-term
periods under consideration. As this method assumes that linkages between markets
remain constant over the entire period, this would indicate a permanent shift in cross-
market linkages, as opposed to contagion (Spring 2001; Claessens & Forbes 2001).
The final method under review is the assessment of transmission mechanisms. This method
attempts to measure directly how different macro- and microeconomic factors affect a
country’s vulnerability to financial crises (Forbes & Rigobon 2002). The methods used to
determine transmission mechanisms varied considerably between papers, making it difficult
to compare results. Goldfajn (1998) found that daily news in one country impacted the
stock market of another. Forbes (2000) and Suliman (2011) found trade to be the most
important factor; while Reinhart and Calvo (1996) attributed geographical closeness to the
transmission of contagion.
To conclude, the aforementioned methods provide various ways in which to measure
contagion. The CMCC method is notably the most simplistic method with strong extant
literature support. The ARCH model overcame prior shortcomings by considering error and
multiple variables, and is widely adopted in contemporaneous literature. Cointegration
analysis applied a long-term perspective but had minimal academic support. Finally, the
assessment of transmission mechanisms sought to define key economic factors which
attributed to contagion and is widely applied. Irrespective of the method selected, however,
a general consensus across literature is that contagion occurs during a time of crises.
It was therefore decided to test for the contagion effect during the most recent financial
crisis, the GFC in 2008, which was said to impact a variety of nations across the globe. Given
the scope of the forthcoming report, the CMCC method was selected due to its simplistic
nature and its wide support in extant literature. The five nations under analysis (to be
discussed in the coming section) were selected as no such analysis using this method had
been undertaken previously and a gap in the literature was present.
2.2 Case nations
The five nations selected for this analysis are the United States, Australia, Canada, Japan and
the United Kingdom. The US was selected as it was the instigator of the GFC (Cheung, Fung
& Tsai 2010). The remaining four nations were selected due to an apparent gap in the

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literature with regards to the CMCC method. All nations are geographically diverse with
varying macro- and micro-economic drivers (Knox, Agnew & McCarthy 2014). This accounts
for Reinhart and Calvo (1996), Forbes (2002) and Buliman (2011) study’s which attributed
geographical closeness and trade as transmission mechanisms for contagion. Furthermore,
the five nations are a part of the Organisation for Economic Co-operation and Development
and are considered developed nations (OECD 2016).
2.3 Research question
Based upon Forbes and Rigobon’s (2002) narrow definition of contagion and the CMCC
method adopted in this study, the following research questions will be addressed to assess
the presence of contagion during the GFC between the US and the selected countries:
1. Did cross-market correlation between the US with Australia, Canada, Japan and the
UK increase significantly during the GFC, relative to tranquil times?
2. To what extent was each selected nation impacted?
3. Does the contagion effect decrease the effectiveness of international diversification
(i.e. for the selected countries)?
2.4 Significance of research
The forthcoming research may impact governments and monetary systems, and investors
and financial institutions, as well as contribute to existing literature. Governments and
monetary systems, in the form of a nation’s central bank, are entrusted to maintain
economic stability for the said country (Hunt & Terry 2015). Failure to appropriately
measure and monitor potential contagion prevents a government from implementing
potentially economy-saving policy and procedures. Hunt and Terry (2012) state how
financial crisis have expensive consequences for governments and financial institutions.
Evidence of this was present during the GFC, in which the US government (i.e. taxpayers)
were forced to bailout institutions that were judged to be to-big-to-fail and implement
unparalleled fiscal stimulus and monetary expansion (Sorkin 2009; Chakrabarti 2011). These
activities were repeated worldwide by governments and monetary systems under pressure
to minimize the impact of the GFC. By learning from the contagion effect during the GFC,
the governments and monetary systems of the case nations would be able to implement
appropriate policy and procedure to decrease the impact of possible future contagion.

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Investors and financial institutions around the world were also negatively impacted during
the GFC and would, therefore, benefit from an understanding of the contagion effect.
Investors rely on international investing to diversify away country-specific risk (Bodie, Kane
& Marcus 2014). The increased correlation between independent markets during times of
crisis undermines the benefit of international diversification (Barunik & Vacha 2013). The
forthcoming report will, therefore, determine if the contagion effect decreases the
effectiveness of international diversification in relation to the case nations during the GFC
and can, therefore, assist investors in their future decision-making process.
3 Research methodology
3.1 Method
To address the aforementioned research questions, a cross-market correlation analysis
using quantitative secondary data was undertaken. Quantitative data was selected because
it can be statically analysed in the form of correlations (Creswell 2014). Secondary data was
collected, since it has already been obtained for other purposes and therefore suits the
retrospective nature of this analysis (Saunders, Lewis & Thornhill 2009).
Daily data was collected from the stock market indices of each nation under investigation.
The period under investigation stemmed from 2005 to 2015 and was assessed on a year-to-
year basis. Correlations were then run between the US and each case nation and results
displayed via tables and graphs.
3.2 Data collection
The stock market indices of each nation from 03 January 2005 to 31 December 2015 was
collected for the purpose of this analysis (Figure 1). All the stock market indices are in local
currency, dividend un-adjusted and are based on the daily closing price in each national
market. When data was not available -due to national holidays- stock market prices were
assumed to stay the same as those of the previous trading day. This method is supported by
previous studies as it is the most accurate representation of the nation’s economic health
and also maintains consistency (Min & Hwang 2012; Barunik & Vacha 2013; Chakrabarti
2011; Forbes & Rigobon 2002).

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Figure 1 Case nations share market index
(Source: Yahoo Finance 2016)
The period under investigation during this analysis was selected on a theoretical basis.
Despite signs from 2007, the GFC is believed to have ‘officially’ commenced with the
bankruptcy of the Lehman Brothers in September 2008 and continued until December 2009
(Barunik & Vacha 2013; Su & Yip 2014). Therefore, the GFC is said to have occurred from
2008-2009. To obtain sufficient comparable data of tranquil times, data from three years
before and six years following was collected (Figure 2).
Figure 2 Data breakdown: tranquil versus crisis
Note: T – tranquil, C – crises
(Source: Developed by author for this project)
Wang and Thi (2013) and Baumohl, Lyocsa and Vyrost (2011) suggested the use of a
mathematical method - iterated cumulative sum of squares algorithms - to spot structural
breaks in the data when selecting periods of tranquillity versus crises. Although statistically
accurate, this process is laborious and is, therefore, unwarranted when sufficient qualitative
data is available.
3.3 Statistical analysis
Cross-market correlation analysis was selected as the statistical tool for the forthcoming
report. A correlation shows how strongly two pairs of variables are related on a scale from
-1 to +1 (see Figure 3) (Creswell 2014). To calculate the correlation between the US and
each case nation, the indices raw data for all five nations from 2005-2015 was exported into
Microsoft Excel 2016 from the Yahoo Finance database (2016). Before computation, the
dataset was checked for errors and outliers through the descriptive statistics function (Table
1). The check found no evidence of errors, outliers or anomalies. From there, each data set
Index
Number of
stocks
Market
capitalisation
Australia S&P(200) 200 80%
Canada S&P(TSX) 235 70%
Japan Nikkei(225) 225 64%
United Kingdom FTSE(250) 250 15%
United States S&P(500) 500 80%
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
T T T C C T T T T T T

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was divided into yearly packages. Following, the correlation between the US and the case
nations year-on-year was computed using the Data Analysis: Analysis Tool: Correlation
software available in Microsoft Excel. Results were tabulated and graphically displayed for
analysis.
Figure 3 Correlation scale
(Source: Creswell 2014)
Table 1 Descriptive statistics of sample data set: 2005-2015
Note: SD – standard deviation
(Source: Yahoo Finance 2016, developed by author for this project)
3.4 Ethical consideration
No ethical consideration was undertaken during this process as only secondary data was
obtained. However, all data which was collected was stored securely.
4 Presentation of findings
4.1 Analysis of data
The correlation between the US and each case nation over the 10-year period is displayed
graphically in Figures 4-7 (Appendix A). The predefined period of crisis (2008-2009) is
denoted by a red box, while the highest point of correlation during the period is symbolised
by a peak (i.e. yellow star).
US Australia Canada Japan UK
Mean 1415 4921 12483 13033 5841
Median 1335 4898 12521 12781 5891
Mode 1190 4263 12111 17451 6087
Minimum 677 3146 7567 7055 3512
Maximum 2131 6829 15658 20868 7104
SD 334 696 1635 3480 716
Relative SD 23.62% 14.14% 13.10% 26.70% 12.26%

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Figure 4 Correlation between Australia and United States (2005-2015)
Figure 5 Correlation between Canada and United States (2005-2015)
Figure 6 Correlation between Japan and United States (2005-2015)
Figure 7 Correlation between the UK and United States (2005- 2015)

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Graphically, it is apparent that correlation is highest in all cases - except for Japan - during
2008 and 2009 (i.e. the GFC). Japan is the exception in which the highest point of correlation
occurred in 2013. However, it is worth noting that during 2008-2009, correlation with the
US increases relative to shouldering periods. The UK also experienced unusually high levels
of correlation with the US during 2013 and 2015 which will be investigated later.
On average, correlations between the US and each nation was lower during periods of
tranquillity, as opposed to periods of crises (i.e. see Table 2: tranquil average versus crises
average). Australia and Canada both shared a high average correlation with the US during
periods of tranquillity with 0.62 and 0.60 respectively. Japan and the UK, on the other hand,
showed relatively low levels of correlation during tranquil periods, at 0.38 and 0.35
respectively, despite the uncharacteristically high levels of correlation in 2013 and 2015.
During the GFC, all nations showed an increase in average correlation with the US (Table 2).
The UK increase most significantly from 0.35 to 0.75, an increase of 117%. Japan increased
by 102%, while Australia and Canada both increased by 46%. Overall, the correlation
between the US and each case nation increased considerably during the GFC relative to
tranquil times.
Table 2 Correlation data
Note: T – tranquil, C – crises
Australia Canada Japan UK
2005 0.67 0.61 0.31 0.48
2006 0.85 0.65 0.03 0.50
2007 0.63 0.50 0.24 -0.21
2008 0.95 0.90 0.72 0.71
2009 0.94 0.87 0.82 0.80
2010 0.57 0.71 -0.08 0.26
2011 0.75 0.79 0.53 0.46
2012 0.75 0.37 0.37 0.20
2013 0.69 0.58 0.92 0.69
2014 0.27 0.64 0.65 0.03
2015 0.62 0.58 0.46 0.71
T average 0.65 0.60 0.38 0.35
C average 0.95 0.88 0.77 0.75
% change 46% 46% 102% 117%

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4.2 Answering the research questions
4.2.1. Did the correlation between the US with Australia, Canada, Japan and the UK
increase significantly during the GFC, relative to tranquil times?
Based upon the results, there was an increase in correlation between the US and each host
nation during the GFC, relative to tranquil times. Each nations correlation increased
between 46-117% which appears significant. However, based on King and Wadhwani (1990)
classification of contagion, who cited an increase of 225% (from 0.23-0.75), contagion did
not occur in these cases. Lee and Kim’s (1993) classification of contagion was slightly more
liberal, a 69% increase (from 0.23-0.39), which would indicate contagion between the US
with Japan and the UK, but not with Australia and Canada. Therefore, correlation did
increase between the US and each case nation during the GFC relative to tranquil times, but
the level of significance is questioned.
4.2.2. To what extent was each selected nation impacted?
Impact, in this case, is defined as the percentage increase in correlation, in conjunction with
Forbes and Rigobon’s (2002) definition of contagion. Those nations with the lowest average
correlation during tranquil times, Japan and the UK, were affected the most during the GFC.
An increase in correlation, of 102% and 117% respectively, indicates that the crisis impacted
Japan and the UK the most. Australia and Canada, on the other hand, saw a relatively lower
increase of 46%.
However, it is worth noting that the increase in correlation seen in Japan and the UK were
only marginally above the average tranquil correlation experienced by Australia and Canada.
During the GFC, US and Australia shared a nearly perfect correlation of 0.95, with Canada
slightly lower at 0.88. These exceptionally high correlations are still worth noting, and it
should be stated that each nation was impacted, just to varying degrees.
4.2.3 Does the contagion effect decrease the effectiveness of international diversification?
The premise of diversification is to reduce/eliminate country-specific risk through
international investing. During the GFC, all markets under investigation increased in
correlation, thus reducing the effectiveness of international diversification. However, this
correlation was short-lived, with all markets showing lower levels of correlation before the

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GFC and returning to lower correlation immediately following (Figure 8). It can, therefore,
be concluded that although contagion does decrease the effectiveness of international
diversification during a crisis, the impact is short-lived and markets quickly return to being
independent.
Figure 8 Correlation between US and case nations before, during, and after GFC
4.2.4 Questions raised during analysis of data
It is also worth addressing questions raised during the analysis process to gain a more
holistic overview of the dataset. The first set of questions relates to the uncharacteristic
spikes in the correlation between the US with Japan and the UK in 2013 and 2015.
The spikes in the correlation between the US with Japan and the UK in 2013 can be
attributed to the positive growth experienced by all three markets. The rally in shareholder
confidence was experienced globally, with financial commentator Chu (2014) noting strong
positive gains across America, Asia, Europe, and the UK in 2013 (see Figure 9). The global
market rally can be accredited to fiscal and monetary stimulus policies undertaken by
international governments and monetary systems, led by the US, Japan, and Germany (Chu
2014).
Figure 9 Stock market returns in 2013
America 30%
Australia 14%
Canada 8%
Japan 63%
UK 14%

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Prior to 2013, the Japanese stock market had been relatively flat following the GFC, whilst
the US market had shown a steady recovery (see Figure 10). In 2013, Japan showed the first
positive signs of growth, in correlation with the US, with the Nikkei225 doubling in value
over 12 months. This is attributed to a series of initiatives by the Japanese government to
boost investor confidence, weaken the Yen and encourage economic expansion (Chu 2014).
However, this stimulus was short-lived when increased taxes saw consumer spending dive
and investor confidence plummet by mid-2014, resulting in a poor correlation with the US
going forward.
Figure 10 The US versus Japanese stock market index (2005-2015)
Prior to 2013, the UK had the lowest average correlation with the US during tranquil times
(0.28 when 2013 and 2015 outliers are removed). This is due to different economic drivers
of each nation (Chu 2014). Like Japan, fiscal policy by the British government, in the form of
tax cuts and availability of funds, post-GFC fostered positive growth in the national stock
market and economy (Chu 2014). The UK market underwent its strongest rally since the GFC
in 2013, by 14%, in correlation with strong gains in the US and other global markets (Figure
9).

20
America -2.23%
Australia -2.58%
Canada -10.95%
Japan 7.95%
UK -4.65%
During 2015, the US and the UK markets both stalled with markets closing down by end-of-
year at -2.23% and -4.65% respectively, leading to a subsequently high correlation (Figure
11). This can be attributed to fear of instability felt across the globe with the slowing down
of China, which makes up 15% of the world’s GDP (Chu 2014). Financial commenters have
since warned of a possible future global financial crisis, with all markets under investigation
closing down in 2015, except for Japan (Figure 12).
Figure 11 The US versus the UK stock market index (2005-2015)
Figure 12 Stock market returns in 2015
It is worth noting that the aforementioned peaks in the correlation between the US with
Japan and the UK are relatively high, compared to the nation’s average correlation.
However, these ‘peaks’ are simply the average level of correlation shared between the US
with Australia and Canada during tranquil times. This leads to a second set of questions –
Why is Australia and Canada so highly correlated with the US during tranquil times, whilst
the Japan and the UK are not?

21
Akarm (2009) states that Australia and Canada have very similar economies; both founded
on the exportation of commodities. Commodities are traded in US dollars and therefore
intrinsically rely on the US dollar to make profits. When the US economy strengths, the US
dollar increases. When the US dollar increases, commodity prices will typically decrease. In
response, the Australian and Canadian government would decrease interest rates to
weaken their respective currencies. Lower currencies favour nations like Australia and
Canada whose economies rely on exportation. This is reflected in a bullish market, as miners
post strong growth and profits. The strong US dollar previously mentioned indicates a strong
economy and therefore also a bullish market, in correlation with Australia and Canada.
Therefore, the US, Australian and Canadian stock markets operate in relatively high
correlation during tranquil times (Akram 2009). This cannot be said, however, for Japan and
the UK who are not resource-driven economies and rely equally on exportation and
importation (Chu 2014).
5 Implications and recommendations
5.1 Literature and further research
The previous report adds to the plethora of literature available surrounding the contagion
effect. Whilst this report is the first to apply the CMCC method to determine the presence of
contagion during the GFC between the US and Australia, Canada, Japan and the UK, extant
literature which utilises different methods provide supporting evidence in the majority of
the cases (Cheung, Fund & Tsai 2010; Min & Hwang 2012; Baumohl, Lyocsa & Vyrost 2011;
Lupu & Lupu 2009; Chakrabarti 2011).
This report solidifies the difficulty faced by researchers when attempting to define and
measure contagion. Under Forbes and Rigobon (2002) narrow definition of contagion,
subtle evidence of contagion is quickly dismissed. The definition of ‘significant’ is also
unclear when applying basic correlation, opposed to more advanced statistical analysis. In
this report, a theoretical approach was used based on the seminal findings of King and
Wadhwani (1990) and Lee and Kim (1993). However, these findings in of themselves
produced mixed results. Therefore, it is concluded that the simplicity of the CMCC method
used in this report impacts the statistical integrity of the findings, as originally suggested by
Forbes and Rigobon (2002).

22
Due to the limited scope and time restraints of this report, it is thus recommended that the
following extensions in research be undertaken:
 Apply the same dataset to alternative methods, such as Forbes and Rigobon’s
methods and the G/M-ARCH model. These methods provide a greater level of
statistical integrity and can, therefore, provide more statically valid conclusions.
 Alter the dataset to include larger stock market indexes from each nation, for
example, Australia S&P300 or Japanese S&P500. By increasing the market
capitalisation represented, the data will more accurately display the economic health
of the nation.
 Change the dataset to national currency prices, interest rates and sovereign spreads,
as mentioned by Baig and Goldfajn (1999). This provides an alternative perspective
for assessing the contagion effect, which could lead to different conclusions being
drawn.
 Alter the time period definition of tranquil and crises to be more specific. For
example, the GFC is said to have started in September 2008. By running the data on
a bi-annual basis, as opposed to annually, alternative evidence of contagion may
have been determined.
5.2 Government and monetary systems
The previous report provides the Australian, Canadian, Japanese and British government
with a greater understanding of how their economy interacts in correlation with the US
during times of tranquillity and crisis. Australia and Canada must remain conscious of their
high dependence on the US dollar and subsequent correlation with the US economy. Japan
and the UK must note that although they are not typically correlated with the US, they are
not immune to a global financial crisis. By having a greater understanding of the contagion
effect during the GFC, the case nations government and monetary system can, therefore,
implement policies and procedures to mitigate the impact of future contagion.
Other nations, not just those in this study, can also assess their correlation relationship
between themselves and other key markets during tranquil and crisis times, and implement

23
appropriate policy and procedure accordingly. They too can be prepared for the growing
presence of contagion as globalisation becomes the norm.
It is therefore recommended that any government and monetary system:
 Determine the strength of correlation between its nation and other key nations
during tranquil and crisis times. By doing so, governments and monetary system will
be aware of which nations play an important role in shaping their home nation’s
economy.
 Determine the driving forces or transmission mechanisms to this correlation, for
example - the exportation of commodities by Australia and Canada.
 By determining the relationship between the home nation and other key nations,
and evaluating the transmission mechanisms driving this correlation, governments,
and monetary systems can implement appropriate policy and procedure to protect
the individual nation against future contagion. This may be in the form of limiting
trade between nations or reducing the level of leverage by banks, as noted by Forbes
(2012).
5.3 Investors and financial institutions
The previous report provides investors and financial institutions with a greater
understanding of the relationship between the US, Australia, Canada, Japan and the UK
markets during a crisis. The report noted Australia and Canada’s high correlation with the
US during tranquil times, while correlation with Japan and the UK was usually minimal.
Investors may, therefore, select to invest in the Japanese or British stock market during
tranquil times, oppose to Australia or Canada, as it provides a greater level of diversification.
The report also found that, although all nations were impacted by the GFC, the contagion
effect does not decrease the effectiveness of international diversification in the long term.
This conclusion is supported by Su and Yip (2014) study of contagion during the GFC, who
found that the impact of contagion is short-lived and dissipates quickly. Su and Yip (2014)
research found that the relationship between the US and nations examined were not
cointegrated over the period from 2000 to 2013; suggesting a global portfolio is still
valuable over a long-term investment horizon.

24
It is therefore recommended that investors and financial institutions:
 Be aware of those nations with strong/weak correlation during times of tranquillity
and crisis. Investors are likely to seek those investments with the lowest correlation
to diversify away country-specific risks.
 Monitor the level of correlation between certain markets continuously. When
correlation appears to be increasing significantly, investors should prepare their
portfolio to capitalise on a potential financial crisis, for example – holding cash or
shorting the market.
 Be aware that the contagion effect leads to global markets operating in correlation,
but its impact is short-lived. Investors must consider the time horizon of each of its
investments and be prepared to hold during turmoil times.
6 Conclusion
This study found evidence of increased cross-market correlation between the US with
Australia, Canada, Japan and the UK during the GFC. This is supportive of the majority of
extant literature which found evidence of contagion during the GFC using alternative
methods. The report also found that nations which the lowest average correlation during
tranquil times (i.e. Japan and the UK), were impacted the most during the GFC. This report
provides global governments and monetary systems, and investors and financial institutions
recommendations to evaluate, monitor, mitigate and capitalise during an event of
contagion.
The unresolved issues found in this report, like the majority of previous research, is the
contention surrounding the definition of contagion and the method used to measure it. By
following the aforementioned recommendations for future research, a more statistically
sound conclusion could be drawn regarding the presence and impact of contagion during
the GFC upon the case nations. It is encouraged that future researchers keep in mind the
advantages, assumptions, and disadvantages of each method, while expanding on this
research. As global markets only become more interconnected, it is vital that researchers
continue their investigation into the definition, courses, and impact of contagion, in order to
guide governments and monetary systems, and investors and financial institutions alike.

25
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29
Appendix A – Correlation data
Figure 13 Correlation computation between US and host nations in 2005
2005 US Australia Canada Japan UK
US 1
Australia 0.674845 1
Canada 0.612678 0.908626 1
Japan 0.312419 0.597366 0.683053 1
UK 0.48097 0.812655 0.850397 0.776684 1
US 1
Canada 0.64973 0.635929 1
Japan 0.03457 0.338816 0.10098 1
UK 0.498626 0.426586 0.403532 -0.03986 1
US 1
Canada 0.504354 0.716934 1
Japan 0.238989 0.22264 0.601569 1
UK -0.20955 -0.36975 -0.09831 0.3156 1
US 1
Australia 0.95034 1
Canada 0.895901 0.88455 1
Japan 0.717293 0.713818 0.537848 1
UK 0.705469 0.759706 0.715259 0.607085 1

30
US 1
Canada 0.865152 0.91755 1
Japan 0.818594 0.813534 0.717449 1
UK 0.802398 0.889133 0.891277 0.796498 1
US 1
Canada 0.706268 0.358971 1
Japan -0.08295 -0.05608 -0.38881 1
UK 0.261642 0.149987 0.622201 -0.54039 1
US 1
Canada 0.792684 0.921635 1
Japan 0.528124 0.716762 0.793927 1
UK 0.45847 0.661727 0.578725 0.476074 1
US 1
Canada 0.370229 0.513229 1
Japan 0.370706 0.227843 0.211764 1
UK 0.199197 0.506044 0.44165 -0.28699 1

31
US 1
Australia 0.69428 1
Canada 0.579242 0.574906 1
Japan 0.920655 0.580485 0.370279 1
UK 0.694776 0.801685 0.318596 0.675811 1
US 1
Australia 0.26835 1
Canada 0.641458 0.567447 1
Japan 0.646295 -0.37748 0.090432 1
UK 0.033725 0.443311 0.322349 -0.25674 1
US 1
Canada 0.582512 0.903573 1
Japan 0.458659 0.139879 0.143604 1
UK 0.713294 0.93022 0.936691 0.197468 1

Dykstra, K 2016, Cross-market correlation coefficient analysis of contagion during the Global Financial Crises.

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