Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
Has BRICS Countries Decoupled from the Effect of Contagion
during the 2008 Financial Crisis? Comparison of VECM Model
and DCC GARCH Model
Ka-lok Kan
In the light of the recent financial crisis, this paper investigates the contagion effect of
the 2008 financial crisis shedding light on the decoupling recoupling theory. This paper
investigates 5 BRICS countries (Brazil, Russia, India, China and South Africa) and the
contagion effect from the US and UK indices. This paper compares DCC GARCH
model and VECM model to conduct the investigation. This paper has found that with
the exception of China, BRICS countries generally have been effected by the contagion
effect.
1. Introduction
The current financial crisis has done considerable global damage in the period between
2008 and 2010, and therefore it is particularly interesting to understand the impact of the
crisis. It originated from Anglo-Saxon economies, the US particularly, and to a lesser
extent the UK. Much of the literature has done extensive research into the reasons behind
the crisis but there is a lack of research into the impact of the crisis upon other countries
such as the BRICS.
The BRICS (Brazil, Russia, India, China and South Africa) was a term for these countries
coined by Goldman Sachs in 2002 and consists of five of the highest GDP growth
countries in the world. It is also forecasted that the BRICS will become the dominant
economies by 2050, representing half of the global GDP (Chittedi 2014). Currently, the
BRICS are all in the G20 organisation and already representing a significant proportion of
the world’s population and economic activities.
The economic activities of BRICS are not only significant globally, they are also significant
in their respective regions as they are the regional hubs in their own right, such as Brazil
in Latin America and China in East Asia. The macro-economics and the climate for
financial markets are very different from the traditional Anglo-Saxon countries, such that
many scholars have thought that these countries have already decoupled from the US
and UK economies (Leduc, Spiegel 2013, Liang 2010). The reason is that the economic
model for the BRICS is predominately export-led, whereas the growth of the Anglo-Saxon
economies has been reliant on domestic consumption for many years. The export-led
economies have resulted in a significant amount of foreign reserves in the current
accounts of the BRICS countries and, leading the above-average economic growth,
regionally driving global economies.
Investors invest in these countries for a number of reasons. First the growth rate of the
assets tends to be in the region of double figures, meaning that it can satisfy some risktaking investment strategies. Investors investing into these assets are usually seeking a
better return than mature markets. Secondly, most scholars thought that because of the
decoupling effect mentioned earlier,
________________________________________________________________________
Ka-lok Kan, Brunel University, Department of Economic and Finance, Email: ecpgkkk@brunel.ac.uk
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
it satisfies the primary principle of the portfolio theory that traders would diversify internationally to
reduce the risk of the portfolio from the extreme sudden volatility of a shock in one particular
country.
Because of this reason, it is particularly interesting to study the co-movement and the contagion of
these emerging markets so that the effect of one of the most damaging financial crises can be
understood. It is important to study the performance of the markets in the BRICS countries during
the recent financial crisis so that the decoupling–recoupling theory can be investigated, as there is
currently a lack of literature in this area. Studying this area will enable the academic world to gain
an understanding of the behaviour of the financial markets, assisting the fiscal policy-making of
emerging countries and also the formulation of crisis-prevention regulation. Secondly,
diversification is an important area for investment and the study of contagion will enable us to
understand how a crisis may transmit from one country to another. This will enable investors to
gain an understanding of the underlying contagion that is hidden in regions and countries,
enabling them to make informed decisions when undertaking investment activities. This will
provide more insight for them to make better decisions in terms of diversification into emerging
countries.
2. Literature Review
A great deal of the literature has focused on finding out the presence of the contagion effect in
emerging markets in a given period of time (Morales, Andreosso-O'Callaghan 2014, Ahmad,
Sehgal et al. 2013, Dimitriou, Kenourgios et al. 2013, Kotkatvuori-Ornberg, Nikkinen et al. 2013,
Didier, Love et al. 2012, Junior, Franca 2012, Aloui, Aissa et al. 2011, Kenourgios, Samitas et al.
2011, Guo, Chen et al. 2011, Naous, Khemiri et al. 2010). Most of the literature lacks cohesion
when analysing the presence of the contagion effect in a collection of countries. It is therefore the
intention of the researcher that a focused analysis be undertaken in the form of a select few
emerging countries, and the BRICS bloc has been chosen for this purpose.
2.1 Contagion channels
Some of the literature argues that the only way BRICS countries would be able to weather the
next financial crisis would be by undertaking trade (Kim, Lee et al. 2011). Through trade and
collection, and a large amount of foreign reserves together with fiscal reform, emerging countries
would be able to isolate themselves from the external shocks that may negatively impact on the
financial markets and economies. These studies explain the reason behind this as trade
eventually leading to export-led growth of economies, ensuring above-average growth year on
year.
However, it is trade that led the emerging countries into lowered growth from mid-2008. The
falling demand of consumption led to a fall in exports. This has hit the export-led countries hard
and resulted in the below average performance of macro-economic figures in 2008 and 2009.
Combined with the pull of the currency contagion, channel presence in the traditional dollartrading markets and those artificially pegged to the dollar, the financial markets experienced a
decrease in market capitalisation and liquidity, the performance of which was found to be affected
by the intensive bad news from the West.
This thesis will provide a comprehensive study of the contagion channels by comparing two
mathematical econometric models, the VECM and the Dynamic Conditional Correlation
(DCC) GARCH models. It will explain how contagion happened and the delay of its
occurrence.
2.2 Decoupling–recoupling theory
Prior to the beginning of the 2008 sub-prime mortgage financial crisis, some of the literature
argued that the BRICS economies had decoupled from the traditional Anglo-Saxon economies
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
(Kizys, Pierdzioch 2013, Kim, Lee et al. 2011, Dooley, Hutchison 2009). This is because the
BRICS have been employing export-led growth strategies, whereas Anglo-Saxon economies
favour domestic consumption. The differences in these strategies led to a global imbalance and
the trade deficit between these countries widened. Countries like China, Russia and India were
hence able to collect a large amount of foreign reserves. Therefore, some of the literature was
arguing that the emerging countries may be able to weather the next financial crisis prior to the
2008 crisis. This argument led into a debate over the strength of the linkage between emerging
countries and the industrialised countries.
However, the 2008 financial crisis revealed to us that the BRICS economies were at best
recoupled to the world economies, despite the fact that they outperformed the US and the UK in
2007 in many areas, such as outperforming by 40% in the financial markets. Since the collapse of
Lehman Brothers and other financial institutions, the contagion of the bad news resulted in the comovement of financial markets. Financial markets in emerging countries experienced a decline in
liquidity and an increase in volatility following the intense bad news in the West. This has led to
the consistent findings of many studies that the linkage between the economies has been greatly
enchanted since the occurrence of 2008 financial crisis. Some even argued that they did not
decouple in the calm period as the emerging countries could not weather the financial crisis
technically.
This thesis will continue to shed light on this area and will use two mathematical models to
contribute to the debate that the decoupling theory was at best weakly supported during the
tranquil period.
1.3 Contagion in BRICS
Much of the literature focuses on examining contagion in a group of emerging countries and there
is a lack of a focus on a group of similar economies such as the BRICS (Kotkatvuori-Ornberg,
Nikkinen et al. 2013, Samarakoon 2011). Consistently, the literature has found that the correlation
of financial contagion has increased since the collapse of Lehman Brothers and the later stage of
the 2008 financial crisis. Some of the literature divided the contagion into two phases: the
difference in the correlation level in the tranquil and the crisis period, providing an in-depth
analysis that the intensive bad news since the collapse of Lehman Brothers has greatly affected
the financial markets of the BRICS countries.
Few elements within the literature rejected the presence of contagion from the US to the BRICS
and this thesis will provide further information and the reasons behind the delay of the contagion
from mid-2008 onwards. In Section 3, the methodologies section, I will explain the testing of the
contagion of the BRICS. Section 4 presents the data describing the performance of the financial
markets in the BRICS during the financial crisis. Section 5 provides a discussion comparing the
VECM and the DCC GARCH model. It provides explanations of the presence of contagion.
Section 6 concludes the paper.
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
3.
Methodology
3.1Data Collection
The sample was taken from January 2007 to December 2010, during the peak of the financial
crisis. Closing share prices of each of the BRICS countries were collected on Yahoo Finance. The
sample was analysed using Microsoft Excel and EVIEWS software.
Many studies have defined contagion as the sudden increase of co-movement of stock markets
after a shock (Dimitriou, Simos 2013, Samarakoon 2011). Traditionally, the literature a number of
different kinds of models to test the co-movement: the Chow test, the co-integration test, the
causality test, a DCC GARCH model and a direct estimation of specific transmission mechanisms.
Since most of the literature that employed the last methods was not especially tested for
contagion, I have proposed to use the three most objective methods in EVIEWS so that the comovement can be tested.
3.2 Johansen co-integration test
The co-integration test is a common tool since it was introduced by measuring the present and the
changes in the co-integrating vector in a long-run sample. It is particularly good for a sample with
a long period of observations explaining the linkages of the financial markets and the variables for
testing.
Its advantage being with an assumption of unit root such that it is excellent in testing variables
that often modelled as unit root such as interest rates, inflation, real exchange rates and
unemployment rates. Hence it is very useful to test the impact of the current financial crisis in
terms of the macro-economic determinates on the financial markets in the BRICS. In the
Johansen framework, all the variables are near-integrated, testing the H0 r≤0 in trace test and the
H0 r=0 in eigenvalue test.
Johansen’s methodology takes its starting point in the vector autoregression (VAR) of order p
given by:
1
is an n x 1 vector of variables that are integrated of order one – commonly denoted as
I(1), and is an n x 1 vector of innovations. This VAR can be re-written as:
∑
2
Where:
∑
∑
and
3
Johansen proposed two different likelihood ratio tests of the significance of these canonical
correlations and thereby the reduced rank of the
atrix; the trace test and the maximum
eigenvalue test, shown in equations 4 and 5.
∑
4
5
One disadvantage of this model is that because it is particularly good at long-running relationships,
it risks missing the periods of co-movement in the short-term relationships of markets for a short
period of time after a crisis. But this can be addressed using a causality test that is designed to
test short-run relationships.
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
3.3.
VECM
Granger and Lee (1989) formulated the non-linearity in the adjustment mechanism or Error
Correction Model (ECM). This representation was then developed by (Escribano, Pfann 1998)
who considered that the linear ECM models are based on restrictive conditions as follows.
The uniqueness of the long-term equilibrium and adjustment with respect to the equilibrium is
symmetrical. Yet according to (Masson 1999), during periods of crisis, financial markets may give
rise to situations of multiple equilibrium, reflecting sudden changes in investor expectations with
respect to the risk.
These carry out operations of re-adjustment and the reallocation of portfolios that thus move the
market from a steady state to another. Also, and as has been suggested by several studies, the
adjustment with respect to the equilibrium is asymmetric. The markets’ reaction to the shock
differs according to its positive or negative nature. (Escribano, Pfann 1998) shared the error
correction term in the ECM model in two positive and negative parts such as:
{
{
6
7
This transformation allows for the creation of two situations of equilibrium, each characterised by
a specific speed of adjustment. The first equilibrium of stability is captured by the term:
.
Therefore, the new representation of the ECM is:
∑
∑
8
By testing the beta coefficient of the granger term ∑
by Wald Test: H0:
, we will be able to know if the granger term has caused significant external induction to the
indices of the BRICS.
Because there is structural break in all of the indices as illustrated on graphs in Appendix B, it is
proposed to include a control variable to estimate the structural break that occurred during the last
quarter of 2008, approximately when Lehman Brothers collapsed. It is proposed that a control
variable of the announcement date of the recession in the UK is used representing the pre crisis
period and post crisis period. The UK government announced the reality of recession in October
2008 and the control variable is set to 0 before the announcement date and equals to 1 after the
announcement date.
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
3.3Dynamic Conditional Correlation (DCC) GARCH model
The DCC GARCH model enables researchers to estimate the co-variance transmission
mechanism between the sample countries. This type of model allows practitioners to construct a
portfolio that can be forecasted using a set of co-variance based variables of asset returns. It is
particularly important as the hedge ratio often changes according to the assets’ co-variance and
volatilities. Hence, this type of GARCH model is commonly employed by sophisticated traders and
researchers.
We can specify a multivariate conditional variance as:
9
Where
is the (n x n) diagonal matrix of time-varying standard deviations from univariate
GARCH models with √
on the ith diagonal, i = 1,2....,n. is the (n x n) time-varying correlation
matrix. The DCC model proposed by Engle (2002) involves a two-stage estimation of the
conditional co-variance matrix . In the first stage, univariate volatility models are fitted for each
of the stock returns and estimates of √
are obtained. In the second stage, stock-return
residuals are transformed by their estimated standard deviations from the first stage. That is:
, where
is then used to estimate the parameters of the conditional correlations.
⁄
√
The evolution of the correlation in the DCC model is given by:
̅
10
[
] is the n x n
Where
(
) is the n x n time-varying co-variance matrix of , ̅
unconditional variance matrix of , and and are non-negative scalar parameters satisfying
. Since
does not generally have ones on the diagonal, we scale it to obtain a proper
correlation matrix . Thus:
11
(
Where
√
)
√
Now
in Eq (11) is a correlation matrix with ones on the diagonal and off diagonal elements less
than one in absolute value, as long as
is definitely positive. A typical element of is of the form:
12
√
Expressing the correlation coefficient in a bivariate case, we have:
̅
̅
√[
̅
]√[
13
]
As proposed by (Engle 2002), the DCC model can be estimated using a two-stage approach to
maximise the log-likelihood function. Let denote the parameters in
and the parameters in
, then the log-likelihood fund is:
[
∑
|
|
]
[
∑
|
|
]
14
The first part of the likelihood function in Eq. 14 is volatility, which is the sum of individual GARCH
likelihoods. The log-likelihood function can be maximised in the first stage over the parameters in
. Given the estimated parameters in the first stage, the correlation component of the likelihood
function in the second stage (the second part of Eq. 14) can be maximised to estimate correlation
coefficients.
This DCC GARCH model has a number of advantages over the multivariate GARCH model.
Firstly, it can potentially analyse a larger sample of matrices. Secondly, it has been tested that it is
often more accurate for its estimation compared to the multivariate GARCH model and superior to
other estimation models such as moving average methods.
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
4.
Data
4.1Descriptive statistics
Appendix A shows the descriptive statistics of the natural log value of the financial markets of the
BRICS and the Anglo-Saxon financial centres. It is illustrated that Brazil has the highest mean
value in the BRICS with 10.9326, while South Africa has the lowest mean value of 4.257997.
South Africa in the meantime has the highest standard deviation, meaning that it has the highest
risk of all five BRICS countries, with a value of 0.489503. In contrast, Brazil has the lowest
standard deviation with a value of 0.197598. The number of observations was 972.
Appendix B shows the graphs illustrating the performance of the indices of all countries during
2007 to 2010.
Appendix C shows the DCC GARCH correlation graphs. It shows that in most countries, the DCC
correlation was in high volatility in 2008 when Lehman Brothers collapsed. It also shows that
South Africa in general has the highest correlation with the US market, while India appeared to
have the lowest correlation with the US market. Apart from Russia, it appears that all five
countries experienced an increase or no change of correlation since the credit crunch. South
Africa experienced the highest increase of correlation with the UK market since the credit crunch.
Appendix D shows the DCC GARCH statistical model result and it shows that, apart from China,
the correlation coefficients for the other four BRICS countries are mostly found to be significant. It
means that there is an underlying financial linkage between the Anglo-Saxon financial markets
and the BRICS.
Appendix E shows the VECM coefficient result and also the corresponding Wald Test and the
announcement date dummy result. The VECM result shows that China, India and South Africa
has significant result with the UK share index. Furthermore, Russia shows a slight contagion
result in lag one with the US share index. This means that contagion was presence during the pre
crisis period in most countries. It is also found that the dummy variable was found significant in
South Africa and Brazil meaning that during post crisis period, contagion was presence from the
US and UK for these countries.
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
5.
Results
5.1BRICS countries
Interestingly, apart from China, the other four countries in the BRICS have shown significant
results in both the DCC GARCH and VECM models. This means that the null hypothesis for both
models can be statistically rejected for the BRICS. The result means that the BRICS did not
decouple from the Anglo-Saxon economies during the 2008 sub-prime financial crisis and, in fact,
were fundamentally influenced by the developed financial markets.
The 2008 financial crisis presents the perfect environment to study the contagion because it is
rare that financial crisis originates in the developed world. The nature and cause of financial crisis
also fit into the theory of contagion that the financial crisis spread from the US to the rest of the
world as a result of asset-backed securities being traded all over the world. The results of this
research show that the underlying linkage between the US financial markets and the BRICS
financial markets is particularly strong and might have become even stronger during this financial
crisis. This can be seen from the DCC GARCH model: the correlation is generally high in the
study period. The high level of correlation in the study period presented in the correlation graphs
is no coincidence and they were the induced external shock that was caused by the financial
crisis in the US. The shockwaves of the failing of the large financial institutions during 2007 and
2008, especially Lehman Brothers, were felt throughout the world and primarily resulted in the
contagion of the sub-prime financial crisis.
The DCC GARCH models also noticed similar patterns between the UK and all five BRICS bloc
countries. This could mean that the health of the banking industry in the UK was also a concern
affecting the rest of the world, resulting in some form of contagion in the BRICS. The banking
industry of the UK was also seriously damaged and a number of financial institutions were
financially rescued by the government, affecting the confidence of the markets. This study has
since confirmed that the linkage between the UK and the BRICS countries is also strong,
particularly India and South Africa as they are part of the Commonwealth. Their business and
financial linkages towards the UK are comparatively higher for historical reasons.
Although insignificant results were found in China, the DCC GARCH model shows that the
collapse of the US and UK financial institutions had a negative impact on the Chinese financial
markets. Data show a slight increase in correlations in both the US and UK graphs, meaning that
in general there were negative impacts of the sub-prime financial markets but they were found not
to be statistically significant.
Prior to undertaking the VECM model, all five countries were tested using the Johansen cointegration model and co-integrations were found in all five countries. This further corroborates the
GARCH results that contagion was present during the financial crisis. Co-integration models are
used to test the long-term equilibrium of data so that its presence means that the tested financial
markets were all in long-run contagion during the study period. This is consistent with the
literature that found evidence against the decoupling theory for the BRICS countries. This means
that the BRICS bloc countries are strongly linked to the Anglo-Saxon economies and the recent
financial crisis only made the underlying linkage stronger, as the DCC GARCH model has shown.
The VECM models show statistically significant results between the UK and the four BRICS
countries China, Russia and India and South Africa. Compared to the DCC GARCH model, the
VECM model did not show significant linkage between the BRICS and the US initially but has
shown a significant result on the interest rate dummy variable meaning that the BRICS countries
has shown some degree of contagion to both of the US and the UK market. The shockwaves of
the collapse of major financial institutions affected global financial markets and resulted in the
contagion of the financial crisis.
The resulting global recession has affected demand for imports in the developed world. This has
caused a slowdown of growth in the export-led economies of the BRICS countries. This is
particularly true to natural resources export countries like Russia, Brazil and South Africa where
the contagion was found during the post crisis period. There was some insulation of contagion for
manufactured and service export countries like China and India as these countries experienced a
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
delay of slow down of economic growth that only occurred in the late stage of 2009. Hence the
control variable that was set in 2008 did not manage to measure this. This resulted in the
decreased level of investment and the outflow of liquidity in the financial markets of the BRICS
countries. Therefore, the financial markets in the emerging economies faced significantly
increased volatility, which resulted in slowdown and partial collapse in some regions. As a result,
the capitalisation of the financial markets in the BRICS decreased and they also experienced a
crisis directly caused by the sub-prime mortgage crisis in the US.
Secondly, the decreased price of commodities from mid-2008 also affected the confidence of the
financial markets. This significantly affected the resource export-led countries like Russia, South
Africa and Brazil. Hence the financial performance of the major enterprises that are listed in their
respected financial markets decreased. Thus the performance of the index was affected, and
confidence for the future was dented during 2008 and 2009 within the BRICS.
Thirdly, apart from China, the value of all four currencies depreciated against the dollar from 2008
as a result of low confidence in the midst of the recession. This somehow did not augment
exportation for the BRICS; hence it caused major concern regarding the capacity to earn foreign
currencies in the emerging countries. Furthermore, the depreciation of local currencies is one of
the pieces of evidence of the outflow of liquidity of financial markets. This further caused the
decreased value of capitalisation of the financial markets in the BRICS.
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
5.2 Comparison of models
This section will compare the findings for both the DCC GARCH model and the VECM model.
VECM, instead of a conventional vector auto regressive model, is used when the model detects
the existence of co-integration. In econometrics, it measures the internal shocks of lagged
financial variables and also the causality of the external shocks. This thesis seeks to identify the
external causalities that caused the contagion of the financial markets. Thus, as presented in the
methodology section, the mathematical model of this VECM measures the impact of the lagged
index data of the US and UK financial markets. This part of the VECM model can be compared to
the granger causality model as it essentially measures the identical factors that might have
caused the contagion. The VECM model is good at measuring the short-run equilibrium in the
already identified long-run co-integration. The short-run equilibrium can be seen as the
adjustment that might be needed for financial markets to stay in contagion. In this thesis, these
identified short-run adjustments might be the decreased demand for exports, the decreased price
of commodities and the depreciation of local currencies. These factors have all played a part in
lowering the growth of the export-led economies of the BRICS countries, resulting in contagion
from the Anglo-Saxon countries of the US and the UK. The collapse of the banking institutions in
the US and the UK probably only had a limited impact on the BRICS countries as most of the
literature reported that only limited exposure to the asset-backed securities was found in the
banking systems within the BRICS.
In comparison, the DCC GARCH model predicts the correlations, measuring the strength of the
underlying co-movement linkage between the financial markets of the US, the UK and the BRICS
countries. In theory, the correlation of the financial markets can be argued to be background
contagion as these are usually caused by the natural characteristics and the behaviour of the
financial markets rather than significant external forces that can be measured by VAR and VECM
models. It is also arguable that this underlying co-movement tends to be passive, hence the DCC
GARCH model is an excellent tool to analyse and measure this underlying co-movement
behaviour. Furthermore, most of the literature suggests that this co-movement can be developed
through three traditional channels: financial, trade and competitive devaluation of local currency.
In this thesis, these three channels will be analysed so that the results of the DCC GARCH model
can be explained.
Financial linkage is the underlying co-movement behaviour due to historical and geographical
reasons. Trade refers to the trading partnership and relationship that the financial markets may
have, therefore affecting the performance of the organisations at the same time. The trade linkage
can also be reflected through economic indicators and the relationship between the examined
countries in their respective financial markets. This is particularly interesting as the economies of
the export-led BRICS countries severely slowed during the recession as a result of the lowered
demand for imports from developed countries. Competitive devaluation of currencies relates to
the behaviour of emerging countries in which they may aggressively lower the foreign exchange
rate in order to stimulate the export industry. During the study period, four of the five countries
experienced the depreciation of local currencies. This has arguably had an impact on the
correlation of financial markets through the reduction of liquidity mechanisms.
5.2.1VECM
This sub-section will discuss the finding of the VECM model. As previously mentioned, VECM
discovers and quantifies the external shocks by investigating the granger causality term in the
equation. This thesis has identified a number of external forces that have dramatically increased
the level of contagion between the US, the UK and the BRICS financial markets. These factors
include the exposure of the toxic assets, the reduction of demand for imports from developed
countries, the collapse of commodities prices and the depreciation of local currencies. This subsection will discuss these findings that were generated through the VECM model.
Lehman Brothers and other financial institutions
Proceedings of 4th European Business Research Conference
9 - 10 April 2015, Imperial College, London, UK, ISBN: 978-1-922069-72-6
The initial shock of the global financial crisis was the collapse of the major financial institutions in
the US and the UK. This sent a shockwave through the world such that many large multinational
financial institutions required governmental rescue. Many other lesser affected institutions also
needed significant investment injection to either repair the balance sheet or re-capitalise. Although
most of the literature suggested that the exposure of toxic assets for the banks within the BRICS
was limited, the indirect effect of the trading of CDOs and other securities means that they were
also under significant pressure to re-capitalise balance sheets. Asian banks, like India’s, came out
of the crisis in better shape compared to other geographical regions because they had
implemented measures during the Asian Financial Crisis in the early 2000s and these measures
limited exposure to toxic foreign investment. In China, the trading of asset-backed securities was
only recently introduced and this further insulated it from the crisis. Indeed, according to The
Economists, four of the top ten most capitalised banks are Chinese.
In Brazil, due to historical and inheritance reasons, banks are strongly linked to Spanish
institutions and exposure to the crisis for Brazilian financial institutions was very indirectly coming
from continental Europe. Countries such as Russia are also more traditionally closely linked with
Europe than with the Anglo-Saxon economies. Hence, the finding of this thesis is consistent with
the literature; there was only a limited exposure to toxic assets for the financial institutions located
within the BRICS. Therefore, the causality of the collapse of the US and UK institutions, although
found to be significant, was considered to be small in the VECM model.
Reduction of demand of import from industrialised countries
The success story of the export-led countries employed by BRICS in the early 2000s to the
present has raised questions about whether the emerging countries have decoupled from the
industrialised countries. Many of the smaller countries and to some extent the western countries
were looking at the secret of the BRICS because the export-led economies had been delivering
near double figures of growth for BRICS members. Export-led growth countries were also able to
turn a budget deficit into budget surplus, particularly in Brazil and Russia. In China the sheer size
of the exports accumulated a significant sum of foreign reserves.
However, the answer to this question has been provided during this financial crisis: manufactured
goods have to be consumed and if developed countries cannot do so, the economies of these
export-led countries will suffer. The resulting recession in the developed countries has impacted
the confidence of the consumption. This affected the desire of the western countries, which
directly resulted in the decline in demand for the manufactured goods and services provided by
the BRICS. As a result, the service industry in India recorded an export decline of 60% to the US
and a sharp decline of growth for China also was recorded.
This affected the performance of export-led companies and the confidence of the financial
markets as the recession in the West slowed the economies of the BRICS substantially. In China,
the government resorted to increasing domestic consumption by using a stimulus package. This
created a cushion for the manufacturing sector in China, hence reducing the decline of
employment opportunities and utilising the reserve capacity in the factories.
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Collapse of commodities prices in mid-2008
While China and India are manufactured goods and service export economies, Russia, Brazil and
South Africa are natural resources export countries. In these countries, over a quarter of the GDP
is related to the export industry of natural resources and this sector is the major earner of foreign
currencies. The financial markets in these countries are dominated by petroleum and multinational
mining companies, which also have significant influence upon the national interests as well as
respective indices.
Hence the BRICS not only suffered a major setback with the decline in demand for exported
manufactured goods; Brazil, Russia and South Africa also suffered significantly during mid-2008
when the price of most of the commodities collapsed. This directly resulted in a decline in the
performance of the indices, affecting the confidence of investors investing in the long term in
these multinational companies. The indirect impact of lowered employment and investment
opportunities spread among the export sector, resulting in a lowered growth of the national GDP
and affecting the respective indices. This resulted in a prolonged period of high volatility, low
growth opportunities and ultimately the contagion effect from the US and UK financial markets.
Outflow of liquidity
During the early part of the financial crisis, the financial markets in the developed world were
suffering a liquidity crisis as a result of sub-prime mortgage events. Financial institutions in the US
and the UK were instructed to rebuild their balance sheet by recapitalising. Hence, they were
selling assets to replace the toxic assets on the balance sheets. The emerging markets became a
very attractive region for recapitalising as this region represents some of the most liquid markets,
like China and Brazil. The assets within the BRICS are also very attractive to investors for a
number of reasons. First of all, they are also very liquid and can turn back to cash easily. This is
very important for struggling banks as time was crucial to regain the confidence of investors.
Secondly, and maybe most importantly, these assets are insulated from the toxic assets in the US
and the UK. Due to local fiscal regulations, the exposure to toxic assets was found to be limited by
most of the literature. Thirdly, the risk premium in the emerging markets has become lower
compared to the US and the UK markets. This ensures that the assets in the emerging markets
are very attractive for opportunistic investors. As a result, financial markets in the BRICS
experienced a drain in liquidity, and hence an increase in volatility and decrease in value.
Furthermore, some of the literature has found that individual and institutional investors may sell off
BRICS assets to repay the margin that they might have lost when the US market collapsed in
2008. This affected the financial markets and increased the contagion effect.
5.2.2DCC GARCH
The DCC GARCH model predicts the correlation of two financial markets over a period of time. As
previously mentioned, the contagion that the DCC GARCH estimates consists of three channels
and this sub-section will discuss these channels.
Underlying financial linkage
This channel measures the passive linkage between two financial markets depending on the
characteristics and the behaviour of investors in the local region. The DCC GARCH model is
useful to estimate this passive contagion channel as it uses the historical return and correlation to
predict the time series’ present correlation. It can take into account the relatively unknown and
hard-to-quantify historical characteristics of local markets. These historical and present
characteristics can be factors like cultural inheritance, fiscal policies, governmental regulations,
the behaviour of local and foreign investors, political position within the geographical region and
other hidden / passive relationships between financial markets such as treaties.
Governmental regulations is a popular topic to be discussed in literature that result in a contagion
effect to a particular financial market. In emerging countries, there is often regulation restricting
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the outflow of investment, such that profits may be reinvested back to local regions or
communities. In Asian countries like China and India, governmental regulations have been
amended since the Asian Financial Crisis in the early 2000s such that there is insulation to this
channel of contagion. Financial markets and fiscal are also heavily regulated in Russia to prevent
an outward flow of investment. There are similar governmental policies, to a lesser extent, in
Brazil such that there is a restriction of the outflow of liquidity in respective financial markets.
However, in South Africa regulations are relatively weaker; hence, the contagion effect is
relatively strong compared to the other four countries. The regulations are not adequate enough
to prevent a withdrawal from investing countries. Thus, South Africa experienced a stronger
decline of market capitalisation compared to the other four countries and a much stronger
contagion effect.
Trade linkage
This channel is particularly important to the BRICS as an export-led growth strategy has been
employed. All five countries are in the G20, showing the significance of the trade for global
importance too. There is a positive relationship between trade and correlation of financial markets
because the performance of trade will affect both markets. The healthy growth of trade for both
markets will also increase the confidence and the capitalisation of financial markets. This will
ensure that the financial markets will grow together, resulting in a higher correlation and contagion
effect at the end.
For example, Russia has a lower correlation with the UK compared to India because of the
traditional heritage and business partnership environment. India belongs to the Commonwealth
and has been the beneficiary of the outsourcing exercise that the UK has been doing since the
1990s. Hence businesses in India are highly integrated with the UK economy compared to Russia.
In contrast, Russia’s economy depends on the exportation of natural resources to Holland,
Germany and Eastern Europe and only 6% of natural gas is delivered to the UK. Similarly, the
business relationship between the UK and Brazil is relatively immature compared to India and
South Africa. The majority of the oil products from Brazil would be delivered to the US and China.
Hence the correlations of Russia and Brazil found by the DCC GARCH model are insignificant
and low when compared to India and South Africa.
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Currency devaluation
In mid-2008, four of the BRICS currencies apart from the Chinese Yuan experienced a sharp
depreciation against the dollar. Although this was not intentional and not enough to start the
devaluation war against each other, this effectively became a competitive devaluation of local
currencies to boost export. On the other hand, the Chinese government employed a strict policy of
not allowing the Yuan to appreciate despite pressure from the US. This has further weakened the
demand for Chinese exports and the growth of the Chinese GDP has fallen below a single
percentage digit for a decade. As a result, the trade link between the Chinese and the world
weakened when exports became less competitive compared to the rest of the BRICS.
Because of this, most of the literature has argued that Chinese contagion through this channel
has been weakened. This is due to the fact that the Yuan became less competitive compared to
the other four BRICS countries, affecting the trade channel as discussed previously. Together
with the strict government policy for foreign investment and relatively limited exposure to the toxic
assets, this has formed a further insulation layer for China to move with the US and UK markets.
Therefore, the contagion effect experienced by the Chinese markets was small and insignificant.
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6.
Conclusion
Contagion has been an interesting subject to study during the recent 2008 sub-prime financial
crisis as it is rare that a crisis originated from a developed country. From this paper, we have
gained an understanding that the contagion effect was present and has affected most of the
BRICS countries apart from China. Therefore, the decoupling theory that some of the literature
maintained was not conclusive, as the economies of the emerging countries possessed
underlying links to the US and the UK markets. This paper has found that, although the model of
the BRICS economies is different from that of Anglo-Saxon countries, the underlying financial
linkages are too strong for economies to have decoupled.
This paper has studied two models of common econometrics to analyse the contagion effect. The
researcher has made a comparison between what the models measure and what they mean in
the contagion study as well as the decoupling–recoupling theory. The correlation found by the
DCC GARCH model is that the linkages between these countries are still very high and became
even higher during the financial crisis. This is because the negative impacts of the turmoil that
occurred in the western economies filtered into the BRICS bloc and resulted in a significant
downturn in the financial markets. This has been confirmed in the discussion section: the trade
linkage and currencies linkage are also very closely related across the studied countries. The
external shock that was analysed in the VECM model enabled us to conclude that the fall in
demand from industrialised countries dented the growth of the export-led economies of the BRICS.
This analysis concluded that the global economies are strongly linked as the export-led
economies must find countries to buy their goods and if demand for these goods falls, so does
growth in the economies of the BRICS.
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Appendix A – Descriptive Statistics
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Appendix B – Performance of indices
Graph A – Brazil
Brazil
80000
70000
60000
50000
40000
30000
20000
10000
0
1/2/2007
1/2/2008
1/2/2009
1/2/2010
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Graph B – Russia
Russia
2500
2000
1500
1000
500
0
1/2/2007
1/2/2008
1/2/2009
1/2/2010
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Graph C – India
India
25000
20000
15000
10000
5000
0
1/2/2007
1/2/2008
1/2/2009
1/2/2010
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Graph D – China
China
7000
6000
5000
4000
3000
2000
1000
0
1/2/2007
1/2/2008
1/2/2009
1/2/2010
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Graph E – South Africa
South Africa
180
160
140
120
100
80
60
40
20
0
1/2/2007
1/2/2008
1/2/2009
1/2/2010
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Appendix C – DCC GARCH correlation graphs
Correlation with the US
Graph 1: Brazil
Graph 2: Russia
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Proceedings of 4th European Business Research Conference
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Graph 3: India
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Graph 4: China
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Graph 5: South Africa
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Correlation with the UK
Graph 6: Brazil
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Graph 7: Russia
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Graph 8: India
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Graph 9: China
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Graph 10: South Africa
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Appendix D – DCC GARCH statistic results
Correlation Table
Brazil - US
Brazil - UK
Russia - US
Russia -UK
India- US
India - UK
China - US
China- UK
South Africa - US
South Africa - UK
T1
0.08*
0.08**
0.03
0.11
-0.01
-0.01*
-0.02
-0.01
0.36*
0.56*
Table 3: DCC GARCH Results
T1 initial value = 0.2
T2 initial value = 0.7
*1% significant
**5% significant
T2
0.65*
0.15
0.92*
0.29
0.87**
0.95*
0.40
-0.31
0.48*
0.95*
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Appendix E – VECM statistic results
VECM
Brazil - US
Brazil - UK
Russia - US
Russia -UK
India- US
India - UK
China - US
China- UK
South Africa - US
South Africa - UK
β1
0.72
0.26
0.30
0.18
0.79
0.58
0.87
0.01*
0.74
0.16
β2
0.27
0.90
0.08***
0.43
0.13
0.04**
0.93
0.55
0.74
0.03**
Table 2: VECM Results
+
β : Wald Test β1=β2=0
*1% significant
**5% significant
β+
0.52
0.51
0.15
0.34
0.33
0.09***
0.98
0.02*
0.91
0.05**
Dummy
0.03**
0.80
0.48
0.19
0.00*
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