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Short-term impacts of the 2004 Indian Ocean Tsunami on stock markets: A DCC-GARCH analysis


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This paper examines the impacts of the 2004 Indian Ocean Tsunami on the stock markets of four affected countries namely India, Indonesia, Malaysia and Thailand. More specifically, we investigate the existence of contagion effects of the tsunami between these markets. In order to capture potential contagion effects, we use a dynamic-conditional-correlation-multivariate GARCH model developed by Engle and Sheppard (2001), Engle (2002) and Tse and Tsui (2002). Tsunami impacts are modelled using dummy variables. Natural disasters often cause sharp declines in stock markets followed by recoveries that are almost as intense. Despite the significant devastation and massive loss of life, the Asian markets remained relatively calm and even increased during the week following the tsunami: there were no contagion effects between the indices in the post-tsunami period.
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Bankers, Markets & Investors nº 145 november-december 2016
Short-term Impacts of the 2004
Indian Ocean Tsunami on Stock
Markets: A DCC-GARCH Analysis
I. Introduction
In the past decade, there have been more frequent
occurrences of natural catastrophes that have affected
different parts of the world. Perhaps still fresh in the
mind, is the devastating 2004 Indian Ocean earthquake
and tsunami that killed more than 220,000 people across
all countries affected. Figure 1 shows the main areas affec-
ted by the tsunami. The economic damage resulting from
the tsunami was close to US$9.5 billion in the worst-hit
countries such as Indonesia, Malaysia, Sri Lanka, India
and Thailand (Inderfurth, Fabrycky, and Cohen, 2005).
To take a more recent example, the north-east coast of
Japan was struck by an earthquake and a tsunami in 2011,
which cost at least US$235 billion or around 4% of the
country’s Gross Domestic Product (GDP) according to
World Bank estimates (The Economist, 2011).
The direct and indirect costs of these catastrophic events
are enormous, and better aftermath disaster-management
is needed to curb these potential sums. As a result, there are
emerging interests from industry professionals, as well as
academics who try to address the economic and nancial
impacts of natural disasters. On the one hand, academic
studies on the economic impacts of natural disasters are
abundant and widely recognised. For instance, some stu-
dies such as Hallegatte and Dumas (2009), Kim (2011),
Noy (2009), Skidmore and Toya (2002) have examined the
economic consequences of natural disasters in the short
and long runs. On the other hand, to date, studies on the
nancial impacts of natural disasters have been limited
and have been primarily focused on specifi c industries or
companies such as the insurance industry and its players.
While there are a few studies on the market-wide effects
of natural disasters, very few have focused only on one
major disaster and its effects on stock markets.
While academic studies have not addressed the in-depth
effects of the 2004 tsunami on stock markets, indepen-
dent organisations have published numerous reports
suggesting that stock markets were not affected during
the post-Indian Ocean tsunami period. In fact, it appears
that stock markets have shown better returns or perfor-
mance. For instance, a report published by the Economist
Intelligence Unit in 2005 showed that stock markets in
affected countries did not collapse. Interestingly, obser-
vations, one month after the disaster, showed that stock
markets in Indonesia, Sri Lanka and Thailand experienced
better performance, compared with the period prior to
the disaster (Economist Intelligence Unit, 2005).
In an attempt to further investigate these phenomena
and to fi ll the gap in the existing literature, this study aims
to examine the short-term effects of natural disasters
on stock markets in the most affected countries – India,
Indonesia, Malaysia and Thailand. The fi ndings of this
study can be used to help professionals to anticipate
* Corresponding author
Adress: 17 rue de la Sorbo nne, 75231 Paris cedex, France
Mail: erwan@l
Figure 1. Countries affected by the
2004 Indian Ocean earthquake
Maître de
Paris 1
Labex REFI
Paris 1
Bankers, Markets & Investors nº 145 november-december 2016 5
and manage better in the event of occurrences of natural
disasters. The remainder of the paper proceeds as follows.
Section 1 reviews the existing and related literature on the
topic. Section 2 presents a dynamic-conditional-correla-
tion-multivariate GARCH model. Section 3 shows data
sets and preliminary statistics of stock returns. Section
4 presents our empirical findings and discussion. The
paper ends with a conclusion.
II. Literature review
Economic growth has always been a centre of interest
for academics and industry professionals alike. Investors,
in particular, are interested in growing economies and in
identifying opportunities for growth where their wealth
can be grown and expanded.
Natural disasters can be viewed as exogenous or external
shocks that may potentially cause massive destruction to
economies; they are regarded as acting as a kind of Schum-
peterian creative destruction (Cuaresma, Hlouskova, and
Obersteiner, 2008). Some research studies have focused
on their impacts on the short, medium and long-term
growth of economies. Earlier studies of the economic
consequences of natural disasters have concentrated on
short-run responses; for example, Dacy and Kunreuther
(1969) found that GDP tends to increase after a natural
catastrophe occurs. Noy and Vu (2010) examined the
impact of natural disasters on annual output-growth
in Vietnam. More studies in the past two decades have
emerged focusing on short to medium-term impacts of
catastrophes such as Albala-Bertrand (1993a, 1993b),
Noy (2009), and Tol and Leek (1999). From a long-term
perspective, the widely cited work by Skidmore and Toya
(2002) examined long-run relationships between disas-
ters, capital accumulation, total factor productivity and
economic growth. Other studies by Cuaresma, Hlouskova,
and Obersteiner (2008) and Kim (2011) asserted that a
positive correlation between the frequency of natural
disasters and long-run economic growth was found after
conditioning other determinants. However, the findings
on the long-term effects of disasters are not conclusive.
For instance, an analysis by Hallegatte and Dumas (2009)
found that natural disasters may not necessarily influence
long-term economic growth.
Other studies aim to assess financial impacts of exo-
genous shocks, including natural disasters. Numerous
studies have been done on the impact of exogenous
shocks, from the outbreak of pandemic diseases, to ter-
rorist attacks, and the effect of natural disasters on stock
or equity markets. Intuitively, stock market returns and
volatility differ across countries, and can be affected by
major events around the world. The occurrence of natural
disasters is sudden and unexpected, even though they
can be forecast and anticipated to a degree as a result of
advances in technology. Nevertheless, the consequences
of natural disasters can be intense for the countries that
experience them. It is therefore crucial to possess systematic
studies on the impacts in order that investors and policy
makers alike can better prepare for any repercussions in
financial markets after a natural disaster.
At the company level, Lamb (1995, 1998), for example,
investigated the stock price reactions of property and
casualty-related businesses to occurrences of various
hurricanes in the United States. The findings showed
that the market was able to differentiate between the
magnitudes of hurricanes and the degree of exposure to
loss, which indicates market efficiency in incorporating
information. Similarly, Bolak and Suer (2008) observed
negative effects on insurance firms’ stocks shortly after
an earthquake in Turkey. Shelor, Anderson and Cross
(1990) investigated stock returns for real-estate firms
after an earthquake in California. They found that returns
are significantly negative affected for firms operating
near to the area affected by the earthquake. Nippani and
Washer (2011) asserted that returns of the top European
airline indices were negatively affected by the eruption of
Eyjafjallajokull, once the effects of global equity markets
and oil prices had been removed.
Beyond analysis at industry and individual company
levels, few studies have also explored the impacts of catas-
trophes on capital markets or, specifically, equity markets
as a whole. Worthington and Valadkhani (2004) analysed
the impact of natural disasters on the Australian equity
market using daily prices and accumulated returns data
from the All Ordinaries Index. After normalising other
factors, the findings showed that cyclones, bushfires
and earthquakes exerted influence on market returns,
with cyclones and bushfires generally more significant.
Worthington (2008), who used a GARCH-M to model
return series, found that natural events and disasters
had no significant impact on Australian market returns,
however defined.
The majority of existing research has primarily focused
on analysing a great number of natural disaster occur-
rences and their impacts on capital markets as a whole,
or on specific industries, or even specific companies.
There have been very few studies analysing specific
major disasters and their financial impacts. Nippani
and Bathala (2008) analysed the impact of the Asian
Tsunami on four affected countries. They concluded
that the tsunami had no immediate negative impact
on the Indonesian, Thai, Sri Lankan and Indian stock
markets. The authors used a
-test to test changes in
the mean of returns prior to, and after, the tsunami.
Using conditional correlation coefficients, Lee, Wu
and Wang (2007) found no contagion effects between
the stock market indices of countries, but they did find
contagion effects in exchange rate markets, mainly
for a number of Asian countries. Lee, Wu and Wang
(2007) used conditional correlations without a dynamic
model. Moreover, they used a Fisher test to compare
correlations during different periods, while we use
dummy variables.
III. Methodology
Conditional correlation models are widely employed
in the literature to measure contagion effects and more
specifically the dynamic conditional correlation model
Bankers, Markets & Investors nº 145 november-december 2016
(Chiang et al., 2007; Syllignakis and Kouretas , 2011; Min
and Hwang, 2012; Dua and Tujeta, 2016).
Dynamic Conditional Correlation Models, which were
developed by Engle (2002), Engle and Sheppard (2001)
and Tse and Tsui (2002), as an original specification for
multivariate models’ conditional correlations, allow the
examination of time-varying dependence between series.
The advantages are numerous because they allow an
estimation of time-varying correlations, which are more
representative of the dynamic than the unconditional
correlation measure or Constant Conditional Correlation
(CCC model) of Bollerslev (1990). Dynamic conditional
correlations rely on two GARCH processes that themselves
capture the time-varying volatilities. The model avoids
computational effort, in contrast with first-generation
models, and relies on two parameters (alpha and beta)
to describe the whole dynamic. The DCC-GARCH model
and its extensions deal with financial contagion which can
be defined as a significant increase in the cross-market
correlation during the crisis (Forbes and Rigobon, 2002).
These models have been used, in particular, to study the
1998 Asian asset-markets crisis (Tse and Tsui, 2002, and
Chiang, Jeon and Li, 2007).
Our approach involves two stages. First we produce an
estimated DCC GARCH model. Second, we model pro-
cesses, following by estimated dynamic correlations. The
estimated DCC-GARCH model is created by a two-step
procedure. In the first step, univariate GARCH models
are fitted for each return, and estimates of their variances
are thus obtained. Nevertheless, it should be noted that,
prior to this, autocorrelation of the series is removed using
a VAR (p). In the second step, the standardised residuals
are used to estimate the time-varying correlation matrix.
The multivariate VAR-DCC-GARCH is defined as follows:
t t p
t t
= + + ⋅
μ (1)
t t t t
= (2)
is the multivariate conditional variance,
t t t Nt
= …
( )
1 2
, is the vector of the past observa-
tions, µ0 is a constant, and ε ε ε ε
t t t Nt
= …
( )
1 2
, is the
vector of the standardised residuals.
D diag h h h
t t t NNt
= …
11 22
is defined as the conditional variance obtained from the
univariate GARCH models.
R diag Q Q Q Q diag Q Q
t t t NNt t t t
= …
− − − −
1 2
1 2 1 2
1 2
1 2/ / / / /
, ,
1 2/
is a time invariant
× symmetric dynamic correla-
tions matrix, where
t is the
× symmetric positive
definite matrix of standardised residuals εt.
Q a b Q a b Q
t t t t
= − −
( )
+ ⋅ + ⋅
− −
11 1
ε εʹ (3)
Therefore, following Engle (2002), we can write the
correlation coefficients as:
ρij t
ij t
ii t jj t
q q
, ,
i j n
, , ,= …1 2 and
i j
The specification of the model is selected, based on
Log Likelihood, Akaike (AIC) and Schwartz (SIC) infor-
mation criteria.
Then, as Chiang and al. (2007), Min and Hwang (2012)
and Dua and Tujeta (2016) did, we model the estimated
DCC coefficients ρij t, for each of the two indices as an
AR model. This provides further results on the contagion
effect during different periods following the tsunami.
For each correlation series, we estimate three AR models
where dummy variables for different time horizons are
included. These latter are created to represent different
periods after the tsunami, where values of 1 are assigned
for 1 trading day, 5 trading days (one week), and 23 tra-
ding days (one month) after the tsunami; and a value of
0 for all other times.
ρ γ ρ β
ij,t 0
p ij,t-p k k,t
=C + + Dummy +
ηij t, (4)
is a constant term,
is the pairwise
conditional correlation between the stock returns of
Asian national indices, and
is the dummy
variable, k
corresponds to the number of trading days
after the tsunami (
=5, and
=23 ), and ηij t,.
is a residual term.
IV. Data and preliminary
Data sets employed in this study include stock indices’
daily returns for four of the countries most affected by
the tsunami: India, Indonesia, Malaysia and Thailand.
We used extracts of Bloomberg market data from 4
January 2000 to 31 December 2007. This period of study
encompasses the Indian Ocean tsunami that took place
on 26 December 2004.
The Bursa Malaysia KLCI Index was an open capitalisa-
tion-weighted index which comprised some 500 companies
listed on Bursa Malaysia’s Main Board. In 2009, it was
replaced by the FTSE KLCI Index. The Jakarta Stock Price
Index (JCI) is a modified capitalisation-weighted index
of all stocks listed on the regular board of the Indonesia
Stock Exchange. There were 335 stocks in 2004. The
BSE Sensex Index is a capitalisation-weighted index of 30
well-established, and financially sound, companies listed
on the Bombay Stock Exchange (India). The Index was
shifted to a free-float methodology on 1 September 2003.
The Bangkok SET 50 Index is a capitalisation-weighted
index of the stocks of the largest 50 companies traded
on the Stock Exchange of Thailand.
Table 1 represents sector allocation for the four Asian
national indices, following the Global Industry Clas-
sification Standard (GICS). The sectoral distribution
differs depending on the national indices concerned.
Nevertheless, we can observe the significant presence of
telecommunications services and the financial sector in
at least three indices (JCI, BSE and SET 50).
Bankers, Markets & Investors nº 145 november-december 2016 7
Table 3. Testing for
autocorrelation and
heterocedasticity in VAR (3)
Q(5) Prob Q(10) Prob
Portmanteau test 41.13 0.13 117.75 0.34
LM autocorrelation 19.05 0.27 15.35 0.50
Chi 2 Df Prob
White no cross term 1271.65 240 0
White with cross term 3059.87 900 0
Table 2 reports the descriptive statistics of stock
indices’ daily returns. The Jacques-Bera statistic rejects
the null hypothesis that the returns distribution is
normally distributed. In fact, we can observe nega-
tive skewness in the distribution that indicates that
the observed values have a long tail to the left. Kur-
tosis, for all series, is higher than 3 which indicates
fatter tails in the distribution. These characteristics
Table 1. Sector allocation of Asian stock indices
Energy 3.43 4.92 15.22 21.78
Materials 5.70 9.46 6.83 23.01
Industrials 21.88 3.51 11.21 8.82
Cons. Discretionary 9.76 10.24 6.55 1.05
Consumer Staples 12.6 16.73 10.31 -
Health Care 0.87 1.98 2.55 -
Financials 8.33 32.31 25.97 25.65
Info. Technology 1.99 0.12 14.01 0.47
Telecom. Services 17.14 19.52 3.91 16.33
Utilities 18.3 1.21 3.44 2.89
Table 2. Summary statistics of the stock index returns
Mean 0.070% 0.027% 0.066% 0.028%
Standard deviation 1.382% 0.926% 1.540% 1.595%
Skewness -1.00 -0.84 -0.62 -0.55
Kurtosis 10.90 11.29 7.07 12.58
Jarque-Bera 5551.9 5976.9 1513.6 7764.6
suggest that an asymmetric GARCH model should
be more adapted to our series. We also note that BSE
and SET 50 indices are the most volatile, while JCI is
the least so. During the study period, returns of our
four indices are positive: JCI and SET 50 are the less
profitable indices, while the KLCI mean return is the
highest. Augmented Dickey–Fuller tests conclude that
all return series are stationary.
Table 4. Selection of DCC
DCC Model Log
Likelihood AIC BIC
GARCH (1,1) 24433.50 -24.38 -24.32
EGARCH (1,1) 24327.60 -24.26 -24.18
GJR (1,1,1) 24484.40 -24.42 -24.36
Bankers, Markets & Investors nº 145 november-december 2016
V. Empirical findings
Prior to the determination of the DCC-GARCH model,
we determined the optimal number of lags of the VAR(p)
model to remove autocorrelation in the return series.
Our results are reported in Table 3. According to the
Portmanteau and LM tests, the null hypothesis of no
serial correlation is not rejected when the number of
selected lags is equal to 3. White tests indicate that the
null hypothesis of no autocorrelation in the squared resi-
duals is rejected. These various results demonstrate that
the presence of volatility clustering and residual series
are heteroscedastic. They justify our choice to retain the
DCC-GARCH model.
Table 4 reports information criteria and likelihood
ratio tests applied on different specifications of the DCC-
GARCH class – namely, standard GARCH of Bollerslev
(1986), EGARCH of Nelson (1991) and GJR-GARCH of
Glosten et al. (1993). Considering the results, we selected
a DCC model with GJR-GARCH (1,1,1) specification for
the equations of conditional variances. Following Table
4, we include a GJR process to account for asymmetrical
behaviour in the correlation process.
h c h I
t t t t t
= + ⋅ + + ⋅ ⋅
− −
0 1 1
1 1 1 1
α ε β γ ε (5)
Where Iif
1 0
0 0
The condition for existence of the second moment
of the
GJR-GARCH is observed : this condition is alpha(1) + beta(1)
+ k gamma(1) < 1 (with k = 0.5). This result implies that
the variance is stationary (see Table 5). Volatility is more
persistent for KCLI and BSE (the sum of the coefficients
is closer to 1). The GARCH coefficients are material but
ARCH coefficients are very low. These results show that
recent information has a small influence in the light of
past information. The GJR coefficients are significant for
BSE, JCI and SET50 which means there are asymmetric
effects in volatility. The positive value means that when
the market is bearish, volatility is higher. A Ljung-Box
Q-tests for autocorrelation in the residual series indicate
that there is no heteroscedasticity (Q² (5) and Q² (10)).
The GJR-GARCH estimates have captured the volatility
persistence effect (see Table 5). According to Table 6,
the DCC alpha and beta coefficients are both significant
at the 1% level.
Table 7 reports descriptive statistics of dynamic corre-
lations for all pairs. The coefficient is quite low but they
are significantly different from zero. The correlations
between the Indonesian, Malaysian and Thailand indices
Table 5. Conditional variance parameter estimates
Coef. SE Coef. SE Coef. SE Coef. SE
Constant 0.160*** 0.048 0.482 0.437 0.321*** 0.115 0.363* 0.216
ARCH 0.030 0.020 0.037** 0.015 0.008 0.020 0.039* 0.024
GARCH 0.779*** 0.047 0.938*** 0.028 0.678*** 0.099 0.752*** 0.080
GJR 0.225*** 0.056 0.045 0.030 0.282*** 0.096 0.121** 0.047
Sum 0.921 0.997 0.827 0.852
Q²(5) 4.018 3.311 2.495 0.2
Q²(10) 5.678 4.835 9.464 0.511
Table 6. DCC GJR-GARCH (1,1,1)
Coefficient SE
a 0.013*** 0.005
b 0.974*** 0.015
Table 7. Descriptive statistics of dynamic
Mean 0.175 0.268 0.224 0.293 0.290 0.289
Median 0.174 0.267 0.223 0.289 0.286 0.283
Maximum 0.506 0.534 0.440 0.643 0.534 0.556
Minimum -0.115 0.042 -0.001 0.055 0.071 0.080
Deviation 0.093 0.086 0.068 0.090 0.073 0.084
Skewness 0.284 0.404 -0.055 0.838 0.232 0.353
Kurtosis 3.805 3.491 3.531 4.381 3.625 3.180
Bankers, Markets & Investors nº 145 november-december 2016 9
are the highest. Table 8 reports ADF unit root tests for
conditional correlations. It appears that four series
to have a stationary trend. Given these results, we add a
trend in our AR processes to model the dynamic condi-
tional correlations series. Table 9 presents the estimated
coefficients of dynamic correlation processes (Equation
4). Except for the SET50_KCLI conditional correlations
series, the trend is significant at 10%. We find that the
returns are auto-correlated to order 1. The AR coefficients
are close to 1, as Chiang and al. (2007), Min and Hwang
(2012) and Dua and Tujeta (2016) found. The coefficients
of the dummy variables d1, d5 and d23 are not significant
except for the SET50_BSE series. Specifically, d1 and d5
are significant at a 5% level. They indicate a positive effect
of the tsunami on the dependence between Thai index
returns and Indian index returns. Given this set of results,
the contagion effect among Asian national indices in the
post-tsunami period appears to be very low or non-exis-
tent. Figure 2 illustrates this result. Here, the evolution
of dynamic correlations in time and the negligible impact
of the tsunami can be observed.
Our results confirm studies that have concluded that
stock markets in the region generally were not affected
by the disaster and, in fact, behaved differently to what
was expected, compared with performance prior to the
disaster. Such behaviour may seem surprising given that
the media highlighted the unprecedented nature of this
disaster. Nevertheless, it seems that markets behaved
rationally. Tsunami damages had a negligible impact on
Table 8. ADF unit root tests for conditional correlations (DCC-TGARCH)
Constant Trend
Coef. SE Coef. SE Test value
KCLI_BSE 0.001 0.001 1.788E-06** 6.390E-07 -3.967***
JCI_BSE 0.004*** 0.001 3.665E-06*** 7.841E-07*** -5.538***
SET50_BSE 0.003** 0.001 8.417E-07 4.890E-07 -4.150***
JCI_KCLI 0.004** 0.001 1.835E-06** 6.584E-07 -4.352***
SET50_KCLI 0.004** 0.001 6.203E-07 5.276E-07 -4.179***
SET50_JCI 0.004** 0.001 2.139E-06** 6.374E-07 -4.534***
Table 9. Estimation of conditional correlation processes with White standard errors
C Trend AR(1) d1 d5 d23
Coef. SE Coef. SE Coef. SE Coef. SE Coef. SE Coef. SE
KCLI_BSE 0.061 0.049 0.000*** 0.000 0.984*** 0.004 -0.005 0.004
JCI_BSE 0.141*** 0.022 0.000*** 0.000 0.971*** 0.006 -0.001 0.001
SET50_BSE 0.175*** 0.037 0.000* 0.000 0.983*** 0.004 0.002** 0.001
JCI_KCLI 0.191*** 0.042 0.000*** 0.000 0.982*** 0.004 0.002 0.002
SET50_KCLI 0.252*** 0.040 0.000 0.000 0.983*** 0.004 -0.001 0.001
SET50_JCI 0.180*** 0.033 0.000*** 0.000 0.980*** 0.004 0.007 0.006
KCLI_BSE 0.061 0.049 0.000*** 0.000 0.984*** 0.004 0.003 0.009
JCI_BSE 0.141*** 0.022 0.000*** 0.000 0.971*** 0.006 0.001 0.003
SET50_BSE 0.175*** 0.037 0.000* 0.000 0.983*** 0.004 0.002** 0.001
JCI_KCLI 0.191*** 0.042 0.000*** 0.000 0.982*** 0.004 0.000 0.001
SET50_KCLI 0.252*** 0.040 0.000 0.000 0.983*** 0.004 -0.001 0.001
SET50_JCI 0.180*** 0.033 0.000*** 0.000 0.980*** 0.004 -0.000 0.000
KCLI_BSE 0.061 0.049 0.000*** 0.000 0.984*** 0.004 0.007 0.013
JCI_BSE 0.141*** 0.022 0.000*** 0.000 0.971*** 0.006 0.004 0.005
SET50_BSE 0.175*** 0.037 0.000* 0.000 0.983*** 0.004 0.005 0.003
JCI_KCLI 0.191*** 0.042 0.000*** 0.000 0.982*** 0.004 -0.000 0.001
SET50_KCLI 0.252*** 0.040 0.000 0.000 0.983*** 0.004 -0.002 0.001
SET50_JCI 0.180*** 0.033 0.000*** 0.000 0.980*** 0.004 -0.000 0.001
Bankers, Markets & Investors nº 145 november-december 2016
VI. Conclusion
This study presents an analysis of the impacts of the
2004 Indian Ocean tsunami on the stock markets of four
affected countries, namely India, Indonesia, Malaysia
and Thailand. We used a dynamic conditional correlation
(DCC) multivariate GARCH model developed by Engle
(2002), Engle and Sheppard (2001) and Tse and Tsui
(2002) in order to capture potential contagion effects
between these markets after the tsunami. The major
advantage of employing this approach is the detection
of potential changes in time-varying conditional cor-
relations. Our results indicate that stock markets in
the region stayed calm, compared with performance
prior to the disaster: natural disasters often provoke
stock market declines, followed by recoveries, when
they occur. This lack of reaction on the Asian financial
markets can be explained by the fact that the damage
was done in outlying areas where there is little concen-
tration of production, and also by the huge amount of
assistance provided which made a positive contribution
to growth.
1 An augment ed Dickey–Fuller test (A DF) tests the null hyp othesis of whether a unit
root is present in a conditional v ariance ser ies. Table A1 in the Appendix r eports
ADF tests realized with a const ant and a trend. We reject the unit root hypothe sis at
a 1% level for all series.
the industrial capacity of countries although it caused many
fatalities. The damage was done in outlying areas where
there is little concentration of production. Therefore, there
are few reasons to suppose that companies that make up
the different stock indices would experience declining
results. Furthermore, although it may sound cynical on
the face of it, the disaster may have even produced econo-
mic and commercial benefi ts as a result of the necessary
reconstruction period. Moreover, as the sectoral repre-
sentation is different from one index to another, there
was little probability that the level of correlation increased
signifi cantly following the tsunami. The huge amount of
assistance provided by other countries sensitised by this
tragedy also made a positive contribution to growth. The
tourism and insurance sectors were affected in varying
proportions, but some have claimed that the insurance
industry could ultimately gain from the tsunami due to the
scope for higher premiums and more insurance contracts.
In fact, in Asian countries, companies and individuals
are traditionally underinsured. In addition, the disaster
also boosts the sectors involved in the reconstruction
of coastal areas.
Figure 2. Evolution of dynamic correlations
Bankers, Markets & Investors nº 145 november-december 2016 11
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Bankers, Markets & Investors nº 145 november-december 2016
Table A1. ADF unit root tests for conditional variances (TGARCH)
Coef. SE Coef. SE Coef. SE Coef. SE
Constant 3.757e-05 6.389e-06 5.189e-06 1.154e-06 5.918e-05 7.702e-06 5.338e-05 6.586e-06
Trend -9.529e-09 4.791e-09 -2.149e-09 7.8486e-10 -4.491e-09 6.037e-09 -6.775e-09 4.731e-09
ADF -11.262*** -6.359*** -18.097*** -14.408***
The number of lags is determined by the Schwarz criterion
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