1

The Impact of the Boxing Day Tsunami on the World Capital Markets

Abstract

The effects of the Boxing Day Tsunami on the world equity markets are investigated in

this paper. In particular, this paper studies how the risks and returns of industry and

market portfolios are altered as a result of the tsunami. The analysis includes

countries that were directly and/or indirectly exposed by this catastrophe. Both

parametric and non-parametric tests are employed to explore the relationship

between equity stock returns and the tsunami, and the CAPM is utilised to assess the

variation in systematic risks. Given that the literature in this area is underdeveloped,

we draw on the economic theories of flooding. In that sense, our results are consistent

with the flooding literature, whereby it is predicted that the Boxing Day Tsunami has

minimal impact on the risk and return of equity markets. This paper documents few

abnormal return changes and a general increase in the long term systematic risk of

equity portfolios in the study.

JEL Classification: G1, G11, G14, H56, Q54

Keywords: tsunami, equity market, abnormal returns, systematic risk,

non-parametric test, parametric test, CAPM

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I. Introduction

According to Roll (1988), stock markets around the world react differently to major

events. Furthermore, Rietz (1988) and Barro (2006) postulate that rare and extreme

events change risk premia in financial markets. On the 26th of December 2004, an

earthquake measuring around 9.3 on the Richter scale occurred under sea in the

Indian Ocean, triggering what is commonly referred to in the literature as the Boxing

Day Tsunami. This event resulted in the loss of lives, missing and displaced people,

property damage and psychological scars in the countries that were directly hit. Whilst

the humanitarian data has been documented, little is known regarding how the stock

markets in those countries reacted to this incident. Further, the literature in the area of

how international markets are affected by this incident is also sparse. As such, the

objective of this paper is to test the hypotheses of Roll (1988), Rietz (1988) and Barro

(2006) regarding whether a major event, such as the Boxing Day Tsunami, affects the

volatility and return of capital markets around the world.

In the political economic literature, Keys, Masterman-Smith and Cottle (2006) argue

that the tsunami impaired the life of poor people-regions and countries that do not

have the power to drive the market in the first place. Such argument is upheld by

subsequent regional studies. For instance, Bandara and Naranpanawa (2007)

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uncovers significant firm losses due to damages of assets and infrastructure in some

geographical regions of Sri Lanka. Bird et al. (2007) discusses the impact of

socio-economic disturbances on local communities within the tourism industry in

Langkawi and Malaysia. Other researchers refuse to limit the consequences to

geographical regions and attempt to extend that literature to the macroeconomic

level. Ichinosawa (2006) led by showing that the tourism industry was affected in

Thailand; Lee, Wu and Wang (2007) demonstrates that the FOREX markets of certain

countries were affected and this study adds to the debate. Specifically, we contribute

to the financial literature by exploring how the tsunami influences risks and returns in

the equity markets. Interestingly, literature on the impact of the tsunamis does not

exist and so we rely on the natural disasters literature. A study carried out by

Worthington and Valadkhani (2004) shows that bushfires, cyclones and earthquakes

tend to change market returns, whilst severe storms and floods do not.

There is another growing literature on the effect of disasters on GDP, wages, labour

markets and they show that disasters in general have important and sizable effects

[see Kahn (2005), Raschky (2008), Strömberg (2007) and Toya and Skidmore

(2007)]. In a similar vein, there is a developing literature on the impact of

environmental news on the stock market [see Dasgupta et al. (2006); Dasgupta et al.

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(2001) and; Laplante and Lanoie (1994)]. Therefore the main objective of this paper is

to bridge the gap in the literature on the effects of tsunamis on capital markets.

The countries that were directly struck by the tsunami provide an ideal testing ground

for the above arguments. According to the UNDP, there were 12 countries that were

affected: Indonesia, Sri Lanka, India, Thailand, Malaysia, Tanzania, Bangladesh,

Kenya, Maldives, Somalia, Myanmar and the Seychelles. The likelihood of another

tsunami with the same magnitude is relatively small, but due to climate change, a

similar flooding incident may occur as the result of an increase in the sea level. It is

thus important to know what the consequences of such a calamity are for these

countries in the event that history repeats itself. As a result, we study all of the market

segments within these countries (conditional on data availability and the existence of

a stock market). We then extend our analysis to the rest of the world to determine

whether there are any externalities or spillover effects. In total, we study 41 countries

that were not directly hit by the tsunami.

We hypothesize the following. First, if investors perceive an increase in their expected

costs or a decrease in their revenue after the tsunami, they will respond negatively to

such that event. For instance, the tourism industry will be adversely affected in the

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countries that were directly hit as tourists will move to alternative tourist destinations

that were not affected by the incident. Secondly, if investors perceive a decrease in

their expected costs or an increase in their revenue, then they will react positively to

the tsunami. It is not unusual to find booming businesses during these catastrophic

events, for instance there will be an increase in demand for cleaning, maintenance,

health care and telecommunication companies around that time of the tsunami.

Finally, investors may not react at all if they do not foresee any change in their cost

and revenue analysis. One of the reasons to believe that the tsunami will not move

share prices is that this event does not contain any information effect for

company-industry in question. Systemic risk, conversely, will either increase or

remain unchanged by the amount of the tsunami risk if the cost and revenue structure

is altered. As it is not clear as to which of these possible outcomes will prevail, this

paper investigates these relationships.

The contributions of this research are as follows. First, a detailed industry analysis is

carried out, and this identifies precisely which sectors within a country that was

directly hit by the tsunami are affected. Secondly, to the best of our knowledge, there

is no current study on the aftermath of the tsunami on the world’s capital markets.

Hence, the objective of this paper is to bridge the gap between the tsunami and equity

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markets around the world. This study will be of interest to market participants who

trade internationally, more specifically in emerging markets, as it documents how

these markets reacted to the tsunami. In the event of another similar incident,

investors can use the results of this report as guidance for making their investment

decisions. This analysis will be beneficial to portfolio managers who use the top-down

investment process, as it also provides the industry effects of tsunamis. Third, this

paper is meant to be historical and unique in nature, given that tsunamis of this

magnitude are extremely rare. Additionally, the last tsunami in the Pacific region

occurred on the 30th of September 2009, killing over 140 civilians in Samoa and this

incident is an indication that a tsunami can occur at anytime and that this threat

cannot be ignored.

The findings of this paper provide some support for the hypotheses of Roll (1988),

Rietz (1988) and Barro (2006) in that we find that the Boxing Day Tsunami has the

potential to increase the long run systematic risk of equity portfolios. There were,

nonetheless, minimal statistical changes in abnormal returns for most of the portfolios

studied. This leads us to give more consideration to the conclusions of Keys,

Masterman-Smith and Cottle (2006) and Worthington and Valadkhani (2004) in terms

of the insensitivity of financial markets to the tsunami. Section II presents the data and

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methods used in this paper. Section III presents the empirical findings, and Section IV

provides some concluding remarks.

II. Data and Methods

Data

Table 1 briefly summarises the consequences of the tsunami on the 12 countries that

were directly affected. We state whether they are regarded as tourist destinations, the

number of recorded fatalities and the number of missing/displaced persons as a direct

result of the calamity. Out of these 12 nations, only eight has a registered stock

exchange, and we manage to get the equity data for seven of these countries,

namely, Indonesia, Sri Lanka, India, Thailand, Malaysia, Bangladesh and Kenya. We

downloaded daily stock return indexes and risk-free rates for the period January 1999

to March 2007 for these seven countries from Datastream. The sample deliberately

excluded 2008 and 2009 in an effort to isolate the impact of the subprime crisis. Using

the Global Industry Classification Standards (GICS), we organise 3,097 firms in the

sample into 11 industry-based portfolios. One practical issue we faced in the process

is the small number of firms populating some industry sectors. To overcome this

issue, we modify the GICS standard and created a sector called Other. Table 2

provides a breakdown of these industrial portfolios according to country of domicile.

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The impact of the tsunami on other capital markets is also studied in this paper. These

countries were not directly hit by the tsunami, and in total, we downloaded 41 equity

market indices from Datastream. Countries such as Iran, Nigeria, Pakistan and

Saudi Arabia were excluded, as there were no relevant stock market data for these

countries. Furthermore, we were unable to obtain the relevant risk-free rate data for

Botswana, Bangladesh, Bahrain, Egypt, Iceland, Jamaica, Jordan, Latvia,

Luxembourg and Zimbabwe. In order to control for the international differences, we

converted return data into US dollars, and the MSCI world index is used as a proxy for

the world market return.

Methodology

Return Analysis

Worthington and Valadkhani (2004) studied the impact of natural events using a long

term time series analysis. In an effort to differentiate our work from their study, the

event study methodology of Brown and Warner (1985) is used in this empirical

analysis to determine whether the returns of the industrial and market portfolios are

impacted by the tsunami. First, the daily returns on individual stocks are calculated

using the natural log of the price relative. The ex-post abnormal returns (ARit) for each

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firm are then calculated as the difference between the observed returns of firm i at

event day t and the expected return- whereby the daily expected return is estimated

using the CAPM, in excess return forms over the last 260 observed daily returns. Next,

the abnormal return for each industry I, ARIt at time t is obtained by averaging the

abnormal return of each firm within the industry.

N

i

itIt ARN

AR1

1

.

(1)

The market portfolio for each country is estimated in a similar manner. On the first

trading day after the tsunami, the industry abnormal return calculated using Equation

1 will predominantly respond to two factors: firm-specific information and the actual

tsunami event. In an effort to capture only the impact of the tsunami and to be free

from other firm-specific information, all firms with firm-specific information 15 days

before and after the event day are excluded from the industry portfolio. However, not

all of the countries sampled has readily accessible information on the existence of

relevant announcements. Thailand, Malaysia, Sri Lanka, and India were found to

have data available on the release of firm-specific information, but we were not

successful in obtaining the necessary data for Indonesia, Bangladesh1, Kenya, and

Tanzania. Firm-specific information is defined as any announcement made on the

1 An attempt was made to collect the data for Bangladesh and Kenya. Given, the poor quality of the

data, it was not possible to use in our study. The poor quality is predominantly because of the old

technology used to store such data.

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domestic stock exchange and around the December period a large number of

companies released their end of financial year reports. If we do not isolate the effects

of these firm specific information, then our results will have two components namely

earnings announcements and tsunami effects. In an effort to capture the sole effects

of the tsunami, we exclude the firms with firm specific information. It should be noted

that for the market index of the countries that were not affected by the tsunami, we do

not exclude any firm-specific information.

The parametric t-statistic for the industry abnormal return is estimated as abnormal

return for each industry divided by their corresponding standard deviations. One of the

underlying assumptions of this parametric test is that abnormal returns are normally

distributed. Nevertheless, the literature dealing with abnormal returns show that they

are not normally distributed. In particular, the distribution of abnormal returns tends to

exhibit fat tails and positive skewness. In event studies such as this one, the abnormal

returns can be potentially large on the first trading of trading right after the tsunami.

Consequently, the following two biases may occur. We are more likely to reject the

null hypothesis for positive abnormal performance and are less likely to reject the

hypothesis of negative abnormal returns. As a result, we turn to an alternative test

developed by Corrado (1989), which is a non-parametric ranked t-test. It should be

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noted that Corrado and Truong (2008) argue in favour of this non-parametric test in

event studies around the Asia-Pacific financial markets. The basic principle of this

method is to convert the abnormal returns into a sequential rank. As ranks are

generally not very distant from each other, the ranked distribution is less prone to

non-normality. However, we use this test as a robustness test because it can only

indicate the direction of change of the returns and cannot provide a magnitude of

change. Given that this non-parametric test does not use the actual values, some

academics do not regard this test as a strong quantitative technique. As a result, we

will favour the parametric test results over the non-parametric ones, when we have

conflicting results. Finally, the periodic abnormal returns are aggregated for the

individual industries over five days to obtain the cumulative abnormal returns (CAR).

This represents the excess return investors receive over the five day period and

measures the ability of the equity market to rebound or deteriorate further five trading

days after the tsunami.

Risk Analysis

According to the efficient market hypothesis, investors assess all risks before making

their financial decisions. Trading investors who perceive the tsunami as a risk will

result in a general increase in market risk. We therefore test whether the tsunami

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has an impact on the systemic risk of the industry and market portfolios. We use the

standard CAPM model and apply the Chow breakpoint test to see whether systematic

risk changes following the tsunami. The shortcoming of this approach, however, is

that it only reveals a structural change in the model and will not identify the element

that changes. Under the CAPM, it is possible to observe either a change in the

intercept or a change in the slope (market risk). Nonetheless, the Chow test does not

differentiate between these two factors and reports the results in aggregate. Further,

the CAPM does not differentiate between long-run and short-run changes in the risk

level. To that end, we fit the CAPM with dummy variables to capture the short term

and long term changes in systematic risk that may be generated by the tsunami.

Equation 2 below is used to test whether the tsunami has an impact on the systemic

risk of each industry on the first day of trading following the calamity.

itftmtIftmtIIftIt STErrrrrr ~*]~~[]~~[~~ 21 , (2)

where Itr~ is the industry i’s portfolio return at time t, ftr~ is the risk-free rate at time

t, and mtr~ is the return on the market at time t. STE stands for short-term effect and is

a multiplicative dummy variable that takes the value of 1 on the first day of trading

following the event and 0 otherwise. This variable is meant to capture the short-term

effect of the tsunami on systematic risk. it~ ,

1

I , and 2

I are the error term, the

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beta and the coefficient on the dummy variable, respectively. i is the intercept of

the regression equation with E( i ) = 0 expected.

To test for a change in the intercept, an additive dummy variable is required. The

inclusion of an additive dummy variable in Equation 2 results in a near-singular

variance-covariance matrix. This is because the additive dummy variable and the

multiplicative dummy variable are highly correlated. Consequently, we estimate a

separate equation to test whether the intercept was affected by the event:

itIftmtIIftIt STErrrr ~]~~[~~ 21 . (3)

Equation 3 is estimated for completeness, as the value of the intercept is expected to

be zero. Equations 2 and 3 thus capture the short-term effects of the tsunami on the

systemic risk of equity markets. The long-term effects of the calamity on the

systematic risk of firms are calculated using Equation 4 below:

itIftmtIftmtIIit LTELTErrrrr ~*]~~[]~~[~ 321 . (4)

The test determines whether the level of risk is altered after the event day. The

long-term effect (LTE) is captured by a structural dummy variable that takes a value of

0 prior to the event and a value of 1 after the day of the event (until the end of our

sample). This variable captures structural changes and the influence of the tsunami

on systemic risk over a long-term horizon.

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The above risk models are adjusted for exchange rate changes as we convert all the

returns into US dollar equivalent, and the MSCI world index is used to generate the

international capital asset pricing model (ICAPM) for the market portfolios. Standard

tests and residual diagnostics for normality, autocorrelation, heteroscedasticity and

ARCH effects are conducted for all of the regression models. Most of the regression

models displayed standard non-normality, autocorrelation and ARCH effects. Given

the large sample size, we are not worried about non-normality issue and appropriate

AR terms are added to the model to address the problem of autocorrelation. A

GARCH(1,1) model was added to solve for ARCH effects. We also test whether the

dummy variables are redundant in the above equations using a Wald test.

III. Empirical Findings

This section outlines the empirical findings of the effects of the Boxing Day Tsunami

on the industrial portfolios of the countries that were directly hit and the market

portfolios of 41 countries that were not directly affected by the disaster. Using

parametric tests, a non-parametric test and regression analysis, we test whether the

risk and return of industries and markets were affected by this rare incident. Using

different testing techniques, we observe that the tsunami has a very minimal effect on

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the abnormal returns of the equity portfolios that were studied and results in an

increase in systematic risk for certain sectors.

Return Analysis

The market in Bangladesh opened one day after the Boxing Day Tsunami. It is thus

important to note that we are assessing the performance of the Bangladeshi portfolios

on the 27th of December 2004 and that this is consistent with the event study

methodology. This implies that on that day, the market participants had at least one

night to think about their strategy in the Bangladeshi equity markets. Further, it is

important to remember that firms with firm-specific information surrounding the event

are not excluded from the results reported in Tables 3(A), 4(A) and 6. This implies that

the results contain both the tsunami and information effects. Tables 3(A) and 4(A)

summarise the parametric empirical results for the different sectors in seven countries

that were directly struck by the Tsunami. The abnormal return on the first day of

trading after the event and the five-day cumulative abnormal return, as well as their

respective t-statistics for 11 different industries for these seven nations, are reported

in these two tables. Table 6, on the other hand, reports the parametric and

non-parametric results for the market portfolios of 41 countries. The results reported

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in Tables 3(A), 4(A) and 6 show minimal evidence of statistically significant change in

the returns of the equities markets around the world.

Columns 6 and 7 of Table 3(A) report the abnormal returns and the parametric

t-statistics for the various sectors in Bangladesh. Table 3(A) shows that the returns in

the information technology (IT) sector in Bangladesh increased by 5.27% after the

tsunami, and the t-statistic shows that this value is statistically different from zero.

However, this positive change in return is more likely to have occurred because of the

IT outsourcing launch in Bangladesh at the time rather than the Tsunami.

Surprisingly, no other portfolio exhibits a statistically significant change in abnormal

returns. In other words, the remaining 59 industrial portfolios in the seven countries

were not affected by this event on the first day of trading. Columns 2 and 3 of Table 6

show that Croatia (-3.64%) and Ecuador (-7.55%) responded negatively, whilst Israel

(1.69%) responded positively. These do not appear to be any direct link between the

tsunami and the share market movements. For instance, the Crobex fell because one

of the 12 blue chip stocks in that index, Privedna Banka Zagreb-the second largest

commercial bank in Croatia reported losses. The Ecuadorean stocks index closed

lower in lackluster trading during a holiday-shortened work week whilst the Tel-Aviv

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Stock Exchange (TASE) recorded few positive announcements on the day. In

addition, the remaining 38 nations showed little reaction to the tsunami. We can thus

conclude that the tsunami has weak statistical impact on the returns of capital markets

on the first day of trading. These findings are consistent the results of Keys,

Masterman-Smith and Cottle (2006) and Worthington and Valadkhani (2004), who

reported that tsunamis and flooding-related incidents show little equity market impact.

Other empirical studies focusing on the tsunami reports regional effects in sectors like

tourism and infrastructure; unfortunately, our results are not directly comparable, as

we study industrial portfolios for entire nations.

The efficient market hypothesis (EMH) posits that the market instantly reflects all

available information; perhaps the reaction in these markets was more leisurely. We

therefore assess how the market reacted five days after the tsunami, which enables

us to test whether there was a delay in the share market reaction (i.e., market

stickiness) to this event. The five-day cumulative abnormal returns support the finding

of insensitivity of the equity markets to the tsunami attack. Of the 77 equity portfolios

that are studied, only three portfolios exhibited a significant change in the cumulative

abnormal return over the following five days. The fourth column of Table 4 (A) shows

that the financial sector reacted positively (10.12%) and that the remaining sectors

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showed no statistically significant change in CAR over the next five days in Thailand.

On the 27th of December the Finance Minister Somkid Jatusripitak ordered a

sweeping overhaul of Thailand's bond trading system- a move that would allow an

Asia Bond issue. These measures were expected to inject more liquidity to the local

bond market, with traders better equipped to deal with the variety of risk management

tools available. There were two other industries that were affected in Bangladesh:

energy and materials.

Thus far, the results that we have discussed lead one to believe that the tsunami do

not influence capital markets. However, we must be careful in interpreting these

results because they are contaminated with firm-specific information. After excluding

firm-specific information from our industrial portfolios, a new set of results was

generated, and we report these findings in Tables 3(B), 4(B) and 6. Out of the seven

countries that were directly affected, we only report the results for four countries.

There are two main reasons for this reduction in our sample; namely, either

firm-specific announcements are not available from that country or the country has its

financial reporting around this time. In countries where the financial year is in

December, we exclude the majority of the companies from our portfolios as they

publish their financial reports around the time of our event date. This second issue

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leads to another serious estimation problem ranging from very small portfolios to no

portfolio at all. Table 3(B) shows that only the consumer discretionary sector in Sri

Lanka and the telecommunications industry in India were affected by the tsunami. The

telecommunication sector in India increased by 5.88%; this could be explained by the

fact that individuals used their phones to call the directly affected areas to find out

about the state of their relatives. However, this industry rebounded five days later, as

the CAR reported in Table 4(B) is not statistically significant. Interestingly, we do not

observe the same effects on the telecommunication industry in the other countries

and this discrepancy is hard to explain. The consumer discretionary sector, on the

other hand, was down by 3.61% in Sri Lanka. This industry consists of the

manufacturer of transportation vehicles such as cars, trucks, aeroplanes and travel-

and leisure-related activities. After adjusting for the firm-specific information, the

consumer discretionary sector consisted primarily of the transportation

manufacturers. It is likely that this industry faced a decline in returns, as the travel and

leisure industry was negatively affected around this time. Five days later, the

performance level of this industry dropped by 8.40%; this is shown in Table 4(B).

Another industry that was affected five days after the tsunami was Malaysia’s energy

sector. We found no statistical change in the abnormal return on the first day of

trading, or in the cumulative abnormal return. Even after excluding firm-specific

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information, we cannot conclude that the tsunami has a significant effect on the

majority of the equity markets.

To control for the issue of non-normality in the distribution of AR, a robustness test in

the form of a non-parametric ranking based test is used. Generally speaking, the

results of the non-parametric tests support the results observed in the parametric

analysis. Considering the non-parametric2results in Table 5, it appears that this test

supports the view of the insensitivity of the equity markets with respect to the tsunami,

as most of the results of the non-parametric t-tests were not significant. However,

the non-parametric results for Sri Lanka cannot be ignored. The test supports the

finding that the consumer discretionary sector was down on the first day of trading, but

it also shows that a number of other industries were down around that time. Given that

the results of the non-parametric test for these portfolios conflict with the parametric

results, we favour the parametric test results as it is a more powerful test.

Risk Analysis

2 Generally speaking, when the reported non-parametric t-statistics are less (greater) than negative two

(positive two), we conclude that the abnormal returns were negative (positive) on the day of the event.

Note that this test only provides the direction of change; it does not inform us about the magnitude of

change.

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Based on the above discussion, we can conclude that only the telecommunications

industry in India and the consumer discretionary sector in Sri Lanka were affected on

the day following the Boxing Day Tsunami. With the exception of these two portfolios,

the impact of the tsunami can be viewed as minimal and we can thus hypothesise that

investors did not perceive an increase in risk following the Boxing Day Tsunami. Our

next objective is to test whether the industrial and market portfolios were affected by

the Boxing Day Tsunami in terms of a change in their systematic risk. The

multiplicative regression analysis (see Equation 2) tests this hypothesis immediately

after the attack. Table 7 reports the results of the multiplicative dummy variable model

(Equation 2). A positive (negative) coefficient of the multiplicative dummy variable

(Coef. 3) reflects an increase (decrease) in systematic risk immediately after the event

and thus measures the short-term changes in systematic risk. The sign of the

coefficient (Coef. 3) appears to be mostly positive, and it requires the coefficient of the

multiplicative dummy variable to be statistically different from zero, to imply a

significant statistical change in the systematic risk of the industry. In Table 7,

t-statistics for the short-term changes in the systematic risk show no statistically

significant changes in any of the sectors studied in these countries. In other words,

there is no evidence to support the null hypothesis. The Boxing Day Tsunami altered

the market risk in the short term for any of the sectors in the countries that were

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directly affected. Similar findings (data not shown) were observed in the market

portfolios of the other countries that were not directly affected by this tsunami. Our

results are not directly comparable to the current thin literature in this area. However,

we do observe similarities with Keys, Masterman-Smith and Cottle (2006) and

Worthington and Valadkhani (2004) in that tsunamis and flooding-related incidents

have a minimal impact.

Further, the additive dummy variable in Equation 3 shows the impact of the Boxing

Day Tsunami on the intercept of the CAPM. Once more, we found no statistically

significant change in the intercept, and as a result, we do not report these findings. It

should be noted that in this excess return CAPM, the intercept has no practical

meaning and is meant to be zero. In estimating Equations 2 and 3, we only show the

short-term impact of this tsunami; also, the Chow breakpoint test suggested changes

in the CAPM in certain sectors. As we do not observe any short-term effects, these

changes are more likely to be long term in nature. By applying Equation 4, we can

establish the direction of change in the long-term systematic risk. In particular, we test

whether the market risk changes in the long run. The results presented in Table 8

show that five industries in India, one industry in Sri Lanka and seven industries in

Malaysia exhibited an increase in systematic risk in the long run. For example, the

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systematic risk of financials sector was 0.89 (see column 3 of Table 8) prior to the

event and increased by 0.15 (see column 4 of Table 8) after the calamity. The

systematic risk increased from 0.89 to 1.04 after the tsunami. The Wald test3 reveals

that for this industry (and for the consumer discretionary, consumer staples,

industrials and telecommunications sectors in India; the financial sector in Sri Lanka;

and the consumer discretionary, consumer staples, energy, health care, industrials, IT

and telecommunications sectors in Malaysia), the dummy variables were not

redundant variables, i.e., these sectors experienced an increase in the long-term

market risk. However, we do not observe any change in the long-term systematic risk

of the other 41 market portfolios.

IV. Conclusion

The literature on how tsunamis affect the risk and return of capital markets is almost

nonexistent. At best, we can draw conclusions on how tsunamis affect certain regions

and then speculate on how the international markets react to this sort of calamity.

Intuitively, one would expect a tsunami to have a negative impact on the financial

markets in terms of negative abnormal returns (i.e., a decrease in wealth) and an

3 Note that we do not report the results of the Wald test in this paper.

24

increase in the risk of conducting businesses in the countries affected. However, the

evidence provided in this paper challenges this view. Using the Boxing Day Tsunami

as a tool, this study shows that equity markets, particularly the industrial portfolios of

countries that were directly hit and the market portfolios of other nations, were virtually

insensitive to this event despite the negative sentiment that prevailed following the

incident. The risks and returns of the majority of the industrial portfolios and market

portfolios did not change after this particular event. Surprisingly, only two industrial

portfolios experienced changes in their abnormal returns. Furthermore, we

documented a general increase in the long-term market risk of 13 industrial portfolios

in four countries that were directly hit. This paper finds theoretical consistency with

other related research suggesting that the Boxing Day Tsunami would have minimal

effects. We conclude that there was no major wealth destruction for equity investors

as a result of the Boxing Day Tsunami. These results reinforce the findings of Roll

(1988), Rietz (1988) and Barro (2006). We believe that the impacts are more of a

regional basis as shown by Bird et al. (2007) and Bandara and Naranpanawa (2007).

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27

Table 1: The consequences of the tsunami for the countries that were directly affected.

Country Stock market Tourism

destination Fatalities Missing Displaced

Indonesia Yes No 129,775 36,789 504,518

Sri Lanka Yes Yes 31,147 4,115 546,509

India Yes Yes 12,405 5,640 649,599

Thailand Yes Yes 8,212 2,817 6,000

Malaysia Yes Yes 69 6 -

Tanzania Yes Yes 13 - -

Bangladesh Yes No 2 - -

Kenya Yes Yes 1 - -

Maldives No Yes 82 26 29,577

Somalia No No 78 211 5,000

Myanmar No No 61 - -

Seychelles No Yes 2 - -

Source: Adapted and adjusted from UNDP 2005 and various media releases.

28

Table 2: The number of equity stocks in each of the 11 industrial portfolios

Industry India Thailand Bangladesh Indonesia Kenya Sri Lanka Malaysia Total

Consumer discretionary 96 80 2 48 11 50 109 396

Consumer staples 104 99 94 73 13 67 166 616

Energy 18 6 7 4 2 1 10 48

Financials 71 113 85 98 13 56 126 562

Health Care 66 17 20 11 0 7 17 138

Industrials 161 110 36 41 4 36 271 659

Information technology 123 34 11 17 0 7 124 316

Materials 66 38 13 45 4 14 98 278

Telecommunications 7 11 0 4 0 2 9 33

Utilities 18 5 0 1 3 2 16 45

Other 2 0 1 0 1 0 3 7

Total 732 513 269 342 50 242 949 3097

29

Table 3(A): Abnormal returns on industrial portfolios for countries that were directly affected by the tsunami (with firm-specific information)

This table presents abnormal returns and the parametric t-test results for the seven directly affected countries with capital markets after the tsunami event on 26 December 2004.

Industry

India Thailand Bangladesh Indonesia Kenya Sri Lanka Malaysia

AR T-Stat AR T-Stat AR T-Stat AR T-Stat AR T-Stat AR T-Stat AR T-Stat

Consumer dis. -0.0002 -0.01 -0.0103 -1.40 n/a n/a -0.0074 -0.82 -0.0012 -0.12 -0.0022 -0.12 -0.0052 -0.70

Consumer staples 0.0099 0.59 -0.0051 -0.86 -0.0039 -0.40 -0.0015 -0.19 -0.0007 -0.08 -0.0046 -0.38 -0.00029 -0.04

Energy 0.0008 0.04 -0.0075 -0.44 -0.0126 -0.99 0.0153 0.63 n/a n/a n/a n/a 0.0013 0.10

Financials 0.0057 0.28 -0.0128 -0.78 -0.0046 -0.43 -0.0029 -0.28 -0.0004 -0.03 -0.0158 -0.99 -0.0018 -0.22

Health care 0.0056 0.30 -0.0061 -0.68 -0.0085 -0.83 -0.0040 -0.28 n/a n/a -0.0091 -0.66 -0.0028 -0.32

Industrials 0.0103 0.57 -0.0114 -0.83 -0.0050 -0.49 -0.0008 -0.07 -0.00084 -0.04 -0.0105 -0.73 0.0009 0.11

IT 0.0096 0.49 -0.0082 -0.59 0.0527** 2.43 0.0171 1.02 n/a n/a -0.0022 -0.14 -0.0054 -0.66

Materials -0.0009 -0.05 -0.0021 -0.16 -0.0010 -0.08 0.0115 1.00 -0.0001 0.00 -0.0017 -0.11 -0.0036 -0.44

Telecommunications 0.0273 1.30 -0.0087 -0.34 n/a n/a 0.0212 1.26 n/a n/a n/a n/a 0.0002 0.02

Utilities 0.0143 0.57 -0.0085 -0.67 n/a n/a n/a n/a n/a n/a n/a n/a 0.0114 1.45

Other n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a -0.0097 -1.00

30

Table 3(B): Abnormal returns on industrial portfolios for countries that were directly affected by the tsunami (excluding firm-specific information)

This table presents abnormal returns and parametric t-test results for the seven directly affected countries with capital markets after the tsunami event on 26 December 2004. In this result, we have excluded the firm-specific information.

Industry

India Thailand Sri Lanka Malaysia

AR T-Stat AR T-Stat AR T-Stat AR T-Stat

Consumer dis. 0.0037 0.21 -0.0086 -1.37 -0.0361** -2.28 0.0078 0.78

Consumer staples 0.0099 0.57 -0.0057 -0.99 -0.0049 -0.40 -0.0022 -0.25

Energy -0.0035 -0.16 n/a n/a n/a n/a 0.0097 0.51

Financials 0.0103 0.51 -0.0078 -1.02 -0.0154 -0.95 -0.0028 -0.33

Health care 0.0067 0.34 -0.0062 -0.53 -0.0111 -0.69 -0.0062 -0.40

Industrials 0.0125 0.70 -0.0115 -1.05 -0.0125 -0.74 -0.0020 -0.25

IT 0.0128 0.64 -0.0129 -0.64 -0.0022 -0.14 -0.0106 -1.03

Materials -0.0035 -0.19 0.0119 1.16 -0.0017 -0.11 -0.0023 -0.27

Telecommunication 0.0588** 2.37 -0.0012 -0.05 n/a n/a -0.0010 -0.07

Utilities 0.0450 1.55 n/a n/a n/a n/a 0.0009 0.09

Other n/a n/a n/a n/a n/a n/a n/a n/a

Note: Company Announcements were not available for Bangladesh, Kenya and Indonesia.

31

Table 4(A): Cumulative abnormal returns on industrial portfolios for countries that were directly affected by the tsunami (with firm-specific information)

This table presents the five-day cumulative abnormal returns and the parametric t-test results for the seven directly affected countries with capital markets after the tsunami event on 26 December 2004.

Industry

India Thailand Bangladesh Indonesia Kenya Sri Lanka Malaysia

CAR T-Stat CAR T-Stat CAR T-Stat CAR T-Stat CAR T-Stat CAR T-Stat CAR T-Stat

Consumer dis. 0.0196 0.46 0.0030 0.15 n/a n/a 0.0087 0.43 -0.0049 -0.19 -0.0487 -1.02 0.0096 0.48

Consumer staples 0.0352 0.80 0.0051 0.36 0.0079 0.30 0.0092 0.53 0.0202 0.84 -0.0120 -0.39 0.0098 0.54

Energy 0.0198 0.37 -0.0041 -0.11 -0.0858** -3.18 -0.0244 -0.55 n/a n/a n/a n/a 0.0310 1.10

Financials 0.0364 0.72 0.1012** 2.56 -0.0172 -0.63 -0.0211 -0.83 0.0086 0.22 -0.0284 -0.80 0.0121 0.54

Health care 0.0224 0.50 0.0186 0.92 -0.0350 -1.22 0.0210 0.63 n/a n/a -0.0105 -0.39 0.0219 1.00

Industrials 0.0265 0.60 0.0022 0.06 -0.0261 -1.08 0.0333 1.19 -0.0463 -0.21 -0.0201 -0.58 0.0161 0.80

IT 0.0373 0.79 0.0060 0.17 0.0707 1.60 0.0215 0.87 n/a n/a -0.0479 -1.58 0.0103 0.46

Materials 0.0049 0.11 -0.0090 -0.28 0.0681** 2.11 -0.0095 -0.38 n/a n/a -0.0187 -0.56 0.0108 0.51

Telecommunications 0.0281 0.62 -0.0213 -0.36 n/a n/a 0.0072 0.19 n/a n/a n/a n/a 0.0014 0.06

Utilities 0.0126 0.22 -0.0007 -0.03 n/a n/a n/a n/a n/a n/a n/a n/a 0.0170 0.93

Other n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 0.0002 0.01

32

Table 4(B): Cumulative abnormal returns on industrial portfolios for countries that were directly affected by the tsunami (excluding firm-specific information)

This table presents the five-day cumulative abnormal returns and the parametric t-test results for the seven directly affected countries with capital markets after the tsunami event on 26 December 2004. In this result, we have excluded the firm-specific information.

Industry

India Thailand Sri Lanka Malaysia

CAR T-Stat CAR T-Stat CAR T-Stat CAR T-Stat

Consumer dis. 0.0244 0.56 0.0085 0.54 -0.0840** -2.04 0.0145 0.72

Consumer staples 0.0371 0.82 -0.0005 -0.04 -0.0112 -0.35 0.0061 0.28

Energy 0.0185 0.35 n/a n/a n/a n/a 0.0754* 1.88

Financials 0.0156 0.30 -0.0055 -0.29 -0.0284 -0.78 0.0044 0.21

Health care 0.0233 0.50 0.0145 0.66 -0.0114 -0.37 0.0462 1.43

Industrials 0.0255 0.58 0.0126 0.44 -0.0248 -0.61 0.0084 0.40

IT 0.0389 0.79 -0.0126 -0.27 -0.0479 -1.58 -0.0076 -0.30

Materials 0.0005 0.01 0.0086 0.37 -0.0187 -0.56 0.0134 0.69

Telecommunications 0.0700 1.37 -0.0083 -0.17 n/a n/a 0.0160 0.49

Utilities 0.0132 0.18 n/a n/a n/a n/a 0.0035 0.18

Other n/a n/a n/a n/a n/a n/a n/a n/a

Note: Company Announcements were not available for Bangladesh, Kenya and Indonesia.

33

Table 5: The non-parametric results of the tsunami on industrial portfolios for countries that were directly affected.

This table presents the non-parametric t-test results for the countries directly affected by the tsunami event on 26 December 2004.

Country India Thailand Sri

Lanka Malaysia Kenya Indonesia Bangladesh

With firm-specific information

Consumer dis. -0.74 -1.61 -1.75* -0.83 1.57 -1.45 n/a

Consumer staples 0.70 -0.29 -1.90* 0.12 -1.24 -0.49 -0.39

Energy -0.35 -0.85 n/a 0.66 n/a 1.20 -1.07

Financials 0.22 -1.48 -1.99** -0.02 1.11 -0.69 -1.22

Health care 0.17 -0.20 -1.27 0.13 n/a -0.31 -0.74

Industrials 0.52 -1.40 -1.65* 1.80 0.47 -0.68 -0.55

IT 0.27 -0.97 -3.16** -0.18 n/a 0.73 1.05

Materials -0.42 -0.68 -2.21** -0.26 n/a 0.75 0.78

Telecommunication -0.14 -0.26 n/a 0.10 n/a 1.57 n/a

Utilities 0.67 -0.48 n/a 0.28 n/a n/a n/a

Other n/a n/a n/a -3.16 n/a n/a n/a

Without firm-specific information

Consumer Dis. -0.18 0.71 -2.28** -0.58

Consumer Staples 0.58 -0.33 -1.91* -0.04

Energy -1.08 n/a n/a 0.96

Financials 0.31 -1.35 -1.89* 0.20

Health Care 0.06 -0.42 -1.43 -0.66

Industrials 0.61 -1.90 -1.98** -0.02

IT 0.37 -1.02 -3.16** -1.56

Materials -0.64 0.23 -2.21** -0.12

Telecommunication -0.93 0.15 n/a 0.37

Utilities 1.61 n/a n/a 0.28

Other n/a n/a n/a n/a

34

Table 6: AR, CAR, non-parametric results of the tsunami on world indices.

This table presents abnormal returns, cumulative abnormal returns, the parametric t-test, and the non-parametric results for 41 countries after the tsunami event on 26 December 2004.

Country AR T-Stat CAR5 T-Stat CAR10 T-Stat Corrado T-Stat

Argentina 0.44% 0.25 1.11% 0.28 -3.74% -0.63 0.57

Australia -0.08% -0.21 -0.30% -0.32 -0.45% -0.35 -0.45

Austria 0.02% 0.02 -0.25% -0.16 1.46% 0.66 -0.07

Belgium 0.03% 0.06 -0.72% -0.52 0.78% 0.38 -0.02

Brazil 0.16% 0.09 0.91% 0.23 -4.93% -1 0.29

Bulgaria -0.65% -0.72 -1.70% -0.71 -2.72% -0.8 -1.17

Canada -0.05% -0.07 -0.67% -0.45 -3.48% -1.68 -0.06

Chile -1.06% -1.35 -1.20% -0.63 -3.36% -1.17 -1.47

Croatia -3.64%** -3.63 -0.64% -0.28 -1.14% -0.32 -1.73*

Czech Rep. 0.29% 0.3 1.21% 0.54 4.04% 1.18 0.45

Denmark -0.07% -0.11 -0.22% -0.16 1.14% 0.58 -0.01

Ecuador -7.55%** -4.27 -7.45%* -1.78 -2.47% -0.36 -1.7

Estonia -0.19% -0.24 1.63% 0.89 2.79% 1.08 -0.57

Finland -0.49% -0.41 -0.80% -0.29 0.25% 0.06 -0.79

France -0.13% -0.17 -0.01% -0.01 1.20% 0.53 -0.35

Germany -0.42% -0.42 -0.04% -0.02 1.25% 0.39 -0.71

Hong Kong -0.05% -0.05 0.01% N/A -4.85% -1.38 0.17

Hungary 0.87% 0.81 2.44% 1.07 1.44% 0.47 0.99

India 0.23% 0.13 1.58% 0.4 -1.29% -0.24 0.17

Indonesia 0.83% 0.57 0.36% 0.1 4.32% 0.88 1.02

Ireland -0.09% -0.12 0.21% 0.13 2.57% 1.14 -0.25

Israel 1.69%* 1.93 3.17% 1.53 3.72% 1.26 1.65

Italy 0.11% 0.17 -0.02% -0.02 0.20% 0.09 0.41

Ivory Coast -0.13% -0.07 8.09%** 2.04 1.74% 0.32 -1.37

Japan -0.07% -0.07 1.11% 0.5 0.46% 0.15 -0.01

Kenya 0.05% 0.03 -1.76% -0.44 6.14% 1.06 1.3

Korea -0.16% -0.1 1.63% 0.48 -0.83% -0.17 -0.22

Kuwait -0.31% -0.04 N/A N/A -1.69% -0.22 -0.62

Malaysia -0.09% -0.13 -0.24% -0.14 0.50% 0.18 -0.27

Mexico -0.02% -0.02 0.15% 0.07 -4.16% -1.34 -0.07

Norway 0.29% 0.31 -0.88% -0.39 -1.31% -0.38 0.47

Peru 0.15% 0.15 2.67% 1.02 3.55% 0.92 0.46

Poland 1.00% 0.92 1.79% 0.58 0.44% 0.1 1.35

Portugal 0.03% 0.04 0.25% 0.16 2.45% 1.11 0.1

Romania -0.33% -0.27 -1.63% -0.5 9.27%* 1.93 -0.58

Russia 1.41% 0.77 2.68% 0.62 2.60% 0.41 1.18

Singapore -0.23% -0.34 0.57% 0.37 1.64% 0.76 -0.49

Slovakia -0.23% -0.23 -1.17% -0.48 -2.34% -0.69 -0.96

Slovenia 0.01% 0.02 -0.60% -0.48 1.13% 0.61 0.14

Spain -0.14% -0.18 N/A N/A -0.34% -0.14 -0.46

35

Sri Lanka -0.05% -0.03 -6.50%* -1.65 -3.29% -0.64 -0.21

Sweden -0.07% -0.07 -0.30% -0.15 0.24% 0.08 -0.13

Switzerland -0.19% -0.24 -0.11% -0.07 0.57% 0.25 -0.29

UK -0.07% -0.08 0.64% 0.32 0.73% 0.26 -0.09

USA -0.19% -0.42 N/A N/A -0.91% -0.71 -0.89

36

Table 7: The impact of the Boxing Day Tsunami on the short-term systemic risk of industrial portfolios in India, Thailand, Sri Lanka and Malaysia.

The table presents the regression analysis results for 11 industries following the Boxing Day Tsunami in India, Thailand, Sri Lanka and Malaysia. These are the results of the first multiplicative dummy variable in Equation 2, which indicates changes in the systemic risk of these industrial portfolios on the first day of trading.

Industry

India Thailand Sri Lanka Malaysia

Intercept Coef.

2 Coef.

3 Intercept Coef.

2 Coef. 3 Intercept Coef.

2 Coef.

3 Intercept Coef.

2 Coef.

3

Consumer Dis. 0.00 0.86 0.39 0.00 0.31 -15.84 0.00 0.02 3.30 0.00 0.62 -23.31

Z-Statistics 0.75 36.36 0.00 -3.26 2.01 0.00 1.19 0.32 0.00 -3.27 11.45 0.00

Cons. Staples 0.00 0.76 2.68 0.00 0.08 -8.74 0.00 0.01 4.30 0.00 0.68 3.83

Z-Statistics 3.05 26.17 0.00 -2.39 0.70 0.00 1.17 0.23 0.00 -3.47 9.28 0.00

Energy 0.00 1.07 -2.22 n/a n/a n/a n/a n/a n/a 0.00 0.97 -26.81

Z-Statistics 0.45 32.17 0.00 n/a n/a n/a n/a n/a n/a -2.41 6.45 -0.61

Financials 0.00 0.90 3.08 0.00 0.18 -17.18 0.00 0.05 2.49 0.00 0.77 6.12

Z-Statistics 0.05 32.76 0.00 -0.97 0.87 0.00 0.44 1.62 0.00 -0.86 17.14 0.00

Health Care 0.00 0.89 1.85 0.00 0.39 -11.18 0.00 0.03 5.59 0.00 0.60 6.02

Z-Statistics 0.64 26.26 0.00 0.71 1.76 0.00 1.13 0.51 0.09 -1.66 5.85 0.00

Industrials 0.00 0.84 3.72 0.00 0.41 -23.68 0.00 0.03 8.22 0.00 0.75 3.55

Z-Statistics 2.72 24.75 0.05 -0.36 1.50 0.00 1.85 0.19 0.00 -3.60 16.70 0.00

IT 0.00 0.90 3.83 0.00 0.62 -25.16 0.00 0.14 6.77 0.00 0.89 16.57

Z-Statistics 0.84 28.30 0.00 -1.27 1.23 0.00 1.38 2.26 0.10 -4.42 14.35 0.00

Materials 0.00 0.81 -2.23 0.00 0.41 14.85 0.00 -0.01 7.53 0.00 0.66 4.95

Z-Statistics 1.57 25.11 0.00 -0.97 1.72 0.00 1.64 -0.21 0.12 -1.43 11.02 0.00

Telecom. 0.00 0.83 20.77 0.00 0.07 -11.71 n/a n/a n/a 0.00 0.96 0.31

Z-Statistics 0.91 18.17 0.00 0.85 0.14 0.00 n/a n/a n/a 0.55 8.27 0.01

Utilities 0.00 0.95 7.56 n/a n/a n/a n/a n/a n/a 0.00 0.66 -4.10

37

Z-Statistics 0.51 13.95 0.00 n/a n/a n/a n/a n/a n/a -0.70 7.82 -0.17

Other n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a

Z-Statistics n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a

38

Table 8: The impact of the Boxing Day Tsunami on the long-term systemic risk of industrial portfolios in India, Thailand, Sri Lanka and Malaysia.

The table presents the regression analysis results for 11 industries after the Boxing Day Tsunami in India, Thailand, Sri Lanka and Malaysia. These are the

results of equation 4, and they show changes in the long-run systematic risk of these industrial portfolios.

Industry

India Thailand Sri Lanka Malaysia

Inter. Coef.2 Coef.3 Coef.4 Inter. Coef.2 Coef.3 Coef.4 Inter. Coef.2 Coef.3 Coef.4 Inter. Coef.2 Coef.3 Coef.4

Consumer dis. 0.00 0.82 0.09*** 0.00 0.00 0.22 0.24 0.00 0.00 0.05 -0.03 0.00 0.00 0.55 0.33*** 0.00

Z-Statistics 1.10 30.43 2.73 -1.25 -1.89 1.13 0.45 1.83 0.95 0.39 -0.21 0.47 -1.62 7.52 3.15 0.31

Cons. staples 0.00 0.77 0.12*** 0.00 0.00 0.09 0.32 0.00 0.00 0.02 0.00 0.00 0.00 0.64 0.21*** 0.00

Z-Statistics 2.25 28.87 3.34 -2.16 -2.23 0.60 0.78 1.55 0.97 0.20 -0.01 0.66 -3.16 9.89 2.32 1.33

Energy 0.00 1.06 -0.15 0.00 n/a n/a n/a n/a n/a n/a n/a n/a 0.00 0.97 0.62*** 0.00

Z-Statistics 0.11 34.01 -1.48 -0.09 n/a n/a n/a n/a n/a n/a n/a n/a -2.13 5.53 2.51 2.72

Financials 0.00 0.89 0.15*** 0.00 0.00 0.27 0.58 0.00 0.00 -0.02 0.16*** 0.00 0.00 0.82 0.04 0.00

Z-Statistics -0.09 42.13 4.11 0.67 -1.09 1.07 1.27 2.19 -0.58 -0.72 5.17 3.59 -1.37 14.04 0.44 0.38

Health care 0.00 0.87 -0.05 0.00 0.00 0.55 -0.50 0.00 0.00 0.00 0.02 0.00 0.00 0.60 0.34** 0.00

Z-Statistics 1.85 29.54 -1.30 -2.87 0.39 2.54 -0.84 0.74 0.41 0.01 0.05 -0.35 -0.87 5.01 2.01 0.60

Industrials 0.00 0.86 0.15*** 0.00 0.00 0.50 0.24 0.00 0.00 0.09 -0.10 0.00 0.00 0.77 0.21*** 0.00

Z-Statistics 3.33 37.93 4.84 -1.91 -0.11 1.80 0.39 0.97 1.73 3.07 -1.61 -0.79 -2.94 12.81 2.44 1.35

IT 0.00 0.90 0.05 0.00 0.00 0.64 0.41 0.00 0.00 0.16 -0.10 0.00 0.00 0.90 0.50*** 0.00

Z-Statistics 1.41 36.46 1.43 -1.43 -1.56 1.57 0.45 1.80 1.04 1.53 -0.94 -0.09 -3.05 8.84 3.51 1.51

Materials 0.00 0.81 0.04 0.00 0.00 0.35 -0.24 0.00 0.00 -0.02 0.04 0.00 0.00 0.65 0.10 0.00

Z-Statistics 1.08 28.99 1.04 -1.38 -1.09 1.59 -0.42 0.98 1.43 -0.23 0.30 -0.30 -1.53 10.85 1.21 0.34

39

Telecom. 0.00 0.83 0.17*** 0.00 0.00 -0.22 0.22 0.00 n/a n/a n/a n/a 0.00 0.95 1.08*** 0.00

Z-Statistics 0.88 22.18 3.02 -0.27 0.98 -0.48 0.20 -1.64 n/a n/a n/a n/a 0.33 4.86 3.91 -0.71

Utilities 0.00 0.94 0.08 0.00 n/a n/a n/a n/a n/a n/a n/a n/a 0.00 0.67 -0.32 0.00

Z-Statistics 1.16 13.63 1.00 -0.54 n/a n/a n/a n/a n/a n/a n/a n/a -0.54 6.27 -1.13 -0.14

Other n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a

Z-Statistics n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a