The economic cost of nuclear threats : a North Korea case study

T

HE ECONOMIC COST OF NUCLEAR THREATS

:

A

N

ORTH

K

OREA CASE STUDY

.

Abstract:

The purpose of this paper is to investigate how public announcements of a nation’s nuclear

programme development influence neighbouring countries’ stock markets. The countries

examined in this case study are North and South Korea. To test this relationship I conducted an

event study using MacKinlay’s market model. Six announcement days were studied comprising

out of three nuclear tests and three missile and satellite related tests. The findings of this research

are in contrast to prior research. Empirical researches on the economic effects of terrorism

suggest that acts of terrorism create large impacts on economic activity. The results of our event

study show that the announcements caused both negative and positive reactions on the market.

Author:

Ryan McKee

Student number:

6181066

Supervisor:

Shivesh Changoer

Date:

8

of Jan, 2014

INTRODUCTION

Stock prices represent investor’s expectations about the future. News announcements effect these

expectations on a day to day basis. Terrorist attacks, military invasions, nuclear threats or any

other ambivalent events can alter investor’s expectations and so allow the prices of stocks and

bonds to deviate from their fundamental value. Once such events have taken place investors

often defer from the market in search of safer, more secure financial investments which can lead

to panic and chaos on the markets (Chen and Siems, 2004). Such chaos can also be caused by

threats from other neighbouring countries.

A perfect example of this is the relationship between North and South Korea. Ever since the

division of North and South Korea on September 8, 1945 the two bordering nations have had a

strenuous relationship, ultimately leading to the Korean war of 1950-1953. Since then the two

nations have technically still been at war although no actual acts of war have taken place.

However, since 1991 North Korea has started developing its own nuclear programme and has

threatened South Korea and her allies. Due to the severity of the threats (nuclear arms) North

Korea has caused a widespread stir and predominately negative reactions in the international

community. Since the beginning of North Korea’s nuclear programme in 1991, it has threatened

South Korea on several occasions comprising out of seven verbal threats and three actual nuclear

tests. The seven verbal threats occurred on October 17

, 2002, December 12

, 2002, January

10

, 2003, February 10

, 2005, May 11

, 2005, July 5

, 2006, and October 3

, 2006. Actual

tests were conducted on October 9

2006, May 25

2009 and February 12

2013. All statements

were publicly announced by North Korean state officials. The markets did not seem to respond to

the seven verbal threats. The markets only seemed to react to the announcements on days which

experienced a sudden drop in value in correspondence with a depreciation of the won.

However

the market was quick to recover once news had come out that North Korea was not interested in

starting nuclear warfare but rather signalling they were open to negotiations.

This study examines how news announcements about nuclear warfare affect stock markets and in

particular the South Korean stock market. Therefore the central question of this paper is: how do

announcements of North Korea’s nuclear program influence the South Korean stock market?

Although the media has frequently reported on the North Korea’s nuclear threat, there has been

little investigation to the actual effects on financial markets. This article tries to shed a light on

such effects. This paper adds to previous research by investigating how financial markets

respond to the threat of nuclear attacks from other nations, such research is lacking in the current

literature. This article is organized as follows. First I summarize the existing literature on trading

styles and impacts of terrorism in section 1. Section 2 and 3 consist of the explanation of the

empirical method used and provides a description of the data in the analysis. The empirical

results are shown in section 4. Finally, section 5 provides a discussion and concluding remarks.

LITERATURE REVIEW

In recent years there has been an increase in interest on the macroeconomic effects of terrorist

attacks. A terrorist attack is a form of an external shock to an economy. The threat of nuclear

attacks or nuclear warfare can be seen as a similar form of external shock. Many studies

conducting research on the effects of terrorism use reduced-form models to describe the impact

of terrorist attacks on an economy. These studies show that terrorist attacks have a negative

effect on economic activity (Blomberg et al., 2004; Tavares, 2004). A paper investigating the

impact of terrorism on the behaviour of stock, bond and commodity markets around the world

found that approximately two thirds of the investigated terrorist attacks lead to a significant

negative impact on one of the markets. Drakos (2010) produces a similar paper in which he

investigates the impact of terrorist activity on daily stock returns across a sample of 22 countries

ranging from Asian, European and American. His employed methodology makes use of a

flexible version of the World CAPM model. The research also focuses on how the psychosocial

impact affects the stock returns. According to Edmans et al. (2007), theoretically, terrorism

satisfies the criteria which are known to affect the moods of investors. The results show that

terrorist activity indeed leads to significantly lower returns on the day a terrorist attacks occurs.

He also finds that this negative effect is larger when the terrorist attacks cause a higher

psychosocial impact. This shows how terrorism affects financial markets but also provides

empirical evidence supporting the sentiment effect.

There also appears to be a difference in impact across industries. The airline industry and

insurance sector exhibit the highest vulnerability to terrorism while the banking industry is the

least sensitive. According to their analysis the impact on the aero/defence, pharma/biotech and

oil/gas sectors are both positive and negative (Chesney, Reshetar, Karaman, 2011). In their paper

they use an event study methodology. They estimate the daily excess returns by using the

mean-adjusted-returns approach. For their longer event windows they also compute the cumulative

average returns. They computed the statistical significance by using the test statistics described

by Brown and Warner (1985). There is also evidence that there is a difference in resilience

across countries. Cohen and Shin (2004) use an event study methodology to examine how

markets from different countries respond to terrorism. They find that US markets recover sooner

from terrorist attacks than any other global markets. Arina, Ciferri and Spagnolo examine how

the stock returns and volatility of the markets of six specific countries (Indonesia, Israel, Spain,

Thailand, Turkey and UK) respond to terrorist activity. They also conclude that terrorism

statistically affects the returns and volatility of stock prices in all the examined countries;

however the amount of response is different. They find that the wealthier nations from Europe

(Spain and the UK) are less affected by terrorist activity. Kollias et al. (2011) examine how these

two countries, Spain and the UK, were affected by two major terrorist attacks, the bombings of

Madrid in 2004 and London in 2005. They follow an event study methodology to investigate the

impact of the events on general and sector indices. They use two models to compute the normal

return. First they calculate the normal return by using the constant mean return model secondly

they use the market model as described by MacKinlay. Their findings show that the reactions on

the actual day of the terrorist attacks are similar but that there is a significant difference in the

recovery time needed for the markets to recover. The markets in London recovered in one single

trading day while the markets in Spain needed a few more days to recover. Possible explanations

for the difference in recovery time were argued to be caused by differences in size, structure and

liquidity of the markets involved. However, it could also have been caused due to possibility of

further attacks. The terrorists responsible for the Madrid bombings were not neutralized to a few

days later, while the attacks in London were suicide bombers. The difference in recovery time

needed might also be caused by the different reactions of the countries’ financial institutions.

The Bank of England, HM Treasury and the Financial Services Authority had instigated

contingency plans (created after 9/11) directly after the attacks so that the UK financial markets

could keep trading. Both attacks increased market volatility significantly affecting both countries’

markets equally. Sector indices provide a varied conclusion with Spanish indices being affected

the most however not consistently. Overall conclusion is that the affect of the terrorist attacks

was fairly brief on the markets. Eldor and Melnick (2004) examine daily data to analyze the

impact of Palestinian terror attacks on the stock and foreign exchange markets of Israel. Attacks

occurred after September 27, 2000 had a permanent negative effect on the stock market but not

on the foreign exchange market. The stock market decline suggests that the terrorist attacks have

a damping effect on firms’ expected profit. The lack of response on the foreign exchange market

means the value of the Israeli shekel was not influenced by the attacks. In the paper they

distinguish differences in the types of terror attacks. They find that suicide bombing attacks have

a permanent impact on the markets, other types do not. They find that attacks on transportation

have a brief effect on the stock market; attacks on other targets do not have an effect. They also

find that attacks on major cities do not seem to have any special kind of effect; the amount of

causalities caused by the attack however does cause a permanent effect on both markets. The

paper concludes that the markets remain efficient and continue to perform their economic duties.

A paper by Prakarsh Singh (2013) uses micro-level data from agricultural surveys to analyze

how terrorism affects growth in developing countries. His research focuses on the effect

impacts investment decisions in the long run however there is no significant effect in the short

term.

However, recently there has been the growing view that terrorism should not have a large impact

on economic activity as a single terrorist attack only destroy a small amount of an economy’s

capital (Becker, G., 2001). Empirical evidence, however, suggests that terrorism on average does

create large impacts on economic activity (Abadie, 2003). The magnitude of the market reaction

can thus be larger than would rationally be expected. In order to explain this one can view the

stock market as being made up out of groups of individual investors. These investors impact the

movement of the stock market. Currently there are two main trading styles to explain how

investors can influence and undermine the markets. These are known as the positive feedback

trading style and the herding trading style. Investors who buy when the price goes up and sell

when the price goes down are known as positive feedback traders. This can cause miss pricing of

assets and therefore increase the volatility of financial markets. This assumption has been tested

by De Long, Shleifer, Summers, and Waldmann (1990). In their paper they construct a

theoretical model which shows that investors who follow a positive feedback trading strategy can

indeed destabilize market efficiency (Choi and Park, 2010).

Herding trading style is characterized by a group of investors trading in the same direction over a

period of time. This type of trading style is caused by five reasons: institutional traders’ positive

feedback trades, trades based on the trading strategy of some investor groups in the same

industry or with similar firm characteristics, trades in which managers duplicate that of other

managers to keep up reputation, trades based on inferred information from other investors and

trades which have a trading strategy due to similar analyzing tools and correlated information

(Choi and Sias, 2009; Wermers, 1999). Herding therefore causes successive large volume trades

in one direction which can cause assets prices to deviate from their fundamental value and thus

increasing market volatility (Choi and Park, 2010). Several studies conduct research in how

positive feedback and herding strategies are implemented during financial or economic stress.

Cohen en Shin (2004) analyze how an economy performs under stress and conclude that

investors become more extreme in their strategy implementation when markets become more

volatile.

The argument of terrorism having a large effect on the stock market is further backed by another

academic paper in which the researchers construct a simple economic model that shows that

terrorist events may have large impacts on the allocation of productive capital across countries,

even when it only represents a small fraction of the overall economic risk. The model shows that

terrorism reduces the expected return in addition to increasing uncertainty (Abadie and

Gardeazaba, 2008). Karolyi and Martell (2006) analyze the long-term impact of terrorism. They

employ standard event study methodology using MacKinlay’s market model. They investigate

the impact of terrorist attacks on the stock price of certain companies. They find that the impact

varies according to the country in which the attack occurs and the where the company itself is

from. They conclude that firms domiciled in wealthier nations experience a greater price

fluctuation and higher vulnerability, contrary to the paper published by Arina, Ciferri and

Spagnolo. The explanation of this occurrence is provided in a paper by Krueger and Laitin

(2003). In their paper they conduct a study to determine which countries are most likely to

produce terrorists and which countries are most prone to terrorist attacks. Countries that are

likely to produce terrorists are those that suffer from political oppression; countries they are

likely to attack are in general wealthy nations that benefit from economic prosperity. Hamilton

and Hamilton (1983) conclude that open societies have a harder time dealing with terrorism and

reducing the willingness of terrorist to further attack their nation. Karolyi and Martell (2006)

thus claim that given the vulnerability of a nation to terrorist attacks is related to the probability

of a terrorist attack taking place and the nation’s attributes such as political freedom, wealth and

higher education are related to the incidence of the attacks they therefore conclude that firms in

wealthier, better educated countries experience larger negative returns than poorer, less educated

countries.

I am going to investigate whether public announcements of North Korea’s nuclear programme

significantly affect South Korea’s stock market. According to the literature a terrorist attack

constitutes as a negative external shock to an economy due to the nature of such an attack.

However, a terrorist attack destroys only a small amount of the total production capacity of a

country. Therefore the scale of the impact should not be substantially large. Therefore I test the

following hypothesis, stated in the null form:

H

: Announcements of North Korea’s nuclear program do not cause a negative impact on South

Korea’s stock market.

I also expect to see a difference in the type of tests that were conducted that day. I expect days on

which nuclear test were conducted to show a larger impact that compared to days on which

missile tests were conducted, due to the scale of the threat. Therefore I test my second hypothesis:

H

: Announcements of nuclear tests do not have a larger impact than announcements of missile

METHODOLOGY AND DATA DESCRIPTION

In order to conduct my research I will perform an event study

. If investors react optimistically

to an event, we would expect a positive abnormal return. If investors react pessimistically to an

event we would expect a negative abnormal stock returns. By analyzing the abnormal return of a

firm’s stock or of an index we can investigate the response of the market to specific events. This

methodology is based on the efficient market hypothesis (Fama et al., 1969)

. In accordance to

previous research methodologies used by Kollias et al. and Karolyi and Martell I will perform an

event study using MacKinlay’s market model. In order to test H

I examine the abnormal returns

on the days August 31

1998, July 5

2006, October 9

2006, April 5

2009, May 25

2009, and

February 12

2013. I estimate the abnormal returns using the market model

. More precisely, in

order to use the market model I have defined my estimation window

starting from 230 working

days prior to the event and ending 30 working days prior to the event. I have created several

event windows

ranging from 1 day before and after the event to 10 days before and after the

event. I did this so I could investigate how much of an impact the announcement had and for

how long the impact lasted. For any security the model is

R

= α

+ β

R

+ ε

E(ε

= 0) var(ε

) = σ

1 _{An event study is a statistical method he impact of an event on the value of a firm or index. An event study is an}

approach that focuses on identifying abnormal returns on a specific date.

2 _{This hypothesis asserts that securities are fairly priced based on their future cash flows, given all information that}

is available to investors (Berk and Demarzo, 2007). This means investors immediately revaluate individual firms and their ability to deal with political, economic, environmental and demographic changes.

3 The market model is a statistical model which relates the return of any given security to the return of the market portfolio (MacKinlay, 1997).

4 _{In the estimation window I retrieve the relevant financial data of the stock or index I wish to investigate and of its}

corresponding reference index in order to create a benchmark.

5 The event window signifies when the event takes place between time τ1 and τ2 and the announcement day at time

R

and

R

represent the period returns on the security i and the market portfolio, respectively.

The disturbance term is represented by

ε

which has a mean of zero. The assumption here is that

the returns on the securities are normally distributed. The

α

,

β

and

σ

are the parameters of the

market model.

Previous researches, like that of Kollias et al. calculate the CARs of the individual markets. This

way you can investigate the impact of the terrorist attack on the general and sector indices. The

security i in my research is represented by all firms listed on the Korea Composite Stock Price

Index or KOSPI. It consists out of all common stocks traded on the Stock Market Division of the

Korea Exchange. The KOPSI is the representative stock market index of South Korea. For the

market portfolio I use the MSCI World index. The MSCI World is a stock market index of 1,606

‘world’ stocks. It is maintained by MSCI Inc., formerly Morgan Stanley Capital International,

and is often used as a common benchmark for 'world' or 'global' stock funds (MSCI, 2013). I

have chosen to use the MSCI World Index to calculate the beta as the KOSPI Index contains

country specific risk. This risk can be diversified by holding world stocks if the indexes of the

countries listed in the world stock and Korea are not perfectly positively correlated. As the world

index holds a large variety of stocks from over the world this is very plausible.

The parameters of the model can be estimated by using Ordinary Least Squares; the

corresponding alpha, beta and sigma coefficients. The coefficients explain the normal

relationship between the security and the reference index. By doing so, I can predict the expected

normal return of my event window. By deducting the ‘expected normal return’ from the ‘actual

return’ I get the ‘abnormal return’. The abnormal return is what I need.

The abnormal return can be expressed in formula form as:

AR

= R

− E(R

)

Now that I have my abnormal returns I need to measure and analyze whether or not they

statically differ from zero. I order to draw a conclusion for the event window of interest I must

aggregate the abnormal return observations. The aggregate of the abnormal returns is defined by

MacKinlay as the sample cumulative abnormal return (CAR) from time period

τ

to τ

In formula

form as:

𝐶𝐴𝑅

� (τ

, τ

) = � AR

The variance of

𝐶𝐴𝑅

� is defined as:

𝜎

(τ

, τ

) = (τ

− τ

+ 1)σ

The variance

σ

can be measured by Ordinary Least Squares. By multiplying the number of days

in the event window with the variance of the sample provides the variance of the

𝐶𝐴𝑅

� as shown

in the formula above.

To test the null hypothesis I divide the cumulative abnormal return by the square root of the

variance. This allows me to calculate the significance the announcement had on the entire event

window. In formula form:

𝛷 =

𝐶𝐴𝑅

� (τ

, τ

)

𝑣𝑎𝑟(𝐶𝐴𝑅

�(𝜏

, 𝜏

))

1 2

I calculated the level of significance of the event on the actual event day in a similar way. To

calculate the level of significance I divided the abnormal return of the event day by the standard

deviation calculated under OLS.

After having analysed the impact of North Korea’s nuclear announcements on the firms listed on

the KOSPI Index, I will conduct further analysis of the impact on specific industries listed on the

KOSPI. To do so I will implement the same methodology I used in finding the relationship

between the KOSPI Index and the MSCI Index. I will filter the firms according to industry by

using the SIC Codes. I will then group the firms together and test the industries against the MSCI

Index which I found before. This will give me a representation of which industries were affected

more or less by the announcements than others.

DATA

My data consist of the price indexes of the firms listed on the KOPSI Index and the price index

of the MSCI World Index. I obtained my data from DataStream. I have chosen six different

announcement days ranging over 15 years. On these particular days the North Korean state

media announced they had conducted tests for the further technological development of their

nuclear program. The table below provides a short descriptive chronology of the event days. The

table shows the six event days which can be divided into two types of events: nuclear testing and

missile testing. The three days in which North Korea conducted nuclear tests are October 9

2006, May 25

2009 and February 12

2013. The remaining dates are days on which North

Korea tested its long range missile and satellite programme which is viewed by the international

community as a further development of their overall nuclear programme.

Table 1: North Korean activity on announcement days

Announcement Day Activity

August 31st _{1998 North Korean state media announces its first satellite launch named Kwangmyŏngsŏng-1.}

July 5th _{2006 North Korean state media announced they conducted two rounds of missile tests, including long range missile} Taepodong-2.

October 9th _{2006 North Korean state media announces it has conducted an underground nuclear test near the village of} P’unggye.

April 5th _{2009 North Korea state media announces launch of Unha-2 rocket, believed by the US to be a modified version of} the previous Taepodong-2 missile.

May 25th _{2009 North Korea conducts its second underground nuclear test near the village of P’unggye.}

February 12th 2013 Seismic activity recorded radiating from the previous nuclear tests site of 2006 and 2009, near the village of

P’unggye. Later that same day a North Korean spokesman confirmed North Korea has successfully conducted its third nuclear test. International reactions following the testing have all been negative.

I had to create a unique market model for each individual event day. This means I have defined

six different estimation windows and six event windows corresponding to the aforementioned

announcement days.

The table below provides an accurate description of the estimation windows.

Table 2: Description of estimation windows for all announcement days.

Announcement

Estimation Window

Start End August 31st _{1998 28/10/1997 17/07/1998} July 5th _{2006 01/09/2005 23/05/2006} October 9th _{2006 06/12/2005 25/08/2006} April 5th _{2009 03/06/2008 20/02/2009} May 25th 2009 20/06/2008 10/04/2009 February 12th 2013 11/04/2012 13/12/2012

The table below provides the returns of the KOSPI and MSCI Index on the aforementioned event

days. As you can for see for the KOSPI Index the returns on the event days are primarily

negative (4 out of 6 providing a negative return). The significance of these returns will be

discussed in the ‘results and analysis’ section and ‘discussion’ section.

A graph showing the

returns can be seen in the appendix.

Table 3: Return on KOSP and MSCI on the event days.

Event Return on KOSPI Return on MSCI

31 August 1991 0.27% -3.37% 05 July 2006 -0.47% -1.26% 09 October 2006 -2.41% 0.12% 05 April 2009 1.10% -0.74% 25 May 2009 -0.20% 0.15% 12 February 2013 -0.26% 0.58%

The following table provides brief descriptive statistics of the sample data of the event windows

for each separate event day. The values in the table are general descriptions of the return on the

KOSPI during the event windows. Noticeable is how the average returns of the event windows

are fairly low in comparison to the standard deviation. This suggests that the KOSPI exhibited

some volatility during the event windows. This can also be seen when observing the Min and

Max values of the week.

Table 4: Descriptive statistics of sample data on the event days.

Event Day

Mean

SD

Min

Median

Max

31 August 1998 0.35% 2.14% -3.62% 0.12% -1.47% 05 July 2006 0.36% 1.28% -1.24% -0.04% 2.54% 09 October 2006 -0.09% 1.05% -2.41% 0.00% 1.26% 05 April 2009 0.74% 2.31% -3.24% 0.73% 4.30% 05 May 2009 0.16% 1.53% -2.06% -0.20% 2.99% 12 February 2013 0.15% 0.63% -0.77% 0.04% 1.56%

In order to analyse the impact of the announcements on specific industries I gathered the price

index of the firms listed on the KOSPI and used the corresponding SIC Codes to group them into

specific industries. I gathered the data for the price indexes of the firms using DataStream.

Unfortunately, due to the lack of data I won’t be able to analyse how the announcement had an

effect on the specific industries on the 31

of August 1998. The SIC Codes were also obtained

from DataStream. Depending on the data available I will analyse the following industries:

agriculture, mining, manufacturing, retail trade, financial, health care, arts and entertainment,

administrative services and public administration.

RESULTS OF THE ANALYSIS

In this section the results of the event study will be presented and analyzed. These results include

the cumulative abnormal returns (CAR) of the event window and the corresponding t-values of

all the event windows. The tables can be found in the appendix.

Tables 5 up until 10 shown in the appendix show the level of significance of the announcements

on the South Korean stock market. I have calculated the level of significance for multiple event

windows ranging from a day before and after the event day until 10 days before and after the

event day. These values are identified by the variable CAR in the table. I have also calculated

event windows ranging from the event day and onward. In order to prevent confusion I have

named this the variable KAR in the tables. I have added this variable KAR to see whether it

would yield different results from the other variable CAR. The reasoning behind it is due to the

nature of a ‘nuclear attack’. Normally when conducting an event study one must create an event

window consisting out of days before the event because there is often activity before the event

day itself as in most cases the event day is planned and thus known about beforehand. However

the nature of a terrorist attack or nuclear threat is that is unknown when or where it will happen.

The nature of such attacks/threats is to be shocking and thus unanticipated. For both variables I

have calculated the level of significance. This is represented by the t-statistic. The level of

significance is indicated for each variable and each event period.

According to my regression results all event days were cases on which the announcements of

North Korea’s nuclear program had effect on the KOSPI. However, the event days do not seem

to exhibit an equal reaction. As shown in tables 6, 7 and 9 respectively we observe that the

negative reaction. However as shown in tables 5, 8 and 10 respectively we observe that the

announcements on the event days of August 31

1998, April 5

2009 and February 12

2013 in

fact cause a contradictory positive reaction.

Therefore the findings of this research do not specifically coincide with prior research. As

mentioned in the beginning of this paper, empirical research on the economic effects of terrorism

suggests that acts of terrorism create large negative impacts on economic activity. Thus

according to prior research we would expect to see that North Korea’s nuclear announcements

should have a significant negative impact on South Korea’s economy which is the case in three

instances however this does not explain why we also observe three positive reactions.

Tables 11 up until 15 in the appendix provide the level of significance the announcements had on

specific industries on the aforementioned dates. The tables 11 until 15 show primarily that

agriculture, mining, manufacturing, retail trade, financials and health care are primarily affected

by the announcements. I find that the effect on arts and entertainment administrative services and

public administration varies. Table 12 shows that the announcements on October 9

, 2006 had

no effect on both entertainment and administrative services. Tables 13 and 14 respectively show

that the announcement in April saw no effect on public administration as did the announcements

in May have no effect on public administration and the entertainment industry. However, there

does not seem to be any pattern showing that a particular industry is repeatedly more susceptible

to nuclear threats than any other as the industries affected are different depending on the

announcement day.

DISCUSSION & CONCLUDING REMARKS

The purpose of the event study was to investigate whether public announcements of North

Korea’s nuclear programme had any significant effect on the South Korean stock market.

Additionally, the study wanted to examine whether there was a difference between

announcement that involved nuclear development and missile development. This section

discusses the discovered results and interprets them in order to be able to answer whether there

are such effects.

As mentioned before the results of tables 5 until 10 show that public announcements of nuclear

development did have a significant impact on the stock market. On all six event days the

announcements created a significant impact on the KOPSI index. However the impact was not

the same for each event day, there were in fact three instances on which the impact created a

negative effect and three instances where the announcement caused a positive reaction. The days

on which the announcement caused a negative reaction a long range missile was tested and two

nuclear tests were conducted. This supports the first hypothesis stating that announcements of

North Korea’s nuclear programme shall cause a negative impact of South Korea’s stock market.

However, on February 12

2013 North Korea conducted its third nuclear test but this caused an

apparent positive reaction on the South Korean stock market. This means that there is evidence

against the first hypothesis and so it must be rejected. The same applies to hypothesis two which

stated that announcements of nuclear tests have a larger impact than announcements of missile

tests. This hypothesis is rejected based on the fact that on days which there were missile tests the

impact created, whether it is positive or negative, had a larger significance than the impact

created by nuclear tests.

There is however one announcement day which can be explained in further detail. Looking at the

chronology of North Korea’s arms development we find that October 9

2006 is the first time

North Korea conducted an actual nuclear test. The seven other nuclear announcements prior to

October 9th had all been verbal, and no actual nuclear testing had been conducted. The fact that

this was the first instance in which North Korea had conducted a nuclear test would have caused

panic and chaos on the markets as investors would initially perceive the announcement as a

signalling of North Korea’s intention to engage in warfare. This explains the level of significance

and the drop of the stock market for that particular day.

However, once the actual purpose of the test was revealed, namely that North Korea was not

interested in starting nuclear warfare but rather signalling they were open to negotiations, the

market recovered. This can be seen in the table of October 9

2006. Initially, the reaction was

abrupt but during the course of the week the market recovered.

The fact that North Korea had revealed the actual purpose of its nuclear tests also provides a

possible explanation as to why the reaction to the subsequent test on February 12

2013 was

positive and thus did not create a negative impact on the South Korean stock market. Investors,

now possessing all information, no longer view the announcements as a potential threat to the

economy and thus do not alter their future expectations. In fact it might have given the investors

the idea that North Korea would want to negotiate more and perhaps improve the situation

between the two countries. However the results do provide a varied conclusion and unfortunately

do not provide hard evidence proving that North Korea’s nuclear announcement do in fact cause

exclusively a negative reaction.

The results from the individual industries provide a varied conclusion. We find that industries as

agriculture, mining, manufacturing, retail trade, financials and health care are affected each time

there was a nuclear threat. We do see some sort of a visible pattern showing that particular

industries are repeatedly more susceptible to nuclear. There is some evidence that other

industries are somewhat more resilient when it comes to nuclear announcements. Such industries

are entertainment, administrative services and public administration however this wasn’t the case

for all the announcements.

In order to expand on this topic, ongoing research could focus on other nations which have

recently announced to expand their nuclear programme. From an academic point of view it

would be interesting to research how Iran’s nuclear announcements affect the stock markets of

her neighbouring countries. This could provide more empirical evidence of the impact nuclear

announcements have on financial markets. Ongoing research could also focus on how the stock

markets of allies of the neighbouring countries are affected by a nation’s nuclear announcements.

In the case of South Korea, the Unites States is an important ally. Further research could thus

analyse if and how US stock markets react to North Korea’s nuclear provocations. The same

analysis can be conducted using Iran’s nuclear announcements. Ongoing research could

investigate how US stock markets react to threats from Iran and observe if such a study yields

the same results. Improvement could also be made by implementing different research

methodologies to create a more comprehensive understanding of the impact of nuclear threats on

financial markets.

REFERENCES

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market returns and volatility. Economics Letters 101 (2008) pp.164–167

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(Accessed: 17 June 2013)

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29, 2001

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Hamilton, Lawrence C. and James D. Hamilton, (1983). Dynamics of Terrorism, International

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the Madrid and London bomb attacks, International Review of Economics and Finance, 20, 532–

541

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Krueger, Alan B. and David D. Laitin, (2003). Kto Kogo?: A Cross-Country Study of the Origins

and Targets of Terrorism, The National Bureau of Economic Research, pp 1-24.

Larocque D., Lincourt G., Normandin M., (2010). Macroeconomic effect of terrorist shocks in

Israel. Defence and Peace Economics, 2010 Vol. 21 (4), August, pp.317–336

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http://www.msci.com/products/indices/country_and_regional/all_country/ (Accessed 24 June

2013)

Singh P., (2013). Impact of Terrorism on Investment Decisions of Farmers Evidence from the

Punjab Insurgency. Journal of Conflict Resolution, 2013, Vol.57 (1), pp.143-168

APPENDIX

The results include the cumulative abnormal returns (CAR) of the event window and the

corresponding t-values of all the event windows. These values can be found in the tables below.

Table 5: Cumulative Average Return in response to nuclear threat on August 31st, 1998

Event Day Variable Mean t-statistic Variable Mean t-statistic August 31st _{1998 CAR1 [-1;+1] 7.1154 13.54*** KAR1 [0;+1] 6.7918 15.05***}

CAR2 [-2;+2] 9.7548 17.43*** KAR2 [0;+2] 7.0042 16.02*** CAR3 [-3;+3] 12.2627 20.13*** KAR3 [0;+3] 7.6991 16.61*** CAR4 [-4;+4] 12.8747 19.24*** KAR4 [0;+4] 6.7491 14.56*** CAR5 [-5;+5] 15.3575 18.69*** KAR5 [0;+5] 9.1787 18.50*** CAR6 [-6;+6] 15.4526 17.98*** KAR6 [0;+6] 7.3021 13.98*** CAR7 [-7;+7] 22.6706 23.86*** KAR7 [0;+7] 10.1149 18.88*** CAR8 [-8;+8] 29.2254 28.23*** KAR8 [0;+8] 12.8313 22.44*** CAR9 [-9;+9] 22.8442 21.04*** KAR9 [0;+9] 11.6487 18.99*** CAR10 [-10;+10] 22.1288 18.55*** KAR10 [0;+10] 11.9809 16.63*** *, **, *** indicate significance levels of 10%, 5% and 1% respectively.

Table 6: Cumulative Average Return in response to nuclear threat on July 5th, 2006

Event Day Variable Mean t-statistic Variable Mean t-statistic July 5th 2006 CAR1 [-1;+1] -2.2284 -13.30*** KAR1 [0;+1] -1.5973 -11.83***

CAR2 [-2;+2] -1.8822 -8.67*** KAR2 [0;+2] -0.9206 -6.20*** CAR3 [-3;+3] -0.0901 -0.35 KAR3 [0;+3] -0.2183 -1.22 CAR4 [-4;+4] 0.5291 1.69 KAR4 [0;+4] -0.1661 -0.83 CAR5 [-5;+5] 0.3326 1.02 KAR5 [0;+5] 0.2548 1.15 CAR6 [-6;+6] 0.9377 2.62* KAR6 [0;+6] 0.4089 1.64 CAR7 [-7;+7] -0.2168 -0.56 KAR7 [0;+7] -0.5793 -1.99 CAR8 [-8;+8] -0.8937 -2.21 KAR8 [0;+8] -0.3522 -1.19 CAR9 [-9;+9] -2.2014 -5.11*** KAR9 [0;+9] -2.3904 -7.57 CAR10 [-10;+10] -3.6039 -8.14*** KAR10 [0;+10] -3.5133 -10.72*** *, **, *** indicate significance levels of 10%, 5% and 1% respectively.

Table 7: Cumulative Average Return in response to nuclear threat on October 9th, 2006

Event Day Variable Mean t-statistic Variable Mean t-statistic

October 9th _{2006 CAR1 [-1;+1] -3.6047 -22.70*** KAR1 [0;+1] -3.9379 -24.64***}

CAR2 [-2;+2] -3.8006 -19.99*** KAR2 [0;+2] -3.8112 -20.18*** CAR3 [-3;+3] -4.0799 -17.90*** KAR3 [0;+3] -2.5962 -13.96*** CAR4 [-4;+4] -2.8768 -12.24*** KAR4 [0;+4] -1.5457 -7.80*** CAR5 [-5;+5] -2.4410 -8.84*** KAR5 [0;+5] -0.9918 -4.45*** CAR6 [-6;+6] -2.2114 -6.94*** KAR6 [0;+6] -1.3803 -5.79*** CAR7 [-7;+7] -1.0716 -3.08*** KAR7 [0;+7] -0.9919 -3.72*** CAR8 [-8;+8] -0.4523 -1.19 KAR8 [0;+8] -0.7445 -2.50** CAR9 [-9;+9] -0.6289 -1.63 KAR9 [0;+9] 0.0413 0.14 CAR10 [-10;+10] -0.0728 -0.18 KAR10 [0;+10] 0.4276 1.41 *, **, *** indicate significance levels of 10%, 5% and 1% respectively.

Table 8: Cumulative Average Return in response to nuclear threat on April 5th, 2009

Event Day Variable Mean t-statistic Variable Mean t-statistic

April 5th _{2009 CAR1 [-1;+1] 5.2094 20.36*** KAR1 [0;+1] 5.1356 22.77***}

CAR2 [-2;+2] 5.4776 16.34*** KAR2 [0;+2] 4.5678 16.31*** CAR3 [-3;+3] 9.4631 22.58*** KAR3 [0;+3] 7.1802 21.96*** CAR4 [-4;+4] 11.4299 24.78*** KAR4 [0;+4] 8.5403 23.90*** CAR5 [-5;+5] 13.2049 26.75*** KAR5 [0;+5] 10.5797 25.36*** CAR6 [-6;+6] 14.3736 26.43*** KAR6 [0;+6] 11.5507 25.57*** CAR7 [-7;+7] 14.1639 24.12*** KAR7 [0;+7] 10.5262 22.90*** CAR8 [-8;+8] 15.1815 24.97*** KAR8 [0;+8] 10.7345 23.08*** CAR9 [-9;+9] 14.4839 23.75*** KAR9 [0;+9] 8.7258 19.29*** CAR10 [-10;+10] 16.4562 25.170*** KAR10 [0;+10] 11.3474 23.67*** *, **, *** indicate significance levels of 10%, 5% and 1% respectively.

Table 9: Cumulative Average Return in response to nuclear threat on May 25th, 2009

Event Day Variable Mean t-statistic Variable Mean t-statistic

May 25th _{2009 CAR1 [-1;+1] -4.4472 -15.58*** KAR1 [0;+1] -3.8498 -17.39***}

CAR2 [-2;+2] -5.4192 -12.96*** KAR2 [0;+2] -4.9307 -14.35*** CAR3 [-3;+3] -5.5770 -11.72*** KAR3 [0;+3] -5.2003 -13.74*** CAR4 [-4;+4] -4.3694 -9.51*** KAR4 [0;+4] -5.3595 -15.25*** CAR5 [-5;+5] -5.6025 -11.41*** KAR5 [0;+5] -5.2719 -14.67*** CAR6 [-6;+6] -4.9863 -9.61*** KAR6 [0;+6] -5.3773 -14.61*** CAR7 [-7;+7] -4.4582 -8.15*** KAR7 [0;+7] -4.1213 -10.69*** CAR8 [-8;+8] -4.1797 -7.45*** KAR8 [0;+8] -6.5159 -16.69*** CAR9 [-9;+9] -3.7451 -6.30*** KAR9 [0;+9] -5.8170 -14.35*** CAR10 [-10;+10] -1.9686 -3.16*** KAR10 [0;+10] -5.5521 -13.35*** *, **, *** indicate significance levels of 10%, 5% and 1% respectively.

Table 10: Cumulative Average Return in response to nuclear threat on February 12th, 2013

Event Day Variable Mean t-statistic Variable Mean t-statistic

February 12th _{2013 CAR1 [-1;+1] 0.4652 4.14*** KAR1 [0;+1] 0.3953 3.56***}

CAR2 [-2;+2] 1.1549 7.12*** KAR2 [0;+2] 0.9291 6.73*** CAR3 [-3;+3] 1.4647 7.22*** KAR3 [0;+3] 1.2244 7.22*** CAR4 [-4;+4] 2.0350 8.62*** KAR4 [0;+4] 1.3797 7.41*** CAR5 [-5;+5] 1.1462 4.63*** KAR5 [0;+5] 1.4825 7.43*** CAR6 [-6;+6] 2.2049 8.06*** KAR6 [0;+6] 2.5429 11.2*** CAR7 [-7;+7] 1.3154 4.14*** KAR7 [0;+7] 2.4005 9.44*** CAR8 [-8;+8] 1.4420 4.33*** KAR8 [0;+8] 2.5028 9.29*** CAR9 [-9;+9] 1.4397 4.10*** KAR9 [0;+9] 2.3568 8.02*** CAR10 [-10;+10] 1.3029 3.57*** KAR10 [0;+10] 2.1523 7.28***

*, **, *** indicate significance levels of 10%, 5% and 1% respectively.

Table 11: Cumulative Average Return for various industries in response to nuclear threat on July 5th, 2006

Agriculture Mining Manufacturing

Retail Trade Financial Health Care Entertainment Administrative Service Public Administration CAR1 -6.63*** -5.86*** -5.91*** -2.64*** -4.60*** -3.70*** 1.37** -2.73*** 1.33** CAR2 -8.76*** -7.21*** -7.80*** -3.26*** -5.80*** -4.85*** 1.99** -3.63*** 1.53** CAR3 -10.71*** -8.77*** -9.85*** -3.87*** -6.93*** -5.99*** 2.46*** -4.32*** 1.83** CAR4 -12.54*** -11.13*** -12.59*** -4.93*** -8.25*** -7.40*** 2.47*** -5.02*** 1.94** CAR5 -13.6*** -12.93*** -14.31*** -5.55*** -8.87*** -8.05*** 2.56*** -5.67*** 1.97** CAR6 -13.75*** -13.02*** -14.27*** -5.41*** -8.59*** -7.81*** 3.13*** -6.27*** 2.16** CAR7 -14.03*** -12.64*** -13.96*** -5.07*** -8.17*** -7.33*** 3.90*** -6.77*** 2.30** CAR8 -14.47*** -12.54*** -13.92*** -4.83*** -8.03*** -6.96*** 4.49*** -7.19*** 2.12** CAR9 -14.83*** -12.61*** -14.20*** -4.58*** -8.06*** -6.93*** 4.88*** -7.82*** 1.77** CAR10 -14.78*** -12.06*** -13.86*** -4.03*** -7.75*** -6.60*** 5.60*** -8.13*** 1.53** KAR1 -5.12*** -4.60*** -4.46*** -2.01** -3.64*** -2.71*** 1.17* -2.08** 1.16 KAR2 -6.63*** -5.86*** -5.91*** -2.64*** -4.60*** -3.70*** 1.37* -2.73*** 1.33** KAR3 -7.81*** -6.66*** -6.95*** -2.97*** -5.24*** -4.39*** 1.68** -3.23*** 1.38** KAR4 -8.76*** -7.21*** -7.80*** -3.26*** -5.80*** -4.85*** 1.99** -3.63*** 1.53** KAR5 -9.84*** -7.97*** -8.84*** -3.59*** -6.39*** -5.43*** 2.22** -4.01*** 1.65** KAR6 -10.71*** -8.77*** -9.85*** -3.87*** -6.93*** -5.99*** 2.46*** -4.32*** 1.83** KAR7 -11.62*** -9.87*** -11.17*** -4.4*** -7.61*** -6.69*** 2.51*** -4.67*** 1.91** KAR8 -12.54*** -11.13*** -12.59*** -4.93*** -8.25*** -7.40*** 2.47*** -5.02*** 1.94** KAR9 -13.41*** -12.69*** -14.15*** -5.56*** -8.90*** -8.14*** 2.25** -5.40*** 1.91** KAR10 -13.6*** -12.93*** -14.31*** -5.55*** -8.87*** -8.05*** 2.56*** -5.67*** 1.97**

Table 12: Cumulative Average Return for various industries in response to nuclear threat on October 9th, 2006

*, **, *** indicate significance levels of 10%, 5% and 1% respectively.

Agriculture Mining Manufacturing

Retail

Trade Financial

Health

Care Entertainment Administrative Service Public Administration

CAR1 2.73*** 4.83*** 2.69*** 0.72 1.00 1.52* 0.20 0.91 4.75*** CAR2 3.81*** 7.02*** 4.31*** 1.15 1.56* 2.27** 0.70 1.06 5.73*** CAR3 4.50*** 8.62*** 6.03*** 1.42** 2.01** 2.99*** 1.09 1.13 6.26*** CAR4 5.38*** 10.55*** 8.16*** 1.97** 2.52*** 4.05*** 1.6* 1.36* 6.88*** CAR5 6.25*** 12.32*** 10.11*** 2.5*** 2.98*** 5.03*** 1.96** 1.66** 7.32*** CAR6 8.12*** 15.75*** 14.04*** 3.94** 3.97*** 6.96*** 2.89*** 2.10** 7.87*** CAR7 9.75*** 18.44*** 17.12*** 5.02*** 4.82*** 8.52*** 3.62*** 2.18** 8.39*** CAR8 11.09*** 20.74*** 19.59*** 5.88*** 5.47*** 9.64*** 4.19*** 2.19** 8.75*** CAR9 12.51*** 23.39*** 22.25*** 6.89*** 6.11*** 10.81*** 4.92*** 2.00** 9.04*** CAR10 13.76*** 25.4*** 24.27*** 7.55*** 6.62*** 11.61*** 5.44*** 1.8** 9.33*** KAR1 2.17** 3.66*** 1.87** 0.42 0.70 1.02 0.05 0.68 4.07*** KAR2 2.73*** 4.83*** 2.69*** 0.72 1.00 1.52* 0.20 0.91 4.75*** KAR3 3.25*** 5.84*** 3.35*** 0.89 1.23* 1.8** 0.38 1.10 5.24*** KAR4 3.81*** 7.02*** 4.31*** 1.15 1.56* 2.27** 0.70 1.06 5.73*** KAR5 4.12*** 7.78*** 5.1*** 1.2* 1.78** 2.56*** 0.88 1.09 5.97*** KAR6 4.5*** 8.62*** 6.03*** 1.42* 2.01** 2.99*** 1.09 1.13 6.26*** KAR7 4.85*** 9.43*** 6.92*** 1.62* 2.23** 3.44*** 1.29* 1.21 6.56*** KAR8 5.38*** 10.55*** 8.16*** 1.97** 2.52*** 4.05*** 1.60* 1.36* 6.88*** KAR9 5.82*** 11.52*** 9.22*** 2.28** 2.77*** 4.62*** 1.81** 1.54* 7.11*** KAR10 6.25*** 12.32*** 10.11*** 2.5** 2.98*** 5.03*** 1.96** 1.66** 7.32***

Table 13: Cumulative Average Return for various industries in response to nuclear threat on April 5th, 2009

Agriculture Mining Manufacturing

Retail Trade Financial Health Care Entertainmen t Administrative Service Public Administration CAR1 6.09*** 14.73*** 14.63*** 7.99*** 6.77*** 7.97*** 5.49*** 2.76*** 1.01 CAR2 7.57*** 18.63*** 18.57*** 9.94*** 8.46*** 10.09*** 6.84*** 3.41*** 1.15 CAR3 8.76*** 21.45*** 21.67*** 11.71*** 9.62*** 11.61*** 8.01*** 3.71*** 1.19 CAR4 9.50*** 23.34*** 23.94*** 13.05*** 10.40*** 12.49*** 8.85*** 3.95*** 1.3* CAR5 10.02*** 24.83*** 25.64*** 14.15*** 10.94*** 13.14*** 9.45*** 4.16*** 1.46* CAR6 10.66*** 26.53*** 27.43*** 14.99*** 11.51*** 13.93*** 10.15*** 4.65*** 1.72** CAR7 11.16*** 27.6*** 28.49*** 15.51*** 11.87*** 14.53*** 10.61*** 4.96*** 1.94** CAR8 11.27*** 27.88*** 28.77*** 15.49*** 11.82*** 14.54*** 10.89*** 5.01*** 2.15** CAR9 10.97*** 27.58*** 28.48*** 15.35*** 11.47*** 14.27*** 11.00*** 4.91*** 2.24** CAR10 10.55*** 27.13*** 28.22*** 15.20*** 11.17*** 13.95*** 11.07*** 4.85*** 2.25** KAR1 5.00*** 12.32*** 12.11*** 6.7*** 5.71*** 6.63*** 4.66*** 2.29** 0.90 KAR2 6.09*** 14.73*** 14.63*** 7.99*** 6.77*** 7.97*** 5.49*** 2.76*** 1.01 KAR3 6.89*** 16.89*** 16.80*** 8.99*** 7.74*** 9.18*** 6.21*** 3.16*** 1.17 KAR4 7.57*** 18.63*** 18.57*** 9.94*** 8.46*** 10.09*** 6.84*** 3.41*** 1.15 KAR5 8.24*** 20.11*** 20.21*** 10.83*** 9.05*** 10.89*** 7.53*** 3.59*** 1.16 KAR6 8.76*** 21.45*** 21.67*** 11.71*** 9.62*** 11.61*** 8.01*** 3.71*** 1.19 KAR7 9.16*** 22.49*** 22.89*** 12.40*** 10.06*** 12.06*** 8.45*** 3.85*** 1.25* KAR8 9.50*** 23.34*** 23.94*** 13.05*** 10.40*** 12.49*** 8.85*** 3.95*** 1.3* KAR9 9.76*** 24.08*** 24.78*** 13.62*** 10.69*** 12.85*** 9.16*** 4.02*** 1.38* KAR10 10.02*** 24.83*** 25.64*** 14.15*** 10.94*** 13.14*** 9.45*** 4.16*** 1.46*

Table 14: Cumulative Average Return for various industries in response to nuclear threat on

May 25

, 2009

Agriculture Mining Manufacturing

Retail

Trade Financial Health Care Entertainment

Administrative Service Public Administration CAR1 -5.33*** -8.7*** -5.53*** -1.98*** -4.42*** -3.04*** 0.16 -2.34*** 1.52 CAR2 -7.83*** -12.8*** -8.84*** -3.00*** -6.18*** -4.67*** -0.6 -3.40*** 1.40 CAR3 -9.73*** -15.93*** -11.01*** -0.38*** -7.33*** -5.68*** -1.22 -4.13*** 1.16 CAR4 -11.35*** -18.94*** -13.20*** -4.29*** -8.20*** -6.54*** -1.87** -4.65*** 0.88 CAR5 -13.04*** -21.7*** -15.35*** -4.66*** -9.27*** -7.48*** -2.6*** -5.23*** 0.37 CAR6 -14.19*** -23.56*** -16.65*** -4.80*** -10.16*** -7.86*** -3.11*** -5.51*** 0.24 CAR7 -14.66*** -24.33*** -17.23*** -4.88*** -10.44*** -7.66*** -3.31*** -5.56*** 0.25 CAR8 -15.15*** -25.17*** -17.58*** -4.87*** -10.69*** -7.39*** -3.59*** -5.60*** 0.34 CAR9 -15.38*** -25.93*** -17.64*** -4.84*** -11.06*** -7.30*** -3.77*** -5.61*** 0.46 CAR10 -15.4*** -26.2*** -17.30*** -4.61*** -11.32*** -6.88*** -3.72*** -5.65*** 0.71 KAR1 -4.15*** -6.96*** -4.40*** -1.57** -3.61*** -2.45*** 0.19 -2.01** 1.36* KAR2 -5.93*** -8.70*** -5.53*** -1.98** -4.42*** -3.04*** 0.16 -2.34*** 1.52* KAR3 -6.69*** -11.11*** -7.49*** -2.62*** -5.46*** -4.06*** -0.36 -2.87*** 1.51* KAR4 -7.83*** -12.80*** -8.84*** -3.00*** -6.18*** -4.67*** -0.6 -3.40*** 1.40 KAR5 -8.83*** -14.57*** -10.09*** -3.45*** -6.86*** -5.32*** -0.91 -3.86*** 1.27* KAR6 -9.73*** -15.93*** -11.01*** -3.77*** -7.33*** -5.68*** -1.22 -4.13*** 1.16 KAR7 -10.56*** -17.50*** -12.10*** -4.06*** -7.77*** -6.15*** -1.55** -4.42*** 1.04 KAR8 -11.35*** -18.94*** -13.20*** -4.29*** -8.20*** -6.54*** -1.87** -4.65*** 0.88 KAR9 -12.21*** -20.30*** -14.23*** -4.46*** -8.66*** -6.99*** -2.19** -4.85*** 0.61 KAR10 -13.04*** -21.70*** -15.35*** -4.66*** -9.27*** -7.48*** -2.6*** -5.23*** 0.37

Table 15: Cumulative Average Return for various industries in response to nuclear threat on February 12th 2013

Agriculture Mining Manufacturing

Retail

Trade Financial

Health

Care Entertainment

Administrative

Service Public Administration

CAR1 3.6*** 8.65*** 7.58*** 1.65*** 4.38*** 5.92*** 3.83*** 2.00** 0.48 CAR2 5.06*** 11.67*** 10.49*** 2.60*** 5.69*** 8.31*** 5.30*** 2.62*** 1.08 CAR3 6.39*** 14.11*** 12.74*** 3.54*** 6.76*** 10.08*** 6.70*** 3.38*** 1.91** CAR4 7.47*** 16.02*** 14.2*** 4.43*** 7.59*** 11.25*** 7.75*** 4.04*** 2.24** CAR5 8.44*** 17.66*** 15.48** 5.28*** 8.27*** 12.37*** 8.56*** 4.65*** 2.55** CAR6 9.25*** 18.97*** 16.40*** 5.93*** 8.81*** 13.14*** 9.24*** 5.04*** 2.95*** CAR7 10.04*** 20.06*** 17.00*** 6.38*** 9.15*** 13.63*** 9.69*** 5.39*** 3.36*** CAR8 10.73*** 20.93*** 17.38*** 6.48*** 9.50*** 14.10*** 9.91*** 5.64*** 3.69*** CAR9 11.42*** 21.90*** 17.72*** 6.54*** 9.86*** 14.59*** 9.92*** 5.89*** 4.05*** CAR10 12.19*** 22.83*** 18.03*** 6.71*** 10.14*** 14.85*** 9.87*** 6.17*** 4.49*** KAR1 2.83*** 6.90*** 6.00*** 1.21 3.63*** 4.61*** 2.99*** 1.72** 0.30 KAR2 3.60*** 8.65*** 7.58*** 1.65* 4.38*** 5.92*** 3.83*** 2.00** 0.48 KAR3 4.32*** 10.05*** 8.95*** 2.05** 4.93*** 7.11*** 4.50*** 2.25** 0.73 KAR4 5.06*** 11.67*** 10.49*** 2.6*** 5.69*** 8.31*** 5.30*** 2.62*** 1.08 KAR5 5.73*** 12.88*** 11.64*** 3.06*** 6.18*** 9.25*** 6.02*** 2.99*** 1.48* KAR6 6.39*** 14.11*** 12.74*** 3.54*** 6.76*** 10.08*** 6.70*** 3.38*** 1.91** KAR7 6.95*** 15.16*** 13.50*** 3.96*** 7.20*** 10.69*** 7.21*** 3.74*** 2.10** KAR8 7.47*** 16.02*** 14.20*** 4.43*** 7.59*** 11.25*** 7.75*** 4.04*** 2.24** KAR9 8.00*** 16.92*** 14.93*** 4.88*** 7.96*** 11.88*** 8.21*** 4.37*** 2.41** KAR10 8.44*** 17.66*** 15.48*** 5.28*** 8.27*** 12.37*** 8.56*** 4.65*** 2.55**

The following charts provide brief descriptive statistics of both indexes for their subsequent

estimation windows. The charts show that the KOSPI and MSCI have a very similar pattern.

Graph 1: Estimation window for August 31

, 1998

Graph 2: Estimation window for July 5

, 2006

0 200 400 600 800 1000 1200 1400

August 31st 1998

July 5th 2006

Graph 3: Estimation window for October 9

, 2006

Graph 4: Estimation window for April 5

, 2009

1000 1050 1100 1150 1200 1250 1300 1350 1400 1450 1500

October 9th 2006

April 5th 2009

Graph 5: Estimation window for May 25

, 2009

Graph 6: Estimation window for February 12

, 2013

0 200 400 600 800 1000 1200 1400 1600

May 25th 2009

February 12th 2013

Table 16: Descriptive statistics of the estimations windows for all event days.

Date Index Mean SD Min Median Max

August 31st 1998 MSCI 1066.167102 37.852178 937.092 1073.263 1142.95 KOSPI 366.8340157 69.975519 280 336.63 536.16 July 5th 2006 MSCI 1321.408406 33.247995 1243.938 1318.539 1406.275 KOSPI 1343.320234 62.933443 1203.86 1336.315 1464.7 October 9th 2006 MSCI 1336.087323 38.596394 1243.938 1337.718 1406.275 KOSPI 1326.71252 61.880082 1203.86 1325.49 1464.7 April 5th 2009 MSCI 854.6164252 66.359014 688.638 857.929 1007.645 KOSPI 1132.099449 79.974745 938.75 1128.73 1338.26 May 25th 2009 MSCI 859.9918819 66.838816 688.638 862.248 994.498 KOSPI 1217.323937 116.37268 1018.81 1179.84 1435.7 February 12th 2013 MSCI 1333.980031 43.148178 1253.305 1326.6005 1419.815 KOSPI 1953.620078 39.700146 1860.83 1954.18 2031.1

Table 17: Summary Cumulative Average Return per firm on August 31st, 1998 Firms CAR10 AMOREPACIFIC GROUP 0.7319489 ANAM ELECTRONICS 9.468202 ASIA CEMENT 33.38024 ASIA PAPER MNFG. 39.7361 AUK 37.54309 AUTOMOBILE & PCB 45.47725 BAEKKWANG MRL.PRDS. 9.321064 BING-GRAE 22.21329 BOHAE BREWERY 37.33018 BOLAK 47.59152 BOOKOOK SECURITIES 15.83495 BOOKOOK STEEL 18.502 BORNEO INTL.FRTR. -8.785048 BORYUNG PHARM. 16.19285

BUKWANG PHARMACEUTICAL INDUSTRIAL 43.97079

BUSAN CITY GAS 4.801938

BUSAN INDUSTRIAL 144.5082

BYC 22.11299

BYUCKSAN 28.56193

BYUCKSAN ENGR.& CON. 9.126229

CAPRO 5.979022

CENTURY -6.413226

CHARM ENGINEERING -17.18414

CHEIL INDUSTRIES 12.28667

CHEJU BANK -11.10738

CHIN HUNG INTL. 8.379176

CHIN YANG INDUSTRY 17.29218

CHINYANG POLY URETHANE 38.65115

CHO BI 44.77628

CHO KWANG LEATHER 27.95481

CHOHEUNG 28.23335 CHOIL ALUMINIUM 43.96423 CHOKWANG PAINT 66.16788 CHONBANG 65.49998 CHONGKUNDANG 5.7202 CHOSUN REFRACTORIES 35.07112 CHUNG HO COMNET 8.991701 CHUNIL EXPRESS 60.0205 CJ 7.879602

CJ KOREA EXPRESS 22.77245

CJ SEAFOOD -10.06954

COMTEC SYSTEMS 61.16887

COSMO ADVANCED MATERIALS &

TECHNOLOGY 32.62505 COSMO CHEMICAL -30.4381 CROWN CONFECTIONERY 65.71386 CS HOLDINGS 63.97439 D I 39.41497 DAE HYUN 39.39027 DAECHANG 10.04839 DAECHANG FORGING 77.26146 DAEDONG INDUSTRIAL 30.34912 DAEDUCK ELECTRONICS 18.62165 DAEDUCK GDS 14.91454 DAEGU DEPT.STORE -8.412375

DAEHAN FLOUR MILLS -3.560673

DAEHAN SYNTHETIC FIBER 14.59314

DAEKYUNG MCH.& ENGR. 25.42917

DAELIM B&CO 72.35249

DAELIM INDUSTRIAL 15.09054

DAELIM TRADING 16.01485

DAESANG 36.56187

DAESUNG GROUP PARTNERS 20.43067

DAEWON CABLE -71.24176 DAEWONKANGUP 38.34573 DAEWOO ELT.COMPNS. 12.33786 DAEWOO SECURITIES 12.66121 DAEWOONG -11.15357 DAEYOUNG PACKAGING 11.10431

DAI YANG METAL 12.34517

DAIDONG ELECTRONICS 20.35002

DAISHIN SECURITIES 25.39888

DAOU TECHNOLOGY 18.6861

DAOUINCUBE 59.31543

DAYOU AUTOMOTIVE SEAT TECHNOLOGY -6.973487

DAYOU SMART ALUMINUM -18.43571

DONG IL 38.48977

DONG WHA PHARM. -21.48847

DONG-A SOCIO HOLDINGS 11.14804

DONGAH TIRE & RUB. 3.8264

DONGAONE 25.71013

DONGBANG TRAN.& LOGIST. 74.56927 DONGBU 35.60564 DONGBU CNI 47.68414 DONGBU HITEK 34.28003 DONGBU INSURANCE 22.33626 DONGBU SECURITIES 22.43289 DONGBU STEEL 29.00329 DONGIL PAPER 29.86101 DONGKOOK IND. 100.7821

DONGKUK STEEL MILL 26.22054

DONGNAM CHEMICAL 17.32354 DONGSUNG CHEMICAL 16.01651 DONGSUNG PHARM. 19.90509 DONGWON 115.7214 DONGWON FISHERIES 116.5062 DONGWON INDUSTRY 10.10157 DONGWON METAL 54.16776 DONGWON SYSTEMS 9.736417

DONGYANG ENGR. & CON. 36.66916

DONGYANG MECHATRONICS 40.63124

DONGYANG STEEL PIPE 16.29543

DOOSAN 0.7274632

DOOSAN ENGR.& CON. 5.65922

DRB HOLDING 29.79757

DUCK YANG IND. 41.70207

DUKSUNG 11.70196 DUZONBIZON 6.180321 E-STARCO 13.77739 E1 10.76325 EAGON INDL. 31.84618 ENEX -1.839785

EUGENE INV.& SECURITIES 31.43528

F&F 5.715934

FIRSTEC 43.89247

FURSYS 25.34102

GAON CABLE 27.65761

GLOBAL &YUASA BTRY. -8.448967

GOLDEN BDG.INV.& SECS. SUSP - SUSP.10/06/13 27.39303

GREEN CROSS 3.67632

GREEN CROSS HDG. 13.88137

GS ENGR. & CON. 11.30475

HAE IN -3.888645

HALLA ENGR.& CON. 89.17872

HALLA VISTEON CLIMATE CONTROL 22.4166

HAN CHANG 16.55008 HANALL BIOPHARMA -2.958545 HANCHANG PAPER 21.65788 HANDOK 50.95435 HANDSOME 22.40072 HANEXPRESS 25.30302 HANIL CEMENT 18.55527 HANIL E-HWA 40.9036

HANIL IRON & STEEL 50.63826

HANJIN HVIND.& CON.HDG. -4.058708

HANJIN SHIPPING HDG. 12.7417

HANJIN TRANPORTATION 56.22943

HANKOOK COSMETICS MNFG. 10.64385

HANKOOK SHELL OIL 26.54544

HANKOOK STEEL 34.45727

HANKOOK TIRE WORLDWIDE -1.34746

HANKUK CARBON 12.0226

HANKUK GLASS INDUSTRIES 11.98788

HANKUK PAPER MNFG. 8.590999

HANMI SCIENCE 10.35099

HANSHIN CONSTRUCTION 6.620008

HANSHIN MACHINERY 23.79852

HANSOL ARTONE PAPER 17.07071

HANSOL CHEMICAL 25.78391 HANSOL CSN 21.61385 HANSOL PAPER MNFG. 6.967689 HANSOL PNS 12.20316 HANSOL TECHNICS 97.20813 HANSUNG ENTERPRISE 88.38101 HANWHA 20.65323 HANWHA CHEMICAL 4.820106

HANWHA GENERAL INSURANCE 12.49668

HANWHA INVESTMENT&SECS. 16.62529 HANWHA TIMEWORLD 21.31061 HANYANG SECURITIES 23.18776 HEUNG-A SHIPPING 28.79412 HEUNGKUK F&M.IN. 9.420395 HITEJINRO HOLDINGS 13.65709 HMC INVESTMENT SECS. 24.9948

HS INDS. 28.87101

HS R & A 39.68509

HUNEED TECHNOLOGIES 23.75011

HUSTEEL 31.06062

HWACHEON MACHINERY 8.782304

HWANGKUM STL.& TECH. 13.32086

HWASHIN 60.96615 HWASUNG INDUSTRIAL 16.92205 HYOSUNG 12.47779 HYUNDAI 12.59381 HYUNDAI BNG STEEL 13.20199 HYUNDAI CEMENT 13.60451 HYUNDAI DEV. 1.018458 HYUNDAI ELEVATOR -6.938964

HYUNDAI ENGR.& CON. 4.166767

HYUNDAI GREEN FOOD -14.42798

HYUNDAI HYSCO 27.67595

HYUNDAI MARINE & FIRE IN. 17.36242

HYUNDAI MERCHANT MARINE 5.796825

HYUNDAI MIPO DOCKYARD 1.453468

HYUNDAI MOBIS 13.71679

HYUNDAI MOTOR 10.24654

HYUNDAI P&C SUSP - 23/07/13 38.75655

HYUNDAI PHARM. 33.89008 HYUNDAI SECURITIES 8.293625 HYUNDAI STEEL 2.312196 IB WORLDWIDE 11.04497 IHQ 21.983 IL DONG PHARM. -38.95037 IL JEONG INDUSTRIAL 23.66647 IL SHIN STONE -14.04302 IL SUNG CONSTRUCTION -10.8066 ILJIN HOLDINGS 53.88354 ILSHIN SPINNING -3.625596 ILSUNG PHARMS. 0.7012669 ILYANG PHARM. 7.040098 IN THE F 16.86941

INDUSTRIAL BANK OF KOREA 9.424062

INFAC 19.7071

INZICONTROLS 64.34191

IS DONGSEO 49.33123

JICO 45.76011

JW PHARMACEUTICAL 4.005587

KC GREEN HOLDINGS 10.05909

KCC 16.31452

KCTC 0.7406847

KEANG NAM ENTERPRISES 20.06956

KEC HOLDINGS 13.29181 KEUNWHA PHARM. 10.71507 KEYANG ELEC.MCH. 16.87372 KEYSTONE GLOBAL 68.87664 KG CHEMICAL 37.85242 KIA MOTORS 34.48147 KISCO HOLDINGS 30.76898 KISWIRE 5.852744 KLEANNARA 65.06294 KOLON 11.73604 KOLONGLOBALCORPORATION 20.90196

KOREA AIRPORT SER. 21.26938

KOREA CAST IRON PIPE IND. 11.68406

KOREA CIRCUIT 0.8407404

KOREA DEV.FINANCING 7.346065

KOREA DEVELOPMENT 7.939385

KOREA ELEC. TERMINAL -8.913083

KOREA ELECTRIC POWER 13.26752

KOREA EXPORT PACK.INDL. 36.91422

KOREA FLANGE 55.37488

KOREA INDL. 122.0628

KOREA KOLMAR HOLDINGS 34.24161

KOREA LINE 13.62753

KOREA PETROLEUM INDL. 56.97102

KOREA REFRACTORIES -2.739898

KOREA STEEL SHAPES 68.67089

KOREA ZINC 8.538456

KOREAN AIR LINES -2.780465

KOREAN REINSURANCE -27.83161 KP&L 56.20776 KPX CHEMICAL 19.27525 KPX FINE CHEMICAL 7.451065 KTB INVESTMENT&SECS. 2.540079 KUKBO TRSP. 64.02599 KUKDO CHEMICAL 16.81516 KUKDONG 42.69832

KUKJE PHARM.INDL. 20.626

KUM YANG 12.68747

KUMBI 96.58881

KUMHO ELECTRIC 47.02148

KUMHO INDUSTRIAL 18.12814

KUMHO INVESTMENT BANK -1.032392

KUMHO PETRO CHEMICAL 12.37496

KUMKANG KIND -0.502948

KUNSUL CHEMICAL INDL. 5.502645

KWANG DONG PHARM. 4.30864

KWANG MYUNG ELEC.ENGR. -2.498165

KYE-RYONG CON.INDL. 29.0697

KYUNG DONG NAVIEN 28.80961

KYUNG IN ELT. 42.62862

KYUNG NONG 25.4304

KYUNGBANG 45.85622

KYUNGDONG CITY GAS 12.14416

KYUNGIN SYNTHETIC 27.41664 KYUNGNAM ENERGY 11.55462 LEE KU INDL. -14.47083 LG 11.57954 LG INTL. 14.15996 LIG INSURANCE 18.48877

LOGISTICS ENERGY KOREA 80.38253

LOTTE CHEMICAL 3.589952

LOTTE CHILSUNG 2.458263

LOTTE CONFECTIONERY -0.002741

LOTTE FOOD 6.674119

LOTTE NON-LIFE IN. 9.669961

LS 23.01431

LS INDUSTRIAL SYS. 24.08767

LS NETWORKS -56.8461

MANHO ROPE & WIRE -35.91157

MERITZ FIRE & MAR.IN. 15.4703

MERITZ SECS. 15.58632

MHETHANOL 10.901

MICHANG OIL IND. 30.16308

MIRAE 4.257063

MIWON COMMERCIAL 37.44949

MONALISA 0.6306809

MONAMI 27.24483