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Does nuclear uncertainty threaten financial markets?: The attention paid to North Korean nuclear threats and its impact on

South Korea’s financial markets*

Ju Hyun Pyun† Korea University Business School

In Huh‡ The Catholic University of Korea

This version: October 1, 2016

Abstract

We explore how the investor attention paid to dangerous nuclear tests in an adjacent country influences financial market outcomes. To measure the attention paid to North Korean nuclear threats, we introduce a weekly Google search volume index for keywords related to North Korean nuclear events. Using a time-varying structural vector auto-regression model with block exogeneity restrictions, we find that the investor attention paid to nuclear threats has heterogeneous effects on South Korea’s stock price both across industries and over time: only attention paid to the first nuclear test was negatively related to the stock price index, and this negative association vanished thereafter. The attention paid to the second test had a significant depreciation impact on the foreign exchange rate. Our result also reveals that the investor attention paid to the nuclear risk reduced stock price, especially in the banking industry, during the whole sample period. JEL Classification: F3; G1 Key words: Investor attention, North Korean Nuclear Risk, Google SVI, Structural Vector Auto-Regression Model, Block Exogeneity, Political Risk

* We are grateful to Boyoung Choi, Minsoo Han, Zonglai Kou, Abul Shamsuddin, Haizhi Wang and participants in the 9th Annual Conference between Fudan University and Chonnam National University, China, the KIEP seminar, Korea, 7th IFABS conference, China and 2016 WEAI meeting for their helpful comments. This work was supported by the Catholic University of Korea, Research Fund 2015. All remaining errors are our own. † Korea University Business School, 145 Anam-Ro, Seoungbuk-Gu, Seoul 136-701, Korea, Tel:+82-2-3290-2610, E-mail: jhpyun@korea.ac.kr ‡ The Catholic University of Korea, Department of Economics, 43 Jibong-ro, Wonmi-gu, Bucheon-si, Gyeonggi-do 14662, Korea, Tel:+82-2-2164-4569, E-mail: inhuh@catholic.ac.kr

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1. Introduction

North Korea has been provoking South Korea since the Korean Armistice Agreement

was signed in 1953. As recently as the 2000s, North Korea conducted several serious military

actions, such as the Yellow Sea battle, attacks on the Cheonan ship and Yeonpyeong Island,

and various missile launches, which can only be construed as grave threats to South Korea.

North Korea also started conducting nuclear tests in 2006, which have been heavily criticized

the world over and have intensified the political and geographical uncertainty in the Korean

peninsula. North Korea’s ambition to be a nuclear state has not only raised concerns about a

potential war between the two Koreas but also put world peace in peril. As the New York

Times noted in April 2013, there are various views on North Korea’s nuclear tests: “Many

analysts believe that North Korea is again seeking aid and other concessions, while some

suggest that it merely wants to be recognized as a nuclear state, like Pakistan. Still others

suggest that the North genuinely fears an attack by the United States or South Korea and

views the warnings as deterrence. Highlighting a perceived threat from abroad is also a

favorite tool the North Korean government uses to ensure internal cohesion.” Since it is

difficult to pin down the real intentions behind North Korea’s provocations and threats (i.e.,

whether the threats are empty), it is hard to identify the “real” effect of such risks on South

Korea’s financial markets.

In this study, we analyze how North Korea’s nuclear risk influences the South Korean

financial markets from an investor’s viewpoint. We provide a new measurement for the

investor attention paid to North Korean nuclear threats in the form of a Google search volume

index (SVI) (e.g., Da et al., 2011), which measures the online search frequencies of related

keywords pertaining to North Korea’s nuclear events. This measurement helps quantify

investor’s demand for information on North Korea’s nuclear threats and provocations. Then,

using weekly data for 2004–2012, we employ a structural vector auto-regression (VAR)

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model with block exogeneity restrictions by treating the attention paid to the nuclear threats

as a given exogenous variable in the system and considering endogeneity among financial

market variables.

We find that the market’s attention to very early North Korean nuclear threats was

negatively related to the South Korean stock market price index (KOSPI). This negative

effect on the stock price of the attention paid to the nuclear threats attenuated afterward.

Interestingly, only the attention paid to the second nuclear test was significantly associated

with depreciation of the Korean won. Further, we investigate the heterogeneous responses of

industry-specific stock prices to the nuclear risk. Our results show that only the banking

industry was considerably hit by the nuclear risk in terms of industry stock price; the stock

prices of other industries did not respond significantly. We show that our results are robust by

controlling for alternative measures, specifications, and identification.

Many previous studies show significant financial market reactions (price changes and

volatility) to exogenous political conflicts or social crises. Frey and Kucher (2000) and

Waldenstrom and Frey (2002) examine the impact of events during World War II on the

prices of several countries’ government bonds traded in Sweden and Zurich (Switzerland),

respectively. Amihud and Wohl (2004) and Rigobon and Sack (2005) focus on the Iraq War

and its consequences on financial markets. Amihud and Wohl (2004) find that the likelihood

of Saddam Hussein’s fall from power, as reflected in a traded futures contract that paid out if

Saddam were to be ousted, is related to U.S. stock market returns.1 Rigobon and Sack (2005)

investigate the impact of the news shock about the Iraq War on several U.S. financial

variables. They find that an increase in the war risk caused a rise in oil prices, declines in

treasury yields and equity prices, a widening of corporate yield spreads, and depreciation of

1 Wolfers and Zitzewitz (2009) use the keywords “Saddam Security” by to estimate the expected cost of the Iraq War. They show that the Iraq War was expected to lower the value of U.S. equities by around 15%, equivalent to USD 1.1 trillion, the market value of all stocks in the S&P 500 Index.

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the U.S. dollar. Berkman et al. (2011), using a large sample of major international political

crises, show that disaster risk affects future consumption growth and expected stock returns.

Fisman et al. (2014) show that interstate political conflicts between China and Japan2 have

significant negative effects on firm-level stock returns and profit measures in both countries.3

Our study contributes to the existing literature on the effects of political crises on the

financial market in several ways. First, we shed light on the unique and real-time political

crisis posed by North Korea, which has often taken center stage in world affairs. We utilize

the latest weekly data for the period 2004–2015, which include thrice as many nuclear threats

and tests by North Korea, and implement comprehensive analysis by comparing the effects of

these nuclear risks on South Korea’s financial markets across industries and over time.

Despite the importance of such events, few studies examine the effects of North Korea’s

military or nuclear provocations on the South Korean financial market or the markets of

adjacent countries. Even some existing studies show mixed findings. For instance, Kollias et

al. (2014) show a greater adverse effect of North Korea’s second nuclear test on the stock

prices of South Korea and other Asian countries than that of the first nuclear test.4 They

argue that the qualitative difference between the two events was that the first was announced

whereas the second was unexpected, which led to different reactions in the financial market.5

2 They consider two events: Japan’s approval of the new textbook and the Senkaku (Diaoyu in China) disputes. 3 Another strand of research focuses on the relationship between political crises and the second moment of financial variable (i.e., stock market volatility) or financial linkages. Bittlingmayer (1998) finds that political uncertainty in the early 1920s in Germany generated greater stock volatility. Brown et al. (2006) argue that the low volatility of Consols during Pax Britannica (1816–1913) may be attributed to the political stability in that period. Choudhry (2010) shows that wartime events during World War II resulted in structural breaks in stock price movement and volatility. Frijns et al. (2012) examine the relationship between political crises and stock market integration and show that political crises significantly reduced the degree of stock market integration in 19 emerging markets for 1991-2006. In a similar vein, previous studies on international trade also find significantly negative effects of interstate military conflicts on trade, and vice versa (e.g., Glick and Taylor, 2010; Lee and Pyun, 2016; Martin et al., 2008). 4 Lee (2006), writing in Korean, finds that the daily news about North Korean nuclear threats decreased South Korea’s stock market index and devalued the won during 2002 and 2003. 5 Koillas et al. (2014) suggest that the prior announcement of nuclear weapon development provided time for markets to absorb the nuclear shock, whereas the unannounced second nuclear test was more of an exogenous shock that markets did not expect.

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However, Kim and Roland (2011) do not find any significant effects of North Korean

military provocations on South Korea’s financial markets from 2000 to 2008.

A novel feature of this study is that, motivated by previous studies on investor

attention such as Da et al. (2011), Mondria and Wu (2012), and Vlastakis and Markellos

(2012), we introduce Google’s SVI using keywords about North Korean nuclear tests to

measure the demand for information on the political risk posed by North Korea. This measure

captures valuable information on whether the nuclear risk is realized in the market, as it

reflects the “attention” economic agents pay to real-time nuclear threats.6 In this regard, our

findings of a significant effect of the nuclear risk on financial market outcome are not limited

to specific political risk but provide more general implications for how investors respond to

political risk without loss of generality.

Further, many previous studies on political risks (including those orchestrated by

North Korea) approach such issues with an event study analysis or identify each catastrophic

event using a dummy variable (e.g., Kim and Roland, 2011, Kollias et al., 2014). While these

works deal with each political crisis event with the same degree of importance, the Google

SVI successfully measures the intensity and variation of the risks perceived by agents and

overcomes the shortcomings of treating various North Korean risks as identical events, as in

previous studies. Thus, our work contributes to the literature not only on political risk and

financial markets but also on investor attention and/or information demand and financial

market outcome.

Methodologically, we employ a time-varying structural VAR model. While

exogeneity of political risk is endorsed in many previous studies, endogeneity and feedback

of financial market variables are still important concerns. Our VAR approach with block

6 The term “attention” has been studied in psychology and is widely used to explain investor behavior while studying the financial market. The terms “inattention” and/or “limited attention” are even more widely used (Huang and Liu, 2007; Kahneman, 1973).

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exogeneity restrictions allows for both exogenous political risk and endogeneity among

financial market variables, which contributes to identifying the pure effect of market attention.

The rest of the paper is organized as follows. Section 2 introduces the data and our

measure of North Korean nuclear risks and also describes the movement of the variables of

the South Korean financial market in response to the nuclear tests. Section 3 shows the

empirical model, which considers possible endogeneity among financial market variables and

reports the empirical results. Section 4 concludes.

2. Data

2.1. Measuring the attention paid to North Korea’s nuclear threats

In this section, we introduce the method used in this study to quantify the attention

paid to risks posed by North Korea’s nuclear threats. Table 1 shows the series of North

Korean military provocations and nuclear threats since 2000. There have been various types

of dissimilar events. For example, it is clear that North Korea’s announcements of missile

launches have very different effects on the South Korean financial market than do real

military attacks like those on the Cheonan ship and Yeonpyeong Island. Even the same types

of nuclear tests may have different effects on South Korea; investors pay more attention to

some events than others, depending not only on the type and intensity of North Korea’s

provocations but also the accessibility of information about each event. An incident itself

does not affect investment in financial markets. Rather, investors may later actively search

online for more specific information about the incident and change their investing decisions

because they may be unaware of the event when it just breaks out. Hence, it is important to

identify the “real” risks posed by North Korea, namely, the risks that investors or market

participants perceive or pay attention to.

[Insert Table 1]

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A new indicator that reflects the attention economic agents pay to the nuclear risks

posed by North Korea is proposed by using weekly Google SVI data collected from the

Google trend database (https://www.google.com/trends/). Barber and Odean (2001) provide

evidence of a growing tendency among investors to rely more on the Internet for information

and brokerage services because they are unwilling to pay for advisory services from off-line

brokers. This supports the important role of Internet-based search in investment decisions. In

fact, the SVI has been previously used to measure the attention paid by investors to a specific

stock in the stock market (Da et al., 2011; Mondria and Wu, 2012; Vlastakis and Markellos,

2012). For instance, if investors are interested in Microsoft Corporation, they can look for

financial information and real-time news about Microsoft by typing the stock’s ticker symbol,

“MSFT,” into the Google search bar. Then, Google provides information on Microsoft, its

financial statements, and news about that stock.

The Google SVI project summarizes this searching activity on the Internet and

constructs the index after computing the search frequency. The index standardizes the

number of searches between 0 and 100, wherein the higher the index, the greater the number

of searches. Da et al. (2011) propose this Google SVI for stock ticker symbols as a direct

proxy for investor attention and find that an increase in SVI predicts higher stock prices in the

following two weeks and an eventual price reversal within the year. Mondria and Wu (2012)

analyze the SVI for overseas stocks (i.e., stocks outside the United States) to examine U.S.

investors’ scrutiny of overseas stocks. A subsequent study by Vlastakis and Markellos (2012)

finds that the demand for information on the specific stock at the market level (measured by

the SVI) is closely associated with stock market volatility, particularly when there is an

economic boom. Previous studies also apply the SVI measure to various fields in finance.

Kita and Wang (2012) and Smith (2012) construct SVIs to measure the intensity of the global

financial crisis and investigate its effect on the foreign exchange market.

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We construct the SVI for North Korean nuclear threats using the following keywords:

“North Korea nuclear” or “Korea nuclear.” We also use Google to search for words reflecting

the same meaning in Korean and devise another SVI to analyze the effects on domestic

investors of South Korean nationality only. Note that the SVIs using Korean keywords are

only available at a monthly frequency.

We believe that the SVI can measure the market attention paid to the nuclear risks

posed by North Korea. Nevertheless, there are several caveats to constructing the index. First,

unlike the SVI used by previous studies to capture investor scrutiny of the stock ticker

symbol7, the SVI for North Korean nuclear threats could be noisy because the index may

capture not only the attention of investors to North Korean nuclear threats but also that of any

other agents interested in this issue but having no intention to invest. Another concern is

whether this measure entirely captures the North Korean nuclear risks. One may argue that

our baseline measure could also capture surprising positive news about North Korea’s nuclear

status (e.g., progress in the six-party talks).

To avoid the above concerns, we first refine our measure by limiting the index

construction to investors/regions that tend to search for information about Korea, such as the

United States, which is a major investor in South Korea. However, this refined alternative

measure shows a very high correlation with the original measure without regional restriction,

and the results are not sensitive to the refinement. Furthermore, to rule out the possibility that

the SVI index of the nuclear risk captures good news about North Korea, we introduce

another proxy for positive information shocks about North Korea, the SVI constructed using

the keyword “six-party talks,” and compare the effects of this positive measure with our

baseline SVI on financial market outcomes.

Fig. 1 describes two SVIs using English and Korean keywords for North Korean

7 It could be rare for non-investors to search the ticker name of stocks without any intention of investing.

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nuclear threats, as well as the SVI using the English keyword “six-party talks.” Gray areas

indicate when North Korea conducted nuclear tests. We observe high correlation between the

SVIs for the nuclear threat and actual North Korean nuclear tests. However, the SVI utilizes

more detailed information on the investor attention paid to each nuclear test. For example, the

SVI takes the highest value after the first nuclear test, which was conducted in October 2006.

The second-highest value is observed for February 2013, when North Korea conducted its

third nuclear test. The SVI for six-party talks also shows a somewhat different fluctuation

from the baseline SVIs. It has the highest value in September 2005 and another peak right

after the first nuclear test, but it has been decreasing over time.

[Insert Fig. 1]

2.2. Other financial market variables

Data used are weekly average data for the South Korean foreign exchange rate

(won/U.S. Dollar), three-year bond interest rate, and Korean stock market index (KOSPI)

from 2004 to 2015. We source daily data for the three-year bond interest rate (the most

traded bond in South Korea), the won/dollar exchange rate, and the KOSPI from the

Economic Statistics System of the Bank of Korea. We use weekly averages instead of values

at the end of the week to allow all financial variables to reflect any changes owing to the

North Korean risk within a given week. Unfortunately, industrial production is not available

at a weekly frequency, so we interpolate monthly industrial production to weekly data to

measure real economic activities. However, our results do not change by excluding the

industrial production variable from the system. The descriptive statistics of the sourced data

appear in Appendix Table 1.

Further, in order to analyze heterogeneous industry stock price responses to the

nuclear risk, we collect the monthly industry-specific stock price index from the KIS Line,

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which accumulates financial information on Korean firms.8 This source provides data for the

stock price index, trading volume, and turnover for 20 industries. Appendix Table 2

summarizes the means and standard deviations for industry stock price data.

2.3. Eyeball test: Responses in South Korea’s financial market on the day of North Korean

nuclear threats

It is natural to expect negative impacts on the South Korean financial markets arising

from North Korea’s escalating nuclear threats. To motivate our analysis, Table 2 reports

changes in prices in the South Korean financial markets on the days North Korea conducted

nuclear tests. In general, the risks posed by North Korea decreased the value of the stock

index, the value of the Korean won, and the interest rate of long-term government bonds. In

the foreign exchange market, the Korean won showed a very small rage of depreciation:

about 0.1% to 0.4%. The value of the Korean won against the U.S. dollar depreciated after

the first and third nuclear tests. KOSPI exhibited a more dramatic fall in response to the first

nuclear test than to the other tests. Interestingly, the magnitude of the negative price shocks

on KOSPI reduced as North Korea repeated the nuclear tests. It seems that the effects of the

nuclear risks on the South Korean stock markets vary over time.

[Insert Table 2]

However, the descriptive changes in the prices of the South Korean financial markets

in Table 2 could have been caused not only by the nuclear tests but also by other shocks in

South Korea. Therefore, we should be very careful while interpreting these price changes as

the real (and sole) consequences of the nuclear tests. We can evaluate the cost of negative

spillovers from the nuclear risk and prepare policy instruments for future provocations by

North Korea only if we are able to identify the pure effect of the nuclear threats on the South

8 KISLINE: Knowledge Inside. http://www.kisline.com. Accessed on March 08, 2015.

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Korean financial market. Thus, we try to estimate the pure effects of the nuclear risks on the

South Korean financial markets by adopting a new method and variable.

3. Empirical analysis

3.1. Empirical model

We estimate the impact of the investor attention to North Korean nuclear threats on South

Korean financial markets using the structural VAR model with block exogeneity restrictions

(see Lastrapes, 2005; Maćkowiak, 2007).

∑ (1)

where is a vector of South Korea’s real macro-economic variables and financial

variables: industrial production, foreign exchange rate, long-term interest rate, and stock price

index. is the measure of the attention paid to North Korean nuclear risk based on

Google’s SVI. A21(s) = 0 for s = 0,1,…,T, which indicates the assumption of block

exogeneity: the residuals in the South Korean financial markets, , do not affect the

nuclear risks posed by North Korea, , even with time lags. ε ≡ ; , where

and are residuals of the system that satisfy E[ | , 0 0 and

E[ ′| , 0 . Additionally, we impose recursive zero restrictions on

contemporaneous structural parameters by applying the Cholesky decomposition (i.e., we set

the order of variables in the vector from the most exogenous to the most endogenous

variable as follows: industrial production, long-term bond yield, foreign exchange rate, and

stock index). This identification scheme allows changes in the interest rate to have a

contemporaneous impact on the other financial market variables, such as the exchange rate

and stock price index, but not the reverse. To check the robustness of the results, we change

the order of the variables in vector , but the results are not sensitive to this change. We

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include the Chicago Board Options Exchange (CBOE) volatility index (VIX) as an

exogenous variable in the system to control for movements in the U.S./international equity

market and/or the uncertain environment of the world economy. This VIX also captures

possible exogenous shocks during the sample period, such as the global financial crisis.

Lastly, we allow time lags of up to two weeks for the VAR system using the final prediction

error (FPE) test and the Akaike information criterion. In the VAR system, we de-trend all

variables except for the SVI with a quadratic time trend after taking a logarithm of them. For

the robustness check, we also use first differenced variables, but the results do not alter.9

3.2. Empirical results

3.2.1. Benchmark results: Full sample vs. sub-samples

Fig. 2 shows the impulse response functions of South Korea’s long-term interest rate,

foreign exchange rate and stock price index to the SVI for North Korean nuclear threats

(nuclear risk perceived by agents) using weekly data for 2004–2015. We compute the

standard errors of the impulse response functions and draw 90% confidence intervals. During

the whole sample period, while North Korean nuclear risk had insignificant effects on foreign

exchange rate, it had negative impacts on South Korea’s long-term bond yield and stock price:

the stock price and long-term interest rate responded significantly and negatively to the

impulse of the shock.

[Insert Fig. 2]

Further, motivated by the data description in Table 2, we split our full sample into

three subsamples around the time of each nuclear test within a four-year window and

examine whether the effects of the nuclear risk on South Korea’s financial markets are

9 We perform the (augmented) Dickey-Fuller unit root test for de-trended (with quadratic trend and first differenced) variables. The null hypothesis that each time-series has a unit root is rejected at the 5% level. The test results are not sensitive to the number of lagged difference terms.

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constant over time. The second column of Fig. 2 shows the impulse response functions to the

nuclear risk for the first subsample, from 2004 to 2007. The attention paid to the nuclear

threats significantly decreases the long-term bond rate and stock price two and three weeks

after the impulse, respectively. A decrease in stock price in response to the nuclear risk

implies that investors’ perception of nuclear risk may discourage their investment in the

Korean stock market. The negative response of the bond yield to the nuclear risk also implies

that the demand for South Korea’s long-term bonds (as safe assets) may increase as the risk

emerges.10

The third and fourth columns in Fig. 2 show the impulse response functions to the

nuclear risk shock for other subsample periods: 2008–2011 and 2012–2015. Unlike the result

of the first sub-sample, the SVI for nuclear threats does not have significant impacts on stock

prices and long-term interest rate in these cases. The significant effect of the SVI observed in

the very first subsample period disappears. Interestingly, the impulse responses to the nuclear

risk in the first sub-sample are only negative and much greater than those in the full sample,

so we conjecture that the full sample result could be driven by the subsample result around

the very first nuclear test. Moreover, the response of the long-term interest rate during 2012-

2015 is negative but marginally significant.

However, the response of foreign exchange rate to the SVI during the second sub-

sample period turns out to be significantly positive three weeks after the impulse, which

implies that the nuclear risk that emerged around the second nuclear test depreciated the

South Korean won. This finding is consistent with a previous study by Kollias et al. (2012)

but requires careful interpretation. Kollias et al. (2012) find that the second nuclear test had a

greater negative effect on the currency markets of 10 Asian countries, including South Korea,

10 According to the data on portfolio flows provided by International Financial Statistics at the International Monetary Fund, equity liability inflows to South Korea shrank while debt liability inflows to South Korea increased from 2004 to 2007, indicating that foreign investors had been buying bonds and selling stocks, respectively, in South Korea’s financial market in that period.

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than the first nuclear test did. They argue that North Korea announced the first test not only

to South Korea but also to the international community (e.g., North Korea’s secession from

the the Non-Proliferation Treaty (NPT) occurred in January 2003, and the market might have

expected this test based on North Korea’s preparations for nuclear development), whereas the

second test was relatively unexpected.

Why, then, did domestic stock price and bond yield respond more to the SVI around

only the first test while foreign exchange rate was sensitive to the SVI around the second test?

Our stock price findings suggest that the nuclear risk affected the stock price significantly

around the time of the first nuclear test, but that its impact has receded. In this regard,

repeated risks of the same form of nuclear tests may have dulled investors’ sentiments toward

the South Korean financial market. From the perspective of investor learning, investors may

realize that the risks posed by North Korea’s nuclear tests tend to be quite short-lived and are

not realized; thus, the impacts of the nuclear risk on financial markets appear to have become

subdued over time.

However, foreign exchange rate which is mainly driven by foreign investors in Korea

did not respond significantly to the SVI for the nuclear threats around the first test. In fact,

what news investors pay attention to totally depends on investors’ access to information. It is

possible that the “information content” of the second test was larger for foreign investors than

for domestic or residential investors in South Korea. In line with Kollias et al. (2012), the

second test may have been more surprising to foreigners than to domestic investors and

subsequently attracted more attention from foreigners. In the robustness check section, we

also show that the impacts of the English SVI and the Korean SVI on exchange rate around

the second nuclear test differ.

3.2.2. Time-varying structural VAR with a four-year rolling window

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Previous subsample analysis treated each nuclear event as a separate sample, thus

making the investor attention paid to each North Korean nuclear test mutually exclusive

across the subsample analysis. One may argue that this arbitrary separation of subsamples can

bias the results. In this subsection, we use the continuous time-varying structural VAR

approach with a four-year rolling window and check whether the implication from the

previous subsample regression continues to hold.

The results are reported in Figs. 3 and 4. Fig. 3 is our baseline result and Fig. 4 is the

result with first differenced variables for robustness. We trace the time-varying responses of

each financial market variable to the SVI at three weeks from the impulse of the shock.

Overall, the results in Figs. 3 and 4 are consistent with those in Fig. 2. In particular, the stock

market response to the SVI shows distinctive changes over time: the nuclear risk significantly

reduced the stock price index in the early 2000s, when North Korea conducted its first

nuclear test. However, the negative impacts vanished over time, although North Korea has

continued its provocations with its second and third nuclear tests.

Around the time of the first nuclear test, the SVI also reduced the long-term bond

yield significantly, which was related to the increased demand for bonds as safe assets.

However, during the period 2008 to 2015, the nuclear risk did not decrease the yield

significantly, as already shown in our subsample results in Fig. 2. These results show that

investors had already become acclimatized to the “benign” effects of the North Korean

nuclear risks and, hence, they even stopped seeking investment in safer assets.11 When

discussing the combined responses of the stock price and bond yield to the SVI for North

Korean nuclear threats, we observe that the significant negative effects of the perceived

nuclear risk on stock price and bond yield are only observed around the first nuclear test.

11 It might also be possible that the investors became more cautious after the global financial crisis (namely, the Lehman Brothers bankruptcy in September 2008), which heightened global financial uncertainty. When events adding to the risk occurred, bond investors decided to withdraw investments from the bond market as well.

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Again, we show that the depreciation effects of the nuclear risk on foreign exchange rate

were distinct around the second nuclear tests.

To evaluate the economic significance of the result, we compute a maximum marginal

effect of the nuclear risk on stock price using the time-varying impulse responses. One

standard deviation of the nuclear risk decreases stock price by 0.8% from the mean of the

stock price index for the whole sample period, which corresponds to 13 points. This change is

not negligible compared to the mean of stock price growth over the sample period, which is

0.05% (or the mean of stock price change during 2004–2007: 0.3%).

[Insert Fig. 3]

[Insert Fig. 4]

3.2.3. Validity of the measurement for nuclear risk

One of the main contributions in this study is to introduce the SVI as a new

measure for the North Korean nuclear risk from an investor’s viewpoint. To check the

validity of our measurement of the attention to the nuclear threats, we introduce an

alternative index that measures a positive news shock on the nuclear deadlock. As

introduced in section 2.1, we use the SVI constructed using the keyword “six-party talks”

and reiterate our time-varying analysis. Fig. 5 reports the results. Unlike the baseline

results in Figs. 3 and 4, this new measurement had a positive effect on the South Korean

financial market. Between the first and second nuclear tests, attention to the six-party

talks is associated with appreciation of the Korean won. This implies that after the first

nuclear test, the effort to resolve this nuclear issue via the six-party talks might have

attracted more foreign capital flows into South Korea, which increased the demand for

the Korean won. The measurement of this positive information shock about North Korea

is also positively related to stock price. Significant positive effects of this measure on

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stock price (two peaks in Fig. 5) are observed during 2007–2010 and 2010–2014. Thus,

the results in Fig. 5 support that our baseline measurement related to North Korean

nuclear threats is not contaminated with investor attention to positive news about North

Korea.

[Insert Fig. 5]

3.2.4. Cross-industry variation: Industry-specific stock price index

We have focused on aggregate analyses to investigate the effect of the attention to

North Korean nuclear threats on South Korea’s macro-financial variables. In particular, the

stock price was hit severely by the perceived nuclear risk in the early 2000s. However, it is

possible that the risk had a different effect on disaggregate stock prices (e.g., industry- or

firm-level stock price indexes) for different market or industry characteristics. This

subsection examines the responses of industry stock prices to the SVI for the nuclear threats.

By introducing industry-specific stock price indexes, we gauge which industry was more

sensitive to the nuclear risk. Thus, we include three de-trended industry stock variables

following the aggregate stock price index in the structural VAR system of Equation (1). We

consider industry stock price as the most endogenous variable, followed by turnover and

industry stock trading volume.

Fig. 6 shows the responses of the industry stock price index to the nuclear risk.12

Interestingly, our results show that the stock prices were significantly lowered by the

attention to the risk only in banking industries, whereas those of the other industries showed

insignificant responses.13 The vulnerability of the domestic banking industry to the nuclear

risk is not surprising. It implies that the risk increased investors’ loan withdrawals from

12 The impulse responses of trading volume and turnover to the SVI are available from the authors upon request. 13 In terms of trading volume and turnover, banking industry consistently shows negative responses to the SVI as well.

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domestic banks, thus hurting the profitability of the banking industry.

[Insert Fig. 6]

3.3. Robustness checks

3.3.1. Monthly frequency data, different identifications, etc.

We conduct various tests to confirm the robustness of our results. First, we introduce

a different identification scheme by changing the order of the financial variables and setting

up the foreign exchange rate as the most endogenous. Second, we use different lags for our

VAR model. Our baseline results use de-trended financial market variables. In the robustness

check, we also introduce variables without de-trending. The results of all the robustness

checks do not change our main findings in Figs. 3 and 4. Lastly, we repeat our baseline time-

varying structural model using monthly data. In this analysis, we use real effective exchange

rate instead of nominal foreign exchange rate.14 Fig. 7 reports the impulse response functions

of three financial variables to the nuclear risk measurement at the impulse of the shock. The

results in Fig. 6 are consistent with our main results in Figs. 3 and 4.

[Insert Fig. 7]

3.3.2. Behavior of domestic South Korean investors

We investigate whether the nationality of investors influenced their access to the

information on the North Korean nuclear threats and shaped the responses to the nuclear risk.

Different nationalities may reflect varying degrees of access to information, leading to

different responses to the same incident. To distinguish the attention of the South Korean

investors from that of all investors, we employ two different attention measurements to

represent the differences in the degree of attention paid to the nuclear risk by South Korean

and all investors and compare the results. For South Korean investors, the measure of

14 Monthly South Korean real effective exchange rate is used instead of nominal exchange rate, and is obtained from the Bank of International Settlement (BIS).

19

attention is constructed using the SVI, which refers to keywords for North Korea’s nuclear

events in Korean only.15 Fig. 8 shows the results of the time-varying impulse responses to

the nuclear risk. Overall, the results are consistent with our main findings in Figs. 3 and 4.

The results also reveal that the attention to the nuclear threats paid by South Korean investors

only has a slightly larger negative effect on stock price than that indicated by the same

measure for all investors. However, the response of real exchange rate to the attention to the

risk paid by South Korean investors (using the Korean SVI) is weaker than that paid by all

investors (using the English SVI). Thus, it is arguable that while the second nuclear test was

not as surprising as the first test for South Korean investors, this distinction is more clear for

foreign investors. This is also consistent with our baseline findings on foreign exchange rate

in Figs. 3 and 4.

[Insert Fig. 8]

4. Conclusions and implications

What is the “real” risk from political shocks and how does it affect financial market? Does an

incident itself matter for investors in the financial market? Otherwise, what kinds of events

do investors pay attention to and respond to? To answer these questions, this study focuses on

the nuclear tests done by North Korea since early 2000s and investigates the dynamic impact

of the investor attention paid to North Korean nuclear threats on South Korea’s financial

markets. Using weekly data for the period 2004–2015, we first quantify the degree of investor

attention paid to the North Korean nuclear threats using Google’s SVI. Then, we can

determine the impact of the nuclear risk perceived by market participants on the South

15 One might argue that it is also possible that some South Korean investors search for information about North Korea’s nuclear threats in English. For example, we can imagine that a South Korean investor might use Google to search for “North Korean threats” and find and read a New York Times article about the topic. However, when discussing investments in the South Korean financial market, information about North Korea is likely to be delivered in more detail and faster through the Korean media than through the media in other countries. Thus, we rule out South Korean investors using Google to search for news about North Korea in the English media.

20

Korean financial markets more precisely over time.

While the attention paid to nuclear threats has had negative impacts on the long-term

bond yield and stock price index in South Korea, the impacts of the nuclear risk were

particularly large and significant in the earlier period (2004–2007) compared to the later

period (2008–2015). More importantly, its negative effects on the stock price and the bond

yield had become subdued after the first North Korean nuclear test was completed. Another

finding is that only the attention to the second nuclear threat was significantly associated with

the depreciation of the Korean won. We also find heterogeneous responses of industry-

specific stock prices to the nuclear risk. Stock price in the banking industry was particularly

sensitive to the investor attention paid to the nuclear threats.

North Korea’s continuing nuclear tests and claim to be a nuclear state have escalated

geopolitical risk in the Korean peninsula and attracted severe criticism from the international

community. However, our results imply that stock market investors did not respond

significantly to the repeated North Korean nuclear threats, whereas a new provocation from

North Korea, like the first North Korean nuclear test that occurred in 2006, could have a

significant impact on the South Korean financial market outcome. Thus, additional nuclear

threats from North Korea in the future could have different consequences for the South

Korean financial market, which would depend not only on the characteristics of the threat but

also how seriously investors pay attention to it. Our findings also support the idea that the

investor attention paid to information plays an important role in their investing decision.

Further, we provide the South Korean government and international organizations with

practical ideas how to stabilize the financial market, especially in preparation for new types

of threats from North Korea: Impeding the access to information never be a remedy. Rather,

policy makers provide a clear source of information and help reduce uncertainty in the

financial market, which will then make investor’s investing decision more predictable.

21

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24

Appendix

Appendix Table 1. Descriptive statistics of weekly data during 2004-2015

Variable Obs. Mean S.D. Min Max

SVI 626 2.37 5.27 0.00 89.50

Won/USD 626 1091.73 108.08 906.70 1554.80

INT 626 3.78 1.08 1.58 6.07

KOSPI 626 1631.02 399.84 730.55 2205.94

VIX 626 19.36 9.21 10.19 72.92

Appendix Table 2. Monthly Industry stock price index, trading volume, and turnover

Industry Variables Mean S.D. Min Max Food & Beverage Stock price index 2626.06 840.4699 1191.67 4667.82

Trading volume 7852.628 8106.909 1050.8 54043 Turnover 93961.13 44753.46 28353 433018

Textile & Apparel Stock price index 179.6952 54.53294 70.23 299.22 Trading volume 10725.94 11310.25 1054 79927.5 Turnover 28672.84 22211.12 5157 158037

Forestry & Paper Stock price index 299.7197 64.99655 156.53 489.56 Trading volume 12080.09 12902.1 1984.3 107263.8 Turnover 21860.87 11980.57 6473 95767

Chemicals Stock price index 3005.767 1305.395 1030.53 6158.12 Trading volume 22389.58 10174.35 6972.5 59210.7 Turnover 548198.8 380103.8 118022 2410348

Pharmaceuticals Stock price index 3494.927 973.3381 1146.92 4902.18 Trading volume 14629.16 13889.03 1959.5 65526.2 Turnover 87175.55 65892.19 9053 477876

Nonferrous Metals Stock price index 855.5948 194.6133 531.95 1399.88 Trading volume 4842.979 4308.385 995.7 25855.7 Turnover 16818.46 11836.52 3733 61014

Iron & Steel Stock price index 4875.975 1700.293 1586.8 7904.44 Trading volume 16988.42 12763.94 2720.6 68014.6 Turnover 253549.1 153437.7 53568 986151

Industrial Engineering Stock price index 1118.056 431.1462 356.03 2526.01 Trading volume 22197.3 16749.03 3620.9 79943.2 Turnover 138958.2 79422.69 28731 445052

Electronic & Electrical Equipment

Stock price index 7379.558 2181.545 4111.47 11804.37 Trading volume 61176.65 32174.07 20761.2 195626.2 Turnover 977043.9 325812.3 392293 1730793

Biotechnology Stock price index 1211.214 576.0944 227.31 2179.51 Trading volume 18920.98 40770.26 1179.8 312277.9 Turnover 43525.49 51012.7 1487 259661

Industrial Transportation Stock price index 1719.864 836.0887 472.46 3452.65 Trading volume 26102.46 14133.71 6427.4 86611.6 Turnover 601080.3 367170 82257 2161468

Retail trade & distribution Stock price index 434.2785 103.4008 216.23 642.48 Trading volume 27849.99 19699.09 5210.9 115529.7

25

Turnover 256742.1 120387 49826 753017 Utilities (Electricity & Gas) Stock price index 996.1322 191.3609 585.04 1380.87

Trading volume 2190.262 1881.661 730.8 20148.5 Turnover 69651.89 45180.01 17033 390340

Construction Stock price index 190.1113 78.53776 55.09 444.31 Trading volume 12993.37 9953.31 2865.2 52402 Turnover 186220.3 109350.5 22376 676137

Warehousing & Storage Stock price index 2151.79 720.7117 774.69 4379.89 Trading volume 22219.88 33898.98 3176.5 232650 Turnover 149422.1 102686.9 25253 754818

Telecommunication Stock price index 310.3886 36.68737 205.61 400 Trading volume 3366.605 1943.273 921.1 12583.1 Turnover 107896.8 47340.12 39906 300866

Banks Stock price index 275.0801 65.44944 136.96 383.75 Trading volume 5760.145 4946.91 350.7 35311.3 Turnover 105348.2 89444.06 3930 464196

Equity Investments Stock price index 2222.577 837.9159 751.07 4714.93 Trading volume 27986.67 26540.12 3628.2 152463.8 Turnover 221986.5 216918.8 26740 1212461

Insurance Stock price index 14748.34 4576.432 4787.14 25294.5 Trading volume 4499.335 2662.931 946.1 14253.5 Turnover 106020.2 83775.93 13790 770036

Services Stock price index 759.6942 216.0594 355.96 1110.03 Trading volume 16697.84 10753.16 2731.3 66425.1 Turnover 354548.8 239121.7 30405 1270463

26

Table 1. North Korea’s military and nuclear threats.

Date Event Note Type

2002. 6. 29

Military dispute in the Yellow Sea around the Northern Limit Line (NLL)

The dispute began with the unprovoked shooting by North Korean patrols.

Provocation

2002. 10. 26 Revelations of its highly enriched uranium (HEU) program

- Nuclear

2003. 1. 10 Secession from the NPT - Nuclear

2003. 2. 24 Missile launch - Missile

2005. 2. 10

Withdrawal from the Six Party Talks and official announcement of its capability to manufacture a nuclear weapon

North Korea officially announced its country’s capability to manufacture nuclear weapons.

Nuclear

2005. 5. 11 Extracts more fuel for nuclear weapons

North Korea shut down the Yongbyon Reactor, a move which could allow it to extract more fuel for nuclear weapons.

Nuclear

2006. 7. 5 Missile launched

North Korea test-fires six missiles, including a long-range Taepodong-2 rocket believed to be capable of reaching the west coast of the U.S. The test was conducted to coincide with the U.S.’ celebration of its Independence Day.

Missile

2006. 10. 9 First nuclear test conducted

The test was conducted to coincide with the U.S.’ celebration of Columbus Day.

Nuclear

2009. 4. 5 Missile launch North Korea launched Bright Star-2. Missile

2009. 4. 29 Warning about another nuclear test and missile launch

North Korea launched the long-range Taepodong missile.

Nuclear /Missile

2009. 5. 25 Second nuclear test conducted

The test was conducted to coincide with the U.S.’ celebration of Memorial Day.

Nuclear

2009. 11. 10 Military dispute occurs near the NLL

- Provocation

2010. 3. 26 Cheonan ship attack - Provocation

2010. 11. 23 Yeonpyeong island attacks - Provocation

2012. 4. 13 Satellites launched (ended in failure)

North Korea launched Bright Star-3.

27

2012. 12. 12 Satellites launched (successful)

North Korea relaunched Bright Star-3 a week before the Presidential election was held in South Korea (2012. 12. 19)

2013. 2. 12 Third nuclear test conducted

- Nuclear

2013. 3. 11 1953 War Truce nullified

North Korea declared that it will no longer abide by the 1953 Armistice that halted the Korean War. The announcement was conducted to coincide with the joint military drills between the U.S. and South Korea.

-

2013. 5.18 Missile launch North Korea launched a total of six short-range projectiles.

Missile

2014. 3. 31 Exchange fire across the disputed western sea border

North Korea and South Korea fired hundreds of artillery shells across their disputed western sea border

Provocation

Source: Kim (2009), Jun (2011), and the NY Times (http://www.nytimes.com/interactive/2014/11/20/world/asia/northkorea-timeline.html?_r=0#/#time238_7128).

28

Table 2. Eyeball test: North Korea’s nuclear tests and their spillovers.

Foreign exchange market

(Won/US Dollar)

Bond market (3-year Korean

treasury bill rate)

Stock market (KOSPI)

1st nuclear test (2006/10/09) 0.8 ₩/$↑ (0.1%)

0.02%p ↑ (0.43%)

33p ↓ (2.4%)

2nd nuclear test (2009/5/25) 5.2 ₩/$↓ (0.4%)

0.04%p ↓ (1.03%)

3p ↓ (0.19%)

3rd nuclear test (2013/2/12) 4.9 ₩/$↑ (0.4%)

0.01%p ↓ (0.37%)

5p ↓ (0.26%)

Source: Bank of Korea, ECOS. Notes: Table 2 reports daily financial market responses on the days of North Korean nuclear tests. Arrows indicate the movement of financial variables. Values in the parentheses report the percent changes. Note that the won/U.S. dollar exchange rates data from another source of Federal Reserve Economic Data (FRED) show missing values on the days of the first and the second nuclear tests (October 9th, 2006 and May 25th, 2009). However, the exchange rate on the day after the first test (October 10th, 2006) is 959.2, which increases by 10.3 ₩/$ from that on October 6th, 2006. The exchange rate on the day after the second test (May 26th, 2009) is 1264.25, which also increases by 22.25 ₩/$ from that on May 22th, 2009.

29

Fig. 1. Google’s search volume index for North Korean nuclear threats

Notes: 1) The red line with square markers indicates Google’s search volume index (SVI) measuring the attention agents give to the North Korean nuclear risk. The SVI is constructed by collecting data on Internet search activities that include the keywords “Korea nuclear” and “North Korea nuclear.” 2) The purple line with cross markers indicates Google’s SVI measuring the attention only South Korean investors give to the North Korean nuclear risk. The keywords include

“북한핵실험” and “북한핵” in Korean.

3) The green line with circle markers indicates Google’s SVI using the keyword “six-party talks.” 4) We use data from January 2004 onwards because Google’s SVI debuted at that time. The gray area depicts the months in which North Korea conducted nuclear tests.

0

10

20

30

40

50

60

70

80

90

100Ja

n-04

May

-04

Sep

-04

Jan-

05M

ay-0

5S

ep-0

5Ja

n-06

May

-06

Sep

-06

Jan-

07M

ay-0

7S

ep-0

7Ja

n-08

May

-08

Sep

-08

Jan-

09M

ay-0

9S

ep-0

9Ja

n-10

May

-10

Sep

-10

Jan-

11M

ay-1

1S

ep-1

1Ja

n-12

May

-12

Sep

-12

Jan-

13M

ay-1

3S

ep-1

3Ja

n-14

May

-14

Sep

-14

Jan-

15M

ay-1

5S

ep-1

5

SVI (Nuclear) Korean SVI (Nuclear) SVI (Six-party talks)

30

Fig. 2. Impulse responses of the South Korean financial markets to the SVI for the nuclear risk Full sample Sub-sample 1: 2004–2007 Sub-sample 2: 2008–2011 Sub-sample 1: 2011–2015

-0.035

-0.03

-0.025

-0.02

-0.015

-0.01

-0.005

0

0.005

0.01

0 1 2 3 4 5 6 7 8 9 10

NK risk → Long-term bond yield

-0.08

-0.07

-0.06

-0.05

-0.04

-0.03

-0.02

-0.01

0

0.01

0 1 2 3 4 5 6 7 8 9 10

NK risk → Long-term bond yield

-0.05

-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

0.04

0.05

0 1 2 3 4 5 6 7 8 9 10

NK risk → Long-term bond yield

-0.035

-0.03

-0.025

-0.02

-0.015

-0.01

-0.005

0

0.005

0 1 2 3 4 5 6 7 8 9 10

NK risk → Long-term bond yield

-0.002

-0.001

0

0.001

0.002

0.003

0.004

0 1 2 3 4 5 6 7 8 9 10

NK risk → Foreign exchange rate

-0.003

-0.002

-0.001

0

0.001

0.002

0.003

0.004

0 1 2 3 4 5 6 7 8 9 10

NK risk → Foreign exchange rate

-0.004

-0.002

0

0.002

0.004

0.006

0.008

0.01

0 1 2 3 4 5 6 7 8 9 10

NK risk → Foreign exchange rate

-0.003

-0.002

-0.001

0

0.001

0.002

0.003

0.004

0 1 2 3 4 5 6 7 8 9 10

NK risk → Foreign exchange rate

-0.012

-0.01

-0.008

-0.006

-0.004

-0.002

0

0.002

0.004

0.006

0 1 2 3 4 5 6 7 8 9 10

NK risk → Stock price

-0.02

-0.015

-0.01

-0.005

0

0.005

0 1 2 3 4 5 6 7 8 9 10

NK risk → Stock price

-0.008

-0.006

-0.004

-0.002

0

0.002

0.004

0.006

0 1 2 3 4 5 6 7 8 9 10

NK risk → Stock price

-0.006

-0.004

-0.002

0

0.002

0.004

0.006

0.008

0 1 2 3 4 5 6 7 8 9 10

NK risk → Stock price

31

Note: The first column reports the impulse responses of three financial market variables (bond, foreign exchange rate, stock price) to one standard deviation shock on the SVI for the nuclear risk. The remaining columns include sub-sample analyses for 2004–2007, 2008–2011, and 2012–2015. The red lines indicate the impulse response over weeks. The dotted lines represent 90% confidence intervals. Financial market variables are de-trended with a quadratic time trend.

32

Fig. 3. Time-varying impulse-responses to the SVI for the nuclear risk

Note: The red lines indicate the impulse response of each variable three weeks after the impulse of one standard deviation shock on the SVI for the nuclear risk. The dotted lines represent 90% confidence intervals. Financial market variables are de-trended with a quadratic time trend. We use a four-year rolling window from the first week of 2004 and the last week (52nd) of 2007.

-0.08

-0.06

-0.04

-0.02

0

0.02

0.0420

04w

1~20

07w

5220

04w

11~2

008w

1020

04w

21~2

008w

2020

04w

31~2

008w

3020

04w

41~2

008w

4020

04w

51~2

008w

5020

05w

9~20

09w

820

05w

19~2

009w

1820

05w

29~2

009w

2820

05w

39~2

009w

3820

05w

49~2

009w

4820

06w

7~20

10w

620

06w

17~2

010w

1620

06w

27~2

010w

2620

06w

37~2

010w

3620

06w

47~2

010w

4620

07w

5~20

11w

420

07w

15~2

011w

1420

07w

25~2

011w

2420

07w

35~2

011w

3420

07w

45~2

011w

4420

08w

3~20

12w

220

08w

13~2

012w

1220

08w

23~2

012w

2220

08w

33~2

012w

3220

08w

43~2

012w

4220

09w

1~20

12w

5220

09w

11~2

013w

1020

09w

21~2

013w

2020

09w

31~2

013w

3020

09w

41~2

013w

4020

09w

51~2

013w

5020

10w

9~20

14w

820

10w

19~2

014w

1820

10w

29~2

014w

2820

10w

39~2

014w

3820

10w

49~2

014w

4820

11w

7~20

15w

620

11w

17~2

015w

1620

11w

27~2

015w

2620

11w

37~2

015w

3620

11w

47~2

015w

46

NK risk→ Long-term bond yield

-0.006

-0.004

-0.002

0

0.002

0.004

0.006

0.008

0.01

2004

w1~

2007

w52

2004

w11

~200

8w10

2004

w21

~200

8w20

2004

w31

~200

8w30

2004

w41

~200

8w40

2004

w51

~200

8w50

2005

w9~

2009

w8

2005

w19

~200

9w18

2005

w29

~200

9w28

2005

w39

~200

9w38

2005

w49

~200

9w48

2006

w7~

2010

w6

2006

w17

~201

0w16

2006

w27

~201

0w26

2006

w37

~201

0w36

2006

w47

~201

0w46

2007

w5~

2011

w4

2007

w15

~201

1w14

2007

w25

~201

1w24

2007

w35

~201

1w34

2007

w45

~201

1w44

2008

w3~

2012

w2

2008

w13

~201

2w12

2008

w23

~201

2w22

2008

w33

~201

2w32

2008

w43

~201

2w42

2009

w1~

2012

w52

2009

w11

~201

3w10

2009

w21

~201

3w20

2009

w31

~201

3w30

2009

w41

~201

3w40

2009

w51

~201

3w50

2010

w9~

2014

w8

2010

w19

~201

4w18

2010

w29

~201

4w28

2010

w39

~201

4w38

2010

w49

~201

4w48

2011

w7~

2015

w6

2011

w17

~201

5w16

2011

w27

~201

5w26

2011

w37

~201

5w36

2011

w47

~201

5w46

NK risk→ Foreign exchange rate

-0.02

-0.015

-0.01

-0.005

0

0.005

0.01

0.015

2004

w1~

2007

w52

2004

w11

~200

8w10

2004

w21

~200

8w20

2004

w31

~200

8w30

2004

w41

~200

8w40

2004

w51

~200

8w50

2005

w9~

2009

w8

2005

w19

~200

9w18

2005

w29

~200

9w28

2005

w39

~200

9w38

2005

w49

~200

9w48

2006

w7~

2010

w6

2006

w17

~201

0w16

2006

w27

~201

0w26

2006

w37

~201

0w36

2006

w47

~201

0w46

2007

w5~

2011

w4

2007

w15

~201

1w14

2007

w25

~201

1w24

2007

w35

~201

1w34

2007

w45

~201

1w44

2008

w3~

2012

w2

2008

w13

~201

2w12

2008

w23

~201

2w22

2008

w33

~201

2w32

2008

w43

~201

2w42

2009

w1~

2012

w52

2009

w11

~201

3w10

2009

w21

~201

3w20

2009

w31

~201

3w30

2009

w41

~201

3w40

2009

w51

~201

3w50

2010

w9~

2014

w8

2010

w19

~201

4w18

2010

w29

~201

4w28

2010

w39

~201

4w38

2010

w49

~201

4w48

2011

w7~

2015

w6

2011

w17

~201

5w16

2011

w27

~201

5w26

2011

w37

~201

5w36

2011

w47

~201

5w46

NK risk→Stock price

33

Fig. 4. Time-varying impulse responses with first differenced variables

Note: The red lines indicate the impulse response of each variable three weeks after the impulse of one standard deviation shock on the SVI for the nuclear risk. The dotted lines represent 90% confidence intervals. Financial market variables are first differenced to avoid non-stationarity. We use a four-year rolling window from the first week of 2004 and the last week (52nd) of 2007.

-0.02

-0.015

-0.01

-0.005

0

0.005

0.01

0.015

0.02

2004

w1~

2007

w52

2004

w11

~200

8w10

2004

w21

~200

8w20

2004

w31

~200

8w30

2004

w41

~200

8w40

2004

w51

~200

8w50

2005

w9~

2009

w8

2005

w19

~200

9w18

2005

w29

~200

9w28

2005

w39

~200

9w38

2005

w49

~200

9w48

2006

w7~

2010

w6

2006

w17

~201

0w16

2006

w27

~201

0w26

2006

w37

~201

0w36

2006

w47

~201

0w46

2007

w5~

2011

w4

2007

w15

~201

1w14

2007

w25

~201

1w24

2007

w35

~201

1w34

2007

w45

~201

1w44

2008

w3~

2012

w2

2008

w13

~201

2w12

2008

w23

~201

2w22

2008

w33

~201

2w32

2008

w43

~201

2w42

2009

w1~

2012

w52

2009

w11

~201

3w10

2009

w21

~201

3w20

2009

w31

~201

3w30

2009

w41

~201

3w40

2009

w51

~201

3w50

2010

w9~

2014

w8

2010

w19

~201

4w18

2010

w29

~201

4w28

2010

w39

~201

4w38

2010

w49

~201

4w48

2011

w7~

2015

w6

2011

w17

~201

5w16

2011

w27

~201

5w26

2011

w37

~201

5w36

2011

w47

~201

5w46

NK risk→ Long-term bond yield

-0.002

-0.0015

-0.001

-0.0005

0

0.0005

0.001

0.0015

0.002

2004

w1~

2007

w52

2004

w11

~200

8w10

2004

w21

~200

8w20

2004

w31

~200

8w30

2004

w41

~200

8w40

2004

w51

~200

8w50

2005

w9~

2009

w8

2005

w19

~200

9w18

2005

w29

~200

9w28

2005

w39

~200

9w38

2005

w49

~200

9w48

2006

w7~

2010

w6

2006

w17

~201

0w16

2006

w27

~201

0w26

2006

w37

~201

0w36

2006

w47

~201

0w46

2007

w5~

2011

w4

2007

w15

~201

1w14

2007

w25

~201

1w24

2007

w35

~201

1w34

2007

w45

~201

1w44

2008

w3~

2012

w2

2008

w13

~201

2w12

2008

w23

~201

2w22

2008

w33

~201

2w32

2008

w43

~201

2w42

2009

w1~

2012

w52

2009

w11

~201

3w10

2009

w21

~201

3w20

2009

w31

~201

3w30

2009

w41

~201

3w40

2009

w51

~201

3w50

2010

w9~

2014

w8

2010

w19

~201

4w18

2010

w29

~201

4w28

2010

w39

~201

4w38

2010

w49

~201

4w48

2011

w7~

2015

w6

2011

w17

~201

5w16

2011

w27

~201

5w26

2011

w37

~201

5w36

2011

w47

~201

5w46

NK risk→ Foreign exchange rate

-0.004-0.003-0.002-0.001

00.0010.0020.0030.0040.0050.006

2004

w1~

2007

w52

2004

w11

~200

8w10

2004

w21

~200

8w20

2004

w31

~200

8w30

2004

w41

~200

8w40

2004

w51

~200

8w50

2005

w9~

2009

w8

2005

w19

~200

9w18

2005

w29

~200

9w28

2005

w39

~200

9w38

2005

w49

~200

9w48

2006

w7~

2010

w6

2006

w17

~201

0w16

2006

w27

~201

0w26

2006

w37

~201

0w36

2006

w47

~201

0w46

2007

w5~

2011

w4

2007

w15

~201

1w14

2007

w25

~201

1w24

2007

w35

~201

1w34

2007

w45

~201

1w44

2008

w3~

2012

w2

2008

w13

~201

2w12

2008

w23

~201

2w22

2008

w33

~201

2w32

2008

w43

~201

2w42

2009

w1~

2012

w52

2009

w11

~201

3w10

2009

w21

~201

3w20

2009

w31

~201

3w30

2009

w41

~201

3w40

2009

w51

~201

3w50

2010

w9~

2014

w8

2010

w19

~201

4w18

2010

w29

~201

4w28

2010

w39

~201

4w38

2010

w49

~201

4w48

2011

w7~

2015

w6

2011

w17

~201

5w16

2011

w27

~201

5w26

2011

w37

~201

5w36

2011

w47

~201

5w46

NK risk→Stock price

34

Fig. 5. Time-varying impulse responses to the SVI for six-party talks

Note: The red lines indicate the impulse response of each variable three weeks after the impulse of one standard deviation shock on the SVI for the six-party talks. The dotted lines represent 90% confidence intervals. Financial market variables are de-trended with a quadratic time trend. We use a four-year rolling window from the first week of 2004 and the last week (52nd) of 2007.

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.0620

04w

1~20

07w

5220

04w

12~2

008w

1120

04w

23~2

008w

2220

04w

34~2

008w

3320

04w

45~2

008w

4420

05w

4~20

09w

320

05w

15~2

009w

1420

05w

26~2

009w

2520

05w

37~2

009w

3620

05w

48~2

009w

4720

06w

7~20

10w

620

06w

18~2

010w

1720

06w

29~2

010w

2820

06w

40~2

010w

3920

06w

51~2

010w

5020

07w

10~2

011w

920

07w

21~2

011w

2020

07w

32~2

011w

3120

07w

43~2

011w

4220

08w

2~20

12w

120

08w

13~2

012w

1220

08w

24~2

012w

2320

08w

35~2

012w

3420

08w

46~2

012w

4520

09w

5~20

13w

420

09w

16~2

013w

1520

09w

27~2

013w

2620

09w

38~2

013w

3720

09w

49~2

013w

4820

10w

8~20

14w

720

10w

19~2

014w

1820

10w

30~2

014w

2920

10w

41~2

014w

4020

10w

52~2

014w

5120

11w

11~2

015w

1020

11w

22~2

015w

2120

11w

33~2

015w

3220

11w

44~2

015w

43

Six-party talks → Long-term bond yield

-0.01-0.008-0.006-0.004-0.002

00.0020.0040.0060.008

2004

w1~

2007

w52

2004

w12

~200

8w11

2004

w23

~200

8w22

2004

w34

~200

8w33

2004

w45

~200

8w44

2005

w4~

2009

w3

2005

w15

~200

9w14

2005

w26

~200

9w25

2005

w37

~200

9w36

2005

w48

~200

9w47

2006

w7~

2010

w6

2006

w18

~201

0w17

2006

w29

~201

0w28

2006

w40

~201

0w39

2006

w51

~201

0w50

2007

w10

~201

1w9

2007

w21

~201

1w20

2007

w32

~201

1w31

2007

w43

~201

1w42

2008

w2~

2012

w1

2008

w13

~201

2w12

2008

w24

~201

2w23

2008

w35

~201

2w34

2008

w46

~201

2w45

2009

w5~

2013

w4

2009

w16

~201

3w15

2009

w27

~201

3w26

2009

w38

~201

3w37

2009

w49

~201

3w48

2010

w8~

2014

w7

2010

w19

~201

4w18

2010

w30

~201

4w29

2010

w41

~201

4w40

2010

w52

~201

4w51

2011

w11

~201

5w10

2011

w22

~201

5w21

2011

w33

~201

5w32

2011

w44

~201

5w43

Six-party talks → Foreign exchange rate

-0.01

-0.005

0

0.005

0.01

0.015

2004

w1~

2007

w52

2004

w13

~200

8w12

2004

w25

~200

8w24

2004

w37

~200

8w36

2004

w49

~200

8w48

2005

w9~

2009

w8

2005

w21

~200

9w20

2005

w33

~200

9w32

2005

w45

~200

9w44

2006

w5~

2010

w4

2006

w17

~201

0w16

2006

w29

~201

0w28

2006

w41

~201

0w40

2007

w1~

2010

w52

2007

w13

~201

1w12

2007

w25

~201

1w24

2007

w37

~201

1w36

2007

w49

~201

1w48

2008

w9~

2012

w8

2008

w21

~201

2w20

2008

w33

~201

2w32

2008

w45

~201

2w44

2009

w5~

2013

w4

2009

w17

~201

3w16

2009

w29

~201

3w28

2009

w41

~201

3w40

2010

w1~

2013

w52

2010

w13

~201

4w12

2010

w25

~201

4w24

2010

w37

~201

4w36

2010

w49

~201

4w48

2011

w9~

2015

w8

2011

w21

~201

5w20

2011

w33

~201

5w32

2011

w45

~201

5w44

Six-party talks→Stock price

35

Fig. 6. Impulse responses of the industry stock price indexes to the SVI for the nuclear risk

Note: Monthly industry stock price variables are used. In the structural VAR system, both industry stock price and stock trade volume and turnover are included. Industry stock price is considered most endogenous in the VAR system. The red lines indicate the impulse responses of industry stock prices to the SVI for the nuclear risk. The dotted lines represent 90% confidence intervals. All financial market variables are de-trended with a quadratic time trend.

-.04

-.02

0

.02

0 5 10

Food & Beverage

NK risk -> Stock price

-.04

-.02

0

.02

.04

0 5 10

Textile & Apparel

NK risk -> Stock price

-.02

0

.02

.04

0 5 10

Forestry & Paper

NK risk -> Stock price

-.02

0

.02

.04

0 5 10

Chemicals

NK risk -> Stock price

-.02

-.01

0

.01

.02

0 5 10

PhamaceuticalsNK risk -> Stock price

-.04

-.02

0

.02

0 5 10

Nonferrous MetalsNK risk -> Stock price

-.02

0

.02

.04

0 5 10

Iron & SteelNK risk -> Stock price

-.04

-.02

0

.02

0 5 10

Industrial EngineeringNK risk -> Stock price

-.02

-.01

0

.01

.02

0 5 10

Electronic & Eletronical Equip.NK risk -> Stock price

-.05

0

.05

0 5 10

BiotechnologyNK risk -> Stock price

-.04

-.02

0

.02

.04

0 5 10

Industrial TransportationNK risk -> Stock price

-.04

-.02

0

.02

0 5 10

Retail trade & DistributionNK risk -> Stock price

-.02

-.01

0

.01

0 5 10

Electricity & GasNK risk -> Stock price

-.04

-.02

0

.02

0 5 10

ConstructionNK risk -> Stock price

-.04

-.02

0

.02

0 5 10

Warehousing & StroageNK risk -> Stock price

-.01

0

.01

.02

0 5 10

TelecommunicationNK risk -> Stock price

-.08

-.06

-.04

-.02

0

0 5 10

BanksNK risk -> Stock price

-.06

-.04

-.02

0

.02

0 5 10

Equity InvestmentsNK risk -> Stock price

-.02

-.01

0

.01

.02

0 5 10

InsuranceNK risk -> Stock price

-.02

0

.02

0 5 10

ServicesNK risk -> Stock price

36

Fig. 7. Time-varying impulse responses to the SVI for the nuclear risk with monthly data

Note: The red lines indicate the impulse response of each variable three weeks after the impulse of one standard deviation shock on the SVI for the nuclear risk. The dotted lines represent 90% confidence intervals. Financial market variables are de-trended with a quadratic time trend. We use a four-year rolling window from the first month of 2004 and the last month of 2007.

-0.12-0.1

-0.08-0.06-0.04-0.02

00.020.040.060.08

2004

m4~

2007

m12

2004

m9~

2008

m5

2005

m2~

2008

m10

2005

m7~

2009

m3

2005

m12

~200

9m8

2006

m5~

2010

m1

2006

m10

~201

0m6

2007

m3~

2010

m11

2007

m8~

2011

m4

2008

m1~

2011

m9

2008

m6~

2012

m2

2008

m11

~201

2m7

2009

m4~

2012

m12

2009

m9~

2013

m5

2010

m2~

2013

m10

2010

m7~

2014

m3

2010

m12

~201

4m8

2011

m5~

2015

m1

2011

m10

~201

5m6

2012

m3~

2015

m11

NK risk → Long-term bond yield

-0.015

-0.01

-0.005

0

0.005

0.01

0.015

2004

m4~

2007

m12

2004

m9~

2008

m5

2005

m2~

2008

m10

2005

m7~

2009

m3

2005

m12

~200

9m8

2006

m5~

2010

m1

2006

m10

~201

0m6

2007

m3~

2010

m11

2007

m8~

2011

m4

2008

m1~

2011

m9

2008

m6~

2012

m2

2008

m11

~201

2m7

2009

m4~

2012

m12

2009

m9~

2013

m5

2010

m2~

2013

m10

2010

m7~

2014

m3

2010

m12

~201

4m8

2011

m5~

2015

m1

2011

m10

~201

5m6

2012

m3~

2015

m11

NK risk → REER

-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

2004

m4~

2007

m12

2004

m9~

2008

m5

2005

m2~

2008

m10

2005

m7~

2009

m3

2005

m12

~200

9m8

2006

m5~

2010

m1

2006

m10

~201

0m6

2007

m3~

2010

m11

2007

m8~

2011

m4

2008

m1~

2011

m9

2008

m6~

2012

m2

2008

m11

~201

2m7

2009

m4~

2012

m12

2009

m9~

2013

m5

2010

m2~

2013

m10

2010

m7~

2014

m3

2010

m12

~201

4m8

2011

m5~

2015

m1

2011

m10

~201

5m6

2012

m3~

2015

m11

NK risk → Stock Price

37

Fig. 8. Time-varying impulse responses to the English SVI and Korean SVI English SVI Korean SVI only

Note: The red lines indicate the impulse response of each variable three weeks after the impulse of one standard deviation shock on the SVI for the nuclear risk. The dotted lines represent 90% confidence intervals. Financial market variables are de-trended with a quadratic time trend. We use a four-year rolling window from the first month of 2004 and the last month of 2007.

-0.15

-0.1

-0.05

0

0.05

0.1

0.1520

04m

4~20

07m

1220

04m

9~20

08m

520

05m

2~20

08m

1020

05m

7~20

09m

320

05m

12~2

009m

820

06m

5~20

10m

120

06m

10~2

010m

620

07m

3~20

10m

1120

07m

8~20

11m

420

08m

1~20

11m

920

08m

6~20

12m

220

08m

11~2

012m

720

09m

4~20

12m

1220

09m

9~20

13m

520

10m

2~20

13m

1020

10m

7~20

14m

320

10m

12~2

014m

820

11m

5~20

15m

120

11m

10~2

015m

620

12m

3~20

15m

11

NK risk → Interest rate

-0.15

-0.1

-0.05

0

0.05

0.1

2004

m4~

2007

m12

2004

m9~

2008

m5

2005

m2~

2008

m10

2005

m7~

2009

m3

2005

m12

~200

9m8

2006

m5~

2010

m1

2006

m10

~201

0m6

2007

m3~

2010

m11

2007

m8~

2011

m4

2008

m1~

2011

m9

2008

m6~

2012

m2

2008

m11

~201

2m7

2009

m4~

2012

m12

2009

m9~

2013

m5

2010

m2~

2013

m10

2010

m7~

2014

m3

2010

m12

~201

4m8

2011

m5~

2015

m1

2011

m10

~201

5m6

2012

m3~

2015

m11

NK risk → Interest rate

-0.01-0.008-0.006-0.004-0.002

00.0020.0040.0060.0080.01

0.012

2004

m4~

2007

m12

2004

m9~

2008

m5

2005

m2~

2008

m10

2005

m7~

2009

m3

2005

m12

~200

9m8

2006

m5~

2010

m1

2006

m10

~201

0m6

2007

m3~

2010

m11

2007

m8~

2011

m4

2008

m1~

2011

m9

2008

m6~

2012

m2

2008

m11

~201

2m7

2009

m4~

2012

m12

2009

m9~

2013

m5

2010

m2~

2013

m10

2010

m7~

2014

m3

2010

m12

~201

4m8

2011

m5~

2015

m1

2011

m10

~201

5m6

2012

m3~

2015

m11

NK risk → REER

-0.012-0.01

-0.008-0.006-0.004-0.002

00.0020.0040.0060.0080.01

2004

m4~

2007

m12

2004

m9~

2008

m5

2005

m2~

2008

m10

2005

m7~

2009

m3

2005

m12

~200

9m8

2006

m5~

2010

m1

2006

m10

~201

0m6

2007

m3~

2010

m11

2007

m8~

2011

m4

2008

m1~

2011

m9

2008

m6~

2012

m2

2008

m11

~201

2m7

2009

m4~

2012

m12

2009

m9~

2013

m5

2010

m2~

2013

m10

2010

m7~

2014

m3

2010

m12

~201

4m8

2011

m5~

2015

m1

2011

m10

~201

5m6

2012

m3~

2015

m11

NK risk → REER

-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

2004

m4~

2007

m12

2004

m9~

2008

m5

2005

m2~

2008

m10

2005

m7~

2009

m3

2005

m12

~200

9m8

2006

m5~

2010

m1

2006

m10

~201

0m6

2007

m3~

2010

m11

2007

m8~

2011

m4

2008

m1~

2011

m9

2008

m6~

2012

m2

2008

m11

~201

2m7

2009

m4~

2012

m12

2009

m9~

2013

m5

2010

m2~

2013

m10

2010

m7~

2014

m3

2010

m12

~201

4m8

2011

m5~

2015

m1

2011

m10

~201

5m6

2012

m3~

2015

m11

NK risk → Stock Price

-0.035-0.03

-0.025-0.02

-0.015-0.01

-0.0050

0.0050.01

0.0150.02

2004

m4~

2007

m12

2004

m9~

2008

m5

2005

m2~

2008

m10

2005

m7~

2009

m3

2005

m12

~200

9m8

2006

m5~

2010

m1

2006

m10

~201

0m6

2007

m3~

2010

m11

2007

m8~

2011

m4

2008

m1~

2011

m9

2008

m6~

2012

m2

2008

m11

~201

2m7

2009

m4~

2012

m12

2009

m9~

2013

m5

2010

m2~

2013

m10

2010

m7~

2014

m3

2010

m12

~201

4m8

2011

m5~

2015

m1

2011

m10

~201

5m6

2012

m3~

2015

m11

NK risk → Stock Price