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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: [email protected] ‡ 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: [email protected]
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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