Description
Natural disasters may inflict significant damage upon international financial markets.
The purpose of this study is to investigate if any contagion effect occurred in the immediate aftermath
of the Japanese earthquake, tsunami and subsequent nuclear crisis.
Journal of Financial Economic Policy
The 2011 Japanese earthquake, tsunami and nuclear crisis: Evidence of contagion from
international financial markets
Simplice A. Asongu
Article information:
To cite this document:
Simplice A. Asongu, (2012),"The 2011 J apanese earthquake, tsunami and nuclear crisis", J ournal of
Financial Economic Policy, Vol. 4 Iss 4 pp. 340 - 353
Permanent link to this document:http://dx.doi.org/10.1108/17576381211279307
Downloaded on: 24 January 2016, At: 21:45 (PT)
References: this document contains references to 22 other documents.
To copy this document: [email protected]
The fulltext of this document has been downloaded 369 times since 2012*
Users who downloaded this article also downloaded:
Salman Khan, Pierre Batteau, (2012),"Government intervention in Russian bourse: a case
of financial contagion", J ournal of Financial Economic Policy, Vol. 4 Iss 4 pp. 320-339 http://
dx.doi.org/10.1108/17576381211279299
Dimitrios Dimitriou, Theodore Simos, (2013),"Contagion channels of the USA subprime financial crisis:
Evidence from USA, EMU, China and J apan equity markets", J ournal of Financial Economic Policy, Vol. 5
Iss 1 pp. 61-71http://dx.doi.org/10.1108/17576381311317781
Richard J . Buttimer, (2011),"The financial crisis: imperfect markets and imperfect regulation", J ournal of
Financial Economic Policy, Vol. 3 Iss 1 pp. 12-32http://dx.doi.org/10.1108/17576381111116795
Access to this document was granted through an Emerald subscription provided by emerald-srm:115632 []
For Authors
If you would like to write for this, or any other Emerald publication, then please use our Emerald for
Authors service information about how to choose which publication to write for and submission guidelines
are available for all. Please visit www.emeraldinsight.com/authors for more information.
About Emerald www.emeraldinsight.com
Emerald is a global publisher linking research and practice to the benefit of society. The company
manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as
providing an extensive range of online products and additional customer resources and services.
Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee
on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive
preservation.
*Related content and download information correct at time of download.
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
5
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
The 2011 Japanese earthquake,
tsunami and nuclear crisis
Evidence of contagion from international
?nancial markets
Simplice A. Asongu
HEC-Management School, University of Lie `ge, Lie `ge, Belgium
Abstract
Purpose – Natural disasters may in?ict signi?cant damage upon international ?nancial markets.
The purpose of this study is to investigate if any contagion effect occurred in the immediate aftermath
of the Japanese earthquake, tsunami and subsequent nuclear crisis.
Design/methodology/approach – Using 33 international stock indices and exchange rates, this
paper uses heteroscedasticity biases based on correlation coef?cients to examine if any contagion
occurred across ?nancial markets after the March 11, 2011 Japanese earthquake, tsunami and nuclear
crisis. The sample period is partitioned into two sections: the 12-month pre-earthquake period
(March 11, 2010 to March 10, 2011) and the 2-month post-earthquake period (March 11, 2011 to May 10,
2011). While the stability period is de?ned as the pre-earthquake period, the turbulent (turmoil) period
is de?ned as the post-earthquake period. In a bid to ensure robustness of the ?ndings, the turmoil
period is further partitioned into two equal sections: the 1-month (short-term) post-earthquake period
(March 11, 2011 to April 10, 2011), and the 2-month (medium-term) post-earthquake (March 11, 2011 to
May 10, 2011).
Findings – Findings reveal that, while no sampled foreign exchange markets suffered from contagion,
stock markets of Taiwan, Bahrain, Saudi Arabia and South Africa witnessed a contagion effect.
Practical implications – The results have two paramount implications. First, the paper has
con?rmed existing consensus that in the face of natural crises that could take an international scale,
emerging markets are contagiously affected for the most part. Second, the empirical evidence also
suggests that international ?nancial market transmissions not only occur during ?nancial crisis; natural
disaster effects should not be undermined.
Originality/value – This paper has shown that the correlation structure of international ?nancial
markets are also affected by high pro?le natural disasters.
Keywords Japanese earthquake, Contagion, International ?nancial markets, Japan, Earthquakes,
Financial markets, Stock markets
Paper type Research paper
1. Motivation
Natural disasters have in?icted serious damage on human life, property, and economy.
Though many earthquakes occur worldwide on an annual basis and impact all walks of
life in one way or the other, collateral effects resulting fromsuch natural disasters could
be quite detrimental ?nancially and economically. The recent Japanese earthquake
resulted in collateral damage that makes the disaster particularly signi?cant. On
March 11, 2011, a 9.0 magnitude undersea mega thrust earthquake hit Tohoku in Japan.
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1757-6385.htm
JEL classi?cation – G10; G15; F30
The author is highly indebted to the referee(s) for their very useful editorial comments.
JFEP
4,4
340
Journal of Financial Economic Policy
Vol. 4 No. 4, 2012
pp. 340-353
qEmerald Group Publishing Limited
1757-6385
DOI 10.1108/17576381211279307
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
5
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
This powerful shock triggered a tsunami that struck coastlines across the east of the
country, leaving thousands dead and damaging considerable property. But what
appears to have left analysts startled and concerned over the consequences of this
earthquake is the nuclear disaster resulting there-from. Recently classi?ed as a
level-seven event on the International Nuclear Event Scale, the Fukushima nuclear
incident now poses a risk equal to the worst nuclear power plant accident in history
(Chernobyl disaster). With much uncertainty over howthe crisis would be managed, it is
imperative to investigate how international ?nancial markets have so far reacted.
Therefore, the goal of this paper is to examine whether any contagion effect has
occurred two months after the Japanese earthquake, tsunami, and worst nuclear crisis
since Chernobyl. In other words, we seek to provide evidence as to whether such a disaster
has increased the interdependence among ?nancial assets in different countries.
The remainder of the paper is organized as follows. Sections 2 examines related literature.
Data and methodology are presented and outlined, respectively, in Section 3. Empirical
analysis is covered in Section 4. We discuss results in Section 5. Section 6 concludes.
2. Related literature
2.1 Effects of ?nancial market integration
Financial integration is widely believed to improve capital allocation ef?ciency and
diversify risks (Demyanyk and Volosovych, 2008; Coulibaly, 2009; Kose et al., 2011).
However, the recent global ?nancial crisis deemed as the worst since the great depression
has left many analysts concerned about the contagion effects of globalization. A great
body of literature has been dedicated to the potential bene?ts of ?nancial integration.
With respect to Kose et al. (2011), ?nancial globalization in theory should facilitate
ef?cient allocation of capital and improve international risk sharing. They further
profess that bene?ts are much greater for developing countries because they are
relatively scarce in capital and rich in labor availability. According to them, access to
foreign capital should enable them grow faster via new sources of investment. On a
positive note of ?nancial globalization, Kose et al. stress that since developing
countries have more volatile output growth than advanced industrial economies, their
potential welfare gains from international risk sharing are much greater. It is
important to note an important ?nding of theirs: with certain identi?able thresholds in
variables such as ?nancial depth and institutional quality, the cost-bene?t trade-off
from ?nancial openness improves signi?cantly once the threshold conditions are met.
Much earlier, Demyanyk and Volosovych (2008) analyzed the bene?ts of ?nancial
integration resulting from international risk sharing among 25 European Union (EU)
countries. In their case for diversi?cation of risk across EU Member States, they posit
that if risks are fully shared, the ten new members joining the EU should have higher
gains than the long standing 15 members. It may be interesting to note South Africa as
one of the most striking examples of the cost and bene?ts of ?nancial integration. As a
country that experienced ?nancial autarky due to the embargo imposed in 1985 and
removed in 1993, Coulibaly (2009) found a signi?cant decrease in the rates of investment,
capital, and output during the embargo period as compared to pre- and post-embargo
periods. By the same token South Africa might have been immune to contagion from a
global ?nancial meltdown during the embargo period.
It follows that countries in relative ?nancial autarky are less exposed to international
shocks. While the prime advantage of ?nancial integration is risk diversi?cation,
The 2011
Japanese
earthquake
341
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
5
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
paradoxically increased ?nancial globalization can reduce the scope for risk
diversi?cation as integrated markets tend to be more correlated and highly
interdependent. On another negative note, Kose et al. (2011) stress that a country may
stand to reap the bene?ts of ?nancial integration if certain threshold factors like ?nancial
depth and institutional quality are met. This stance is shared by Schmukler (2004)
who has underlined the importance of sound ?nancial fundamentals and strong
macroeconomic institutions; the absence of which will decrease the effectiveness of crises
management and increase the probability of crises and contagion.
2.2 Linkages among natural disasters, globalization, and crises
In the ?rst part of this literature review, we present several bene?ts of ?nancial
integration as well as potential costs. As such, occurrences or crises in one country
often due to domestic factors (human or natural) could be propagated to other countries
through channels of globalization (trade or ?nancial links for instance). There are four
main routes via which natural disasters like the Japanese turmoil could lead to crises at
a global level.
On a ?rst count, as stressed by Schmukler (2004), when a country’s ?nancial system
is more free, it becomes an object of market discipline exercised by both foreign and
domestic investors. As such reactions to unsound fundamentals resulting from natural
disasters are not only the concern of domestic investors as in closed economies. If the
prospects for resolving the disaster are unclear, asymmetric information may lead
investors to make irrational decisions that could result in a crisis depending on the
degree of ?nancial integration.
On a second note, international ?nancial market imperfections could arise from a
natural disaster, especially herding behavior, speculative attacks, irrational responses,
etc. Thus, regardless of market fundamentals, investors could speculate against a
currency in the wake of a natural calamity if they deem the exchange rate unsustainable,
which could lead to self-ful?lling balance-of-payments. This thesis presented by Obstfeld
(1986) has been supported by Schmukler (2004) and more recently Asongu (2011a, b).
Third, even in the presence of sound fundamentals and absence of imperfections in
the international capital market (after a natural disaster), crises might develop due to
external factors (Schmukler, 2004) such as determinants of capital ?ows (Calvo et al.,
1996) and foreign interest rates (Frankel and Rose, 1996). For example, if the country
is foreign capital dependent, shifts in foreign capital after a natural calamity could
create additional ?nancial issues and economic downturns. As pointed out by Frankel
and Rose (1996), foreign interest rates could play an important role in determining the
likelihood of ?nancial crises in developing countries.
Last but not the least, according to Schmukler (2004) natural disasters could lead
to crisis by contagion, notably through shocks by real links, ?nancial links and herding
behavior or unexplained high correlations. Our focus on this Japanese earthquake will
rotate around this fourth example; the de?nition and elucidation of which are worthwhile.
2.3 De?nitions and channels of contagion
2.3.1 De?nitions of contagion. There is yet no established consensus on the de?nition of
contagion by economists. However, according to the World Bank there are three main
de?nitions of contagion. First, froma broadprismthe phenomenoncouldbe seenwiththe
general process of shock transmission across countries. This de?nition takes account
JFEP
4,4
342
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
5
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
of both negative and positive spillovers. Second, contagion could be synonymous with the
propagation of shocks between two countries in excess of what should be expected with
respect to existing fundamentals after considering co-movements triggered by common
shocks. This second de?nition is somehow restrictive as it presupposes the mastery of
what constitutes the underlying fundamentals, without which an assessment of excess
co-movements is impossible. The third and more restrictive de?nition considers the
phenomenon as the change in transmission mechanisms that occur during the crisis
period and is assessed by a signi?cant increase in cross-market correlations.
With respect to this study, we shall limit ourselves to the third de?nition of contagion
because:
.
our study aims to investigate only a crisis period in the Japanese ?nancial market
(as opposed to the ?rst de?nition); and
.
we have no mastery of what constitutes underlying fundamentals of
co-movements we are about to investigate.
Fromanempirical standpoint, Forbes andRigobon(2002) ?rst proposeda methodologyfor
the third de?nition. They viewcontagion as a signi?cant increase in market co-movements
after a shock has occurred in one country. Owing to this conception, the condition for
contagion is a signi?cant increase in co-movements as a result of a shock in one market
(considered the base criterion). It follows that if two markets display a high degree of
co-movement during the stability period, even if they are highly correlated during a
crisis, but if the difference in correlation is insigni?cant, contagion has not occurred. Thus,
in the absence of a signi?cant increase in correlation during the crisis period, the term
“interdependence” is employed to appraise the situation between the two markets.
2.3.2 Channels of contagion. In accordance with Schmukler (2004), three main
channels of contagion have been identi?ed in the literature:
(1) Through real links which are often tied to trade links. As an example, if we
consider two countries trading together and competing in the same external
market, a devaluation of the exchange rate of one country diminishes the other
country’s competitive advantage. In an attempt to rebalance its external sectors,
the losing country would seek to depreciate/devaluate its own currency.
(2) Via ?nancial channels especially when two economies are connected through the
international ?nancial system. If we consider a leveraged institutionfacingmargin
calls as an example, and if the value of the collateral falls due to a negative shock in
a given country, the institution would be poised to sell some of its holdings in
countries not yet affected by the shock in an attempt to mitigate its initial stock.
This response may give rise to ripples of shocks that could engender contagion.
(3) Lastly, as a result of herding behavior or panic resulting from asymmetric
information, a ?nancial market might transmit shocks across other markets.
2.4 Measuring contagion
Quite a number of methods have been suggested in the literature for measuring the
spread of international shocks across countries. Among these, the most widely applied
are cross-market correlation coef?cient measures (Lee et al., 2007; Collins and Biekpe,
2003; Forbes and Rigobon, 2002; King and Wadhwani, 1990), volatility analysis based
on ARCH and GARCH models (King et al., 1994), cross-market co-integration vectors
The 2011
Japanese
earthquake
343
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
5
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
changing techniques (Kanas, 1998) and direct estimation of speci?c transmission
mechanisms (Forbes, 2000). Within the framework of this study, we shall adopt Forbes
and Rigobon (2002) in the context of Lee et al. (2007).
3. Data and methodology
3.1 Data
As we have earlier emphasized, we aim to investigate the correlations among returns of
the Japanese daily stock index (exchange rate) and 33 other international stock indexes
(exchange rates) returns. Adopting the Japanese equity and foreign exchange markets
as the base criterion, we investigate if co-movements among national stock and
foreign exchange markets increased signi?cantly after the major earthquake, tsunami,
and nuclear disaster. The sample period is partitioned into two sections: the 12-month
pre-earthquake period (March 11, 2010-March 10, 2011) and the two-month
post-earthquake period (March 11, 2011-May 10, 2011)[1]. While the stability period
is de?ned as the pre-earthquake period, the turbulent (turmoil) period is de?ned as
the post-earthquake period. In a bid to ensure robustness of our ?ndings, the turmoil
period is further partitioned into two equal sections: the one-month (short-term)
post-earthquake period (March 11, 2011-April 10, 2011), and the two-month
(medium-term) post-earthquake (March 11, 2011-May 10, 2011). The number of days
are, respectively, 365, 31, 62 days for the stable, short- and medium-termturmoil periods.
Data used in the study are obtained from Bloomberg’s database. In the computation of
stock returns, last values are carried over for non-trading days. The US dollar is used
as the common “x” unit of foreign currency for each unit of national/regional currency
in the computation of exchange rate returns. Our use of local currency index return is in
line with Forbes and Rigobon (2002) who have shown that using dollar or local indices
will produce similar results.
3.2 Methodology
Borrowing from Forbes and Rigobon (2002), contagion is a signi?cant increase in
market co-movements after a shock has occurred in one country.
The coef?cient of correlation is de?ned as:
r ¼
s
xy
s
x
s
y
ð1Þ
where x represents the base criterion and y an international market. This correlation
coef?cient is adjusted in the following manner to take account of heteroscedasticity:
r
*
¼
r
??????????????????????????????
1 þ d½1 2ðrÞ
2
?
p ð2Þ
where:
d ¼
s
h
xx
s
l
xx
21
It measures the change in high-period volatility against volatility in the low period.
While the crisis interval is used as the high volatility period, the tranquil or stable
period represents the low volatility period. Contagion is eventually measured as the
JFEP
4,4
344
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
5
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
signi?cant increase of adjusted correlation coef?cients in time-varying turmoil periods
against the stability period.
Borrowing from Lee et al. (2007), the following hypotheses are tested:
H
0
: r
t
2r
s
# 0 versus H
1
: r
t
2r
s
, 0
where r
t
is the adjusted correlation coef?cient during the turmoil period and r
s
the
adjusted correlation coef?cient for the stable period. A comparison of the difference in
correlation between the stable and crisis periods is then carried-out. The null hypothesis
(H0) is the hypothesis of no contagion while H1 is the alternative hypothesis for the
presence of contagion. Fisher’s Z transformations of correlation coef?cients are used
to test pair-wise cross-country signi?cance. This Fisher’s Z transformations change
standard coef?cients to normally distributed Z variables. Therefore, before hypothesis
testing, r-values must be converted to Z
r
values:
H
0
: r
t
2r
s
# 0 ) H
0
: Z
rt
2Z
rs
# 0
H
1
: r
t
2r
s
. 0 ) H
1
: Z
rt
2Z
rs
. 0
where:
Z
rt
¼
1
2
ln
1 þ r
t
1 2r
t
Z
rs
¼
1
2
ln
1 þ r
s
1 2r
s
Z ¼
Z
rt
2Z
rs
?????????????????????????????????????????????????????
ð1=ðn
t
23ÞÞ þ ð1=ðn
s
23ÞÞ
p
4. Empirical results
4.1 Contagion effect in international stock indexes returns after earthquake
Table I shows the conditional (unadjusted) correlation coef?cients of international
stock indexes for the 2011 Japanese Tsunami. Cross-market correlations of stock index
returns are compared before and after the earthquake of March 11, 2011. With the
exceptions of China, Taiwan, New Zealand, Argentina, Bahrain, Egypt, Saudi Arabia,
and South Africa; cross-market correlations between Japan and most countries in the
sample during stable period are higher than those during medium-term turmoil period.
For the short-run interval, correlations are strengthened for China, Hong Kong,
Taiwan, South Korea, Australia, New Zealand, Argentina, Germany, Bahrain,
South Africa, and Saudi Arabia. There is signi?cant evidence of contagion in Taiwan,
Bahrain, Saudi Arabia, and South Africa for the short-term turmoil period and only in
Bahrain and Saudi Arabia for the medium-term turmoil interval. Comparatively, for
the most part volatilities of most countries during the stable period are higher than
those during turmoil periods (short and medium terms).
Unconditional correlation coef?cients are presented in Table II. These adjusted
correlations are higher that their unadjusted counterparts in Table I. Results of Table I
are substantiated by those of Table II.
The 2011
Japanese
earthquake
345
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
5
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
F
u
l
l
p
e
r
i
o
d
S
t
a
b
l
e
p
e
r
i
o
d
S
h
o
r
t
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
M
e
d
i
u
m
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
R
e
g
i
o
n
s
C
o
u
n
t
r
i
e
s
r
s
r
s
r
s
Z
-
t
e
s
t
C
o
r
s
Z
-
t
e
s
t
C
o
S
o
u
t
h
A
s
i
a
a
n
d
S
o
u
t
h
-
E
a
s
t
A
s
i
a
I
n
d
i
a
0
.
2
8
8
0
.
0
0
9
0
.
3
4
3
0
.
0
0
9
0
.
2
4
7
0
.
0
0
9
2
0
.
5
3
8
N
0
.
1
7
1
0
.
0
0
9
2
1
.
3
2
1
N
M
a
l
a
y
s
i
a
0
.
3
7
2
0
.
0
0
5
0
.
4
0
5
0
.
0
0
5
0
.
3
9
2
0
.
0
0
5
2
0
.
0
8
0
N
0
.
3
4
8
0
.
0
0
5
2
0
.
4
7
4
N
P
h
i
l
i
p
p
i
n
e
s
0
.
3
1
7
0
.
0
0
9
0
.
3
5
7
0
.
0
0
9
0
.
2
9
5
0
.
0
0
9
2
0
.
3
5
3
N
0
.
2
6
6
0
.
0
0
8
2
0
.
7
1
5
N
S
i
n
g
a
p
o
r
e
n
.
a
.
0
.
0
0
0
n
.
a
.
0
.
0
0
0
n
.
a
.
0
.
0
0
0
n
.
a
.
n
.
a
.
n
.
a
.
0
.
0
0
0
n
.
a
.
n
.
a
.
T
h
a
i
l
a
n
d
0
.
3
0
8
0
.
0
0
9
0
.
3
6
1
0
.
0
1
0
0
.
2
7
8
0
.
0
0
8
2
0
.
4
7
0
N
0
.
2
0
9
0
.
0
0
8
2
1
.
1
8
0
N
E
a
s
t
A
s
i
a
a
n
d
N
o
r
t
h
-
E
a
s
t
A
s
i
a
C
h
i
n
a
0
.
2
8
3
0
.
0
1
1
0
.
3
0
9
0
.
0
1
2
0
.
4
7
7
0
.
0
0
7
1
.
0
2
2
N
0
.
3
2
1
0
.
0
0
7
0
.
1
0
0
N
H
o
n
g
K
o
n
g
0
.
5
1
0
0
.
0
0
9
0
.
5
4
2
0
.
0
0
9
0
.
5
7
4
0
.
0
0
9
0
.
2
4
0
N
0
.
5
2
5
0
.
0
0
8
2
0
.
1
6
6
N
T
a
i
w
a
n
0
.
5
8
7
0
.
0
0
8
0
.
5
9
1
0
.
0
0
8
0
.
7
8
1
0
.
0
0
8
1
.
8
8
1
*
Y
0
.
6
9
4
0
.
0
0
8
1
.
2
4
7
N
S
o
u
t
h
K
o
r
e
a
0
.
5
7
5
0
.
0
0
8
0
.
6
0
6
0
.
0
0
8
0
.
6
6
0
0
.
0
0
8
0
.
4
5
8
N
0
.
5
6
6
0
.
0
0
8
2
0
.
4
3
7
N
A
u
s
t
r
a
l
a
s
i
a
A
u
s
t
r
a
l
i
a
2
0
.
0
0
4
0
.
0
0
8
0
.
0
0
0
0
.
0
0
8
0
.
0
7
3
0
.
0
0
7
0
.
3
7
3
N
2
0
.
0
2
1
0
.
0
0
7
2
0
.
1
4
7
N
N
e
w
Z
e
a
l
a
n
d
0
.
4
4
0
0
.
0
0
4
0
.
4
5
9
0
.
0
0
4
0
.
6
0
9
0
.
0
0
4
1
.
0
8
0
N
0
.
5
1
5
0
.
0
0
4
0
.
5
2
5
N
N
o
r
t
h
A
m
e
r
i
c
a
C
a
n
a
d
a
2
0
.
0
5
5
0
.
1
9
7
2
0
.
0
1
3
0
.
2
0
8
2
0
.
4
4
1
0
.
1
2
5
2
2
.
3
4
8
N
2
0
.
3
4
3
0
.
1
1
0
2
2
.
4
5
5
N
U
S
A
0
.
1
7
6
0
.
0
1
2
0
.
2
1
7
0
.
0
1
3
0
.
0
5
4
0
.
0
0
8
2
0
.
8
4
8
N
0
.
0
7
4
0
.
0
0
7
2
1
.
0
4
1
N
M
e
x
i
c
o
0
.
1
5
9
0
.
0
0
7
0
.
2
0
8
0
.
0
0
7
0
.
0
4
8
0
.
0
0
6
2
0
.
8
3
1
N
0
.
0
2
7
0
.
0
0
6
2
1
.
3
1
0
N
S
o
u
t
h
A
m
e
r
i
c
a
A
r
g
e
n
t
i
n
a
0
.
1
7
4
0
.
0
1
2
0
.
1
6
3
0
.
0
1
3
0
.
3
1
2
0
.
0
1
1
0
.
8
0
7
N
0
.
2
6
9
0
.
0
1
0
0
.
7
9
5
N
B
r
a
z
i
l
0
.
0
7
6
0
.
0
1
0
0
.
1
2
0
0
.
0
1
0
2
0
.
0
3
3
0
.
0
0
6
2
0
.
7
8
3
N
2
0
.
0
6
9
0
.
0
0
8
2
1
.
3
5
1
N
C
h
i
l
e
0
.
1
1
7
0
.
0
0
7
0
.
1
7
8
0
.
0
0
7
2
0
.
0
8
6
0
.
0
0
8
2
1
.
3
5
7
N
2
0
.
0
3
5
0
.
0
0
7
2
1
.
5
3
2
N
E
u
r
o
p
e
F
r
a
n
c
e
0
.
3
2
1
0
.
0
1
2
0
.
3
6
6
0
.
0
1
2
0
.
2
5
3
0
.
0
1
1
2
0
.
6
3
9
N
0
.
2
5
4
0
.
0
1
0
2
0
.
8
8
3
N
P
o
l
a
n
d
0
.
2
1
8
0
.
0
0
8
0
.
2
8
7
0
.
0
0
8
2
0
.
0
4
5
0
.
0
0
6
2
1
.
7
3
5
N
0
.
0
1
3
0
.
0
0
6
2
2
.
0
1
4
N
G
e
r
m
a
n
y
0
.
3
2
5
0
.
0
0
9
0
.
3
6
6
0
.
0
0
9
0
.
3
5
0
0
.
0
1
2
0
.
0
8
3
N
0
.
3
3
4
0
.
0
1
1
2
0
.
0
1
2
N
I
t
a
l
y
0
.
2
4
8
0
.
0
1
3
0
.
2
9
2
0
.
0
1
3
0
.
1
4
2
0
.
0
0
9
2
0
.
8
0
6
N
0
.
1
6
9
0
.
0
0
9
2
0
.
9
2
8
N
H
o
l
l
a
n
d
0
.
3
3
2
0
.
0
1
0
0
.
3
7
8
0
.
0
1
0
0
.
2
9
6
0
.
0
0
8
2
0
.
4
7
3
N
0
.
2
7
1
0
.
0
0
8
2
0
.
8
5
1
N
S
p
a
i
n
0
.
1
9
3
0
.
0
1
5
0
.
2
5
5
0
.
0
1
6
2
0
.
1
1
6
0
.
0
0
9
2
1
.
9
2
3
N
2
0
.
0
0
1
0
.
0
0
9
2
1
.
8
6
0
N
U
K
0
.
2
9
2
0
.
0
0
9
0
.
3
6
1
0
.
0
0
9
0
.
1
3
5
0
.
0
0
8
2
1
.
2
3
4
N
0
.
1
2
9
0
.
0
0
8
2
1
.
7
6
4
N
M
i
d
d
l
e
E
a
s
t
a
n
d
A
f
r
i
c
a
B
a
h
r
a
i
n
0
.
0
0
6
0
.
0
0
5
2
0
.
0
5
0
0
.
0
0
5
0
.
2
9
0
0
.
0
0
6
1
.
7
7
4
*
Y
0
.
2
0
7
0
.
0
0
5
1
.
8
5
0
*
Y
E
g
y
p
t
0
.
1
1
6
0
.
0
1
3
0
.
1
0
4
0
.
0
1
1
0
.
0
9
8
0
.
0
2
8
2
0
.
0
2
7
N
0
.
1
3
1
0
.
0
2
2
0
.
1
9
8
N
J
o
r
d
a
n
2
0
.
0
3
5
0
.
0
0
5
2
0
.
0
2
0
0
.
0
0
5
2
0
.
1
0
1
0
.
0
0
6
2
0
.
4
1
3
N
2
0
.
0
9
7
0
.
0
0
5
2
0
.
5
5
4
N
K
u
w
a
i
t
2
0
.
0
7
3
0
.
0
0
5
2
0
.
0
2
6
0
.
0
0
5
2
0
.
2
9
8
0
.
0
0
6
2
1
.
4
3
1
N
2
0
.
2
5
6
0
.
0
0
4
2
1
.
6
7
9
N
Q
a
t
a
r
0
.
0
1
9
0
.
0
0
9
0
.
0
4
6
0
.
0
0
9
2
0
.
0
8
0
0
.
0
0
9
2
0
.
6
4
1
N
2
0
.
0
6
4
0
.
0
0
8
2
0
.
7
8
5
N
S
a
u
d
i
A
r
a
b
i
a
0
.
1
8
2
0
.
0
1
1
0
.
1
1
7
0
.
0
1
1
0
.
4
9
3
0
.
0
1
3
2
.
1
5
4
*
*
Y
0
.
4
5
7
0
.
0
1
0
2
.
6
7
8
*
*
*
Y
U
A
E
0
.
0
8
0
0
.
0
0
6
0
.
1
0
9
0
.
0
0
6
2
0
.
0
5
5
0
.
0
0
6
2
0
.
8
3
6
N
0
.
0
1
0
0
.
0
0
5
2
0
.
7
0
6
N
S
o
u
t
h
A
f
r
i
c
a
0
.
3
4
8
0
.
0
0
9
0
.
3
4
3
0
.
0
0
9
0
.
6
3
4
0
.
0
0
8
1
.
9
9
4
*
*
Y
0
.
4
3
4
0
.
0
0
9
0
.
7
6
6
N
O
t
h
e
r
R
u
s
s
i
a
0
.
2
9
0
0
.
0
1
2
0
.
3
7
8
0
.
0
1
2
0
.
0
0
7
0
.
0
1
0
2
1
.
9
9
2
N
0
.
0
6
9
0
.
0
1
2
2
2
.
3
3
8
N
N
o
t
e
s
:
S
t
a
t
i
s
t
i
c
a
l
s
i
g
n
i
?
c
a
n
c
e
a
t
:
*
1
0
,
*
*
5
,
a
n
d
*
*
*
1
p
e
r
c
e
n
t
,
r
e
s
p
e
c
t
i
v
e
l
y
;
t
h
e
t
a
b
l
e
s
h
o
w
s
c
o
n
d
i
t
i
o
n
a
l
(
u
n
a
d
j
u
s
t
e
d
)
c
r
o
s
s
-
m
a
r
k
e
t
c
o
r
r
e
l
a
t
i
o
n
c
o
e
f
?
c
i
e
n
t
s
(
r
)
a
n
d
s
t
a
n
d
a
r
d
d
e
v
i
a
t
i
o
n
s
(
s
)
f
o
r
J
a
p
a
n
a
n
d
o
t
h
e
r
s
t
o
c
k
i
n
d
e
x
e
s
;
t
h
e
t
e
s
t
s
t
a
t
i
s
t
i
c
s
a
r
e
o
b
t
a
i
n
e
d
f
r
o
m
F
i
s
h
e
r
Z
t
r
a
n
s
f
o
r
m
a
t
i
o
n
s
;
t
h
e
s
t
a
b
l
e
p
e
r
i
o
d
i
s
d
e
?
n
e
d
a
s
t
h
e
1
2
-
m
o
n
t
h
p
r
e
-
e
a
r
t
h
q
u
a
k
e
p
e
r
i
o
d
(
M
a
r
c
h
1
1
,
2
0
1
0
-
M
a
r
c
h
1
0
,
2
0
1
1
)
;
t
h
e
s
h
o
r
t
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
i
s
d
e
?
n
e
d
a
s
t
h
e
o
n
e
-
m
o
n
t
h
p
o
s
t
-
e
a
r
t
h
q
u
a
k
e
p
e
r
i
o
d
(
M
a
r
c
h
1
1
,
2
0
1
1
-
A
p
r
i
l
1
0
,
2
0
1
1
)
;
t
h
e
m
e
d
i
u
m
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
i
s
d
e
?
n
e
d
a
s
t
h
e
t
w
o
-
m
o
n
t
h
p
o
s
t
-
e
a
r
t
h
q
u
a
k
e
p
e
r
i
o
d
(
M
a
r
c
h
1
1
,
2
0
1
1
-
M
a
y
1
0
,
2
0
1
1
)
;
t
h
e
f
u
l
l
p
e
r
i
o
d
i
s
t
h
e
s
t
a
b
l
e
p
e
r
i
o
d
p
l
u
s
t
h
e
m
e
d
i
u
m
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
;
C
o
:
c
o
n
t
a
g
i
o
n
;
w
h
i
l
e
“
Y
”
d
e
n
o
t
e
s
t
h
a
t
t
h
e
t
e
s
t
s
t
a
t
i
s
t
i
c
s
i
s
g
r
e
a
t
e
r
t
h
a
n
t
h
e
c
r
i
t
i
c
a
l
v
a
l
u
e
a
n
d
c
o
n
t
a
g
i
o
n
o
c
c
u
r
r
e
d
,
“
N
”
i
n
d
i
c
a
t
e
s
t
h
a
t
t
h
e
t
e
s
t
s
t
a
t
i
s
t
i
c
s
w
a
s
l
e
s
s
o
r
e
q
u
a
l
t
o
t
h
e
c
r
i
t
i
c
a
l
v
a
l
u
e
a
n
d
n
o
c
o
n
t
a
g
i
o
n
o
c
c
u
r
r
e
d
;
c
o
r
r
e
l
a
t
i
o
n
c
o
e
f
?
c
i
e
n
t
s
a
r
e
u
n
a
d
j
u
s
t
e
d
f
o
r
h
e
t
e
r
o
s
c
e
d
a
s
t
i
c
i
t
y
Table I.
International stock
indices returns
conditional (unadjusted)
correlation coef?cients in
2011 Japanese earthquake
JFEP
4,4
346
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
5
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
F
u
l
l
p
e
r
i
o
d
S
t
a
b
l
e
p
e
r
i
o
d
S
h
o
r
t
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
M
e
d
i
u
m
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
R
e
g
i
o
n
s
C
o
u
n
t
r
i
e
s
r
s
r
*
s
t
p
r
*
m
t
p
r
*
d
Z
-
t
e
s
t
C
o
r
*
d
Z
-
t
e
s
t
C
o
S
o
u
t
h
A
s
i
a
a
n
d
S
o
u
t
h
-
E
a
s
t
A
s
i
a
I
n
d
i
a
0
.
2
8
8
0
.
0
0
9
0
.
4
3
0
0
.
4
4
5
0
.
3
1
5
2
0
.
0
1
7
2
0
.
6
7
9
N
0
.
2
2
9
0
.
0
3
2
2
1
.
7
4
5
N
M
a
l
a
y
s
i
a
0
.
3
7
2
0
.
0
0
5
0
.
5
0
0
0
.
5
1
6
0
.
4
8
5
2
0
.
0
7
7
2
0
.
0
9
9
N
0
.
4
5
1
2
0
.
1
1
1
2
0
.
6
0
9
N
P
h
i
l
i
p
p
i
n
e
s
0
.
3
1
7
0
.
0
0
9
0
.
4
4
5
0
.
4
6
0
0
.
3
7
3
0
.
0
0
7
2
0
.
4
4
3
N
0
.
3
5
1
2
0
.
1
4
3
2
0
.
9
3
3
N
S
i
n
g
a
p
o
r
e
n
.
a
.
0
.
0
0
0
n
.
a
.
n
.
a
.
n
.
a
.
n
.
a
.
n
.
a
.
n
.
a
.
n
.
a
.
n
.
a
.
n
.
a
.
n
.
a
.
T
h
a
i
l
a
n
d
0
.
3
0
8
0
.
0
0
9
0
.
4
5
0
0
.
4
6
5
0
.
3
5
2
2
0
.
1
6
5
2
0
.
5
9
1
N
0
.
2
7
9
2
0
.
1
5
5
2
1
.
5
4
9
N
E
a
s
t
A
s
i
a
a
n
d
N
o
r
t
h
-
E
a
s
t
A
s
i
a
C
h
i
n
a
0
.
2
8
3
0
.
0
1
1
0
.
3
8
9
0
.
4
0
3
0
.
5
7
7
2
0
.
4
3
3
1
.
2
6
2
N
0
.
4
1
8
2
0
.
3
8
7
0
.
1
3
0
N
H
o
n
g
K
o
n
g
0
.
5
1
0
0
.
0
0
9
0
.
6
4
2
0
.
6
5
8
0
.
6
7
4
0
.
0
4
8
0
.
2
8
3
N
0
.
6
4
2
2
0
.
1
0
6
2
0
.
2
0
3
N
T
a
i
w
a
n
0
.
5
8
7
0
.
0
0
8
0
.
6
9
0
0
.
7
0
6
0
.
8
5
2
0
.
0
4
7
2
.
1
1
9
*
*
Y
0
.
7
9
4
2
0
.
0
4
3
1
.
4
5
8
N
S
o
u
t
h
K
o
r
e
a
0
.
5
7
5
0
.
0
0
8
0
.
7
0
4
0
.
7
1
9
0
.
7
5
3
0
.
0
1
6
0
.
5
2
8
N
0
.
6
8
2
0
.
0
9
2
2
0
.
5
2
3
N
A
u
s
t
r
a
l
a
s
i
a
A
u
s
t
r
a
l
i
a
2
0
.
0
0
4
0
.
0
0
8
0
.
0
0
0
0
.
0
0
0
0
.
0
9
5
2
0
.
7
0
6
0
.
4
8
5
N
2
0
.
0
2
8
2
0
.
1
2
0
2
0
.
1
9
9
N
N
e
w
Z
e
a
l
a
n
d
0
.
4
4
0
0
.
0
0
4
0
.
5
5
8
0
.
5
7
4
0
.
7
0
7
2
0
.
1
1
9
1
.
2
8
3
N
0
.
6
3
2
2
0
.
1
7
2
0
.
6
5
1
N
N
o
r
t
h
A
m
e
r
i
c
a
C
a
n
a
d
a
2
0
.
0
5
5
0
.
1
9
7
2
0
.
0
1
7
2
0
.
0
1
8
2
0
.
5
3
9
2
0
.
4
0
1
2
2
.
9
8
5
N
2
0
.
4
4
5
2
0
.
4
7
3
2
3
.
2
7
7
N
U
S
A
0
.
1
7
6
0
.
0
1
2
0
.
2
7
8
0
.
2
8
9
0
.
0
7
0
2
0
.
4
1
0
2
1
.
0
9
6
N
0
.
1
0
0
2
0
.
4
5
8
2
1
.
4
0
1
N
M
e
x
i
c
o
0
.
1
5
9
0
.
0
0
7
0
.
2
6
7
0
.
2
7
8
0
.
0
6
3
2
0
.
1
7
4
2
1
.
0
7
4
N
0
.
0
3
7
2
0
.
1
1
8
2
1
.
7
6
7
N
S
o
u
t
h
A
m
e
r
i
c
a
A
r
g
e
n
t
i
n
a
0
.
1
7
4
0
.
0
1
2
0
.
2
1
0
0
.
2
1
9
0
.
3
9
3
2
0
.
1
3
9
1
.
0
2
9
N
0
.
3
5
5
2
0
.
1
9
8
1
.
0
5
9
N
B
r
a
z
i
l
0
.
0
7
6
0
.
0
1
0
0
.
1
5
5
0
.
1
6
2
2
0
.
0
4
3
2
0
.
3
8
1
2
1
.
0
1
8
N
2
0
.
0
9
4
2
0
.
2
2
8
2
1
.
8
3
3
N
C
h
i
l
e
0
.
1
1
7
0
.
0
0
7
0
.
2
2
9
0
.
2
3
8
2
0
.
1
1
2
0
.
2
1
5
2
1
.
7
6
1
N
2
0
.
0
4
8
0
.
0
3
8
2
2
.
0
7
3
N
E
u
r
o
p
e
F
r
a
n
c
e
0
.
3
2
1
0
.
0
1
2
0
.
4
5
6
0
.
4
7
1
0
.
3
2
2
2
0
.
1
2
4
2
0
.
8
0
5
N
0
.
3
3
6
2
0
.
1
8
8
2
1
.
1
5
3
N
P
o
l
a
n
d
0
.
2
1
8
0
.
0
0
8
0
.
3
6
4
0
.
3
7
7
2
0
.
0
5
8
2
0
.
2
9
5
2
2
.
2
4
0
N
0
.
0
1
7
2
0
.
2
9
5
2
2
.
7
0
2
N
G
e
r
m
a
n
y
0
.
3
2
5
0
.
0
0
9
0
.
4
2
1
0
.
4
3
6
0
.
4
3
7
0
.
2
5
5
0
.
1
0
4
N
0
.
4
3
4
0
.
1
4
2
2
0
.
0
1
5
N
I
t
a
l
y
0
.
2
4
8
0
.
0
1
3
0
.
3
6
9
0
.
3
8
3
0
.
1
8
3
2
0
.
3
1
4
2
1
.
0
3
2
N
0
.
2
2
7
2
0
.
3
0
6
2
1
.
2
3
2
N
H
o
l
l
a
n
d
0
.
3
3
2
0
.
0
1
0
0
.
4
6
9
0
.
4
8
5
0
.
3
7
4
2
0
.
2
0
9
2
0
.
5
9
3
N
0
.
3
5
8
2
0
.
2
4
2
2
1
.
1
0
8
N
S
p
a
i
n
0
.
1
9
3
0
.
0
1
5
0
.
3
2
4
0
.
3
3
7
2
0
.
1
5
1
2
0
.
4
0
6
2
2
.
4
8
9
N
2
0
.
0
0
1
2
0
.
4
4
3
2
2
.
5
0
4
N
U
K
0
.
2
9
2
0
.
0
0
9
0
.
4
5
0
0
.
4
6
5
0
.
1
7
5
2
0
.
1
5
2
2
1
.
5
7
1
N
0
.
1
7
5
2
0
.
1
2
0
2
2
.
3
3
3
N
M
i
d
d
l
e
E
a
s
t
a
n
d
A
f
r
i
c
a
B
a
h
r
a
i
n
0
.
0
0
6
0
.
0
0
5
2
0
.
0
6
5
2
0
.
0
6
7
0
.
3
6
7
0
.
0
2
2
2
.
2
9
0
*
*
Y
0
.
2
7
6
2
0
.
1
5
6
2
.
5
0
1
*
*
Y
E
g
y
p
t
0
.
1
1
6
0
.
0
1
3
0
.
1
3
4
0
.
1
4
0
0
.
1
2
7
1
.
6
0
5
2
0
.
0
3
6
N
0
.
1
7
7
0
.
9
9
1
0
.
2
6
8
N
J
o
r
d
a
n
2
0
.
0
3
5
0
.
0
0
5
2
0
.
0
2
6
2
0
.
0
2
7
2
0
.
1
3
1
0
.
0
7
7
2
0
.
5
3
7
N
2
0
.
1
3
2
2
0
.
1
5
9
2
0
.
7
5
2
N
K
u
w
a
i
t
2
0
.
0
7
3
0
.
0
0
5
2
0
.
0
3
4
2
0
.
0
3
6
2
0
.
3
7
6
0
.
0
8
8
2
1
.
8
4
1
N
2
0
.
3
3
9
2
0
.
1
5
7
2
2
.
2
5
7
N
Q
a
t
a
r
0
.
0
1
9
0
.
0
0
9
0
.
0
5
9
0
.
0
6
2
2
0
.
1
0
4
0
.
0
3
7
2
0
.
8
3
4
N
2
0
.
0
8
7
2
0
.
1
3
3
2
1
.
0
6
6
N
S
a
u
d
i
A
r
a
b
i
a
0
.
1
8
2
0
.
0
1
1
0
.
1
5
2
0
.
1
5
8
0
.
5
9
4
0
.
1
7
0
2
.
7
0
5
*
*
*
Y
0
.
5
7
2
2
0
.
1
1
1
3
.
5
0
2
*
*
*
Y
U
A
E
0
.
0
8
0
0
.
0
0
6
0
.
1
4
1
0
.
1
4
7
2
0
.
0
7
1
0
.
0
1
2
2
1
.
0
8
7
N
0
.
0
1
4
2
0
.
0
8
5
2
0
.
9
5
7
N
S
o
u
t
h
A
f
r
i
c
a
0
.
3
4
8
0
.
0
0
9
0
.
4
2
9
0
.
4
4
4
0
.
7
3
0
2
0
.
1
3
6
2
.
3
9
4
*
*
Y
0
.
5
4
8
2
0
.
0
3
6
0
.
9
8
0
N
O
t
h
e
r
R
u
s
s
i
a
0
.
2
9
0
0
.
0
1
2
0
.
4
6
9
0
.
4
8
5
0
.
0
0
9
2
0
.
1
7
6
2
2
.
5
5
0
N
0
.
0
9
4
2
0
.
0
0
6
2
3
.
1
0
0
N
N
o
t
e
s
:
S
t
a
t
i
s
t
i
c
a
l
s
i
g
n
i
?
c
a
n
c
e
a
t
:
*
1
0
,
*
*
5
,
a
n
d
*
*
*
1
p
e
r
c
e
n
t
,
r
e
s
p
e
c
t
i
v
e
l
y
;
t
h
e
t
a
b
l
e
s
h
o
w
s
c
o
n
d
i
t
i
o
n
a
l
(
u
n
a
d
j
u
s
t
e
d
)
c
r
o
s
s
-
m
a
r
k
e
t
c
o
r
r
e
l
a
t
i
o
n
c
o
e
f
?
c
i
e
n
t
s
(
r
)
,
a
d
j
u
s
t
e
r
f
o
r
h
e
t
e
r
o
s
c
e
d
a
s
t
i
c
i
t
y
(
d
)
,
a
n
d
s
t
a
n
d
a
r
d
d
e
v
i
a
t
i
o
n
s
(
s
)
f
o
r
J
a
p
a
n
a
n
d
o
t
h
e
r
s
t
o
c
k
i
n
d
e
x
e
s
;
t
h
e
t
e
s
t
s
t
a
t
i
s
t
i
c
s
a
r
e
o
b
t
a
i
n
e
d
f
r
o
m
F
i
s
h
e
r
Z
t
r
a
n
s
f
o
r
m
a
t
i
o
n
s
;
t
h
e
s
t
a
b
l
e
p
e
r
i
o
d
i
s
d
e
?
n
e
d
a
s
t
h
e
1
2
-
m
o
n
t
h
p
r
e
-
e
a
r
t
h
q
u
a
k
e
p
e
r
i
o
d
(
M
a
r
c
h
1
1
,
2
0
1
0
-
M
a
r
c
h
1
0
,
2
0
1
1
)
;
t
h
e
s
h
o
r
t
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
i
s
d
e
?
n
e
d
a
s
t
h
e
o
n
e
-
m
o
n
t
h
p
o
s
t
-
e
a
r
t
h
q
u
a
k
e
p
e
r
i
o
d
(
M
a
r
c
h
1
1
,
2
0
1
1
-
A
p
r
i
l
1
0
,
2
0
1
1
)
;
t
h
e
m
e
d
i
u
m
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
i
s
d
e
?
n
e
d
a
s
t
h
e
t
w
o
-
m
o
n
t
h
p
o
s
t
-
e
a
r
t
h
q
u
a
k
e
p
e
r
i
o
d
(
M
a
r
c
h
1
1
,
2
0
1
1
-
M
a
y
1
0
,
2
0
1
1
)
;
t
h
e
f
u
l
l
p
e
r
i
o
d
i
s
t
h
e
s
t
a
b
l
e
p
e
r
i
o
d
p
l
u
s
t
h
e
m
e
d
i
u
m
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
;
C
o
:
c
o
n
t
a
g
i
o
n
;
w
h
i
l
e
“
Y
”
d
e
n
o
t
e
s
t
h
a
t
t
h
e
t
e
s
t
s
t
a
t
i
s
t
i
c
s
i
s
g
r
e
a
t
e
r
t
h
a
n
t
h
e
c
r
i
t
i
c
a
l
v
a
l
u
e
a
n
d
c
o
n
t
a
g
i
o
n
o
c
c
u
r
r
e
d
,
“
N
”
i
n
d
i
c
a
t
e
s
t
h
a
t
t
h
e
t
e
s
t
s
t
a
t
i
s
t
i
c
s
w
a
s
l
e
s
s
o
r
e
q
u
a
l
t
o
t
h
e
c
r
i
t
i
c
a
l
v
a
l
u
e
a
n
d
n
o
c
o
n
t
a
g
i
o
n
o
c
c
u
r
r
e
d
;
r
*
s
t
p
,
r
*
m
t
p
,
d
e
n
o
t
e
a
d
j
u
s
t
e
d
c
o
r
r
e
l
a
t
i
o
n
c
o
e
f
?
c
i
e
n
t
s
f
o
r
t
h
e
s
h
o
r
t
-
a
n
d
m
e
d
i
u
m
-
t
e
r
m
p
e
r
i
o
d
s
,
r
e
s
p
e
c
t
i
v
e
l
y
;
d
:
c
o
r
r
e
l
a
t
i
o
n
c
o
e
f
?
c
i
e
n
t
a
d
j
u
s
t
e
r
;
c
o
r
r
e
l
a
t
i
o
n
c
o
e
f
?
c
i
e
n
t
s
a
r
e
a
d
j
u
s
t
e
d
f
o
r
h
e
t
e
r
o
s
c
e
d
a
s
t
i
c
i
t
y
u
s
i
n
g
e
q
u
a
t
i
o
n
(
2
)
Table II.
International stock
indices returns
unconditional (adjusted)
correlation coef?cients in
2011 Japanese earthquake
The 2011
Japanese
earthquake
347
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
5
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
4.2 Contagion effect in international exchange rates returns after earthquake
Findings in Table III present exchange rate conditional (unadjusted) correlation
coef?cients. Cross-market correlations during turmoil periods are higher than those
during the stable period. Strengthened cross-market correlations with insigni?cant
evidence of contagion are noticeable for Thai Baht (THB), Argentinian Peso (ARS),
Egyptian Pound (EGP), and Qatari Riyal (QAR) for the short-term turmoil period. With
regard to the medium-term, the Chinese Yuan (RMB), Canadian Dollar (CAD), EGP, QAR,
and Emirati dirham (AED) also witnessed insigni?cant stronger co-movements with the
Japanese Yen ( JPY). Adjusted results from Table IV con?rm those in Table III. In
summary, no national/regional exchange market is found to have suffered fromcontagion
two months in the aftermath of the Japanese earthquake and ensuing collateral disasters.
5. Discussion of results
This study has investigated if the March 2011 Japanese earthquake, tsunami, and
nuclear disaster affected the stability of the correlation structure in international stock
and foreign exchange markets.
On a ?rst note, with respect to international equity markets there is strong evidence
of contagion in Taiwan, Bahrain, Saudi Arabia, and South Africa. The effect on Saudi
Arabia is not unexpected because it is one of the four countries fromwhich a large part of
Japan’s imports in raw material originate. For the other three, cross-market correlations
strengthened only with China and Australia in the short-term, albeit insigni?cant to
account for contagion. An explanation as to why Saudi Arabia was most strongly
affected both in the immediate and medium terms may be determined from Japan’s
boost in fuel imports in substitution to energy provided by wrecked the Fukushima
nuclear plants. Bahrain, being an oil-export driven economy like her sisterly neighbor
Saudi Arabia, could not have suffered a different fate. As for Taiwan, Japan is its second
largest trading partner and of?cial estimates on the effect of the Japanese earthquake on
the Taiwanese economy stand at a yearly decline in growth by 0.2 percent of gross
domestic product (GDP).
Second, international foreign exchange market results indicate no presence of
contagion. Admittedly, one would have expected the widespread disruption to Japan’s
US$5.5 trillion economy to inevitably affect other countries in the Asia-Paci?c region and
beyond. Regional trade would have been immediately affected by the damage to Japanese
ports. Our unexpected ?ndings could be explained from the fact that major Japanese
manufacturers of automobiles, semiconductors, computers, and other goods immediately
took advantage of their international supply chains and production networks; therefore
moving production elsewhere in Asia or to North America, where capacity utilization is
still low. Also, since Japanese factories generally produce consumer goods rather than
intermediate products, disruptions to outbound shipments should not have been expected
to seriously affect production processes in other countries.
As to what concerns managing and mitigating spillovers and contagion, it is worth
pointing out that globalization comes with costs and bene?ts. Hence managing
?nancial market contagion resulting from natural disasters requires that governments
minimize the costs and maximize the bene?ts of ?nancial market integration. Most
countries in the sample have undoubtedly bene?ted from integration, however based
on the above empirical evidence, measures need to be taken in an effort to manage the
downside rami?cations of integration in the event of a natural disaster.
JFEP
4,4
348
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
5
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
F
u
l
l
p
e
r
i
o
d
S
t
a
b
l
e
p
e
r
i
o
d
S
h
o
r
t
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
M
e
d
i
u
m
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
R
e
g
i
o
n
s
C
o
u
n
t
r
i
e
s
r
s
r
s
r
s
Z
-
t
e
s
t
C
o
r
s
Z
-
t
e
s
t
C
o
S
o
u
t
h
A
s
i
a
a
n
d
S
o
u
t
h
-
E
a
s
t
A
s
i
a
I
n
d
i
a
2
0
.
1
3
6
0
.
0
0
4
2
0
.
1
3
0
0
.
0
0
5
2
0
.
2
8
2
0
.
0
0
2
2
0
.
8
0
7
N
2
0
.
2
0
7
0
.
0
0
2
2
0
.
5
6
0
N
M
a
l
a
y
s
i
a
2
0
.
1
9
7
0
.
0
0
4
2
0
.
1
9
1
0
.
0
0
5
2
0
.
3
3
2
0
.
0
0
2
2
0
.
7
7
3
N
2
0
.
2
6
7
0
.
0
0
3
2
0
.
5
7
0
N
P
h
i
l
i
p
p
i
n
e
s
2
0
.
1
2
9
0
.
0
0
4
2
0
.
1
2
8
0
.
0
0
5
2
0
.
4
3
9
0
.
0
0
3
2
1
.
7
4
6
N
2
0
.
1
4
3
0
.
0
0
3
2
0
.
1
0
5
N
S
i
n
g
a
p
o
r
e
2
0
.
0
2
9
0
.
0
0
3
2
0
.
0
1
5
0
.
0
0
4
2
0
.
1
9
7
0
.
0
0
3
2
0
.
9
4
0
N
2
0
.
1
0
9
0
.
0
0
3
2
0
.
6
7
4
N
T
h
a
i
l
a
n
d
0
.
0
6
1
0
.
0
0
2
0
.
0
6
6
0
.
0
0
4
0
.
2
3
5
0
.
0
0
2
0
.
8
8
5
N
0
.
0
2
3
0
.
0
0
2
2
0
.
3
0
7
N
E
a
s
t
A
s
i
a
a
n
d
N
o
r
t
h
-
E
a
s
t
A
s
i
a
C
h
i
n
a
0
.
0
3
0
0
.
0
0
1
0
.
0
1
8
0
.
0
0
4
2
0
.
0
9
0
0
.
0
0
1
2
0
.
5
5
1
N
0
.
0
8
5
0
.
0
0
1
0
.
4
8
2
N
H
o
n
g
K
o
n
g
2
0
.
0
4
9
0
.
0
0
0
2
0
.
0
2
0
0
.
0
0
4
2
0
.
2
2
3
0
.
0
0
0
2
1
.
0
4
9
N
2
0
.
2
2
5
0
.
0
0
0
2
1
.
4
8
1
N
T
a
i
w
a
n
2
0
.
1
0
4
0
.
0
0
3
2
0
.
0
8
0
0
.
0
0
4
2
0
.
4
0
0
0
.
0
0
2
2
1
.
7
4
8
N
2
0
.
2
5
1
0
.
0
0
2
2
1
.
2
5
6
N
S
o
u
t
h
K
o
r
e
a
2
0
.
2
4
2
0
.
0
0
7
2
0
.
2
2
6
0
.
0
0
7
2
0
.
5
0
0
0
.
0
0
5
2
1
.
6
3
0
N
2
0
.
4
1
5
0
.
0
0
4
2
1
.
5
0
8
N
A
u
s
t
r
a
l
a
s
i
a
A
u
s
t
r
a
l
i
a
2
0
.
0
8
0
0
.
0
0
7
2
0
.
0
4
2
0
.
0
0
7
2
0
.
4
2
8
0
.
0
0
6
2
2
.
1
2
0
N
2
0
.
3
2
5
0
.
0
0
6
2
2
.
0
9
9
N
N
e
w
Z
e
a
l
a
n
d
2
0
.
0
3
1
0
.
0
0
7
0
.
0
3
5
0
.
0
0
7
2
0
.
6
1
2
0
.
0
0
7
2
3
.
8
1
2
N
2
0
.
4
1
9
0
.
0
0
6
2
3
.
4
3
2
N
N
o
r
t
h
A
m
e
r
i
c
a
C
a
n
a
d
a
2
0
.
2
7
2
0
.
0
0
6
2
0
.
2
7
5
0
.
0
0
6
2
0
.
3
9
0
0
.
0
0
4
2
0
.
6
6
2
N
2
0
.
2
7
4
0
.
0
0
4
0
.
0
1
0
N
M
e
x
i
c
o
2
0
.
3
3
8
0
.
0
0
5
2
0
.
3
3
9
0
.
0
0
6
2
0
.
5
2
1
0
.
0
0
3
2
1
.
1
5
1
N
2
0
.
3
8
5
0
.
0
0
4
2
0
.
3
7
8
N
S
o
u
t
h
A
m
e
r
i
c
a
A
r
g
e
n
t
i
n
a
2
0
.
0
3
0
0
.
0
0
1
2
0
.
0
2
6
0
.
0
0
1
2
0
.
0
2
4
0
.
0
0
1
0
.
0
1
0
N
2
0
.
0
5
9
0
.
0
0
1
2
0
.
2
3
7
N
B
r
a
z
i
l
2
0
.
2
0
5
0
.
0
0
6
2
0
.
1
8
1
0
.
0
0
6
2
0
.
4
1
5
0
.
0
0
5
2
1
.
3
1
6
N
2
0
.
3
5
6
0
.
0
0
6
2
1
.
3
4
7
N
C
h
i
l
e
0
.
0
1
2
0
.
0
0
5
0
.
0
4
6
0
.
0
0
6
2
0
.
2
8
1
0
.
0
0
4
2
1
.
7
0
7
N
2
0
.
2
3
4
0
.
0
0
4
2
2
.
0
2
8
N
E
u
r
o
p
e
E
u
r
o
0
.
1
6
6
0
.
0
0
6
0
.
2
1
1
0
.
0
0
6
2
0
.
2
0
4
0
.
0
0
4
2
2
.
1
5
1
N
2
0
.
0
7
9
0
.
0
0
6
2
2
.
0
9
4
N
U
K
0
.
0
4
3
0
.
0
0
5
0
.
0
7
3
0
.
0
0
5
2
0
.
2
1
1
0
.
0
0
5
2
1
.
4
6
7
N
2
0
.
1
4
7
0
.
0
0
4
2
1
.
5
7
6
N
M
i
d
d
l
e
E
a
s
t
a
n
d
A
f
r
i
c
a
B
a
h
r
a
i
n
2
0
.
0
2
1
0
.
0
0
6
2
0
.
0
1
4
0
.
0
0
0
2
0
.
0
2
9
0
.
0
0
0
2
0
.
0
7
7
N
2
0
.
0
4
3
0
.
0
0
0
2
0
.
2
0
8
N
E
g
y
p
t
0
.
0
3
7
0
.
0
0
1
0
.
0
2
2
0
.
0
0
1
0
.
1
3
3
0
.
0
0
1
0
.
5
6
6
N
0
.
1
9
4
0
.
0
0
1
1
.
2
3
9
N
J
o
r
d
a
n
2
0
.
0
2
6
0
.
0
0
1
2
0
.
0
2
4
0
.
0
0
1
2
0
.
1
3
1
0
.
0
0
0
2
0
.
5
4
8
N
2
0
.
0
3
6
0
.
0
0
0
2
0
.
0
8
8
N
K
u
w
a
i
t
0
.
2
4
7
0
.
0
0
1
0
.
2
5
8
0
.
0
0
1
0
.
1
8
2
0
.
0
0
1
2
0
.
4
0
8
N
0
.
1
8
7
0
.
0
0
1
2
0
.
5
3
3
N
Q
a
t
a
r
0
.
0
3
7
0
.
0
0
0
0
.
0
2
9
0
.
0
0
0
0
.
2
1
1
0
.
0
0
0
0
.
9
9
4
N
0
.
1
7
8
0
.
0
0
0
1
.
0
7
2
N
S
a
u
d
i
A
r
a
b
i
a
2
0
.
0
2
7
0
.
0
0
0
0
.
0
0
5
0
.
0
0
0
2
0
.
3
3
4
0
.
0
0
0
2
1
.
7
9
5
N
2
0
.
1
9
4
0
.
0
0
0
2
1
.
4
3
1
N
U
A
E
2
0
.
0
8
6
0
.
0
0
0
2
0
.
0
9
4
0
.
0
0
1
2
0
.
2
1
1
0
.
0
0
5
n
.
a
.
n
.
a
.
2
0
.
0
3
7
0
.
0
0
0
0
.
4
0
6
N
S
o
u
t
h
A
f
r
i
c
a
2
0
.
1
3
0
0
.
0
0
7
2
0
.
0
7
4
0
.
0
0
7
2
0
.
6
0
1
0
.
0
0
7
2
3
.
1
7
0
N
2
0
.
4
4
8
0
.
0
0
7
2
2
.
9
0
6
N
O
t
h
e
r
R
u
s
s
i
a
2
0
.
1
4
0
0
.
0
0
4
2
0
.
1
3
2
0
.
0
0
5
2
0
.
3
7
7
0
.
0
0
3
2
1
.
3
4
7
N
2
0
.
2
1
1
0
.
0
0
4
2
0
.
5
8
0
N
N
o
t
e
s
:
S
t
a
t
i
s
t
i
c
a
l
s
i
g
n
i
?
c
a
n
c
e
a
t
:
*
1
0
,
*
*
5
,
*
*
*
1
p
e
r
c
e
n
t
,
r
e
s
p
e
c
t
i
v
e
l
y
;
n
.
a
.
:
t
h
e
p
r
e
s
e
n
c
e
o
f
z
e
r
o
e
x
c
h
a
n
g
e
r
a
t
e
r
e
t
u
r
n
f
o
r
a
l
l
p
e
r
i
o
d
s
o
f
t
h
e
s
e
r
i
e
s
;
t
h
e
t
a
b
l
e
s
h
o
w
s
c
o
n
d
i
t
i
o
n
a
l
(
u
n
a
d
j
u
s
t
e
d
)
c
r
o
s
s
-
m
a
r
k
e
t
c
o
r
r
e
l
a
t
i
o
n
c
o
e
f
?
c
i
e
n
t
s
(
r
)
a
n
d
s
t
a
n
d
a
r
d
d
e
v
i
a
t
i
o
n
s
(
s
)
f
o
r
J
a
p
a
n
a
n
d
o
t
h
e
r
s
t
o
c
k
i
n
d
e
x
e
s
;
t
h
e
t
e
s
t
s
t
a
t
i
s
t
i
c
s
a
r
e
o
b
t
a
i
n
e
d
f
r
o
m
F
i
s
h
e
r
Z
t
r
a
n
s
f
o
r
m
a
t
i
o
n
s
;
t
h
e
s
t
a
b
l
e
p
e
r
i
o
d
i
s
d
e
?
n
e
d
a
s
t
h
e
1
2
-
m
o
n
t
h
p
r
e
-
e
a
r
t
h
q
u
a
k
e
p
e
r
i
o
d
(
M
a
r
c
h
1
1
,
2
0
1
0
-
M
a
r
c
h
1
0
,
2
0
1
1
)
;
t
h
e
s
h
o
r
t
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
i
s
d
e
?
n
e
d
a
s
t
h
e
o
n
e
-
m
o
n
t
h
p
o
s
t
-
e
a
r
t
h
q
u
a
k
e
p
e
r
i
o
d
(
M
a
r
c
h
1
1
,
2
0
1
1
-
A
p
r
i
l
1
0
,
2
0
1
1
)
;
t
h
e
m
e
d
i
u
m
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
i
s
d
e
?
n
e
d
a
s
t
h
e
t
w
o
-
m
o
n
t
h
p
o
s
t
-
e
a
r
t
h
q
u
a
k
e
p
e
r
i
o
d
(
M
a
r
c
h
1
1
,
2
0
1
1
-
M
a
y
1
0
,
2
0
1
1
)
;
t
h
e
f
u
l
l
p
e
r
i
o
d
i
s
t
h
e
s
t
a
b
l
e
p
e
r
i
o
d
p
l
u
s
t
h
e
m
e
d
i
u
m
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
;
C
o
:
c
o
n
t
a
g
i
o
n
;
w
h
i
l
e
“
Y
”
d
e
n
o
t
e
s
t
h
a
t
t
h
e
t
e
s
t
s
t
a
t
i
s
t
i
c
s
i
s
g
r
e
a
t
e
r
t
h
a
n
t
h
e
c
r
i
t
i
c
a
l
v
a
l
u
e
a
n
d
c
o
n
t
a
g
i
o
n
o
c
c
u
r
r
e
d
,
“
N
”
i
n
d
i
c
a
t
e
s
t
h
a
t
t
h
e
t
e
s
t
s
t
a
t
i
s
t
i
c
s
w
a
s
l
e
s
s
o
r
e
q
u
a
l
t
o
t
h
e
c
r
i
t
i
c
a
l
v
a
l
u
e
a
n
d
n
o
c
o
n
t
a
g
i
o
n
o
c
c
u
r
r
e
d
;
c
o
r
r
e
l
a
t
i
o
n
c
o
e
f
?
c
i
e
n
t
s
a
r
e
u
n
a
d
j
u
s
t
e
d
f
o
r
h
e
t
e
r
o
s
c
e
d
a
s
t
i
c
i
t
y
Table III.
International exchange
rates returns conditional
(unadjusted) correlation
coef?cients in 2011
Japanese earthquake
The 2011
Japanese
earthquake
349
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
5
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
F
u
l
l
p
e
r
i
o
d
S
t
a
b
l
e
p
e
r
i
o
d
S
h
o
r
t
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
M
e
d
i
u
m
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
R
e
g
i
o
n
s
C
o
u
n
t
r
i
e
s
r
s
r
*
s
t
p
r
*
m
t
p
P
*
d
Z
-
t
e
s
t
C
o
r
*
d
Z
-
t
e
s
t
C
o
S
o
u
t
h
A
s
i
a
a
n
d
S
o
u
t
h
-
E
a
s
t
A
s
i
a
I
n
d
i
a
2
0
.
1
3
6
0
.
0
0
4
2
0
.
1
8
1
2
0
.
1
6
8
2
0
.
3
7
9
2
0
.
4
8
7
2
1
.
1
0
4
N
2
0
.
2
6
5
2
0
.
4
0
8
2
0
.
7
2
1
N
M
a
l
a
y
s
i
a
2
0
.
1
9
7
0
.
0
0
4
2
0
.
2
6
2
2
0
.
2
4
5
2
0
.
4
4
1
2
0
.
5
1
0
2
1
.
0
4
5
N
2
0
.
3
3
9
2
0
.
3
5
6
2
0
.
7
2
8
N
P
h
i
l
i
p
p
i
n
e
s
2
0
.
1
2
9
0
.
0
0
4
2
0
.
1
7
8
2
0
.
1
6
6
2
0
.
5
6
4
2
0
.
2
6
7
2
2
.
3
4
1
N
2
0
.
1
8
4
2
0
.
2
8
1
2
0
.
1
3
6
N
S
i
n
g
a
p
o
r
e
2
0
.
0
2
9
0
.
0
0
3
2
0
.
0
2
1
2
0
.
0
2
0
2
0
.
2
7
0
2
0
.
1
5
4
2
1
.
3
0
4
N
2
0
.
1
4
1
2
0
.
0
8
8
2
0
.
8
7
4
N
T
h
a
i
l
a
n
d
0
.
0
6
1
0
.
0
0
2
0
.
0
9
2
0
.
0
8
6
0
.
3
2
0
2
0
.
2
8
2
1
.
2
2
2
N
0
.
0
3
0
2
0
.
1
9
4
2
0
.
3
9
9
N
E
a
s
t
A
s
i
a
a
n
d
N
o
r
t
h
-
E
a
s
t
A
s
i
a
C
h
i
n
a
0
.
0
3
0
0
.
0
0
1
0
.
0
2
4
0
.
0
2
3
2
0
.
1
2
6
2
0
.
1
4
2
2
0
.
7
6
9
N
0
.
1
1
0
0
.
1
3
5
0
.
6
2
6
N
H
o
n
g
K
o
n
g
2
0
.
0
4
9
0
.
0
0
0
2
0
.
0
2
9
2
0
.
0
2
7
2
0
.
3
0
4
0
.
0
7
2
2
1
.
4
5
3
N
2
0
.
2
8
7
2
0
.
0
9
9
2
1
.
9
1
3
N
T
a
i
w
a
n
2
0
.
1
0
4
0
.
0
0
3
2
0
.
1
1
2
2
0
.
1
0
4
2
0
.
5
2
0
2
0
.
0
6
5
2
2
.
3
6
7
N
2
0
.
3
2
0
2
0
.
0
8
5
2
1
.
6
1
7
N
S
o
u
t
h
K
o
r
e
a
2
0
.
2
4
2
0
.
0
0
7
2
0
.
3
0
8
2
0
.
2
8
9
2
0
.
6
2
8
2
0
.
3
2
2
2
2
.
1
3
8
N
2
0
.
5
1
0
2
0
.
3
7
5
2
1
.
8
9
2
N
A
u
s
t
r
a
l
a
s
i
a
A
u
s
t
r
a
l
i
a
2
0
.
0
8
0
0
.
0
0
7
2
0
.
0
5
9
2
0
.
0
5
5
2
0
.
5
5
2
2
0
.
0
9
6
2
2
.
8
6
8
N
2
0
.
4
0
7
2
0
.
1
4
2
2
2
.
6
9
0
N
N
e
w
Z
e
a
l
a
n
d
2
0
.
0
3
1
0
.
0
0
7
0
.
0
4
9
0
.
0
4
6
2
0
.
7
3
4
2
0
.
0
5
1
2
5
.
0
3
2
N
2
0
.
5
1
5
2
0
.
0
7
8
2
4
.
3
7
8
N
N
o
r
t
h
A
m
e
r
i
c
a
C
a
n
a
d
a
2
0
.
2
7
2
0
.
0
0
6
2
0
.
3
7
1
2
0
.
3
4
9
2
0
.
5
0
9
2
0
.
2
7
5
2
0
.
8
7
8
N
2
0
.
3
4
7
2
0
.
2
6
0
0
.
0
1
3
N
M
e
x
i
c
o
2
0
.
3
3
8
0
.
0
0
5
2
0
.
4
4
9
2
0
.
4
2
4
2
0
.
6
4
9
2
0
.
4
0
7
2
1
.
4
8
0
N
2
0
.
4
7
6
2
0
.
3
5
0
2
0
.
4
7
0
N
S
o
u
t
h
A
m
e
r
i
c
a
A
r
g
e
n
t
i
n
a
2
0
.
0
3
0
0
.
0
0
1
2
0
.
2
6
1
2
0
.
0
3
3
2
0
.
0
3
3
2
0
.
2
6
1
0
.
0
1
3
N
2
0
.
0
7
7
2
0
.
0
6
7
2
0
.
3
0
8
N
B
r
a
z
i
l
2
0
.
2
0
5
0
.
0
0
6
2
0
.
2
4
6
2
0
.
2
3
3
2
0
.
5
3
7
2
0
.
2
3
1
2
1
.
7
6
1
N
2
0
.
4
4
4
2
0
.
0
7
2
2
1
.
7
0
8
N
C
h
i
l
e
0
.
0
1
2
0
.
0
0
5
0
.
0
6
4
2
0
.
0
7
2
2
0
.
3
7
8
2
0
.
2
5
3
2
2
.
3
5
9
N
2
0
.
4
4
4
2
0
.
2
3
3
2
2
.
6
2
2
N
E
u
r
o
p
e
E
u
r
o
0
.
1
6
6
0
.
0
0
6
0
.
2
8
9
0
.
2
7
1
2
0
.
2
8
0
2
0
.
2
3
1
2
2
.
9
8
3
N
2
0
.
1
0
3
2
0
.
0
0
1
2
2
.
7
1
2
N
U
K
0
.
0
4
3
0
.
0
0
5
0
.
1
0
2
0
.
0
9
5
2
0
.
2
8
9
2
0
.
0
7
4
2
2
.
0
3
8
N
2
0
.
1
9
0
2
0
.
1
5
0
2
2
.
0
4
5
N
M
i
d
d
l
e
E
a
s
t
a
n
d
A
f
r
i
c
a
B
a
h
r
a
i
n
2
0
.
0
2
1
0
.
0
0
6
2
0
.
0
1
9
2
0
.
0
1
8
2
0
.
0
4
1
9
.
2
1
5
2
0
.
1
0
7
N
2
0
.
0
5
6
6
.
1
8
9
2
0
.
2
7
1
N
E
g
y
p
t
0
.
0
3
7
0
.
0
0
1
0
.
0
3
1
0
.
0
2
9
0
.
1
8
4
2
0
.
4
0
0
0
.
7
8
8
N
0
.
2
4
9
2
0
.
4
9
6
1
.
6
0
3
N
J
o
r
d
a
n
2
0
.
0
2
6
0
.
0
0
1
2
0
.
0
3
4
2
0
.
0
3
1
2
0
.
1
8
1
2
0
.
5
6
0
2
0
.
7
6
3
N
2
0
.
0
4
7
2
0
.
4
9
5
2
0
.
1
1
4
N
K
u
w
a
i
t
0
.
2
4
7
0
.
0
0
1
0
.
3
4
9
0
.
3
2
8
0
.
2
5
0
2
0
.
4
1
9
2
0
.
5
5
7
N
0
.
2
4
0
2
0
.
3
3
8
2
0
.
6
8
1
N
Q
a
t
a
r
0
.
0
3
7
0
.
0
0
0
0
.
0
4
1
0
.
0
3
8
0
.
2
8
9
2
0
.
5
0
0
1
.
3
0
8
N
0
.
2
2
9
2
0
.
6
4
9
1
.
3
8
7
N
S
a
u
d
i
A
r
a
b
i
a
2
0
.
0
2
7
0
.
0
0
0
0
.
0
0
6
0
.
0
0
6
2
0
.
4
4
4
0
.
0
7
9
2
2
.
4
6
4
N
2
0
.
2
4
9
0
.
0
2
5
2
1
.
8
5
2
N
U
A
E
2
0
.
0
8
6
0
.
0
0
0
2
0
.
1
3
1
2
0
.
1
2
2
n
.
a
.
2
1
.
0
0
0
n
.
a
.
n
.
a
.
2
0
.
0
4
8
2
0
.
8
1
6
0
.
5
2
6
N
S
o
u
t
h
A
f
r
i
c
a
2
0
.
1
3
0
0
.
0
0
7
2
0
.
1
0
3
2
0
.
0
9
5
2
0
.
7
2
5
0
.
0
0
3
2
2
.
0
3
8
N
2
0
.
5
4
5
2
0
.
0
4
1
2
3
.
6
7
6
N
O
t
h
e
r
R
u
s
s
i
a
2
0
.
1
4
0
0
.
0
0
4
2
0
.
1
8
3
2
0
.
1
7
0
2
0
.
4
9
4
2
0
.
3
1
6
2
1
.
8
2
1
N
2
0
.
2
7
0
2
0
.
1
8
7
2
0
.
7
4
6
N
N
o
t
e
s
:
S
t
a
t
i
s
t
i
c
a
l
s
i
g
n
i
?
c
a
n
c
e
a
t
:
*
1
0
,
*
*
5
,
a
n
d
*
*
*
1
p
e
r
c
e
n
t
,
r
e
s
p
e
c
t
i
v
e
l
y
;
t
h
e
t
a
b
l
e
s
h
o
w
s
c
o
n
d
i
t
i
o
n
a
l
(
u
n
a
d
j
u
s
t
e
d
)
c
r
o
s
s
-
m
a
r
k
e
t
c
o
r
r
e
l
a
t
i
o
n
c
o
e
f
?
c
i
e
n
t
s
(
r
)
,
a
d
j
u
s
t
e
r
f
o
r
h
e
t
e
r
o
s
c
e
d
a
s
t
i
c
i
t
y
(
d
)
,
a
n
d
s
t
a
n
d
a
r
d
d
e
v
i
a
t
i
o
n
s
(
s
)
f
o
r
J
a
p
a
n
a
n
d
o
t
h
e
r
s
t
o
c
k
i
n
d
e
x
e
s
;
t
h
e
t
e
s
t
s
t
a
t
i
s
t
i
c
s
a
r
e
o
b
t
a
i
n
e
d
f
r
o
m
F
i
s
h
e
r
Z
t
r
a
n
s
f
o
r
m
a
t
i
o
n
s
;
t
h
e
s
t
a
b
l
e
p
e
r
i
o
d
i
s
d
e
?
n
e
d
a
s
t
h
e
1
2
-
m
o
n
t
h
p
r
e
-
e
a
r
t
h
q
u
a
k
e
p
e
r
i
o
d
(
M
a
r
c
h
1
1
,
2
0
1
0
-
M
a
r
c
h
1
0
,
2
0
1
1
)
;
t
h
e
s
h
o
r
t
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
i
s
d
e
?
n
e
d
a
s
t
h
e
o
n
e
-
m
o
n
t
h
p
o
s
t
-
e
a
r
t
h
q
u
a
k
e
p
e
r
i
o
d
(
M
a
r
c
h
1
1
,
2
0
1
1
-
A
p
r
i
l
1
0
,
2
0
1
1
)
;
t
h
e
m
e
d
i
u
m
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
i
s
d
e
?
n
e
d
a
s
t
h
e
t
w
o
-
m
o
n
t
h
p
o
s
t
-
e
a
r
t
h
q
u
a
k
e
p
e
r
i
o
d
(
M
a
r
c
h
1
1
,
2
0
1
1
-
M
a
y
1
0
,
2
0
1
1
)
;
t
h
e
f
u
l
l
p
e
r
i
o
d
i
s
t
h
e
s
t
a
b
l
e
p
e
r
i
o
d
p
l
u
s
t
h
e
m
e
d
i
u
m
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
;
C
o
:
c
o
n
t
a
g
i
o
n
;
w
h
i
l
e
“
Y
”
d
e
n
o
t
e
s
t
h
a
t
t
h
e
t
e
s
t
s
t
a
t
i
s
t
i
c
s
i
s
g
r
e
a
t
e
r
t
h
a
n
t
h
e
c
r
i
t
i
c
a
l
v
a
l
u
e
a
n
d
c
o
n
t
a
g
i
o
n
o
c
c
u
r
r
e
d
,
“
N
”
i
n
d
i
c
a
t
e
s
t
h
a
t
t
h
e
t
e
s
t
s
t
a
t
i
s
t
i
c
s
w
a
s
l
e
s
s
o
r
e
q
u
a
l
t
o
t
h
e
c
r
i
t
i
c
a
l
v
a
l
u
e
a
n
d
n
o
c
o
n
t
a
g
i
o
n
o
c
c
u
r
r
e
d
;
r
*
s
t
p
,
r
*
m
t
p
,
d
e
n
o
t
e
a
d
j
u
s
t
e
d
c
o
r
r
e
l
a
t
i
o
n
c
o
e
f
?
c
i
e
n
t
s
f
o
r
t
h
e
s
h
o
r
t
-
a
n
d
m
e
d
i
u
m
-
t
e
r
m
p
e
r
i
o
d
s
,
r
e
s
p
e
c
t
i
v
e
l
y
;
d
:
c
o
r
r
e
l
a
t
i
o
n
c
o
e
f
?
c
i
e
n
t
a
d
j
u
s
t
e
r
;
c
o
r
r
e
l
a
t
i
o
n
c
o
e
f
?
c
i
e
n
t
s
a
r
e
a
d
j
u
s
t
e
d
f
o
r
h
e
t
e
r
o
s
c
e
d
a
s
t
i
c
i
t
y
u
s
i
n
g
e
q
u
a
t
i
o
n
(
2
)
Table IV.
International exchange
rates returns
unconditional (adjusted)
correlation coef?cients in
2011 Japanese earthquake
JFEP
4,4
350
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
5
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
The following are some recommendations policy makers need to put in place in order
to minimize (mitigate) the adverse ?nancial market effects of disasters:
.
The banking system of a country should not be directly exposed to foreign assets
that natural disasters can easily stress and make worthless. This recommendation
also holds for assets in institutions that natural disasters could render futile. This
will mitigate the knock-on effects through monetary, ?nancial, and real channels.
.
Domestic ?nancial markets (equity, money, foreign exchange, and credit markets)
may also suffer because of the “substitution effect”. As credit lines and credit
channels in the affected and contaminated countries run dry, some of the
credit-demand earlier met by overseas ?nancing could easily shift to the domestic
sector and put pressure on domestic resources. The reversal of capital ?ows arising
fromthe de-leveraging process could put pressure on the foreign exchange market,
leading to sharp ?uctuations in overnight money market rates and depreciation of
currency. It is therefore in the interest of central banks to adopt a monetary policy
stance that is adequate to growth, in?ation, and ?nancial stability concerns.
.
In situations where the natural disaster re?ects an expected decline in in?ation, it
is also in the interest of the central bank to adjust its monetary stance and manage
liquidity: both domestic and foreign exchange to ensure that credit continues to
?ow for productive activities at both aggregate and sector speci?c levels.
.
In order to enable economic agents plan their business activities with more
assurance, the central bank could ensure an orderly adjustment of the pain of its
policies by maintaining a comfortable liquidity position: seeing that the weighted
average overnight money market rate is maintained within the repo-reverse repo
corridor and ensure conditions conducive for ?ow of credit to productive sectors
(particularly the stressed export industry sectors).
Before we conclude, it is important to highlight the implications of this paper to the
future of natural disasters. Though the crisis is over, from a ?nancial standpoint the
following concerns will preoccupy policy makers in future natural disasters:
.
Is self-insurance a viable option for emerging economies? In order words, could
the accumulation of foreign reserves buffer against ?nancial market crises
arising from natural disasters? Whether these reserves derive from current
account surpluses (China for instance) or capital ?ows (India for example),
relying on them to hedge contagion could still represent some form of liability.
Hence the need to ?nd a way of balancing the trade-off between vulnerability to
?nancial contagion and vulnerability to trade contagion in the event of a disaster.
Another important strand within this framework points to the redundancy of
self-insurance if international arrangements (regional and multilateral) could
provide easy, quick and unconditional liquidity during such crises.
.
Howdopolicymakers keepthe ?nancial sector inline withthe real sector inevent of a
natural disaster? Forgotteninthe euphoria of ?nancial alchemyis the basic tenet that
the ?nancial sector has no standing of its own; it derives its strength and resilience
from the real economic sector. Thinking the other way round has led many into
believing that, signi?cant value could be created by slicing and dicing securities.
.
How do we address regulatory arbitrage in times of crisis? If under the nose of
regulators, grows an extensive, and complex network of a “shadowbankingsystem”
The 2011
Japanese
earthquake
351
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
5
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
that encourages loose practices, hunt for quick yields and non-transparent and risky
?nancial products, when systems unravel owing to natural disasters, many of
these institutions will pose a systematic risk to the ?nancial systems. Hence the
regulatory architecture has to be fashioned to keep pace with innovation and the
possibility of natural shocks.
.
Simulating natural disasters and learning how to manage global imbalances
arising from them could also help countries prepare for potential ?nancial and
real sector consequences of natural crises.
6. Conclusion
In this paper, we have used unadjusted and adjusted correlation coef?cients to test for
contagion effects across 33 economies in the aftermath of the Japanese earthquake,
ensuing tsunami, and worst nuclear crisis in recent history. Results indicate no
international foreign exchange markets experienced signi?cantly stronger correlations
with the Japanese Yen two months after. However, for international stock markets,
Taiwan, Bahrain, Saudi Arabia, and South Africa experience contagion; consistent
with the widely held notion that contagion is mostly a concern for emerging countries.
In line with Lee et al. (2007), the effects of natural disasters on ?nancial markets are
important in investment decisions, as the bene?ts of portfolio diversi?cation are severely
limited during periods of high volatility and increased cross-market correlations. With
?nancial globalization, investors can gain from diversi?cation if returns from ?nancial
markets are stable and not correlated. However, with volatility spillovers, increase in
cross-market correlations exist as a real effect and are not taken into account for
asset allocation and portfolio composition.
Our results have two paramount implications. First, we have con?rmed the existing
consensus that in the face of natural crises that could take an international scale,
only emerging markets are contagiously affected for the most part. Second, we have
also shown that international ?nancial market transmissions not only occur during
?nancial crises; natural disaster effects should not be undermined.
Note
1. Differences in pre- and post-earthquake sample periods are in line with Collins and Biekpe
(2003), Lee et al. (2007), and Asongu (2011a, b).
References
Asongu, S.A. (2011a), “Globalization, ?nancial crisis and contagion: time-dynamic evidence from
?nancial markets of developing countries”, MPRA Paper No. 30120.
Asongu, S.A. (2011b), “Political crises and risk of ?nancial contagion in developing countries:
evidence from Africa”, MPRA Paper No. 30391.
Calvo, G.A., Leiderman, L. and Reinhart, C.A. (1996), “In?ows of capital to developing countries
in the 1990s”, Journal of Economic Perspectives, Vol. 10, Spring, pp. 123-39.
Collins, D. and Biekpe, N. (2003), “Contagion: a fear for African equity markets?”, Journal of
Economics and Business, Vol. 55, pp. 285-97.
Coulibaly, B. (2009), “Effects of ?nancial autarky and integration: the case of South Africa
embargo”, Journal of International Money and Finance, Vol. 28, pp. 454-78.
JFEP
4,4
352
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
5
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Demyanyk, Y. and Volosovych, V. (2008), “Gains from ?nancial integration in the European
Union: evidence for new and old members”, Journal of International Money and Finance,
Vol. 27, pp. 277-94.
Forbes, K.J. (2000), “The Asian ?u and Russian virus: ?rm-level evidence on how crises are
transmitted internationally”, NBER Working Paper No. 7807.
Forbes, K.J. and Rigobon, R. (2002), “No contagion, only interdependence: measuring stock
market co-movements”, Journal of Finance, Vol. 57 No. 5, pp. 2223-61.
Frankel, J. and Rose, A.K. (1996), “Currency crashes in emerging markets: an empirical
treatment”, Journal of International Economics, Vol. 41, November, pp. 351-66.
Kanas, A. (1998), “Linkages between the US and European equity markets: further evidence from
cointegration test”, Applied Financial Economics, Vol. 8 No. 6, pp. 607-14.
King, M. and Wadhwani, S. (1990), “Transmission of volatility between stock markets”, Review
of Financial Studies, Vol. 3 No. 1, pp. 5-33.
King, M., Sentana, E. and Wadhwani, S. (1994), “Volatility and links between national stock
markets”, Econometrica, Vol. 62 No. 4, pp. 901-33.
Kose, M.A., Prasad, E.S. and Taylor, A.D. (2011), “Thresholds in the process of international
?nancial integration”, Journal of International Money and Finance, Vol. 30, pp. 147-79.
Lee, H., Wu, H. and Wang, Y. (2007), “Contagion effect in ?nancial markets after the South-East
Asia tsunami”, Research in International Business and Finance, Vol. 21, pp. 281-96.
Obstfeld, M. (1986), “Rational and self-ful?lling balance of payments crises”, American Economic
Review, Vol. 76, March, pp. 72-81.
Schmukler, S.L. (2004), “Financial globalization: gain and pain for developing countries”, Federal
Reserve Bank of Atlanta Economic Review, April, pp. 39-66.
Further reading
Anoruo, E. andMustafa, M. (2007), “Anempirical investigationinto the relationof oil to stockmarket
prices”, North American Journal of Finance and Banking Research, Vol. 1 No. 1, pp. 1-15.
Henry, P.B. (2007), “Capital account liberalization: theory, evidence and speculation”, Journal of
Economic Literature, Vol. XLV, December, pp. 887-935.
In, F., Cui, J. and Mahraj, A. (2008), “The impact of a new term auction facility on LIBOR-OIS
spreads and volatility transmission between money and mortgage market”, unpublished
manuscript, available at:http://papers.ssrn.com/sol3/papers.cfm?abstract id¼1272806
Ji, P.I. and In, F. (2010), “The impact of the global ?nancial crisis on the cross-currency linkage
of LIBOR-OIS spreads”, International Financial Markets, Institutions and Money, Vol. 20,
pp. 575-89.
McAndrews, J., Sarkar, A. and Wang, Z. (2008), “The effects of the term auction facility on the
London inter-bank offered rate”, Federal Reserve Bank of New York Staff Reports No. 335.
Taylor, J.B. and Williams, J.C. (2008), “Further results on a black swan in the money market”,
Research Discussion Paper 07-046, Stanford Institute for Economic Policy.
Corresponding author
Simplice A. Asongu can be contacted at: [email protected]
The 2011
Japanese
earthquake
353
To purchase reprints of this article please e-mail: [email protected]
Or visit our web site for further details: www.emeraldinsight.com/reprints
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
5
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
This article has been cited by:
1. Guangxi Cao, Wei Xu, Yu Guo. 2015. Effects of climatic events on the Chinese stock market: applying
event analysis. Natural Hazards 77, 1979-1992. [CrossRef]
2. Shuhei Nakano, Yoshito Hirata, Koji Iwayama, Kazuyuki Aihara. 2014. Intra-day response of foreign
exchange markets after the Tohoku-Oki earthquake. Physica A: Statistical Mechanics and its Applications
. [CrossRef]
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
5
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
doc_272694864.pdf
Natural disasters may inflict significant damage upon international financial markets.
The purpose of this study is to investigate if any contagion effect occurred in the immediate aftermath
of the Japanese earthquake, tsunami and subsequent nuclear crisis.
Journal of Financial Economic Policy
The 2011 Japanese earthquake, tsunami and nuclear crisis: Evidence of contagion from
international financial markets
Simplice A. Asongu
Article information:
To cite this document:
Simplice A. Asongu, (2012),"The 2011 J apanese earthquake, tsunami and nuclear crisis", J ournal of
Financial Economic Policy, Vol. 4 Iss 4 pp. 340 - 353
Permanent link to this document:http://dx.doi.org/10.1108/17576381211279307
Downloaded on: 24 January 2016, At: 21:45 (PT)
References: this document contains references to 22 other documents.
To copy this document: [email protected]
The fulltext of this document has been downloaded 369 times since 2012*
Users who downloaded this article also downloaded:
Salman Khan, Pierre Batteau, (2012),"Government intervention in Russian bourse: a case
of financial contagion", J ournal of Financial Economic Policy, Vol. 4 Iss 4 pp. 320-339 http://
dx.doi.org/10.1108/17576381211279299
Dimitrios Dimitriou, Theodore Simos, (2013),"Contagion channels of the USA subprime financial crisis:
Evidence from USA, EMU, China and J apan equity markets", J ournal of Financial Economic Policy, Vol. 5
Iss 1 pp. 61-71http://dx.doi.org/10.1108/17576381311317781
Richard J . Buttimer, (2011),"The financial crisis: imperfect markets and imperfect regulation", J ournal of
Financial Economic Policy, Vol. 3 Iss 1 pp. 12-32http://dx.doi.org/10.1108/17576381111116795
Access to this document was granted through an Emerald subscription provided by emerald-srm:115632 []
For Authors
If you would like to write for this, or any other Emerald publication, then please use our Emerald for
Authors service information about how to choose which publication to write for and submission guidelines
are available for all. Please visit www.emeraldinsight.com/authors for more information.
About Emerald www.emeraldinsight.com
Emerald is a global publisher linking research and practice to the benefit of society. The company
manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as
providing an extensive range of online products and additional customer resources and services.
Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee
on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive
preservation.
*Related content and download information correct at time of download.
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
5
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
The 2011 Japanese earthquake,
tsunami and nuclear crisis
Evidence of contagion from international
?nancial markets
Simplice A. Asongu
HEC-Management School, University of Lie `ge, Lie `ge, Belgium
Abstract
Purpose – Natural disasters may in?ict signi?cant damage upon international ?nancial markets.
The purpose of this study is to investigate if any contagion effect occurred in the immediate aftermath
of the Japanese earthquake, tsunami and subsequent nuclear crisis.
Design/methodology/approach – Using 33 international stock indices and exchange rates, this
paper uses heteroscedasticity biases based on correlation coef?cients to examine if any contagion
occurred across ?nancial markets after the March 11, 2011 Japanese earthquake, tsunami and nuclear
crisis. The sample period is partitioned into two sections: the 12-month pre-earthquake period
(March 11, 2010 to March 10, 2011) and the 2-month post-earthquake period (March 11, 2011 to May 10,
2011). While the stability period is de?ned as the pre-earthquake period, the turbulent (turmoil) period
is de?ned as the post-earthquake period. In a bid to ensure robustness of the ?ndings, the turmoil
period is further partitioned into two equal sections: the 1-month (short-term) post-earthquake period
(March 11, 2011 to April 10, 2011), and the 2-month (medium-term) post-earthquake (March 11, 2011 to
May 10, 2011).
Findings – Findings reveal that, while no sampled foreign exchange markets suffered from contagion,
stock markets of Taiwan, Bahrain, Saudi Arabia and South Africa witnessed a contagion effect.
Practical implications – The results have two paramount implications. First, the paper has
con?rmed existing consensus that in the face of natural crises that could take an international scale,
emerging markets are contagiously affected for the most part. Second, the empirical evidence also
suggests that international ?nancial market transmissions not only occur during ?nancial crisis; natural
disaster effects should not be undermined.
Originality/value – This paper has shown that the correlation structure of international ?nancial
markets are also affected by high pro?le natural disasters.
Keywords Japanese earthquake, Contagion, International ?nancial markets, Japan, Earthquakes,
Financial markets, Stock markets
Paper type Research paper
1. Motivation
Natural disasters have in?icted serious damage on human life, property, and economy.
Though many earthquakes occur worldwide on an annual basis and impact all walks of
life in one way or the other, collateral effects resulting fromsuch natural disasters could
be quite detrimental ?nancially and economically. The recent Japanese earthquake
resulted in collateral damage that makes the disaster particularly signi?cant. On
March 11, 2011, a 9.0 magnitude undersea mega thrust earthquake hit Tohoku in Japan.
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1757-6385.htm
JEL classi?cation – G10; G15; F30
The author is highly indebted to the referee(s) for their very useful editorial comments.
JFEP
4,4
340
Journal of Financial Economic Policy
Vol. 4 No. 4, 2012
pp. 340-353
qEmerald Group Publishing Limited
1757-6385
DOI 10.1108/17576381211279307
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
5
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
This powerful shock triggered a tsunami that struck coastlines across the east of the
country, leaving thousands dead and damaging considerable property. But what
appears to have left analysts startled and concerned over the consequences of this
earthquake is the nuclear disaster resulting there-from. Recently classi?ed as a
level-seven event on the International Nuclear Event Scale, the Fukushima nuclear
incident now poses a risk equal to the worst nuclear power plant accident in history
(Chernobyl disaster). With much uncertainty over howthe crisis would be managed, it is
imperative to investigate how international ?nancial markets have so far reacted.
Therefore, the goal of this paper is to examine whether any contagion effect has
occurred two months after the Japanese earthquake, tsunami, and worst nuclear crisis
since Chernobyl. In other words, we seek to provide evidence as to whether such a disaster
has increased the interdependence among ?nancial assets in different countries.
The remainder of the paper is organized as follows. Sections 2 examines related literature.
Data and methodology are presented and outlined, respectively, in Section 3. Empirical
analysis is covered in Section 4. We discuss results in Section 5. Section 6 concludes.
2. Related literature
2.1 Effects of ?nancial market integration
Financial integration is widely believed to improve capital allocation ef?ciency and
diversify risks (Demyanyk and Volosovych, 2008; Coulibaly, 2009; Kose et al., 2011).
However, the recent global ?nancial crisis deemed as the worst since the great depression
has left many analysts concerned about the contagion effects of globalization. A great
body of literature has been dedicated to the potential bene?ts of ?nancial integration.
With respect to Kose et al. (2011), ?nancial globalization in theory should facilitate
ef?cient allocation of capital and improve international risk sharing. They further
profess that bene?ts are much greater for developing countries because they are
relatively scarce in capital and rich in labor availability. According to them, access to
foreign capital should enable them grow faster via new sources of investment. On a
positive note of ?nancial globalization, Kose et al. stress that since developing
countries have more volatile output growth than advanced industrial economies, their
potential welfare gains from international risk sharing are much greater. It is
important to note an important ?nding of theirs: with certain identi?able thresholds in
variables such as ?nancial depth and institutional quality, the cost-bene?t trade-off
from ?nancial openness improves signi?cantly once the threshold conditions are met.
Much earlier, Demyanyk and Volosovych (2008) analyzed the bene?ts of ?nancial
integration resulting from international risk sharing among 25 European Union (EU)
countries. In their case for diversi?cation of risk across EU Member States, they posit
that if risks are fully shared, the ten new members joining the EU should have higher
gains than the long standing 15 members. It may be interesting to note South Africa as
one of the most striking examples of the cost and bene?ts of ?nancial integration. As a
country that experienced ?nancial autarky due to the embargo imposed in 1985 and
removed in 1993, Coulibaly (2009) found a signi?cant decrease in the rates of investment,
capital, and output during the embargo period as compared to pre- and post-embargo
periods. By the same token South Africa might have been immune to contagion from a
global ?nancial meltdown during the embargo period.
It follows that countries in relative ?nancial autarky are less exposed to international
shocks. While the prime advantage of ?nancial integration is risk diversi?cation,
The 2011
Japanese
earthquake
341
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
5
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
paradoxically increased ?nancial globalization can reduce the scope for risk
diversi?cation as integrated markets tend to be more correlated and highly
interdependent. On another negative note, Kose et al. (2011) stress that a country may
stand to reap the bene?ts of ?nancial integration if certain threshold factors like ?nancial
depth and institutional quality are met. This stance is shared by Schmukler (2004)
who has underlined the importance of sound ?nancial fundamentals and strong
macroeconomic institutions; the absence of which will decrease the effectiveness of crises
management and increase the probability of crises and contagion.
2.2 Linkages among natural disasters, globalization, and crises
In the ?rst part of this literature review, we present several bene?ts of ?nancial
integration as well as potential costs. As such, occurrences or crises in one country
often due to domestic factors (human or natural) could be propagated to other countries
through channels of globalization (trade or ?nancial links for instance). There are four
main routes via which natural disasters like the Japanese turmoil could lead to crises at
a global level.
On a ?rst count, as stressed by Schmukler (2004), when a country’s ?nancial system
is more free, it becomes an object of market discipline exercised by both foreign and
domestic investors. As such reactions to unsound fundamentals resulting from natural
disasters are not only the concern of domestic investors as in closed economies. If the
prospects for resolving the disaster are unclear, asymmetric information may lead
investors to make irrational decisions that could result in a crisis depending on the
degree of ?nancial integration.
On a second note, international ?nancial market imperfections could arise from a
natural disaster, especially herding behavior, speculative attacks, irrational responses,
etc. Thus, regardless of market fundamentals, investors could speculate against a
currency in the wake of a natural calamity if they deem the exchange rate unsustainable,
which could lead to self-ful?lling balance-of-payments. This thesis presented by Obstfeld
(1986) has been supported by Schmukler (2004) and more recently Asongu (2011a, b).
Third, even in the presence of sound fundamentals and absence of imperfections in
the international capital market (after a natural disaster), crises might develop due to
external factors (Schmukler, 2004) such as determinants of capital ?ows (Calvo et al.,
1996) and foreign interest rates (Frankel and Rose, 1996). For example, if the country
is foreign capital dependent, shifts in foreign capital after a natural calamity could
create additional ?nancial issues and economic downturns. As pointed out by Frankel
and Rose (1996), foreign interest rates could play an important role in determining the
likelihood of ?nancial crises in developing countries.
Last but not the least, according to Schmukler (2004) natural disasters could lead
to crisis by contagion, notably through shocks by real links, ?nancial links and herding
behavior or unexplained high correlations. Our focus on this Japanese earthquake will
rotate around this fourth example; the de?nition and elucidation of which are worthwhile.
2.3 De?nitions and channels of contagion
2.3.1 De?nitions of contagion. There is yet no established consensus on the de?nition of
contagion by economists. However, according to the World Bank there are three main
de?nitions of contagion. First, froma broadprismthe phenomenoncouldbe seenwiththe
general process of shock transmission across countries. This de?nition takes account
JFEP
4,4
342
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
5
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
of both negative and positive spillovers. Second, contagion could be synonymous with the
propagation of shocks between two countries in excess of what should be expected with
respect to existing fundamentals after considering co-movements triggered by common
shocks. This second de?nition is somehow restrictive as it presupposes the mastery of
what constitutes the underlying fundamentals, without which an assessment of excess
co-movements is impossible. The third and more restrictive de?nition considers the
phenomenon as the change in transmission mechanisms that occur during the crisis
period and is assessed by a signi?cant increase in cross-market correlations.
With respect to this study, we shall limit ourselves to the third de?nition of contagion
because:
.
our study aims to investigate only a crisis period in the Japanese ?nancial market
(as opposed to the ?rst de?nition); and
.
we have no mastery of what constitutes underlying fundamentals of
co-movements we are about to investigate.
Fromanempirical standpoint, Forbes andRigobon(2002) ?rst proposeda methodologyfor
the third de?nition. They viewcontagion as a signi?cant increase in market co-movements
after a shock has occurred in one country. Owing to this conception, the condition for
contagion is a signi?cant increase in co-movements as a result of a shock in one market
(considered the base criterion). It follows that if two markets display a high degree of
co-movement during the stability period, even if they are highly correlated during a
crisis, but if the difference in correlation is insigni?cant, contagion has not occurred. Thus,
in the absence of a signi?cant increase in correlation during the crisis period, the term
“interdependence” is employed to appraise the situation between the two markets.
2.3.2 Channels of contagion. In accordance with Schmukler (2004), three main
channels of contagion have been identi?ed in the literature:
(1) Through real links which are often tied to trade links. As an example, if we
consider two countries trading together and competing in the same external
market, a devaluation of the exchange rate of one country diminishes the other
country’s competitive advantage. In an attempt to rebalance its external sectors,
the losing country would seek to depreciate/devaluate its own currency.
(2) Via ?nancial channels especially when two economies are connected through the
international ?nancial system. If we consider a leveraged institutionfacingmargin
calls as an example, and if the value of the collateral falls due to a negative shock in
a given country, the institution would be poised to sell some of its holdings in
countries not yet affected by the shock in an attempt to mitigate its initial stock.
This response may give rise to ripples of shocks that could engender contagion.
(3) Lastly, as a result of herding behavior or panic resulting from asymmetric
information, a ?nancial market might transmit shocks across other markets.
2.4 Measuring contagion
Quite a number of methods have been suggested in the literature for measuring the
spread of international shocks across countries. Among these, the most widely applied
are cross-market correlation coef?cient measures (Lee et al., 2007; Collins and Biekpe,
2003; Forbes and Rigobon, 2002; King and Wadhwani, 1990), volatility analysis based
on ARCH and GARCH models (King et al., 1994), cross-market co-integration vectors
The 2011
Japanese
earthquake
343
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
5
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
changing techniques (Kanas, 1998) and direct estimation of speci?c transmission
mechanisms (Forbes, 2000). Within the framework of this study, we shall adopt Forbes
and Rigobon (2002) in the context of Lee et al. (2007).
3. Data and methodology
3.1 Data
As we have earlier emphasized, we aim to investigate the correlations among returns of
the Japanese daily stock index (exchange rate) and 33 other international stock indexes
(exchange rates) returns. Adopting the Japanese equity and foreign exchange markets
as the base criterion, we investigate if co-movements among national stock and
foreign exchange markets increased signi?cantly after the major earthquake, tsunami,
and nuclear disaster. The sample period is partitioned into two sections: the 12-month
pre-earthquake period (March 11, 2010-March 10, 2011) and the two-month
post-earthquake period (March 11, 2011-May 10, 2011)[1]. While the stability period
is de?ned as the pre-earthquake period, the turbulent (turmoil) period is de?ned as
the post-earthquake period. In a bid to ensure robustness of our ?ndings, the turmoil
period is further partitioned into two equal sections: the one-month (short-term)
post-earthquake period (March 11, 2011-April 10, 2011), and the two-month
(medium-term) post-earthquake (March 11, 2011-May 10, 2011). The number of days
are, respectively, 365, 31, 62 days for the stable, short- and medium-termturmoil periods.
Data used in the study are obtained from Bloomberg’s database. In the computation of
stock returns, last values are carried over for non-trading days. The US dollar is used
as the common “x” unit of foreign currency for each unit of national/regional currency
in the computation of exchange rate returns. Our use of local currency index return is in
line with Forbes and Rigobon (2002) who have shown that using dollar or local indices
will produce similar results.
3.2 Methodology
Borrowing from Forbes and Rigobon (2002), contagion is a signi?cant increase in
market co-movements after a shock has occurred in one country.
The coef?cient of correlation is de?ned as:
r ¼
s
xy
s
x
s
y
ð1Þ
where x represents the base criterion and y an international market. This correlation
coef?cient is adjusted in the following manner to take account of heteroscedasticity:
r
*
¼
r
??????????????????????????????
1 þ d½1 2ðrÞ
2
?
p ð2Þ
where:
d ¼
s
h
xx
s
l
xx
21
It measures the change in high-period volatility against volatility in the low period.
While the crisis interval is used as the high volatility period, the tranquil or stable
period represents the low volatility period. Contagion is eventually measured as the
JFEP
4,4
344
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
5
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
signi?cant increase of adjusted correlation coef?cients in time-varying turmoil periods
against the stability period.
Borrowing from Lee et al. (2007), the following hypotheses are tested:
H
0
: r
t
2r
s
# 0 versus H
1
: r
t
2r
s
, 0
where r
t
is the adjusted correlation coef?cient during the turmoil period and r
s
the
adjusted correlation coef?cient for the stable period. A comparison of the difference in
correlation between the stable and crisis periods is then carried-out. The null hypothesis
(H0) is the hypothesis of no contagion while H1 is the alternative hypothesis for the
presence of contagion. Fisher’s Z transformations of correlation coef?cients are used
to test pair-wise cross-country signi?cance. This Fisher’s Z transformations change
standard coef?cients to normally distributed Z variables. Therefore, before hypothesis
testing, r-values must be converted to Z
r
values:
H
0
: r
t
2r
s
# 0 ) H
0
: Z
rt
2Z
rs
# 0
H
1
: r
t
2r
s
. 0 ) H
1
: Z
rt
2Z
rs
. 0
where:
Z
rt
¼
1
2
ln
1 þ r
t
1 2r
t
Z
rs
¼
1
2
ln
1 þ r
s
1 2r
s
Z ¼
Z
rt
2Z
rs
?????????????????????????????????????????????????????
ð1=ðn
t
23ÞÞ þ ð1=ðn
s
23ÞÞ
p
4. Empirical results
4.1 Contagion effect in international stock indexes returns after earthquake
Table I shows the conditional (unadjusted) correlation coef?cients of international
stock indexes for the 2011 Japanese Tsunami. Cross-market correlations of stock index
returns are compared before and after the earthquake of March 11, 2011. With the
exceptions of China, Taiwan, New Zealand, Argentina, Bahrain, Egypt, Saudi Arabia,
and South Africa; cross-market correlations between Japan and most countries in the
sample during stable period are higher than those during medium-term turmoil period.
For the short-run interval, correlations are strengthened for China, Hong Kong,
Taiwan, South Korea, Australia, New Zealand, Argentina, Germany, Bahrain,
South Africa, and Saudi Arabia. There is signi?cant evidence of contagion in Taiwan,
Bahrain, Saudi Arabia, and South Africa for the short-term turmoil period and only in
Bahrain and Saudi Arabia for the medium-term turmoil interval. Comparatively, for
the most part volatilities of most countries during the stable period are higher than
those during turmoil periods (short and medium terms).
Unconditional correlation coef?cients are presented in Table II. These adjusted
correlations are higher that their unadjusted counterparts in Table I. Results of Table I
are substantiated by those of Table II.
The 2011
Japanese
earthquake
345
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
5
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
F
u
l
l
p
e
r
i
o
d
S
t
a
b
l
e
p
e
r
i
o
d
S
h
o
r
t
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
M
e
d
i
u
m
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
R
e
g
i
o
n
s
C
o
u
n
t
r
i
e
s
r
s
r
s
r
s
Z
-
t
e
s
t
C
o
r
s
Z
-
t
e
s
t
C
o
S
o
u
t
h
A
s
i
a
a
n
d
S
o
u
t
h
-
E
a
s
t
A
s
i
a
I
n
d
i
a
0
.
2
8
8
0
.
0
0
9
0
.
3
4
3
0
.
0
0
9
0
.
2
4
7
0
.
0
0
9
2
0
.
5
3
8
N
0
.
1
7
1
0
.
0
0
9
2
1
.
3
2
1
N
M
a
l
a
y
s
i
a
0
.
3
7
2
0
.
0
0
5
0
.
4
0
5
0
.
0
0
5
0
.
3
9
2
0
.
0
0
5
2
0
.
0
8
0
N
0
.
3
4
8
0
.
0
0
5
2
0
.
4
7
4
N
P
h
i
l
i
p
p
i
n
e
s
0
.
3
1
7
0
.
0
0
9
0
.
3
5
7
0
.
0
0
9
0
.
2
9
5
0
.
0
0
9
2
0
.
3
5
3
N
0
.
2
6
6
0
.
0
0
8
2
0
.
7
1
5
N
S
i
n
g
a
p
o
r
e
n
.
a
.
0
.
0
0
0
n
.
a
.
0
.
0
0
0
n
.
a
.
0
.
0
0
0
n
.
a
.
n
.
a
.
n
.
a
.
0
.
0
0
0
n
.
a
.
n
.
a
.
T
h
a
i
l
a
n
d
0
.
3
0
8
0
.
0
0
9
0
.
3
6
1
0
.
0
1
0
0
.
2
7
8
0
.
0
0
8
2
0
.
4
7
0
N
0
.
2
0
9
0
.
0
0
8
2
1
.
1
8
0
N
E
a
s
t
A
s
i
a
a
n
d
N
o
r
t
h
-
E
a
s
t
A
s
i
a
C
h
i
n
a
0
.
2
8
3
0
.
0
1
1
0
.
3
0
9
0
.
0
1
2
0
.
4
7
7
0
.
0
0
7
1
.
0
2
2
N
0
.
3
2
1
0
.
0
0
7
0
.
1
0
0
N
H
o
n
g
K
o
n
g
0
.
5
1
0
0
.
0
0
9
0
.
5
4
2
0
.
0
0
9
0
.
5
7
4
0
.
0
0
9
0
.
2
4
0
N
0
.
5
2
5
0
.
0
0
8
2
0
.
1
6
6
N
T
a
i
w
a
n
0
.
5
8
7
0
.
0
0
8
0
.
5
9
1
0
.
0
0
8
0
.
7
8
1
0
.
0
0
8
1
.
8
8
1
*
Y
0
.
6
9
4
0
.
0
0
8
1
.
2
4
7
N
S
o
u
t
h
K
o
r
e
a
0
.
5
7
5
0
.
0
0
8
0
.
6
0
6
0
.
0
0
8
0
.
6
6
0
0
.
0
0
8
0
.
4
5
8
N
0
.
5
6
6
0
.
0
0
8
2
0
.
4
3
7
N
A
u
s
t
r
a
l
a
s
i
a
A
u
s
t
r
a
l
i
a
2
0
.
0
0
4
0
.
0
0
8
0
.
0
0
0
0
.
0
0
8
0
.
0
7
3
0
.
0
0
7
0
.
3
7
3
N
2
0
.
0
2
1
0
.
0
0
7
2
0
.
1
4
7
N
N
e
w
Z
e
a
l
a
n
d
0
.
4
4
0
0
.
0
0
4
0
.
4
5
9
0
.
0
0
4
0
.
6
0
9
0
.
0
0
4
1
.
0
8
0
N
0
.
5
1
5
0
.
0
0
4
0
.
5
2
5
N
N
o
r
t
h
A
m
e
r
i
c
a
C
a
n
a
d
a
2
0
.
0
5
5
0
.
1
9
7
2
0
.
0
1
3
0
.
2
0
8
2
0
.
4
4
1
0
.
1
2
5
2
2
.
3
4
8
N
2
0
.
3
4
3
0
.
1
1
0
2
2
.
4
5
5
N
U
S
A
0
.
1
7
6
0
.
0
1
2
0
.
2
1
7
0
.
0
1
3
0
.
0
5
4
0
.
0
0
8
2
0
.
8
4
8
N
0
.
0
7
4
0
.
0
0
7
2
1
.
0
4
1
N
M
e
x
i
c
o
0
.
1
5
9
0
.
0
0
7
0
.
2
0
8
0
.
0
0
7
0
.
0
4
8
0
.
0
0
6
2
0
.
8
3
1
N
0
.
0
2
7
0
.
0
0
6
2
1
.
3
1
0
N
S
o
u
t
h
A
m
e
r
i
c
a
A
r
g
e
n
t
i
n
a
0
.
1
7
4
0
.
0
1
2
0
.
1
6
3
0
.
0
1
3
0
.
3
1
2
0
.
0
1
1
0
.
8
0
7
N
0
.
2
6
9
0
.
0
1
0
0
.
7
9
5
N
B
r
a
z
i
l
0
.
0
7
6
0
.
0
1
0
0
.
1
2
0
0
.
0
1
0
2
0
.
0
3
3
0
.
0
0
6
2
0
.
7
8
3
N
2
0
.
0
6
9
0
.
0
0
8
2
1
.
3
5
1
N
C
h
i
l
e
0
.
1
1
7
0
.
0
0
7
0
.
1
7
8
0
.
0
0
7
2
0
.
0
8
6
0
.
0
0
8
2
1
.
3
5
7
N
2
0
.
0
3
5
0
.
0
0
7
2
1
.
5
3
2
N
E
u
r
o
p
e
F
r
a
n
c
e
0
.
3
2
1
0
.
0
1
2
0
.
3
6
6
0
.
0
1
2
0
.
2
5
3
0
.
0
1
1
2
0
.
6
3
9
N
0
.
2
5
4
0
.
0
1
0
2
0
.
8
8
3
N
P
o
l
a
n
d
0
.
2
1
8
0
.
0
0
8
0
.
2
8
7
0
.
0
0
8
2
0
.
0
4
5
0
.
0
0
6
2
1
.
7
3
5
N
0
.
0
1
3
0
.
0
0
6
2
2
.
0
1
4
N
G
e
r
m
a
n
y
0
.
3
2
5
0
.
0
0
9
0
.
3
6
6
0
.
0
0
9
0
.
3
5
0
0
.
0
1
2
0
.
0
8
3
N
0
.
3
3
4
0
.
0
1
1
2
0
.
0
1
2
N
I
t
a
l
y
0
.
2
4
8
0
.
0
1
3
0
.
2
9
2
0
.
0
1
3
0
.
1
4
2
0
.
0
0
9
2
0
.
8
0
6
N
0
.
1
6
9
0
.
0
0
9
2
0
.
9
2
8
N
H
o
l
l
a
n
d
0
.
3
3
2
0
.
0
1
0
0
.
3
7
8
0
.
0
1
0
0
.
2
9
6
0
.
0
0
8
2
0
.
4
7
3
N
0
.
2
7
1
0
.
0
0
8
2
0
.
8
5
1
N
S
p
a
i
n
0
.
1
9
3
0
.
0
1
5
0
.
2
5
5
0
.
0
1
6
2
0
.
1
1
6
0
.
0
0
9
2
1
.
9
2
3
N
2
0
.
0
0
1
0
.
0
0
9
2
1
.
8
6
0
N
U
K
0
.
2
9
2
0
.
0
0
9
0
.
3
6
1
0
.
0
0
9
0
.
1
3
5
0
.
0
0
8
2
1
.
2
3
4
N
0
.
1
2
9
0
.
0
0
8
2
1
.
7
6
4
N
M
i
d
d
l
e
E
a
s
t
a
n
d
A
f
r
i
c
a
B
a
h
r
a
i
n
0
.
0
0
6
0
.
0
0
5
2
0
.
0
5
0
0
.
0
0
5
0
.
2
9
0
0
.
0
0
6
1
.
7
7
4
*
Y
0
.
2
0
7
0
.
0
0
5
1
.
8
5
0
*
Y
E
g
y
p
t
0
.
1
1
6
0
.
0
1
3
0
.
1
0
4
0
.
0
1
1
0
.
0
9
8
0
.
0
2
8
2
0
.
0
2
7
N
0
.
1
3
1
0
.
0
2
2
0
.
1
9
8
N
J
o
r
d
a
n
2
0
.
0
3
5
0
.
0
0
5
2
0
.
0
2
0
0
.
0
0
5
2
0
.
1
0
1
0
.
0
0
6
2
0
.
4
1
3
N
2
0
.
0
9
7
0
.
0
0
5
2
0
.
5
5
4
N
K
u
w
a
i
t
2
0
.
0
7
3
0
.
0
0
5
2
0
.
0
2
6
0
.
0
0
5
2
0
.
2
9
8
0
.
0
0
6
2
1
.
4
3
1
N
2
0
.
2
5
6
0
.
0
0
4
2
1
.
6
7
9
N
Q
a
t
a
r
0
.
0
1
9
0
.
0
0
9
0
.
0
4
6
0
.
0
0
9
2
0
.
0
8
0
0
.
0
0
9
2
0
.
6
4
1
N
2
0
.
0
6
4
0
.
0
0
8
2
0
.
7
8
5
N
S
a
u
d
i
A
r
a
b
i
a
0
.
1
8
2
0
.
0
1
1
0
.
1
1
7
0
.
0
1
1
0
.
4
9
3
0
.
0
1
3
2
.
1
5
4
*
*
Y
0
.
4
5
7
0
.
0
1
0
2
.
6
7
8
*
*
*
Y
U
A
E
0
.
0
8
0
0
.
0
0
6
0
.
1
0
9
0
.
0
0
6
2
0
.
0
5
5
0
.
0
0
6
2
0
.
8
3
6
N
0
.
0
1
0
0
.
0
0
5
2
0
.
7
0
6
N
S
o
u
t
h
A
f
r
i
c
a
0
.
3
4
8
0
.
0
0
9
0
.
3
4
3
0
.
0
0
9
0
.
6
3
4
0
.
0
0
8
1
.
9
9
4
*
*
Y
0
.
4
3
4
0
.
0
0
9
0
.
7
6
6
N
O
t
h
e
r
R
u
s
s
i
a
0
.
2
9
0
0
.
0
1
2
0
.
3
7
8
0
.
0
1
2
0
.
0
0
7
0
.
0
1
0
2
1
.
9
9
2
N
0
.
0
6
9
0
.
0
1
2
2
2
.
3
3
8
N
N
o
t
e
s
:
S
t
a
t
i
s
t
i
c
a
l
s
i
g
n
i
?
c
a
n
c
e
a
t
:
*
1
0
,
*
*
5
,
a
n
d
*
*
*
1
p
e
r
c
e
n
t
,
r
e
s
p
e
c
t
i
v
e
l
y
;
t
h
e
t
a
b
l
e
s
h
o
w
s
c
o
n
d
i
t
i
o
n
a
l
(
u
n
a
d
j
u
s
t
e
d
)
c
r
o
s
s
-
m
a
r
k
e
t
c
o
r
r
e
l
a
t
i
o
n
c
o
e
f
?
c
i
e
n
t
s
(
r
)
a
n
d
s
t
a
n
d
a
r
d
d
e
v
i
a
t
i
o
n
s
(
s
)
f
o
r
J
a
p
a
n
a
n
d
o
t
h
e
r
s
t
o
c
k
i
n
d
e
x
e
s
;
t
h
e
t
e
s
t
s
t
a
t
i
s
t
i
c
s
a
r
e
o
b
t
a
i
n
e
d
f
r
o
m
F
i
s
h
e
r
Z
t
r
a
n
s
f
o
r
m
a
t
i
o
n
s
;
t
h
e
s
t
a
b
l
e
p
e
r
i
o
d
i
s
d
e
?
n
e
d
a
s
t
h
e
1
2
-
m
o
n
t
h
p
r
e
-
e
a
r
t
h
q
u
a
k
e
p
e
r
i
o
d
(
M
a
r
c
h
1
1
,
2
0
1
0
-
M
a
r
c
h
1
0
,
2
0
1
1
)
;
t
h
e
s
h
o
r
t
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
i
s
d
e
?
n
e
d
a
s
t
h
e
o
n
e
-
m
o
n
t
h
p
o
s
t
-
e
a
r
t
h
q
u
a
k
e
p
e
r
i
o
d
(
M
a
r
c
h
1
1
,
2
0
1
1
-
A
p
r
i
l
1
0
,
2
0
1
1
)
;
t
h
e
m
e
d
i
u
m
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
i
s
d
e
?
n
e
d
a
s
t
h
e
t
w
o
-
m
o
n
t
h
p
o
s
t
-
e
a
r
t
h
q
u
a
k
e
p
e
r
i
o
d
(
M
a
r
c
h
1
1
,
2
0
1
1
-
M
a
y
1
0
,
2
0
1
1
)
;
t
h
e
f
u
l
l
p
e
r
i
o
d
i
s
t
h
e
s
t
a
b
l
e
p
e
r
i
o
d
p
l
u
s
t
h
e
m
e
d
i
u
m
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
;
C
o
:
c
o
n
t
a
g
i
o
n
;
w
h
i
l
e
“
Y
”
d
e
n
o
t
e
s
t
h
a
t
t
h
e
t
e
s
t
s
t
a
t
i
s
t
i
c
s
i
s
g
r
e
a
t
e
r
t
h
a
n
t
h
e
c
r
i
t
i
c
a
l
v
a
l
u
e
a
n
d
c
o
n
t
a
g
i
o
n
o
c
c
u
r
r
e
d
,
“
N
”
i
n
d
i
c
a
t
e
s
t
h
a
t
t
h
e
t
e
s
t
s
t
a
t
i
s
t
i
c
s
w
a
s
l
e
s
s
o
r
e
q
u
a
l
t
o
t
h
e
c
r
i
t
i
c
a
l
v
a
l
u
e
a
n
d
n
o
c
o
n
t
a
g
i
o
n
o
c
c
u
r
r
e
d
;
c
o
r
r
e
l
a
t
i
o
n
c
o
e
f
?
c
i
e
n
t
s
a
r
e
u
n
a
d
j
u
s
t
e
d
f
o
r
h
e
t
e
r
o
s
c
e
d
a
s
t
i
c
i
t
y
Table I.
International stock
indices returns
conditional (unadjusted)
correlation coef?cients in
2011 Japanese earthquake
JFEP
4,4
346
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
5
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
F
u
l
l
p
e
r
i
o
d
S
t
a
b
l
e
p
e
r
i
o
d
S
h
o
r
t
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
M
e
d
i
u
m
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
R
e
g
i
o
n
s
C
o
u
n
t
r
i
e
s
r
s
r
*
s
t
p
r
*
m
t
p
r
*
d
Z
-
t
e
s
t
C
o
r
*
d
Z
-
t
e
s
t
C
o
S
o
u
t
h
A
s
i
a
a
n
d
S
o
u
t
h
-
E
a
s
t
A
s
i
a
I
n
d
i
a
0
.
2
8
8
0
.
0
0
9
0
.
4
3
0
0
.
4
4
5
0
.
3
1
5
2
0
.
0
1
7
2
0
.
6
7
9
N
0
.
2
2
9
0
.
0
3
2
2
1
.
7
4
5
N
M
a
l
a
y
s
i
a
0
.
3
7
2
0
.
0
0
5
0
.
5
0
0
0
.
5
1
6
0
.
4
8
5
2
0
.
0
7
7
2
0
.
0
9
9
N
0
.
4
5
1
2
0
.
1
1
1
2
0
.
6
0
9
N
P
h
i
l
i
p
p
i
n
e
s
0
.
3
1
7
0
.
0
0
9
0
.
4
4
5
0
.
4
6
0
0
.
3
7
3
0
.
0
0
7
2
0
.
4
4
3
N
0
.
3
5
1
2
0
.
1
4
3
2
0
.
9
3
3
N
S
i
n
g
a
p
o
r
e
n
.
a
.
0
.
0
0
0
n
.
a
.
n
.
a
.
n
.
a
.
n
.
a
.
n
.
a
.
n
.
a
.
n
.
a
.
n
.
a
.
n
.
a
.
n
.
a
.
T
h
a
i
l
a
n
d
0
.
3
0
8
0
.
0
0
9
0
.
4
5
0
0
.
4
6
5
0
.
3
5
2
2
0
.
1
6
5
2
0
.
5
9
1
N
0
.
2
7
9
2
0
.
1
5
5
2
1
.
5
4
9
N
E
a
s
t
A
s
i
a
a
n
d
N
o
r
t
h
-
E
a
s
t
A
s
i
a
C
h
i
n
a
0
.
2
8
3
0
.
0
1
1
0
.
3
8
9
0
.
4
0
3
0
.
5
7
7
2
0
.
4
3
3
1
.
2
6
2
N
0
.
4
1
8
2
0
.
3
8
7
0
.
1
3
0
N
H
o
n
g
K
o
n
g
0
.
5
1
0
0
.
0
0
9
0
.
6
4
2
0
.
6
5
8
0
.
6
7
4
0
.
0
4
8
0
.
2
8
3
N
0
.
6
4
2
2
0
.
1
0
6
2
0
.
2
0
3
N
T
a
i
w
a
n
0
.
5
8
7
0
.
0
0
8
0
.
6
9
0
0
.
7
0
6
0
.
8
5
2
0
.
0
4
7
2
.
1
1
9
*
*
Y
0
.
7
9
4
2
0
.
0
4
3
1
.
4
5
8
N
S
o
u
t
h
K
o
r
e
a
0
.
5
7
5
0
.
0
0
8
0
.
7
0
4
0
.
7
1
9
0
.
7
5
3
0
.
0
1
6
0
.
5
2
8
N
0
.
6
8
2
0
.
0
9
2
2
0
.
5
2
3
N
A
u
s
t
r
a
l
a
s
i
a
A
u
s
t
r
a
l
i
a
2
0
.
0
0
4
0
.
0
0
8
0
.
0
0
0
0
.
0
0
0
0
.
0
9
5
2
0
.
7
0
6
0
.
4
8
5
N
2
0
.
0
2
8
2
0
.
1
2
0
2
0
.
1
9
9
N
N
e
w
Z
e
a
l
a
n
d
0
.
4
4
0
0
.
0
0
4
0
.
5
5
8
0
.
5
7
4
0
.
7
0
7
2
0
.
1
1
9
1
.
2
8
3
N
0
.
6
3
2
2
0
.
1
7
2
0
.
6
5
1
N
N
o
r
t
h
A
m
e
r
i
c
a
C
a
n
a
d
a
2
0
.
0
5
5
0
.
1
9
7
2
0
.
0
1
7
2
0
.
0
1
8
2
0
.
5
3
9
2
0
.
4
0
1
2
2
.
9
8
5
N
2
0
.
4
4
5
2
0
.
4
7
3
2
3
.
2
7
7
N
U
S
A
0
.
1
7
6
0
.
0
1
2
0
.
2
7
8
0
.
2
8
9
0
.
0
7
0
2
0
.
4
1
0
2
1
.
0
9
6
N
0
.
1
0
0
2
0
.
4
5
8
2
1
.
4
0
1
N
M
e
x
i
c
o
0
.
1
5
9
0
.
0
0
7
0
.
2
6
7
0
.
2
7
8
0
.
0
6
3
2
0
.
1
7
4
2
1
.
0
7
4
N
0
.
0
3
7
2
0
.
1
1
8
2
1
.
7
6
7
N
S
o
u
t
h
A
m
e
r
i
c
a
A
r
g
e
n
t
i
n
a
0
.
1
7
4
0
.
0
1
2
0
.
2
1
0
0
.
2
1
9
0
.
3
9
3
2
0
.
1
3
9
1
.
0
2
9
N
0
.
3
5
5
2
0
.
1
9
8
1
.
0
5
9
N
B
r
a
z
i
l
0
.
0
7
6
0
.
0
1
0
0
.
1
5
5
0
.
1
6
2
2
0
.
0
4
3
2
0
.
3
8
1
2
1
.
0
1
8
N
2
0
.
0
9
4
2
0
.
2
2
8
2
1
.
8
3
3
N
C
h
i
l
e
0
.
1
1
7
0
.
0
0
7
0
.
2
2
9
0
.
2
3
8
2
0
.
1
1
2
0
.
2
1
5
2
1
.
7
6
1
N
2
0
.
0
4
8
0
.
0
3
8
2
2
.
0
7
3
N
E
u
r
o
p
e
F
r
a
n
c
e
0
.
3
2
1
0
.
0
1
2
0
.
4
5
6
0
.
4
7
1
0
.
3
2
2
2
0
.
1
2
4
2
0
.
8
0
5
N
0
.
3
3
6
2
0
.
1
8
8
2
1
.
1
5
3
N
P
o
l
a
n
d
0
.
2
1
8
0
.
0
0
8
0
.
3
6
4
0
.
3
7
7
2
0
.
0
5
8
2
0
.
2
9
5
2
2
.
2
4
0
N
0
.
0
1
7
2
0
.
2
9
5
2
2
.
7
0
2
N
G
e
r
m
a
n
y
0
.
3
2
5
0
.
0
0
9
0
.
4
2
1
0
.
4
3
6
0
.
4
3
7
0
.
2
5
5
0
.
1
0
4
N
0
.
4
3
4
0
.
1
4
2
2
0
.
0
1
5
N
I
t
a
l
y
0
.
2
4
8
0
.
0
1
3
0
.
3
6
9
0
.
3
8
3
0
.
1
8
3
2
0
.
3
1
4
2
1
.
0
3
2
N
0
.
2
2
7
2
0
.
3
0
6
2
1
.
2
3
2
N
H
o
l
l
a
n
d
0
.
3
3
2
0
.
0
1
0
0
.
4
6
9
0
.
4
8
5
0
.
3
7
4
2
0
.
2
0
9
2
0
.
5
9
3
N
0
.
3
5
8
2
0
.
2
4
2
2
1
.
1
0
8
N
S
p
a
i
n
0
.
1
9
3
0
.
0
1
5
0
.
3
2
4
0
.
3
3
7
2
0
.
1
5
1
2
0
.
4
0
6
2
2
.
4
8
9
N
2
0
.
0
0
1
2
0
.
4
4
3
2
2
.
5
0
4
N
U
K
0
.
2
9
2
0
.
0
0
9
0
.
4
5
0
0
.
4
6
5
0
.
1
7
5
2
0
.
1
5
2
2
1
.
5
7
1
N
0
.
1
7
5
2
0
.
1
2
0
2
2
.
3
3
3
N
M
i
d
d
l
e
E
a
s
t
a
n
d
A
f
r
i
c
a
B
a
h
r
a
i
n
0
.
0
0
6
0
.
0
0
5
2
0
.
0
6
5
2
0
.
0
6
7
0
.
3
6
7
0
.
0
2
2
2
.
2
9
0
*
*
Y
0
.
2
7
6
2
0
.
1
5
6
2
.
5
0
1
*
*
Y
E
g
y
p
t
0
.
1
1
6
0
.
0
1
3
0
.
1
3
4
0
.
1
4
0
0
.
1
2
7
1
.
6
0
5
2
0
.
0
3
6
N
0
.
1
7
7
0
.
9
9
1
0
.
2
6
8
N
J
o
r
d
a
n
2
0
.
0
3
5
0
.
0
0
5
2
0
.
0
2
6
2
0
.
0
2
7
2
0
.
1
3
1
0
.
0
7
7
2
0
.
5
3
7
N
2
0
.
1
3
2
2
0
.
1
5
9
2
0
.
7
5
2
N
K
u
w
a
i
t
2
0
.
0
7
3
0
.
0
0
5
2
0
.
0
3
4
2
0
.
0
3
6
2
0
.
3
7
6
0
.
0
8
8
2
1
.
8
4
1
N
2
0
.
3
3
9
2
0
.
1
5
7
2
2
.
2
5
7
N
Q
a
t
a
r
0
.
0
1
9
0
.
0
0
9
0
.
0
5
9
0
.
0
6
2
2
0
.
1
0
4
0
.
0
3
7
2
0
.
8
3
4
N
2
0
.
0
8
7
2
0
.
1
3
3
2
1
.
0
6
6
N
S
a
u
d
i
A
r
a
b
i
a
0
.
1
8
2
0
.
0
1
1
0
.
1
5
2
0
.
1
5
8
0
.
5
9
4
0
.
1
7
0
2
.
7
0
5
*
*
*
Y
0
.
5
7
2
2
0
.
1
1
1
3
.
5
0
2
*
*
*
Y
U
A
E
0
.
0
8
0
0
.
0
0
6
0
.
1
4
1
0
.
1
4
7
2
0
.
0
7
1
0
.
0
1
2
2
1
.
0
8
7
N
0
.
0
1
4
2
0
.
0
8
5
2
0
.
9
5
7
N
S
o
u
t
h
A
f
r
i
c
a
0
.
3
4
8
0
.
0
0
9
0
.
4
2
9
0
.
4
4
4
0
.
7
3
0
2
0
.
1
3
6
2
.
3
9
4
*
*
Y
0
.
5
4
8
2
0
.
0
3
6
0
.
9
8
0
N
O
t
h
e
r
R
u
s
s
i
a
0
.
2
9
0
0
.
0
1
2
0
.
4
6
9
0
.
4
8
5
0
.
0
0
9
2
0
.
1
7
6
2
2
.
5
5
0
N
0
.
0
9
4
2
0
.
0
0
6
2
3
.
1
0
0
N
N
o
t
e
s
:
S
t
a
t
i
s
t
i
c
a
l
s
i
g
n
i
?
c
a
n
c
e
a
t
:
*
1
0
,
*
*
5
,
a
n
d
*
*
*
1
p
e
r
c
e
n
t
,
r
e
s
p
e
c
t
i
v
e
l
y
;
t
h
e
t
a
b
l
e
s
h
o
w
s
c
o
n
d
i
t
i
o
n
a
l
(
u
n
a
d
j
u
s
t
e
d
)
c
r
o
s
s
-
m
a
r
k
e
t
c
o
r
r
e
l
a
t
i
o
n
c
o
e
f
?
c
i
e
n
t
s
(
r
)
,
a
d
j
u
s
t
e
r
f
o
r
h
e
t
e
r
o
s
c
e
d
a
s
t
i
c
i
t
y
(
d
)
,
a
n
d
s
t
a
n
d
a
r
d
d
e
v
i
a
t
i
o
n
s
(
s
)
f
o
r
J
a
p
a
n
a
n
d
o
t
h
e
r
s
t
o
c
k
i
n
d
e
x
e
s
;
t
h
e
t
e
s
t
s
t
a
t
i
s
t
i
c
s
a
r
e
o
b
t
a
i
n
e
d
f
r
o
m
F
i
s
h
e
r
Z
t
r
a
n
s
f
o
r
m
a
t
i
o
n
s
;
t
h
e
s
t
a
b
l
e
p
e
r
i
o
d
i
s
d
e
?
n
e
d
a
s
t
h
e
1
2
-
m
o
n
t
h
p
r
e
-
e
a
r
t
h
q
u
a
k
e
p
e
r
i
o
d
(
M
a
r
c
h
1
1
,
2
0
1
0
-
M
a
r
c
h
1
0
,
2
0
1
1
)
;
t
h
e
s
h
o
r
t
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
i
s
d
e
?
n
e
d
a
s
t
h
e
o
n
e
-
m
o
n
t
h
p
o
s
t
-
e
a
r
t
h
q
u
a
k
e
p
e
r
i
o
d
(
M
a
r
c
h
1
1
,
2
0
1
1
-
A
p
r
i
l
1
0
,
2
0
1
1
)
;
t
h
e
m
e
d
i
u
m
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
i
s
d
e
?
n
e
d
a
s
t
h
e
t
w
o
-
m
o
n
t
h
p
o
s
t
-
e
a
r
t
h
q
u
a
k
e
p
e
r
i
o
d
(
M
a
r
c
h
1
1
,
2
0
1
1
-
M
a
y
1
0
,
2
0
1
1
)
;
t
h
e
f
u
l
l
p
e
r
i
o
d
i
s
t
h
e
s
t
a
b
l
e
p
e
r
i
o
d
p
l
u
s
t
h
e
m
e
d
i
u
m
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
;
C
o
:
c
o
n
t
a
g
i
o
n
;
w
h
i
l
e
“
Y
”
d
e
n
o
t
e
s
t
h
a
t
t
h
e
t
e
s
t
s
t
a
t
i
s
t
i
c
s
i
s
g
r
e
a
t
e
r
t
h
a
n
t
h
e
c
r
i
t
i
c
a
l
v
a
l
u
e
a
n
d
c
o
n
t
a
g
i
o
n
o
c
c
u
r
r
e
d
,
“
N
”
i
n
d
i
c
a
t
e
s
t
h
a
t
t
h
e
t
e
s
t
s
t
a
t
i
s
t
i
c
s
w
a
s
l
e
s
s
o
r
e
q
u
a
l
t
o
t
h
e
c
r
i
t
i
c
a
l
v
a
l
u
e
a
n
d
n
o
c
o
n
t
a
g
i
o
n
o
c
c
u
r
r
e
d
;
r
*
s
t
p
,
r
*
m
t
p
,
d
e
n
o
t
e
a
d
j
u
s
t
e
d
c
o
r
r
e
l
a
t
i
o
n
c
o
e
f
?
c
i
e
n
t
s
f
o
r
t
h
e
s
h
o
r
t
-
a
n
d
m
e
d
i
u
m
-
t
e
r
m
p
e
r
i
o
d
s
,
r
e
s
p
e
c
t
i
v
e
l
y
;
d
:
c
o
r
r
e
l
a
t
i
o
n
c
o
e
f
?
c
i
e
n
t
a
d
j
u
s
t
e
r
;
c
o
r
r
e
l
a
t
i
o
n
c
o
e
f
?
c
i
e
n
t
s
a
r
e
a
d
j
u
s
t
e
d
f
o
r
h
e
t
e
r
o
s
c
e
d
a
s
t
i
c
i
t
y
u
s
i
n
g
e
q
u
a
t
i
o
n
(
2
)
Table II.
International stock
indices returns
unconditional (adjusted)
correlation coef?cients in
2011 Japanese earthquake
The 2011
Japanese
earthquake
347
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
5
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
4.2 Contagion effect in international exchange rates returns after earthquake
Findings in Table III present exchange rate conditional (unadjusted) correlation
coef?cients. Cross-market correlations during turmoil periods are higher than those
during the stable period. Strengthened cross-market correlations with insigni?cant
evidence of contagion are noticeable for Thai Baht (THB), Argentinian Peso (ARS),
Egyptian Pound (EGP), and Qatari Riyal (QAR) for the short-term turmoil period. With
regard to the medium-term, the Chinese Yuan (RMB), Canadian Dollar (CAD), EGP, QAR,
and Emirati dirham (AED) also witnessed insigni?cant stronger co-movements with the
Japanese Yen ( JPY). Adjusted results from Table IV con?rm those in Table III. In
summary, no national/regional exchange market is found to have suffered fromcontagion
two months in the aftermath of the Japanese earthquake and ensuing collateral disasters.
5. Discussion of results
This study has investigated if the March 2011 Japanese earthquake, tsunami, and
nuclear disaster affected the stability of the correlation structure in international stock
and foreign exchange markets.
On a ?rst note, with respect to international equity markets there is strong evidence
of contagion in Taiwan, Bahrain, Saudi Arabia, and South Africa. The effect on Saudi
Arabia is not unexpected because it is one of the four countries fromwhich a large part of
Japan’s imports in raw material originate. For the other three, cross-market correlations
strengthened only with China and Australia in the short-term, albeit insigni?cant to
account for contagion. An explanation as to why Saudi Arabia was most strongly
affected both in the immediate and medium terms may be determined from Japan’s
boost in fuel imports in substitution to energy provided by wrecked the Fukushima
nuclear plants. Bahrain, being an oil-export driven economy like her sisterly neighbor
Saudi Arabia, could not have suffered a different fate. As for Taiwan, Japan is its second
largest trading partner and of?cial estimates on the effect of the Japanese earthquake on
the Taiwanese economy stand at a yearly decline in growth by 0.2 percent of gross
domestic product (GDP).
Second, international foreign exchange market results indicate no presence of
contagion. Admittedly, one would have expected the widespread disruption to Japan’s
US$5.5 trillion economy to inevitably affect other countries in the Asia-Paci?c region and
beyond. Regional trade would have been immediately affected by the damage to Japanese
ports. Our unexpected ?ndings could be explained from the fact that major Japanese
manufacturers of automobiles, semiconductors, computers, and other goods immediately
took advantage of their international supply chains and production networks; therefore
moving production elsewhere in Asia or to North America, where capacity utilization is
still low. Also, since Japanese factories generally produce consumer goods rather than
intermediate products, disruptions to outbound shipments should not have been expected
to seriously affect production processes in other countries.
As to what concerns managing and mitigating spillovers and contagion, it is worth
pointing out that globalization comes with costs and bene?ts. Hence managing
?nancial market contagion resulting from natural disasters requires that governments
minimize the costs and maximize the bene?ts of ?nancial market integration. Most
countries in the sample have undoubtedly bene?ted from integration, however based
on the above empirical evidence, measures need to be taken in an effort to manage the
downside rami?cations of integration in the event of a natural disaster.
JFEP
4,4
348
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
5
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
F
u
l
l
p
e
r
i
o
d
S
t
a
b
l
e
p
e
r
i
o
d
S
h
o
r
t
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
M
e
d
i
u
m
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
R
e
g
i
o
n
s
C
o
u
n
t
r
i
e
s
r
s
r
s
r
s
Z
-
t
e
s
t
C
o
r
s
Z
-
t
e
s
t
C
o
S
o
u
t
h
A
s
i
a
a
n
d
S
o
u
t
h
-
E
a
s
t
A
s
i
a
I
n
d
i
a
2
0
.
1
3
6
0
.
0
0
4
2
0
.
1
3
0
0
.
0
0
5
2
0
.
2
8
2
0
.
0
0
2
2
0
.
8
0
7
N
2
0
.
2
0
7
0
.
0
0
2
2
0
.
5
6
0
N
M
a
l
a
y
s
i
a
2
0
.
1
9
7
0
.
0
0
4
2
0
.
1
9
1
0
.
0
0
5
2
0
.
3
3
2
0
.
0
0
2
2
0
.
7
7
3
N
2
0
.
2
6
7
0
.
0
0
3
2
0
.
5
7
0
N
P
h
i
l
i
p
p
i
n
e
s
2
0
.
1
2
9
0
.
0
0
4
2
0
.
1
2
8
0
.
0
0
5
2
0
.
4
3
9
0
.
0
0
3
2
1
.
7
4
6
N
2
0
.
1
4
3
0
.
0
0
3
2
0
.
1
0
5
N
S
i
n
g
a
p
o
r
e
2
0
.
0
2
9
0
.
0
0
3
2
0
.
0
1
5
0
.
0
0
4
2
0
.
1
9
7
0
.
0
0
3
2
0
.
9
4
0
N
2
0
.
1
0
9
0
.
0
0
3
2
0
.
6
7
4
N
T
h
a
i
l
a
n
d
0
.
0
6
1
0
.
0
0
2
0
.
0
6
6
0
.
0
0
4
0
.
2
3
5
0
.
0
0
2
0
.
8
8
5
N
0
.
0
2
3
0
.
0
0
2
2
0
.
3
0
7
N
E
a
s
t
A
s
i
a
a
n
d
N
o
r
t
h
-
E
a
s
t
A
s
i
a
C
h
i
n
a
0
.
0
3
0
0
.
0
0
1
0
.
0
1
8
0
.
0
0
4
2
0
.
0
9
0
0
.
0
0
1
2
0
.
5
5
1
N
0
.
0
8
5
0
.
0
0
1
0
.
4
8
2
N
H
o
n
g
K
o
n
g
2
0
.
0
4
9
0
.
0
0
0
2
0
.
0
2
0
0
.
0
0
4
2
0
.
2
2
3
0
.
0
0
0
2
1
.
0
4
9
N
2
0
.
2
2
5
0
.
0
0
0
2
1
.
4
8
1
N
T
a
i
w
a
n
2
0
.
1
0
4
0
.
0
0
3
2
0
.
0
8
0
0
.
0
0
4
2
0
.
4
0
0
0
.
0
0
2
2
1
.
7
4
8
N
2
0
.
2
5
1
0
.
0
0
2
2
1
.
2
5
6
N
S
o
u
t
h
K
o
r
e
a
2
0
.
2
4
2
0
.
0
0
7
2
0
.
2
2
6
0
.
0
0
7
2
0
.
5
0
0
0
.
0
0
5
2
1
.
6
3
0
N
2
0
.
4
1
5
0
.
0
0
4
2
1
.
5
0
8
N
A
u
s
t
r
a
l
a
s
i
a
A
u
s
t
r
a
l
i
a
2
0
.
0
8
0
0
.
0
0
7
2
0
.
0
4
2
0
.
0
0
7
2
0
.
4
2
8
0
.
0
0
6
2
2
.
1
2
0
N
2
0
.
3
2
5
0
.
0
0
6
2
2
.
0
9
9
N
N
e
w
Z
e
a
l
a
n
d
2
0
.
0
3
1
0
.
0
0
7
0
.
0
3
5
0
.
0
0
7
2
0
.
6
1
2
0
.
0
0
7
2
3
.
8
1
2
N
2
0
.
4
1
9
0
.
0
0
6
2
3
.
4
3
2
N
N
o
r
t
h
A
m
e
r
i
c
a
C
a
n
a
d
a
2
0
.
2
7
2
0
.
0
0
6
2
0
.
2
7
5
0
.
0
0
6
2
0
.
3
9
0
0
.
0
0
4
2
0
.
6
6
2
N
2
0
.
2
7
4
0
.
0
0
4
0
.
0
1
0
N
M
e
x
i
c
o
2
0
.
3
3
8
0
.
0
0
5
2
0
.
3
3
9
0
.
0
0
6
2
0
.
5
2
1
0
.
0
0
3
2
1
.
1
5
1
N
2
0
.
3
8
5
0
.
0
0
4
2
0
.
3
7
8
N
S
o
u
t
h
A
m
e
r
i
c
a
A
r
g
e
n
t
i
n
a
2
0
.
0
3
0
0
.
0
0
1
2
0
.
0
2
6
0
.
0
0
1
2
0
.
0
2
4
0
.
0
0
1
0
.
0
1
0
N
2
0
.
0
5
9
0
.
0
0
1
2
0
.
2
3
7
N
B
r
a
z
i
l
2
0
.
2
0
5
0
.
0
0
6
2
0
.
1
8
1
0
.
0
0
6
2
0
.
4
1
5
0
.
0
0
5
2
1
.
3
1
6
N
2
0
.
3
5
6
0
.
0
0
6
2
1
.
3
4
7
N
C
h
i
l
e
0
.
0
1
2
0
.
0
0
5
0
.
0
4
6
0
.
0
0
6
2
0
.
2
8
1
0
.
0
0
4
2
1
.
7
0
7
N
2
0
.
2
3
4
0
.
0
0
4
2
2
.
0
2
8
N
E
u
r
o
p
e
E
u
r
o
0
.
1
6
6
0
.
0
0
6
0
.
2
1
1
0
.
0
0
6
2
0
.
2
0
4
0
.
0
0
4
2
2
.
1
5
1
N
2
0
.
0
7
9
0
.
0
0
6
2
2
.
0
9
4
N
U
K
0
.
0
4
3
0
.
0
0
5
0
.
0
7
3
0
.
0
0
5
2
0
.
2
1
1
0
.
0
0
5
2
1
.
4
6
7
N
2
0
.
1
4
7
0
.
0
0
4
2
1
.
5
7
6
N
M
i
d
d
l
e
E
a
s
t
a
n
d
A
f
r
i
c
a
B
a
h
r
a
i
n
2
0
.
0
2
1
0
.
0
0
6
2
0
.
0
1
4
0
.
0
0
0
2
0
.
0
2
9
0
.
0
0
0
2
0
.
0
7
7
N
2
0
.
0
4
3
0
.
0
0
0
2
0
.
2
0
8
N
E
g
y
p
t
0
.
0
3
7
0
.
0
0
1
0
.
0
2
2
0
.
0
0
1
0
.
1
3
3
0
.
0
0
1
0
.
5
6
6
N
0
.
1
9
4
0
.
0
0
1
1
.
2
3
9
N
J
o
r
d
a
n
2
0
.
0
2
6
0
.
0
0
1
2
0
.
0
2
4
0
.
0
0
1
2
0
.
1
3
1
0
.
0
0
0
2
0
.
5
4
8
N
2
0
.
0
3
6
0
.
0
0
0
2
0
.
0
8
8
N
K
u
w
a
i
t
0
.
2
4
7
0
.
0
0
1
0
.
2
5
8
0
.
0
0
1
0
.
1
8
2
0
.
0
0
1
2
0
.
4
0
8
N
0
.
1
8
7
0
.
0
0
1
2
0
.
5
3
3
N
Q
a
t
a
r
0
.
0
3
7
0
.
0
0
0
0
.
0
2
9
0
.
0
0
0
0
.
2
1
1
0
.
0
0
0
0
.
9
9
4
N
0
.
1
7
8
0
.
0
0
0
1
.
0
7
2
N
S
a
u
d
i
A
r
a
b
i
a
2
0
.
0
2
7
0
.
0
0
0
0
.
0
0
5
0
.
0
0
0
2
0
.
3
3
4
0
.
0
0
0
2
1
.
7
9
5
N
2
0
.
1
9
4
0
.
0
0
0
2
1
.
4
3
1
N
U
A
E
2
0
.
0
8
6
0
.
0
0
0
2
0
.
0
9
4
0
.
0
0
1
2
0
.
2
1
1
0
.
0
0
5
n
.
a
.
n
.
a
.
2
0
.
0
3
7
0
.
0
0
0
0
.
4
0
6
N
S
o
u
t
h
A
f
r
i
c
a
2
0
.
1
3
0
0
.
0
0
7
2
0
.
0
7
4
0
.
0
0
7
2
0
.
6
0
1
0
.
0
0
7
2
3
.
1
7
0
N
2
0
.
4
4
8
0
.
0
0
7
2
2
.
9
0
6
N
O
t
h
e
r
R
u
s
s
i
a
2
0
.
1
4
0
0
.
0
0
4
2
0
.
1
3
2
0
.
0
0
5
2
0
.
3
7
7
0
.
0
0
3
2
1
.
3
4
7
N
2
0
.
2
1
1
0
.
0
0
4
2
0
.
5
8
0
N
N
o
t
e
s
:
S
t
a
t
i
s
t
i
c
a
l
s
i
g
n
i
?
c
a
n
c
e
a
t
:
*
1
0
,
*
*
5
,
*
*
*
1
p
e
r
c
e
n
t
,
r
e
s
p
e
c
t
i
v
e
l
y
;
n
.
a
.
:
t
h
e
p
r
e
s
e
n
c
e
o
f
z
e
r
o
e
x
c
h
a
n
g
e
r
a
t
e
r
e
t
u
r
n
f
o
r
a
l
l
p
e
r
i
o
d
s
o
f
t
h
e
s
e
r
i
e
s
;
t
h
e
t
a
b
l
e
s
h
o
w
s
c
o
n
d
i
t
i
o
n
a
l
(
u
n
a
d
j
u
s
t
e
d
)
c
r
o
s
s
-
m
a
r
k
e
t
c
o
r
r
e
l
a
t
i
o
n
c
o
e
f
?
c
i
e
n
t
s
(
r
)
a
n
d
s
t
a
n
d
a
r
d
d
e
v
i
a
t
i
o
n
s
(
s
)
f
o
r
J
a
p
a
n
a
n
d
o
t
h
e
r
s
t
o
c
k
i
n
d
e
x
e
s
;
t
h
e
t
e
s
t
s
t
a
t
i
s
t
i
c
s
a
r
e
o
b
t
a
i
n
e
d
f
r
o
m
F
i
s
h
e
r
Z
t
r
a
n
s
f
o
r
m
a
t
i
o
n
s
;
t
h
e
s
t
a
b
l
e
p
e
r
i
o
d
i
s
d
e
?
n
e
d
a
s
t
h
e
1
2
-
m
o
n
t
h
p
r
e
-
e
a
r
t
h
q
u
a
k
e
p
e
r
i
o
d
(
M
a
r
c
h
1
1
,
2
0
1
0
-
M
a
r
c
h
1
0
,
2
0
1
1
)
;
t
h
e
s
h
o
r
t
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
i
s
d
e
?
n
e
d
a
s
t
h
e
o
n
e
-
m
o
n
t
h
p
o
s
t
-
e
a
r
t
h
q
u
a
k
e
p
e
r
i
o
d
(
M
a
r
c
h
1
1
,
2
0
1
1
-
A
p
r
i
l
1
0
,
2
0
1
1
)
;
t
h
e
m
e
d
i
u
m
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
i
s
d
e
?
n
e
d
a
s
t
h
e
t
w
o
-
m
o
n
t
h
p
o
s
t
-
e
a
r
t
h
q
u
a
k
e
p
e
r
i
o
d
(
M
a
r
c
h
1
1
,
2
0
1
1
-
M
a
y
1
0
,
2
0
1
1
)
;
t
h
e
f
u
l
l
p
e
r
i
o
d
i
s
t
h
e
s
t
a
b
l
e
p
e
r
i
o
d
p
l
u
s
t
h
e
m
e
d
i
u
m
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
;
C
o
:
c
o
n
t
a
g
i
o
n
;
w
h
i
l
e
“
Y
”
d
e
n
o
t
e
s
t
h
a
t
t
h
e
t
e
s
t
s
t
a
t
i
s
t
i
c
s
i
s
g
r
e
a
t
e
r
t
h
a
n
t
h
e
c
r
i
t
i
c
a
l
v
a
l
u
e
a
n
d
c
o
n
t
a
g
i
o
n
o
c
c
u
r
r
e
d
,
“
N
”
i
n
d
i
c
a
t
e
s
t
h
a
t
t
h
e
t
e
s
t
s
t
a
t
i
s
t
i
c
s
w
a
s
l
e
s
s
o
r
e
q
u
a
l
t
o
t
h
e
c
r
i
t
i
c
a
l
v
a
l
u
e
a
n
d
n
o
c
o
n
t
a
g
i
o
n
o
c
c
u
r
r
e
d
;
c
o
r
r
e
l
a
t
i
o
n
c
o
e
f
?
c
i
e
n
t
s
a
r
e
u
n
a
d
j
u
s
t
e
d
f
o
r
h
e
t
e
r
o
s
c
e
d
a
s
t
i
c
i
t
y
Table III.
International exchange
rates returns conditional
(unadjusted) correlation
coef?cients in 2011
Japanese earthquake
The 2011
Japanese
earthquake
349
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
5
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
F
u
l
l
p
e
r
i
o
d
S
t
a
b
l
e
p
e
r
i
o
d
S
h
o
r
t
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
M
e
d
i
u
m
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
R
e
g
i
o
n
s
C
o
u
n
t
r
i
e
s
r
s
r
*
s
t
p
r
*
m
t
p
P
*
d
Z
-
t
e
s
t
C
o
r
*
d
Z
-
t
e
s
t
C
o
S
o
u
t
h
A
s
i
a
a
n
d
S
o
u
t
h
-
E
a
s
t
A
s
i
a
I
n
d
i
a
2
0
.
1
3
6
0
.
0
0
4
2
0
.
1
8
1
2
0
.
1
6
8
2
0
.
3
7
9
2
0
.
4
8
7
2
1
.
1
0
4
N
2
0
.
2
6
5
2
0
.
4
0
8
2
0
.
7
2
1
N
M
a
l
a
y
s
i
a
2
0
.
1
9
7
0
.
0
0
4
2
0
.
2
6
2
2
0
.
2
4
5
2
0
.
4
4
1
2
0
.
5
1
0
2
1
.
0
4
5
N
2
0
.
3
3
9
2
0
.
3
5
6
2
0
.
7
2
8
N
P
h
i
l
i
p
p
i
n
e
s
2
0
.
1
2
9
0
.
0
0
4
2
0
.
1
7
8
2
0
.
1
6
6
2
0
.
5
6
4
2
0
.
2
6
7
2
2
.
3
4
1
N
2
0
.
1
8
4
2
0
.
2
8
1
2
0
.
1
3
6
N
S
i
n
g
a
p
o
r
e
2
0
.
0
2
9
0
.
0
0
3
2
0
.
0
2
1
2
0
.
0
2
0
2
0
.
2
7
0
2
0
.
1
5
4
2
1
.
3
0
4
N
2
0
.
1
4
1
2
0
.
0
8
8
2
0
.
8
7
4
N
T
h
a
i
l
a
n
d
0
.
0
6
1
0
.
0
0
2
0
.
0
9
2
0
.
0
8
6
0
.
3
2
0
2
0
.
2
8
2
1
.
2
2
2
N
0
.
0
3
0
2
0
.
1
9
4
2
0
.
3
9
9
N
E
a
s
t
A
s
i
a
a
n
d
N
o
r
t
h
-
E
a
s
t
A
s
i
a
C
h
i
n
a
0
.
0
3
0
0
.
0
0
1
0
.
0
2
4
0
.
0
2
3
2
0
.
1
2
6
2
0
.
1
4
2
2
0
.
7
6
9
N
0
.
1
1
0
0
.
1
3
5
0
.
6
2
6
N
H
o
n
g
K
o
n
g
2
0
.
0
4
9
0
.
0
0
0
2
0
.
0
2
9
2
0
.
0
2
7
2
0
.
3
0
4
0
.
0
7
2
2
1
.
4
5
3
N
2
0
.
2
8
7
2
0
.
0
9
9
2
1
.
9
1
3
N
T
a
i
w
a
n
2
0
.
1
0
4
0
.
0
0
3
2
0
.
1
1
2
2
0
.
1
0
4
2
0
.
5
2
0
2
0
.
0
6
5
2
2
.
3
6
7
N
2
0
.
3
2
0
2
0
.
0
8
5
2
1
.
6
1
7
N
S
o
u
t
h
K
o
r
e
a
2
0
.
2
4
2
0
.
0
0
7
2
0
.
3
0
8
2
0
.
2
8
9
2
0
.
6
2
8
2
0
.
3
2
2
2
2
.
1
3
8
N
2
0
.
5
1
0
2
0
.
3
7
5
2
1
.
8
9
2
N
A
u
s
t
r
a
l
a
s
i
a
A
u
s
t
r
a
l
i
a
2
0
.
0
8
0
0
.
0
0
7
2
0
.
0
5
9
2
0
.
0
5
5
2
0
.
5
5
2
2
0
.
0
9
6
2
2
.
8
6
8
N
2
0
.
4
0
7
2
0
.
1
4
2
2
2
.
6
9
0
N
N
e
w
Z
e
a
l
a
n
d
2
0
.
0
3
1
0
.
0
0
7
0
.
0
4
9
0
.
0
4
6
2
0
.
7
3
4
2
0
.
0
5
1
2
5
.
0
3
2
N
2
0
.
5
1
5
2
0
.
0
7
8
2
4
.
3
7
8
N
N
o
r
t
h
A
m
e
r
i
c
a
C
a
n
a
d
a
2
0
.
2
7
2
0
.
0
0
6
2
0
.
3
7
1
2
0
.
3
4
9
2
0
.
5
0
9
2
0
.
2
7
5
2
0
.
8
7
8
N
2
0
.
3
4
7
2
0
.
2
6
0
0
.
0
1
3
N
M
e
x
i
c
o
2
0
.
3
3
8
0
.
0
0
5
2
0
.
4
4
9
2
0
.
4
2
4
2
0
.
6
4
9
2
0
.
4
0
7
2
1
.
4
8
0
N
2
0
.
4
7
6
2
0
.
3
5
0
2
0
.
4
7
0
N
S
o
u
t
h
A
m
e
r
i
c
a
A
r
g
e
n
t
i
n
a
2
0
.
0
3
0
0
.
0
0
1
2
0
.
2
6
1
2
0
.
0
3
3
2
0
.
0
3
3
2
0
.
2
6
1
0
.
0
1
3
N
2
0
.
0
7
7
2
0
.
0
6
7
2
0
.
3
0
8
N
B
r
a
z
i
l
2
0
.
2
0
5
0
.
0
0
6
2
0
.
2
4
6
2
0
.
2
3
3
2
0
.
5
3
7
2
0
.
2
3
1
2
1
.
7
6
1
N
2
0
.
4
4
4
2
0
.
0
7
2
2
1
.
7
0
8
N
C
h
i
l
e
0
.
0
1
2
0
.
0
0
5
0
.
0
6
4
2
0
.
0
7
2
2
0
.
3
7
8
2
0
.
2
5
3
2
2
.
3
5
9
N
2
0
.
4
4
4
2
0
.
2
3
3
2
2
.
6
2
2
N
E
u
r
o
p
e
E
u
r
o
0
.
1
6
6
0
.
0
0
6
0
.
2
8
9
0
.
2
7
1
2
0
.
2
8
0
2
0
.
2
3
1
2
2
.
9
8
3
N
2
0
.
1
0
3
2
0
.
0
0
1
2
2
.
7
1
2
N
U
K
0
.
0
4
3
0
.
0
0
5
0
.
1
0
2
0
.
0
9
5
2
0
.
2
8
9
2
0
.
0
7
4
2
2
.
0
3
8
N
2
0
.
1
9
0
2
0
.
1
5
0
2
2
.
0
4
5
N
M
i
d
d
l
e
E
a
s
t
a
n
d
A
f
r
i
c
a
B
a
h
r
a
i
n
2
0
.
0
2
1
0
.
0
0
6
2
0
.
0
1
9
2
0
.
0
1
8
2
0
.
0
4
1
9
.
2
1
5
2
0
.
1
0
7
N
2
0
.
0
5
6
6
.
1
8
9
2
0
.
2
7
1
N
E
g
y
p
t
0
.
0
3
7
0
.
0
0
1
0
.
0
3
1
0
.
0
2
9
0
.
1
8
4
2
0
.
4
0
0
0
.
7
8
8
N
0
.
2
4
9
2
0
.
4
9
6
1
.
6
0
3
N
J
o
r
d
a
n
2
0
.
0
2
6
0
.
0
0
1
2
0
.
0
3
4
2
0
.
0
3
1
2
0
.
1
8
1
2
0
.
5
6
0
2
0
.
7
6
3
N
2
0
.
0
4
7
2
0
.
4
9
5
2
0
.
1
1
4
N
K
u
w
a
i
t
0
.
2
4
7
0
.
0
0
1
0
.
3
4
9
0
.
3
2
8
0
.
2
5
0
2
0
.
4
1
9
2
0
.
5
5
7
N
0
.
2
4
0
2
0
.
3
3
8
2
0
.
6
8
1
N
Q
a
t
a
r
0
.
0
3
7
0
.
0
0
0
0
.
0
4
1
0
.
0
3
8
0
.
2
8
9
2
0
.
5
0
0
1
.
3
0
8
N
0
.
2
2
9
2
0
.
6
4
9
1
.
3
8
7
N
S
a
u
d
i
A
r
a
b
i
a
2
0
.
0
2
7
0
.
0
0
0
0
.
0
0
6
0
.
0
0
6
2
0
.
4
4
4
0
.
0
7
9
2
2
.
4
6
4
N
2
0
.
2
4
9
0
.
0
2
5
2
1
.
8
5
2
N
U
A
E
2
0
.
0
8
6
0
.
0
0
0
2
0
.
1
3
1
2
0
.
1
2
2
n
.
a
.
2
1
.
0
0
0
n
.
a
.
n
.
a
.
2
0
.
0
4
8
2
0
.
8
1
6
0
.
5
2
6
N
S
o
u
t
h
A
f
r
i
c
a
2
0
.
1
3
0
0
.
0
0
7
2
0
.
1
0
3
2
0
.
0
9
5
2
0
.
7
2
5
0
.
0
0
3
2
2
.
0
3
8
N
2
0
.
5
4
5
2
0
.
0
4
1
2
3
.
6
7
6
N
O
t
h
e
r
R
u
s
s
i
a
2
0
.
1
4
0
0
.
0
0
4
2
0
.
1
8
3
2
0
.
1
7
0
2
0
.
4
9
4
2
0
.
3
1
6
2
1
.
8
2
1
N
2
0
.
2
7
0
2
0
.
1
8
7
2
0
.
7
4
6
N
N
o
t
e
s
:
S
t
a
t
i
s
t
i
c
a
l
s
i
g
n
i
?
c
a
n
c
e
a
t
:
*
1
0
,
*
*
5
,
a
n
d
*
*
*
1
p
e
r
c
e
n
t
,
r
e
s
p
e
c
t
i
v
e
l
y
;
t
h
e
t
a
b
l
e
s
h
o
w
s
c
o
n
d
i
t
i
o
n
a
l
(
u
n
a
d
j
u
s
t
e
d
)
c
r
o
s
s
-
m
a
r
k
e
t
c
o
r
r
e
l
a
t
i
o
n
c
o
e
f
?
c
i
e
n
t
s
(
r
)
,
a
d
j
u
s
t
e
r
f
o
r
h
e
t
e
r
o
s
c
e
d
a
s
t
i
c
i
t
y
(
d
)
,
a
n
d
s
t
a
n
d
a
r
d
d
e
v
i
a
t
i
o
n
s
(
s
)
f
o
r
J
a
p
a
n
a
n
d
o
t
h
e
r
s
t
o
c
k
i
n
d
e
x
e
s
;
t
h
e
t
e
s
t
s
t
a
t
i
s
t
i
c
s
a
r
e
o
b
t
a
i
n
e
d
f
r
o
m
F
i
s
h
e
r
Z
t
r
a
n
s
f
o
r
m
a
t
i
o
n
s
;
t
h
e
s
t
a
b
l
e
p
e
r
i
o
d
i
s
d
e
?
n
e
d
a
s
t
h
e
1
2
-
m
o
n
t
h
p
r
e
-
e
a
r
t
h
q
u
a
k
e
p
e
r
i
o
d
(
M
a
r
c
h
1
1
,
2
0
1
0
-
M
a
r
c
h
1
0
,
2
0
1
1
)
;
t
h
e
s
h
o
r
t
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
i
s
d
e
?
n
e
d
a
s
t
h
e
o
n
e
-
m
o
n
t
h
p
o
s
t
-
e
a
r
t
h
q
u
a
k
e
p
e
r
i
o
d
(
M
a
r
c
h
1
1
,
2
0
1
1
-
A
p
r
i
l
1
0
,
2
0
1
1
)
;
t
h
e
m
e
d
i
u
m
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
i
s
d
e
?
n
e
d
a
s
t
h
e
t
w
o
-
m
o
n
t
h
p
o
s
t
-
e
a
r
t
h
q
u
a
k
e
p
e
r
i
o
d
(
M
a
r
c
h
1
1
,
2
0
1
1
-
M
a
y
1
0
,
2
0
1
1
)
;
t
h
e
f
u
l
l
p
e
r
i
o
d
i
s
t
h
e
s
t
a
b
l
e
p
e
r
i
o
d
p
l
u
s
t
h
e
m
e
d
i
u
m
-
t
e
r
m
t
u
r
m
o
i
l
p
e
r
i
o
d
;
C
o
:
c
o
n
t
a
g
i
o
n
;
w
h
i
l
e
“
Y
”
d
e
n
o
t
e
s
t
h
a
t
t
h
e
t
e
s
t
s
t
a
t
i
s
t
i
c
s
i
s
g
r
e
a
t
e
r
t
h
a
n
t
h
e
c
r
i
t
i
c
a
l
v
a
l
u
e
a
n
d
c
o
n
t
a
g
i
o
n
o
c
c
u
r
r
e
d
,
“
N
”
i
n
d
i
c
a
t
e
s
t
h
a
t
t
h
e
t
e
s
t
s
t
a
t
i
s
t
i
c
s
w
a
s
l
e
s
s
o
r
e
q
u
a
l
t
o
t
h
e
c
r
i
t
i
c
a
l
v
a
l
u
e
a
n
d
n
o
c
o
n
t
a
g
i
o
n
o
c
c
u
r
r
e
d
;
r
*
s
t
p
,
r
*
m
t
p
,
d
e
n
o
t
e
a
d
j
u
s
t
e
d
c
o
r
r
e
l
a
t
i
o
n
c
o
e
f
?
c
i
e
n
t
s
f
o
r
t
h
e
s
h
o
r
t
-
a
n
d
m
e
d
i
u
m
-
t
e
r
m
p
e
r
i
o
d
s
,
r
e
s
p
e
c
t
i
v
e
l
y
;
d
:
c
o
r
r
e
l
a
t
i
o
n
c
o
e
f
?
c
i
e
n
t
a
d
j
u
s
t
e
r
;
c
o
r
r
e
l
a
t
i
o
n
c
o
e
f
?
c
i
e
n
t
s
a
r
e
a
d
j
u
s
t
e
d
f
o
r
h
e
t
e
r
o
s
c
e
d
a
s
t
i
c
i
t
y
u
s
i
n
g
e
q
u
a
t
i
o
n
(
2
)
Table IV.
International exchange
rates returns
unconditional (adjusted)
correlation coef?cients in
2011 Japanese earthquake
JFEP
4,4
350
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
5
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
The following are some recommendations policy makers need to put in place in order
to minimize (mitigate) the adverse ?nancial market effects of disasters:
.
The banking system of a country should not be directly exposed to foreign assets
that natural disasters can easily stress and make worthless. This recommendation
also holds for assets in institutions that natural disasters could render futile. This
will mitigate the knock-on effects through monetary, ?nancial, and real channels.
.
Domestic ?nancial markets (equity, money, foreign exchange, and credit markets)
may also suffer because of the “substitution effect”. As credit lines and credit
channels in the affected and contaminated countries run dry, some of the
credit-demand earlier met by overseas ?nancing could easily shift to the domestic
sector and put pressure on domestic resources. The reversal of capital ?ows arising
fromthe de-leveraging process could put pressure on the foreign exchange market,
leading to sharp ?uctuations in overnight money market rates and depreciation of
currency. It is therefore in the interest of central banks to adopt a monetary policy
stance that is adequate to growth, in?ation, and ?nancial stability concerns.
.
In situations where the natural disaster re?ects an expected decline in in?ation, it
is also in the interest of the central bank to adjust its monetary stance and manage
liquidity: both domestic and foreign exchange to ensure that credit continues to
?ow for productive activities at both aggregate and sector speci?c levels.
.
In order to enable economic agents plan their business activities with more
assurance, the central bank could ensure an orderly adjustment of the pain of its
policies by maintaining a comfortable liquidity position: seeing that the weighted
average overnight money market rate is maintained within the repo-reverse repo
corridor and ensure conditions conducive for ?ow of credit to productive sectors
(particularly the stressed export industry sectors).
Before we conclude, it is important to highlight the implications of this paper to the
future of natural disasters. Though the crisis is over, from a ?nancial standpoint the
following concerns will preoccupy policy makers in future natural disasters:
.
Is self-insurance a viable option for emerging economies? In order words, could
the accumulation of foreign reserves buffer against ?nancial market crises
arising from natural disasters? Whether these reserves derive from current
account surpluses (China for instance) or capital ?ows (India for example),
relying on them to hedge contagion could still represent some form of liability.
Hence the need to ?nd a way of balancing the trade-off between vulnerability to
?nancial contagion and vulnerability to trade contagion in the event of a disaster.
Another important strand within this framework points to the redundancy of
self-insurance if international arrangements (regional and multilateral) could
provide easy, quick and unconditional liquidity during such crises.
.
Howdopolicymakers keepthe ?nancial sector inline withthe real sector inevent of a
natural disaster? Forgotteninthe euphoria of ?nancial alchemyis the basic tenet that
the ?nancial sector has no standing of its own; it derives its strength and resilience
from the real economic sector. Thinking the other way round has led many into
believing that, signi?cant value could be created by slicing and dicing securities.
.
How do we address regulatory arbitrage in times of crisis? If under the nose of
regulators, grows an extensive, and complex network of a “shadowbankingsystem”
The 2011
Japanese
earthquake
351
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
5
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
that encourages loose practices, hunt for quick yields and non-transparent and risky
?nancial products, when systems unravel owing to natural disasters, many of
these institutions will pose a systematic risk to the ?nancial systems. Hence the
regulatory architecture has to be fashioned to keep pace with innovation and the
possibility of natural shocks.
.
Simulating natural disasters and learning how to manage global imbalances
arising from them could also help countries prepare for potential ?nancial and
real sector consequences of natural crises.
6. Conclusion
In this paper, we have used unadjusted and adjusted correlation coef?cients to test for
contagion effects across 33 economies in the aftermath of the Japanese earthquake,
ensuing tsunami, and worst nuclear crisis in recent history. Results indicate no
international foreign exchange markets experienced signi?cantly stronger correlations
with the Japanese Yen two months after. However, for international stock markets,
Taiwan, Bahrain, Saudi Arabia, and South Africa experience contagion; consistent
with the widely held notion that contagion is mostly a concern for emerging countries.
In line with Lee et al. (2007), the effects of natural disasters on ?nancial markets are
important in investment decisions, as the bene?ts of portfolio diversi?cation are severely
limited during periods of high volatility and increased cross-market correlations. With
?nancial globalization, investors can gain from diversi?cation if returns from ?nancial
markets are stable and not correlated. However, with volatility spillovers, increase in
cross-market correlations exist as a real effect and are not taken into account for
asset allocation and portfolio composition.
Our results have two paramount implications. First, we have con?rmed the existing
consensus that in the face of natural crises that could take an international scale,
only emerging markets are contagiously affected for the most part. Second, we have
also shown that international ?nancial market transmissions not only occur during
?nancial crises; natural disaster effects should not be undermined.
Note
1. Differences in pre- and post-earthquake sample periods are in line with Collins and Biekpe
(2003), Lee et al. (2007), and Asongu (2011a, b).
References
Asongu, S.A. (2011a), “Globalization, ?nancial crisis and contagion: time-dynamic evidence from
?nancial markets of developing countries”, MPRA Paper No. 30120.
Asongu, S.A. (2011b), “Political crises and risk of ?nancial contagion in developing countries:
evidence from Africa”, MPRA Paper No. 30391.
Calvo, G.A., Leiderman, L. and Reinhart, C.A. (1996), “In?ows of capital to developing countries
in the 1990s”, Journal of Economic Perspectives, Vol. 10, Spring, pp. 123-39.
Collins, D. and Biekpe, N. (2003), “Contagion: a fear for African equity markets?”, Journal of
Economics and Business, Vol. 55, pp. 285-97.
Coulibaly, B. (2009), “Effects of ?nancial autarky and integration: the case of South Africa
embargo”, Journal of International Money and Finance, Vol. 28, pp. 454-78.
JFEP
4,4
352
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
5
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Demyanyk, Y. and Volosovych, V. (2008), “Gains from ?nancial integration in the European
Union: evidence for new and old members”, Journal of International Money and Finance,
Vol. 27, pp. 277-94.
Forbes, K.J. (2000), “The Asian ?u and Russian virus: ?rm-level evidence on how crises are
transmitted internationally”, NBER Working Paper No. 7807.
Forbes, K.J. and Rigobon, R. (2002), “No contagion, only interdependence: measuring stock
market co-movements”, Journal of Finance, Vol. 57 No. 5, pp. 2223-61.
Frankel, J. and Rose, A.K. (1996), “Currency crashes in emerging markets: an empirical
treatment”, Journal of International Economics, Vol. 41, November, pp. 351-66.
Kanas, A. (1998), “Linkages between the US and European equity markets: further evidence from
cointegration test”, Applied Financial Economics, Vol. 8 No. 6, pp. 607-14.
King, M. and Wadhwani, S. (1990), “Transmission of volatility between stock markets”, Review
of Financial Studies, Vol. 3 No. 1, pp. 5-33.
King, M., Sentana, E. and Wadhwani, S. (1994), “Volatility and links between national stock
markets”, Econometrica, Vol. 62 No. 4, pp. 901-33.
Kose, M.A., Prasad, E.S. and Taylor, A.D. (2011), “Thresholds in the process of international
?nancial integration”, Journal of International Money and Finance, Vol. 30, pp. 147-79.
Lee, H., Wu, H. and Wang, Y. (2007), “Contagion effect in ?nancial markets after the South-East
Asia tsunami”, Research in International Business and Finance, Vol. 21, pp. 281-96.
Obstfeld, M. (1986), “Rational and self-ful?lling balance of payments crises”, American Economic
Review, Vol. 76, March, pp. 72-81.
Schmukler, S.L. (2004), “Financial globalization: gain and pain for developing countries”, Federal
Reserve Bank of Atlanta Economic Review, April, pp. 39-66.
Further reading
Anoruo, E. andMustafa, M. (2007), “Anempirical investigationinto the relationof oil to stockmarket
prices”, North American Journal of Finance and Banking Research, Vol. 1 No. 1, pp. 1-15.
Henry, P.B. (2007), “Capital account liberalization: theory, evidence and speculation”, Journal of
Economic Literature, Vol. XLV, December, pp. 887-935.
In, F., Cui, J. and Mahraj, A. (2008), “The impact of a new term auction facility on LIBOR-OIS
spreads and volatility transmission between money and mortgage market”, unpublished
manuscript, available at:http://papers.ssrn.com/sol3/papers.cfm?abstract id¼1272806
Ji, P.I. and In, F. (2010), “The impact of the global ?nancial crisis on the cross-currency linkage
of LIBOR-OIS spreads”, International Financial Markets, Institutions and Money, Vol. 20,
pp. 575-89.
McAndrews, J., Sarkar, A. and Wang, Z. (2008), “The effects of the term auction facility on the
London inter-bank offered rate”, Federal Reserve Bank of New York Staff Reports No. 335.
Taylor, J.B. and Williams, J.C. (2008), “Further results on a black swan in the money market”,
Research Discussion Paper 07-046, Stanford Institute for Economic Policy.
Corresponding author
Simplice A. Asongu can be contacted at: [email protected]
The 2011
Japanese
earthquake
353
To purchase reprints of this article please e-mail: [email protected]
Or visit our web site for further details: www.emeraldinsight.com/reprints
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
5
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
This article has been cited by:
1. Guangxi Cao, Wei Xu, Yu Guo. 2015. Effects of climatic events on the Chinese stock market: applying
event analysis. Natural Hazards 77, 1979-1992. [CrossRef]
2. Shuhei Nakano, Yoshito Hirata, Koji Iwayama, Kazuyuki Aihara. 2014. Intra-day response of foreign
exchange markets after the Tohoku-Oki earthquake. Physica A: Statistical Mechanics and its Applications
. [CrossRef]
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
5
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
doc_272694864.pdf