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 Financial
Contagion:
The
Impact
of
 Global
Disasters
 Jory
Fong



Financial
Contagion:
The
Impact
of
Global
Disasters
Jory
Fong
Advisor:
Shannon
Mudd
Senior
Thesis
Haverford
College
Spring
11
TABLE OF CONTENTS
Abstract ..............................................................................................................................3
I. INTRODUCTION .........................................................................................................4
II. LITERATURE REVIEW ...........................................................................................6
III. DATA OVERVIEW …………..................................................................................9
IV. METHODOLOGY ...................................................................................................10
V. RESULTS ...................................................................................................................14
VI. CONCLUSION .........................................................................................................26
VII. BIBLIOGRAPHY ...................................................................................................27
2
ABSTRACT
Most available literature on the contagion effects of crises focuses primarily on
currency crises and does not address the plethora of natural disasters that have occurred
recently. This thesis seeks to compare the effects of natural and non-natural disasters as a
source of contagion due to fundamentals vs. changes perceptions. Events are divided into
two main categories: crises that are expected to remain isolated, and events that are
expected to yield spillover effects. Because there is no reason for a natural disaster to
affect perceptions of the health of a neighboring country’s economy, financial sector, etc.,
any contagion effects should be purely due to fundamental links between the two
economies. For this reason, non-natural disasters, because of the potential for changes in
perceptions are predicted to be a stronger source for financial contagion than natural
disasters. Results from an event study examining government bond spreads indicate that
contrary to opinion, natural disasters yielded more instances of spillover effects for
longer periods of time.
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I. INTRODUCTION
The age of technology and information has given rise to an increasingly
interconnected world. The physical barriers that once segregated countries from one
another have been broken down with innovation that reduces communication costs,
including most recently the advent of the Internet as well as improvements in shipping
technology that has reduced transportation costs. With this increased interconnectivity,
and the access to real-time information, the idea of an “isolated” event no longer exists.
This is particularly so in financial markets which have merged together into a one-stop
shop that can be accessed 24 hours a day, 7 days a week. The segmented nature of
financial markets no longer exists. Anyone with an Internet connection has instant access
to global equity, foreign exchange, commodity and fixed income markets. A change in
interest rates in China will have global repercussions across all different asset classes.
While the new financial landscape has many obvious benefits, there are significant
downfalls that have materialized over the past ten years.
Looking back at the Credit Crisis of 2008, it has become apparent that very few
disasters are isolated. What started as a unique problem to the US housing market quickly
spread into a systematic collapse of financial markets around the world. The phenomenon
of spillover risk has been referred to as financial contagion and has caught the attention
of scholars and economists alike. Much of the previous research regarding this topic
focused around the emerging market twin crises of the 1990’s, involving both banking
and currency market crises in many of the countries. Notably Thailand made the decision
to remove the peg of the Thai Baht to the US Dollar and let it float freely in the
marketplace. This resulted in the entire East Asian region facing several years of
economic weakness. With the new era of economic weakness, contagion studies are
beginning to gain steam again.
While there are several circulating definitions for what constitutes contagion, this
paper defines it as a significant increase in the probability of a crisis occurring in one
country, contingent on a crisis occurring in another country. That is, when a crisis occurs
in Country A, there is an increase in the probability that a crisis occurs in Country B that
cannot be explained by macroeconomic fundamentals such as country’s B’s previous
GDP growth, interest rate levels, exchange rate changes, etc. However, there are a
number of ways that one can operationalize how the probability of a crisis can be
measured. Pericoli (2003) examines five common definitions used in previous literature
that shows the variety of ways researchers have operationalized the study of contagion:
Definition 1: Contagion is a significant increase in the probability of a crisis in one
country, conditional on a crisis occurring in another country.
This definition is most commonly associated with empirical studies of the international
implications of exchange rate collapses in which the probability of an exchange rate crisis
was calculated using a pooled contagion index.
Definition 2: Contagion occurs when volatility of asset prices spills over from the crisis
country to other countries.
A stylized fact in international financial markets is the rise in asset price volatility that
occurs during periods of financial turmoil. Researchers using this definition often use
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event studies to determine whether volatility in country B’s asset prices are affected by a
crisis in country A.
Definition 3: Contagion occurs when cross-country comovements of asset prices cannot
be explained by fundamentals.
If the spread of a crisis reflects an arbitrary switch from one equilibrium to another,
fundamentals alone cannot explain its timing and modalities.
Definition 4: Contagion is a significant increase in comovements of prices and quantities
across markets, conditional on a crisis occurring in one market or group of markets.
By stressing the quantitative dimension (a ‘significant increase’), this definition conveys
the notion of contagion as ‘excessive comovements’, relative to some standard.
Definition 5: (Shift-) contagion occurs when the transmission channel intensifies or, more
generally, changes after a shock in one market.
The international transmission mechanism may strengthen in response to a crisis in one
country. For instance, some channels of transmission might be active only during
financial crises.
Within the available literature, there isn’t always a clear distinction between
interdependence and contagion. While these two concepts share similar qualities, it is
important to differentiate between them. Interdependence describes a relationship
between countries that are mutually connected through macroeconomic factors. These
countries exhibit comovements in financial markets that reflect global macroeconomic
factors such as trade linkages. Therefore, an impact to one country will have predictable
effects on the partner countries due to a common shock such as a decrease in trade and
industry. It is important to note that countries that display interdependence will move
together in predictable patterns.
In contrast, contagion reflects a significant change in the comovements of asset
prices between countries as a result of an isolated shock. When two countries exhibit low
correlations in asset prices, contagion effects would result when a shock changes the
normal relationship between two countries. A financial disaster in one country may cause
investors to reassess their risk evaluations for en entire region. This process illustrates the
spillover effects to neighboring countries while fundamentals remain unchanged. Pericoli
discusses the change in equilibrium in his paper, “Contagion refers to shocks that produce
a discontinuity in the data-generating process of asset prices”(Pericoli 2003). The effects
of a disaster can alter the normal behavior between assets of two countries. As shown in
the Mexican Crisis of 1995 and the Russian Crisis of 1998, asset price comovements
exhibited higher correlations across international borders.
Compared to the definitions and methodologies stated above, for this paper a
combination of definitions 1 and 3 is used. Contagion will be stated as a change in the
probability of a crisis occurring that cannot be explained by fundamentals. To further
clarify, this paper will measure contagion based on abnormal changes in the bond yields
of neighboring countries and major trade partners, contingent on a crisis occurring in
another country. An identified change in yields will be specifically related to the shock.
Because the periodicity of the data is daily, identifying fundamentals that may contribute
5
to volatility in bond prices is difficult. Therefore, I distinguish the effects of links through
interconnectedness as opposed to changes in perception by testing for differences in the
effects of a financial/currency crisis versus a natural disaster.
This paper takes a unique approach by examining the impacts of two types of
disasters: disasters that are expected to remain isolated and disasters that are expected to
yield spillover effects. The division of events can further be categorized into natural
versus non-natural events. The belief is that non-natural disasters are expected to yield
higher instances of spillover effects due to a change in fundamentals. An event such as a
banking crisis may cause investors to reassess their risk evaluations for an entire region.
In contrast, since there is no reason for a natural disaster to affect how people perceive a
neighboring countries health, any contagion effects should purely be due to fundamental
links between the two economies. For this reason, non-natural disasters should be a
stronger source of contagion due to the potential of increased risk perception. Using
different regression techniques, this thesis will test the hypothesis that non-natural
disasters are a stronger source for financial contagion.
II. LITERATURE REVIEW
Empirical literature has shown mixed results regarding the importance of
contagion as a factor of regional weakness during different crises. Positive support is
discussed in Caramazza (2004) and King and Wadhwani (1990).
Francesco Caramazza takes a unique approach in his study of financial linkages
between countries. This paper examines the spillover effects of the Mexican, Asian and
Russian crises, and more specifically, it focuses on the role of the common creditor as a
source for financial contagion. For the purpose of this study, contagion is defined as “the
spread of financial difficulties from one economy to others in the same region and
beyond” (Caramazza 2004). Financial difficulties are indicated by additional pressure on
domestic exchange rates of a country. Like other papers, this paper extends existing
research by looking at the factors that cause a country to become vulnerable to contagion
and increase the risk that a crisis will spillover to neighboring countries and trade
partners. He notes that there are three main categories that help explain the temporal
clustering of crises. They fall into common shocks, trade linkages and financial linkages.
In explaining the simultaneous occurrence of currency crises, a common shock stems
from the interaction of large macroeconomic fundamentals (Caramazza 2004). For
example, macroeconomic change such as interest rates may set off responses in several
different markets. With a trade linkage, depreciation in a currency may lead to a
downturn in economic activity carrying over to major trade partners. Lastly, financial
linkages may be a channel for contagion when a crisis in a given country causes investors
to rebalance their portfolios. Investors may decrease their risk exposure by selling off the
debt of countries believed to have strong financial ties with the crisis country.
The role of the common creditor is believed to have a significant impact on
regional weakness during a currency crisis. A common creditor is a country that lends to
both a crisis country, and a third party country. The idea behind this concept is that a
third country may experience spillover effects through portfolio adjustments made by the
major lender to a crisis country. In the study, a “Common Creditor” variable is created
6
using a combination of the pre-crisis borrowing share and pre-crisis lending share of the
common creditor country.
Caramazza uses a panel probit regression on 41 emerging economies to test for
trade linkages. First, he creates an operational definition of a crisis by creating an
“Exchange Market Pressure” index. Additional variables were added to control for
factors that are determined to move exchange rates. These variables include Real
Exchange Rates, M2 Growth, GDP Growth, Fiscal Balance etc. To determine the effect
of a crisis on the index, the event window used is 6 months. That is, the crisis period that
is observed is the 6-month period from the onset of a given crisis.
The results from this study show that during the 6-month event window, there was
a higher than average change in the amount of crisis countries for all three episodes. The
Mexican Crisis shows 9 countries in crisis, 10 for the Asian Crisis and 13 countries for
the Russian Crisis. The incidence of crisis during these episodes is higher when compared
to other 6-month periods during the 1990’s where the average number of crisis countries
was 6.
Looking at the Common Creditor variable, it is cited as “…the most important,
robust and significant variable” (Caramazza 2004). This factor proves to be very
significant and provides the largest contribution to the probability of a crisis in the study.
The presence of a common creditor also explains the regional pattern specific to each
crisis episode, and that economic factors, not herd behavior is to be blamed. It can be
concluded that the Common Creditor (CC) indicates strong financial ties between the
crisis country and affected spillover countries. Previous studies have shown that the onset
of a crisis in a country may spur a common creditor to readjust its loan portfolio, thus
adversely affecting the debt for a third party country. The common creditor may prepare
for losses in the crisis country by raising additional capital in the way of selling the debt
of the third party country. In the case of a crisis, the perception of increased risk for a
region may cause investors to completely reassess their risk evaluations for an entire
region. Therefore a CC would be less affected by a natural disaster versus a non-natural
or financial disaster. The need to a readjust a loan portfolio is not required for a one time
natural disaster; rather it is required when a failure in the financial markets of country
indicates future problems. A disaster such as an earthquake or tsunami provides no
ground for future risk.
In 1990, Mervyn A. King and Sushil Wadhwani did some seminal work
examining the impact of the October 1987 stock market crash. The one-day decline of 23
percent in the New York Stock Exchange had global implications for financial markets
despite differing economic circumstances (King and Wadhwani 1990). Using an OLS
model, they examine a rational expectations price equilibrium and model contagion
between different equity markets. While it is not surprising that stock markets in different
countries are correlated, what can explain the uniform decline in equity prices across the
globe? The results from the study indicate that an increase in volatility leads to a rise in
correlation of returns across financial markets. King and Wadhwani indicate that the rise
in correlations occur as a result of attempts by rational agents to infer information from
price changes in other markets (King and Wadhwani 1990). The exchange of information
acts as a channel for the contagion effects to spread.
Further support for contagion as a source of weakness during crises is presented
in the works of Eichengreen, Rose and Wyplosz. This study examines the impacts of
7
currency crises and found that the occurrence ofa currency crisis in one country increases
the probability of a speculative attack in other countries by 8 percentage points
(Eichengreen 1996). Thirty years of panel data from twenty industrialized countries
indicates strong spillover effects for various currency crises in the form of speculative
attacks. Within the paper, they cite multiple equilibria as a strong source in propagating
these attacks. This arises when market participants anticipate that a speculative attack
will alter current policy. Examining a currency peg, a multiple equilibrae model exists in
two forms, “the first one features no attack, no change in fundamentals, and indefinite
maintenance of the peg; the second one features a speculative attack followed bt a change
in fundamentals”(Eichengreen 1996). The expectations of a future speculative attack may
present itself as a self-fulfilling prophecy where weakness in the currency market is
created, despite no change in fundamentals. Paul Krugman suggests that this model is
more likely to converge on the artificial equilibrium when fundamentals are wrong.
When fundamentals are clearly inconsistent with a current valuation, investors have little
doubt about the occurrence of a crisis, and the model quickly converges to the
equilibrium where the currency is attacked and devalued (Krugman 1996). In contrast,
when fundamentals are only “good enough” the uncertainty of a crisis continues to
support this multiple equilibrae model. With the uncertainty of many emerging market
economies, weakness in one market may lead to further speculative attacks in other
countries.
The onset of several emerging market crises during the 1990’s gave rise to new
contagion studies. The Mexican, Russian and Asian crises outlined the potential spillover
effects to both neighboring countries as well as other emerging countries. Weak support
for contagion as a source of regional weakness during a crisis is displayed in the works of
Khalid (2002).
The scope of contagion research generally focuses on single markets to identify
different channels for transmission, but Khalid (2002) takes a different approach by
observing multiple markets. The main goal of this study is to examine the interlinkages
between the currency, equity and money markets. By identifying the comovements in
different markets, it becomes possible to isolate specific catalysts for contagion. The East
Asian region suffered significant economic weakness after the Thai Government decided
to float the baht. By cutting the peg to the US Dollar, the region was hit with devalued
currencies and falling asset prices.
The specific indicators used are exchange rates, equity market indices and interest
rates representing the currency, stock and money markets respectively. These indicators
cover both the microeconomic and macroeconomic landscape of neighboring countries
during the crisis period. The sample contains nine East Asian countries including Japan,
and uses a vector auto-regression (VAR) model with daily observations for empirical
estimation (Khalid 2002). Khalid begins by estimating the correlation coefficients for all
three markets across different countries. The VAR approach helps identify potential
causal relationships across different markets and different countries.
The results from the study do not support financial market contagion as the main
source of economic weakness in the region. Equity market values showed the highest
correlation across East Asian countries while interest rates showed the lowest. However,
the finding of low correlations in interest rates may be attributed to the fact that the data
for interest rates is very limited, and many countries have financial controls in place.
8
Given that the VAR and Granger causality tests show only weak support for financial
contagion, other factors are believed to be involved. While strong trade and financial
linkages did generate some spillover effects, the authors point to the presence of weak
fundamentals, financial sector fragility and poor response to the crisis as the main
catalysts. It is important to note that the results do not disprove contagion as a factor for
weakness; rather the findings from this study are weak compared to previous literature.
III. DATA OVERVIEW
The data used in this study is based off of 11 different crisis events. The events
are divided up into 2 categories: events that I expect to remain isolated, and events that I
believe will yield spillover effects. These disasters span from the end of the 20th century
up to the end of 2010. Selective pairing of events occurs for 4 of the 7 disaster countries
for comparison purposes. Using the disaster country, financial spillover effects will be
analyzed for neighboring countries and major trade partners. Disasters range from
tsunamis to terrorist attacks with a full account shown Table 1 below.
Table 1- List of Chosen Disasters, Countries, Start Date and Disaster
Type
Crisis
Peso
Crisis
September
11th
Hurricane
Katrina
Pipeline
Explosion
Glitnir
Bank
Nationalization
Tabasco
Floods
Hungarian
Gov't
Coup
Thailand
Drought
Half
of
Grain
Exports
Red
Sludge
Spill
Indonesian
Tsunami
Country
Mexico
USA
USA
Russia
Iceland
Mexico
Hungary
Thailand
Russia
Hungary
Indonesia
Start
Date
12/1/94
9/11/01
8/29/05
1/22/06
10/7/02
11/8/07
8/15/07
3/26/10
8/15/10
10/7/10
10/26/10
Disaster
Type
Non‐Isolated
Non‐Isolated
Isolated
Non‐Isolated
Non‐Isolated
Isolated
Non‐Isolated
Isolated
Isolated
Isolated
Isolated
This study will analyze the potential for financial contagion through fluctuations
in government bond yield spreads, otherwise known as volatility. In particular,
benchmark 2 YR government bonds will be used to capture short-term effects that may
occur as a result of a disaster. My sample contains bond yields and yield-spreads for 18
different countries around the world. Of the 18 countries, 8 are Asian, 7 are European, 2
are North American and data was also collected for Australia. The time period for the
collected data varies by event, but all data is limited to daily yields 2 years before the
onset of the disaster, and daily yields for 2 years after the onset of the disaster when
possible. For events that occurred less than 2 years before 3/14/2011, data up until that
point will be used. In addition, it should be noted that bond prices and bond yields follow
an inverse relationship.
Yield spread data is created by using the yields of the 2 YR US Treasury note in
conjunction with other government bond data. The daily spread is calculated by taking
the yield of a government bond, and subtracting the corresponding yield on the US 2 YR
9
Treasury. While different factors such as market demand affect bond yields, more risk
generally results in a higher yield. Treasuries are perceived to be the safest asset an
investor can hold due to its liquidity and the high credit rating of the US government.
With low perceived risk, Treasury notes are generally less volatile than other government
securities and serve as a stable benchmark for this study.
Since spillover effects will be measured through the volatility in yield-spreads, it
is important to control for macroeconomic factors that will independently affect bond
yields. The control variables chosen for this study are Real GDP (% change year on year
or YoY), CPI (% change YoY) and Foreign Exchange Rates. Daily exchange rates are
collected for each country using the US Dollar as the base currency. All currencies used
are relevant for the specified time period. Since GDP and CPI are not published daily,
YoY change values are used across the data set. Historically, each indicator shares a
specific relationship with bond yields. GDP has displayed a positive relationship with
yields due to growth. Countries with high GDP rates indicate a positive economic
climate. When individuals are wealthier, they tend to increase their risk appetite towards
higher returning assets such as equities and non-government bonds. Inflation (CPI)
displays a positive relationship with yields as well. The higher the current rate of inflation
and expected future inflation, yields will rise across the yield curve as investors demand
to be compensated for additional inflation risk. Inflation is seen as detrimental to a bond
because it erodes the purchasing power of future cash flows. Lastly, foreign exchange
rates tend to be a lagging indicator of bond yields. Historical data has shown that when
the yield spread increases in favor of a certain currency, that currency will appreciate
against other currencies. For example, if the current yield on the Canadian 2 YR
government bond is 2% and the current yield for the US 2 YR Treasury is 1%, the spread
is 100 basis points (1/100th of a percent) in favor of Canada. If Canada decides to raise
interest rates and the government bond appreciates to 3%, the spread is now 200 basis
points. Historically, this increase in spread has been followed by an appreciation of the
CAD against the USD. While several different factors move bond prices/yields,
controlling for these 3 major factors will allow me to examine the root cause of spillover
effects more closely.
IV. ESTIMATION METHODOLOGY
The methodology used in this paper closely mirrors the approaches used by
Caramazza (2004) and Eichengreen (1996). In particular, I create an operational
definition of contagion to be used in a seemingly unrelated regression model (SUR).
After gathering all relevant data, I used a SUR regression model to test my hypothesis
that manmade disasters exhibit more episodes of financial contagion to neighboring
countries and major trade partners.
5.1. Indentifying contagion (Indices)
To test for the impact of natural and non-natural disasters in the propagation of financial
contagion, it is necessary to create an operational definition of contagion. Following
similar methods used in the previously mentioned studies, I first constructed an index of
10
bond yield spreads (SPREAD), that accounts for movements in a country’s 2 YR bond
relative to the US treasury:
SPREADit=Yit for country i and period t
This index is created for every focus country specific to a given disaster. The sample
period used consists of yield-spread data from 2 years before the onset of the disaster and
up to 2 years following the onset of the disaster.
Countries experiencing contagion effects during the specified disaster are
identified as those experiencing yield-spreads that exceed a specific threshold within six
months of the beginning of the episode. To test for contagion I define a contagion index
(CONTAGION) which is defined as:
CONTAGION Focus Country =1, if SPREAD>µCI + 1.645σCI
=0, otherwise
where µCI represents the pooled mean of the spread index and 1.645σCI represents the
pooled standard deviation of the spread index. For each focus country, the pooled mean
and standard deviation is calculated from data during the 2-year period before the crisis,
and the 2-year period after the onset of the crisis. Means and standard deviations were
calculated in excel for each country. For instances where a country did not publish yields
and the US did, the spread for that day was omitted so that it will not skew the index.
5.2. Dummy Variables
In order to examine the impact of a specific crisis on bond-spreads, I created a
dummy variable for each disaster. The event window consists of the 6-month period after
the onset of the crisis, and closes thereafter. The variables take on the following form
shown below, and all disaster dummies are shown in Table 2:
DummyEvent =1, if Date>Event start date & Date<6 months after Event
start date
=0, otherwise
Table 2- List of Event and Corresponding Dummy Variables
Event
Peso
Crisis
September
11th
Hurricane
Katrina
Pipeline
Explosion
Glitnir
Bank
Nationalization
Tabasco
Floods
Hungarian
Gov't
Coup
Thailand
Drought
Half
of
Grain
Exports
Red
Sludge
Spill
Indonesian
Tsunami
Dummy
Variable
Name
Peso
Sept11
Katrina
Pipeline
GlitnirBank
Tabasco
HungCoup
ThaiDroughy
HaltGrain
RedSludge
IndoTsunami
11
The contagion index above is reused in the probit estimation. The variable is
created in the same manner as previously described based on the calculated means and
standard deviations for each focus country. Any movement outside of the upper or lower
bounds indicate unusual yield volatility. The variables take on the following form shown
below, and all disaster dummies are shown in Table 3:
Table 3- Focus Country Dummy Variables with Corresponding Upper & Lower
Bounds
Crisis
Peso
Crisis
th
September
11 Hurricane
Katrina
Pipeline
Explosion
Tabasco
Floods
Glitnir
Bank
Hungarian
Gov't
Coup
Thailand
Drought
Dummy
Name
pesoCanada
pesoGermany
pesoJapan
pesoSpain
septCanada
septGermany
septJapan
septMexico
sepUK
hurrChina
hurrCanada
hurrGermany
hurrJapan
hurrMexico
hurrUK
pipeChina
pipeGermany
pipeItaly
pipeKorea
tabasCanada
tabasGermany
tabasJapan
tabasSpain
glitChina
glitDenmark
glitJapan
glitUK
hungAustria
hungGermany
hungItaly
hungSlovakia
drouChina
drouIndia
Spread
Mean
Spread
SD
Lower
Bound
Upper
Bound
1.010438
1.053
‐0.042324
2.0632
‐0.1827428
1.629
‐1.8117568
1.4462712
‐3.446783
‐3.447
0
‐6.893566
4.037707
1.993
2.044762
6.030652
0.6076169
0.831
‐0.2231499
1.4383837
‐0.032785
1.187
‐1.219392
1.153822
‐3.620636
1.695
‐5.315529
‐1.925743
7.00301
1.542
5.460695
8.545325
1.077067
0.927
0.1499389
2.0041951
‐2.390023
0.514
‐2.9036255
‐1.8764205
‐0.1629126
0.745
‐0.9083443
0.5825191
‐0.7010429
0.851
‐1.5522477
0.1501619
‐3.277216
0.943
‐4.2201765
‐2.3342555
3.937692
1.456
2.481284
5.3941
1.037152
1.049
‐0.011965
2.086269
‐1.957275
1.142
‐3.099724
‐0.814826
‐0.7371785
0.803
‐1.5397667
0.0654097
‐0.6788682
0.842
‐1.5208274
0.163091
0.7875825
0.937
‐0.1489363
1.7241013
‐0.0263371
0.626
‐0.6523868
0.5997126
‐0.0064683
1.148
‐1.1547503
1.1418137
‐2.512869
1.510
‐4.023129
‐1.002609
0.1334158
1.271
‐1.1373772
1.4042088
0.2703913
1.418
‐1.1479637
1.6887463
0.693265
0.995
‐0.30222
1.68875
‐1.696833
1.349
‐3.045789
‐0.347877
0.8638886
0.795
0.0684708
1.6593064
0.7502977
0.737
0.013519
1.4870764
0.5540422
0.659
‐0.1048866
1.212971
1.130297
0.781
0.3496678
1.9109262
1.567249
0.699
0.8679246
2.2665734
1.221642
0.658
0.5635112
1.8797728
5.495405
1.022
4.473033
6.517777
12
Half
of
Grain
Exports
Red
Sludge
Spill
Indonesian
Tsunami
drouJapan
drouSingapore
drouVietnam
haltChina
haltGermany
haltItaly
haltKorea
sludgeAustria
sludgeGermany
sludgeItaly
sludgeSlovakia
tsuAustralia
tsuChina
tsuJapan
tsuMalaysia
tsuSingapore
tsuThailand
‐0.7434782
‐0.4451564
10.29403
1.212669
0.4774318
1.247095
2.963505
0.6467229
0.4021806
1.193634
1.416318
3.38787
1.196695
‐0.5593338
2.012131
‐0.2740382
1.40302
0.436
0.435
2.706
0.698
0.473
0.616
0.459
0.509
0.366
0.591
0.513
0.874
0.720
0.189
0.412
0.203
0.615
‐1.1798948
‐0.8800896
7.588038
0.5146812
0.0043565
0.6315211
2.5045584
0.1373213
0.0357189
0.6025781
0.9032862
2.5137421
0.4767979
‐0.7480668
1.6000031
‐0.4768343
0.7880897
‐0.3070616
‐0.0102232
13.000022
1.9106568
0.9505071
1.8626689
3.4224516
1.1561245
0.7686423
1.7846899
1.9293498
4.2619979
1.9165921
‐0.3706008
2.4242589
‐0.0712421
2.0179503
5.3. Probit Regression Model
Using the previously defined variables, I employ a probit regression model to
examine the factors that lead to increased bond volatility. For my dependent variable I
use the contagion index dummy variable that corresponds with bond spreads over a given
period of time. The binary outcome of this variable indicates whether volatility levels are
normal at any given period. The independent variables used in the regression are the
event dummy, real GDP, CPI and foreign exchange rates. The event dummy indicates
any potential deviations from the mean bond-spread during the specified event window.
The control variables consider the impact of GDP, CPI and foreign exchange rates on
bond yield spreads. The basic probit model is illustrated below with the focus country
variable as the dependent:
In addition to the probit regression, a marginal effects analysis is run for each regression
to simplify the coefficient interpretation. With a fixed effects model, the coefficients are
read similar to an Ordinary Least Squared (OLS) regression. Since the dependent variable
in this study is a dummy, the coefficients indicate a percent change in the dummy
variable being equal to 1.
13
V. RESULTS
5.1. Contagion Index
Table 1 below represents thAe results from the contagion index empirical model. In
comparing 6 natural disasters versus 5 non-natural disasters, natural disasters yielded a
higher average number of countries exposed to contagion. For the chosen natural
disasters, 4 countries on average were exposed to spillover effects for36.8 days in the six
months following the disaster. It should be noted that the spillover effects are defined by
movements of government bond-spreads outside of 1 standard deviation from the mean in
both directions. Within the natural disaster category, the Tabasco Floods in Mexico
resulted in greatest total of exposure days with 63.25 days of high bond-spread volatility.
The average number of days that bond-yields are outside of their average values can be
seen as a proxy for severity. The more severe an event is, the more days that bond-yields
are expected to remain outside of normal levels. The Indonesian Tsunami resulted in the
highest number of countries exposed in the sample with the second longest average
length of exposure of 58 days amongst focus countries.
Table 1- Contagion Index Results
Disaster
Hurricane Katrina
Tabasco Floods
Thailand Drought
Half of Grain Exports
Red Sludge Spill
Indonesian Tsunami
Average
Peso Crisis
September 11th
Pipeline Explosion
Glitnir Bank Nationalization
Hungarian Gov't Coup
Average
Type
Natural
Natural
Natural
Natural
Natural
Natural
Non-Natural
Non-Natural
Non-Natural
Non-Natural
Non-Natural
# of Countries Exposed
5
4
3
3
3
6
4
0
5
3
3
1
2.4
Avg. Length of Exposure (Days)
34.5
63.25
4.8
33.5
27.25
58
36.88333333
0
11
53.5
23.75
3.5
18.35
In comparison, non-natural disasters resulted in an average of 2.4 countries being
exposed to spillover effects for an average of 18.35 days. Both the average number of
countries exposed and average number of days of exposure is significantly less than the
values for natural disasters. The longest average length of exposure is 53.5 days for the
Pipeline Explosion in North Ossetia, Russia. This is nearly 10 days shorter than the
Tabasco Floods in Mexico. September 11th yielded the highest number of countries
exposed to contagion in the non-natural disaster category with 5 countries. The average
length of exposure for this disaster is 11 days, with abnormal bond volatility beginning
on September 12, 2001 for all countries.
14
5.2. Peso Crisis (probit results)
Variable
Peso
FX Rate
Real GDP
CPI
Canada
-.278
(0.000*)
-4.586
(0.000*)
-.723
(0.001*)
-.326
(0.001*)
Germany
-.406
(0.000*)
2.00
(0.000*)
-1.437
(0.000*)
-.194
(0.000*)
Japan
-.145
(0.000*)
.054
(0.000*)
-1.436
(0.000*)
-.573
(0.000*)
Spain
(dropped)
.202
(0.000*)
(dropped)
(dropped)
- P-values are indicated in parentheses
- * significant at 5% level
The results from the peso crisis indicate that the variable Peso is significant for all focus
countries at the 5% level with the exception of Spain, where the variable was dropped
due to a lack of variation in the data. This indicates that during the 6-month event period,
Spanish bond yield-spreads did not move outside of the index bounds. These results are
surprising because the negative sign indicates a decrease in the likelihood of contagion
during the crisis period. Looking at the coefficient for Germany, it indicates that the
chances of German Bund yield-spreads falling outside of the 1 SD range decreases by
40.6% during the Peso crisis. All other control variables were significant at the 5% level
in this regression. To interpret foreign exchange coefficients over 1, the coefficient is
multiplied by the standard deviation of the FX Rate variable (calculated over the 4 year
event period). Interpreting the coefficient for Canada, it indicates that when the FX rate
increases by 1 SD, the probability that Canada’s debt move outside of the specified range
decreases by 23.8%.
The graph shown above indicates the timeline for German Bund yield-spreads during the
Peso crisis. On the x-axis is the Event Timeline, where 0 is equal to the day of the onset
of the event, and negative values indicate days before the event. The y-axis indicates the
yield-spread on a given day. The black lines show the upper and lower bounds for
calculated yield spreads, and red dots indicate yields during the specified period. This
chart shows that yield-spreads remain wider than average about a third of the way after
the onset of the crisis. Yields continue to remain wide after the 6-month event window
ends, and were also wide about a year and a half before the onset of the event. This must
be attributed to some factor affecting yield spreads that are not accounted for.
15
5.3. September 11th (probit results)
Variable
9/11
FX Rate
Real GDP
CPI
Canada
-.352
(0.000*)
.156
(0.678)
.628
(0.000*)
.847
(0.000*)
Germany
-.295
(0.000*)
-1.008
(0.000*)
.908
(0.000*)
-.155
(0.009*)
Japan
-.284
(0.013*)
-.028
(0.000*)
.990
(0.000*)
2.373
(0.000*)
Mexico
-.108
(0.017*)
.049
(0.040*)
-.000
(0.993)
.136
(0.000*)
UK
-.395
(0.000*)
-3.206
(0.000*)
.436
(0.001*)
-.180
(0.015*)
- P-values are indicated in parentheses
- * significant at 5% level
The terrorist attacks of September 11th were significant for all 5 focus countries in the
regression. The largest effect was on the UK, where during the crisis, the likelihood that
UK Gilts move outside of the specified range decreased by 39.5%. Real GDP was
significant for all countries with the exception of Mexico, where it had no effect on bond
spreads. For Japan, the calculated FX rate coefficient indicates that a 1 SD change in the
FX rate decreases the probability of the dependent variable being equal to one by 18.6%.
This graph shows the time line of UK Gilt spreads during 9/11. The clustering of red dots
around the crisis period shows that spreads were moving in and out of the normal bounds
until around the 250 day mark after the onset of the crisis. It should be noted that the
results for American based crisis might exhibit effects from exogenous variables in
addition to increased risk for a focus country. Since the benchmark in this study is the US
Treasury, an event in the US may cause investors to pour money into the treasury market.
This could compress yields on treasuries, thus widening the yield-spread with another
country. Therefore a widening of yield-spreads during US based events does not
necessarily indicate contagion.
16
5.4. Hurricane Katrina (probit results)
Variable
Katrina
FX Rate
Real GDP
CPI
China
-.338
(0.000*)
1.313
(0.000*)
-.115
(0.039*)
.152
(0.000*)
Canada
-.138
(0.000*)
1.02
(0.000*)
.875
(0.000*)
.201
(0.000*)
Germany
.257
(0.000*)
7.073
(0.000*)
-.005
(0.903)
-.769
(0.000*)
Japan
-.063
(0.167)
-.035
(0.000*)
.615
(0.000*)
.947
(0.000*)
Mexico
-.336
(0.000*)
1.473
(0.000*)
-.695
(0.001*)
-.088
(0.186)
UK
.303
(0.000*)
16.211
(0.000*)
28.957
(0.000*)
-1.833
(0.000*)
- P-values are indicated in parentheses
- * significant at 5% level
Hurricane Katrina is significant for all focus countries at the 5% level. For China,
Canada, Japan and Mexico, the coefficient sign is negative indicating a decrease in
contagion effects during this period. On the other hand, the UK and Germany exhibit
positive signs pointing to an increase in the chance of contagion during the crisis. During
the 6-month period after Hurricane Katrina, the likelihood that UK Gilts would exhibit
unusual bond yield volatility increased by 30.3%. This corresponds with the idea that a
disaster will yield spillover effects to major trade partners in the form of increased bond
yield volatility. All control variables with the exception of CPI for Mexico were
significant at the 5% level.
This graph shows that during the crisis period, Chinese bond yield-spreads rarely moved
outside of the specified range. The clustering of red points is within the upper and lower
bound for the majority of the 6-month period. Yield-spreads exceeded the upper bound of
the threshold after the 6-month mark, which indicates the effects of some other event on
spreads. As previously mentioned, the increase in spreads for US based events does not
necessarily indicate spillover effects to focus countries.
17
5.5. Pipeline Explosion (probit results)
Variable
Pipeline
FX Rate
Real GDP
CPI
China
.394
(0.000*)
.042
(0.797)
-.763
(0.000*)
.330
(0.000*)
Germany
.642
(0.000*)
3.398
(0.000*)
-.468
(0.000*)
.486
(0.000*)
Italy
.558
(0.000*)
3.403
(0.000*)
-1.245
(0.000*)
1.048
(0.003*)
Korea
.296
(0.000*)
-.001
(0.002*)
-.333
(0.003*)
.251
(0.000*)
- P-values are indicated in parentheses
- * significant at 5% level
While all focus countries are significant at the 5% level, the results of this regression
differ in that all coefficients have a positive sign. Specifically, the effects of the North
Ossetia Pipeline Explosion had a significant effect on Germany and Italy. The
coefficients indicate a 64.2% and a 55.8% increase in the likelihood of bond-yields
exceeding the threshold, respectively. All control variables are significant at the 5% level
with the exception of real GDP for China. The FX coefficient for Italy shows that when
FX rates increase by 1 SD, the probability for spillover effects increase by 19%.
The U-shape of this graph shows that yield-spreads have been exhibiting high levels of
volatility during this 4 year period. Spreads were higher than average at the -2 year mark,
and were tightening up until about 6-months before the crisis. German Bund yieldspreads remain around the lower bound until the end of the 6-month event window, and
began to rise again. During the event window, spreads floated in and out of the lower
bound. The shape of the data shows that this specific crisis may not have had a large
effect on yield-spreads, rather some event has been shaping the relationship between the
US treasury and the Germany Bund.
18
5.6. Tabasco Floods (probit results)
Variable
Tabasco
FX Rate
Real GDP
CPI
Canada
.499
(0.000*)
2.427
(0.000*)
.048
(0.284)
.373
(0.000*)
Germany
.154
(0.007*)
2.791
(0.000*)
-.008
(0.822)
.390
(0.000*)
Japan
-.669
(0.000*)
-.087
(0.000*)
.957
(0.000*)
.235
(0.000*)
Spain
-.495
(0.000*)
6.769
(0.003*)
-2.525
(0.000*)
1.320
(0.000*)
- P-values are indicated in parentheses
- * significant at 5% level
The effects of the Tabasco floods were mixed amongst the focus countries. While it was
significant at the 5% level, the impact of the event varied. For Canada and Germany, the
crisis period indicates an increase in the likelihood for contagion, while Japan and Spain
show a decrease in the likelihood for contagion. Real GDP was not significant for Canada
or Germany, while all other controls are significant at the 5% level.
The shape of the graph shows that yield-spreads have been widening over the course of
the 4-year period until around the 250-day mark. Despite the negative coefficient, the
graph indicates a strong response to the Tabasco crisis. After the onset of the floods,
Spanish debt spreads began widening immediately. At the 3-month mark, yield-spreads
breached the upper bound calculated for the index.
19
5.7. Glitnir Bank Nationalization (probit results)
Variable
Glitnir
FX Rate
Real GDP
CPI
China
-.071
(0.215)
1.924
(0.000*)
-.452
(0.000*)
.059
(0.000*)
Denmark
-.008
(0.908)
.319
(0.000*)
-.018
(0.613)
.442
(0.000*)
Japan
.999
(0.000*)
.000
(0.837)
.024
(0.806)
.008
(0.810)
UK
-.179
(0.000*)
-1.259
(0.002*)
-.498
(0.000*)
.216
(0.000*)
- P-values are indicated in parentheses
- * significant at 5% level
The nationalization of Glitnir Bank in Iceland had no significance on the debt spreads of
China and Denmark. In contrast, Japan reacted very strongly to this event with a
coefficient of .999 indicating that the probability of increased bond volatility was nearly
certain. None of the control variables had a significant effect on Japan. The converted FX
coefficient for China indicates that when FX rates increase by 1 SD, the probability that
bond-spreads will breach the bounds increases by 77%.
The yield-spreads on Japanese bond-spreads began increasing immediately after Glitnir
bank was nationalized. Spreads stayed above the upper bound for a short period, while
fluctuating below it at times. The movement of spreads around the upper bound indicate
high levels of volatility. After the crisis period ends, spreads remain close to normal
levels until the very end of the 4-year period.
20
5.8. Hungarian Government Coup (probit results)
Variable
Coup
FX Rate
Real GDP
CPI
Austria
-.312
(0.000*)
-2.669
(0.000*)
-.230
(0.000*)
.158
(0.000*)
Germany
.006
(0.894)
.468
(0.305)
.052
(0.342)
.469
(0.000*)
Italy
-.069
(0.183)
.482
(0.317)
.129
(0.012*)
.307
(0.000*)
Slovakia
.338
(0.000*)
.185
(0.000*)
-.122
(0.000*)
.309
(0.000*)
- P-values are indicated in parentheses
- * significant at 5% level
The Hungarian Government Coup was a significant predictor in determining bond-spread
volatility for Austria and Slovakia, but was insignificant for Germany and Italy.
Comparing Austria and Slovakia, the effects of the event differed in terms of its sign. It
was expected that Slovakia would exhibit spillover due to its close proximity to Hungary,
and the results support the hypothesis that non-natural events would cause a reassessment
of risk for an entire region. The controls were significant predictors for bond-volatility
with the exception of FX rates and real GDP for Germany.
The above chart shows high levels of spread volatility during the 4-year period with large
movements in both directions. The data points during the crisis period all remained
within the upper and lower bound for the duration of the incident. The gap represented on
the chart indicates failure to report government bond data during the immediate period
after the coup.
21
5.9. Thailand Drought (probit results)
Variable
Drought
FX Rate
Real GDP
CPI
China
-.291
(0.000*)
-2.040
(0.000*)
.271
(0.478)
-.085
(0.083**)
India
-.540
(0.000*)
.011
(0.261)
.006
(0.569)
.061
(0.000*)
Japan
-.339
(0.000*)
.000
(0.002*)
.005
(0.016*)
.013
(0.047*)
Singapore
-.016
(0.780)
-3.892
(0.000*)
-.024
(0.000*)
.087
(0.000*)
Vietnam
.083
(0.373)
-.000
(0.000*)
.245
(0.006*)
-.012
(0.171)
- P-values are indicated in parentheses
- * significant at 5% level
- ** significant at 10% level
The Thailand Drought variable had a significant impact on China, India and Japan at the
5% level, while insignificant for Singapore and Vietnam. All signs for significant focus
countries are negative, and India shows that during the crisis period, the probability of
abnormal spread volatility decreases by 54%. Real GDP and FX rates are both significant
in determining volatility for Vietnam, but CPI is not. CPI is significant at the 5% level for
India, Japan and Singapore, the 10% level for China and insignificant for Vietnam.
This graph shows that after the onset of the drought in Vietnam, bond spreads began
widening immediately and breached the upper bound at the very end of the 6-month
period. Prior to the drought, volatility levels were high going from one extreme to the
other. After the 6-month event window closed, spreads continued to widen.
22
5.10. Halt of Grain Exports (probit results)
Variable
Grain Halt
FX Rate
Real GDP
CPI
China
.072
(0.590)
-4.384
(0.000*)
1.964
(0.000*)
-.308
(0.000*)
Germany
-.431
(0.000*)
3.421
(0.000*)
.005
(0.421)
.342
(0.000*)
Italy
-.033
(0.625)
-4.624
(0.000*)
-.029
(0.008*)
.384
(0.000*)
Korea
.004
(0.386)
.000
(0.016*)
.000
(0.017*)
.000
(0.000*)
- P-values are indicated in parentheses
- * significant at 5% level
The halt of grain exports out of Russia was only significant for Germany at the 5% level
and insignificant for all other focus countries. These results are surprising as Russia is
one of the world’s largest grain exporters, and a ban on the good should create concern
for major importers such as China. CPI is significant for all focus countries at the 5%
level, but the effect is negligible for Korea. While all controls for Korea were significant,
all of the coefficients were 0.000 and have no considerable effect on predicting spread
volatility.
The results from the probit regression support the graph depicted above. The coefficient
indicates that during the Grain crisis, the likelihood of bond spreads moving outside of
the 1 SD range decreases by 43.1%. Looking at the crisis period points on the scatter plot,
all values stay within the range for the entirety of the event. While the shape of the graph
indicates that there is fluctuation of spreads during the crisis period, they do not breach
the upper bound until after the 6-month event window.
23
5.11. Red Sludge Spill (probit results)
Variable
Sludge
FX Rate
Real GDP
CPI
Austria
-.326
(0.000*)
-2.770
(0.000*)
-.290
(0.000*)
1.473
(0.000*)
Germany
-.315
(0.000*)
6.426
(0.000*)
-.170
(0.000*)
1.653
(0.000*)
Italy
.354
(0.000*)
-2.635
(0.000*)
-.059
(0.000*)
.512
(0.000*)
Slovakia
-.418
(0.000*)
.169
(0.000*)
-.027
(0.000*)
.689
(0.000*)
- P-values are indicated in parentheses
- * significant at 5% level
All focus countries showed a significant response to the sludge variable. Slovakia
displayed the strongest response with a decrease in probability of contagion effects of
41.8% during this period, while Italy showed a positive relationship with the crisis. The
coefficient indicates that during the crisis period, Italian sovereign bond spreads are
35.4% more likely to be outside of the 1 SD range. Every one of the control variables
were significant predictors in determining bond spread volatility. The FX Rate variable
for Germany indicates that with a 1 SD move in foreign exchange rates, Germany is
22.5% more likely to experience contagion effects.
The shape of the graph shows a clear upward trend of data points during after the onset of
the Red Sludge Spill. The upward trend indicates a steady widening in spreads between
the Austrian sovereign bond and the US treasury. Given the recent occurrence of the
event, data for the 2-year period after the event is limited. Looking at the beginning of the
timeline, spreads were at abnormal levels before peaking around the -600 day mark and
sharply declining until the -300 day mark.
24
5.12. Indonesian Tsunami (probit results)
Variable
Tsunami
FX Rate
Real GDP
CPI
Australia
.765
(0.000*)
2.845
(0.000*)
.497
(0.000*)
-.244
(0.003*)
China
.658
(0.000*)
-1.913
(0.000*)
.809
(0.037*)
-.153
(0.001*)
Japan
.156
(0.091**)
-.006
(0.289)
.016
(0.004*)
.099
(0.001*)
Malaysia
.263
(0.002*)
-.810
(0.001*)
-.035
(0.011)
-.009
(0.755)
Singapore
-.124
(0.098**)
-2.851
(0.000*)
-.017
(0.000*)
.051
(0.000*)
Thailand
.505
(0.000*)
.361
(0.000*)
.200
(0.000*)
-.184
(0.000*)
- P-values are indicated in parentheses
- * significant at 5% level
- ** significant at 10% level
The Indonesian Tsunami was significant for Australia, China, Malaysia and Thailand at
the 5% level, and significant at the 10% level for Japan and Singapore. All control
variables were significant predictors of bond spread volatility for the focus countries with
the exception of FX rates for Japan, and GDP and CPI for Malaysia. The calculated
foreign exchange rate coefficients for Australia and Thailand are 69% and 67%
respectively; indicating a strong correlation between bond spread volatility and daily
exchange rates.
The data over the 4-year period indicates a steady rise in the trend of the yield-spread.
The yield-spreads between Australian sovereign bonds and US treasuries remained wide
of normal values for the majority of the crisis period. The shape of the graph during the
earlier periods of the timeline indicate a response to some exogenous shock causing
spreads to tighten beyond normal levels, before widening at a faster rate until the
tsunami.
25
VI. CONCLUSION
Every event will have international repercussions in the world economy and
financial markets regardless of where it occurs. While this is in no way surprising, the
results indicate that the initial hypothesis that non-natural disasters would be most closely
associated with spillover effects was incorrect. The contagion effects experienced by
neighboring countries and major trade partners were more frequent and more prolonged
in length for events that would be considered to be natural disasters.
The results from the probit regression were surprising as well. The initial idea was
that the majority of the events would be significant predictors in determining bond-spread
volatility and would display a positive relationship. This in fact proved to be true in that
nearly all event variables proved to be significant at the 5% level, but the majority of the
coefficients turned out to be negative. The initial logic was that with some sort of crisis,
neighboring countries and major trade partners would reflect risk and spreads would thus
widen. In actuality, it is difficult to determine the psychology of investors in their
reaction to a large-scale disaster.
The results give us a brief view into how markets react to adverse news, but do
not reveal the entire story. One downside to using the US treasury as a benchmark for
other sovereign bonds is that the benchmark is dynamic itself. When considering
spillover effects based on spreads, there are multiple moving parts. As mentioned earlier,
it is very difficult to determine the cause of wide spreads for American based events. In
addition, there is no way to control for all global news and events at a given time, so there
is significant omitted variable bias to be taken into consideration.
For future studies, additional steps can be taken to get a clearer view of the effects
of large-scale disasters. First, for US based events a different benchmark can be used.
The United States treasury is generally viewed as a traditional safe investment with
limited volatility, thus making it a strong benchmark. When dealing with American
disasters, the German Bund or UK Gilt could be used instead- both of which carry a
AAA rating. Secondly this disaster used a 6-month event window to attribute contagion
effects to a specific event. Future studies could experiment by shortening the event
window in order to further specify contagion effects to a specific event. Lastly, this study
has chosen a very small list of events to examine. In a world with no shortage of
disasters, many more events can be explored.
26
VII. BIBLIOGRAPHY
A. M. Khalid and M. Kawai, Was financial market contagion the source of economic
crisis in Asia? Evidence using a multivariate VAR model, Journal of Asian Economics
14 (2003) 131–156.
Caramazza, F., L. A. Ricci, and R. Salgado, 2004, "International Financial Contagion in
Currency Crises", Journal of International Money and Finance, Vol. 23, pp. 51–70.
Eichengreen, B., Rose, A. and Wyplosz, C. (1996) Contagious currency crises: final tests.
Scandinavian Journal of Economics, 38, 463–494.
King, M. A. and Wadhwani, S. (1990) Transmission of volatility between stock markets.
The Review of Financial Studies, 3, 15–33.
Krugman, Paul (1996), “Are Currency Crises Self-Fulfilling?” NBER Macroeconomics
Annual (forthcoming).
27