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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. 3 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 4 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