Financial contagion during world crisIs in 2007-2009
by
Aliaksei Malashonak
A thesis submitted in partial fulfillment of the requirements for the degree of
MA in Economics
KyivSchool of Economics
2009
Approved by ______
KSE Program Director
______
______
______
Date ______
KyivSchool of Economics
Abstract
Financial contagion during world crisIs in 2007-2009
by Aliaksei Malashonak
KSE Program Director: Tom Coupé
The paper is the first attempt to analyze the phenomenon of financial contagion during the financial crisis that started in 2007. Time-varying copula models of daily market returns are applied to investigate the impact of the global financial crisis on the dependence measures between seven world stock markets (USA, UK, Germany, Japan, Brazil, Russia and China)during the period of 2005-2009, based on time series of daily return rates. The model of the marginal distributions is assumed to follow a GARCH specification. Two copulas, Gaussian and Symmetrized Joe-Clayton, are employed to construct joint distributions. Such technique allows capturing excess changes in correlations and in the structure of dependence which can be interpreted as contagion.
The results indicate upward shift of constant correlation parameters and significant change of the coefficients describing the behavior of dependence parameters for the majority of country pairs, especially those includingUS financial market. However, the size of the effect is not homogeneous. Changes took maximum values for US, China, Russia Brazil and are lower for Japan, Germany and UK.
Overall, estimated time-varying dependence measures were found to exhibit an increase in financial markets co-movement, which supports the hypothesis of contagion during period of the crisis of 2007-2009.
Keywords: contagion, crisis, copula, dependence measure.
Table of Contents
Chapter 1. Introduction
Chapter 2. Literature Review
Chapter 3. Methodology
3.1 Dependence process and a copula function.
3.2 Conditional copula functions and is properties.
3.3 First step: Models of uniform distribution
3.4 Second step: Two models of copulas
3.5 Normal (Gaussian) copula dependence parameter
3.6 The Symmetrized Joe–Clayton copula dependence parameter.
Chapter 4. Data
Chapter 5. Results
5.1 General comments.
5.2 Main sources of contagion: US and developing countries. Optimistic expectations in Russia and Germany.
5.3 Dependence measures dynamics.
5.4 Evidence for the structure of the crisis.
Chapter 6. Conclusions......
Bibliography...... 50
Appendix...... 52
List of figures
NumberPage
Figure 1. Theoretical example of contagion under unchanging constant dependence parameter.
Figure 2. Daily returns of S&P500 and FTSE indices in 2005-2008.
Figure 3. S&P500 daily index in 2005-2008..
Figure 4. Differences in constant correlation in Normal copula
Figure 5. A typical behavior of the time-varying correlation in 2008 with large positive change in level and dynamics structure of correlation parameter. Correlations in the Normal copula for US vs.UK and absolute value of S&P Index for UK.
Figure 6. A typical behavior of the time-varying correlation in 2008 with negative change in level and change in dynamics of correlation parameter. Correlations in the Normal copula for Germany vs. Japan and absolute value of DAX Index for Germany.
Figure A-1. Conditional correlations in Normal and SCJ copulas assuming the structural break in level and structure of dependence in August 2007: S&P(500) and FTSE (UK)..
Figure A-2. Conditional correlations in Normal and SCJ copulas assuming the structural break in level and structure of dependence: FTSE (UK) and BVSP (Brazil) in August 2007..
Figure A-3. Conditional correlations in Normal and SCJ copulas assuming the structural break in level and structure of dependence: FTSE (UK) and SSE (China) in August 2007..
Figure A-4Conditional correlations in Normal and SCJ copulas assuming the structural break in level and structure of dependence: RTS (Russia) and SSE (China) in August 2007.
Figure A-5. Conditional correlations in SCJ copula and difference between tails assuming the structural break in level and structure of dependence: RTS (Russia) and SSE (China) in August 2007.
Acknowledgments
The author wishes to thank to Olesia Verchenko for thoughtful guidance and valuable comments during the research process. Special regards go to Ann for inspiration and creation of challenging environment.
1
Chapter 1
Introduction
A financial crisis is a situation characterized by a significant decrease in asset prices (market fall) and increase in market volatility (market risk). Financial markets which are integrated through spillover channels can transmit this instability across borders. Therefore, if we consider financial markets as investment opportunities for an investor, then information about correlation between returns of markets becomes necessary in the course of portfolio construction. A critical assumption for portfolio risk analysis is that the cross-country transmission of shocks remains constant even during crisis periods. In case the assumption of constant correlation is violated due to the instability caused by the crisis, the presence of a crisis should have direct influence on portfolio and risk management, since the change in market co-movement affects the performance of an internationally diversified portfolio without dynamic rebalancing.
Here one has to distinguish between interdependence and contagion. Markets generally are connected both during stable and unstable periods. Theoretically, long-term relationship between markets should be predetermined by spillover links between counties. The main factor which may cause increase in financial sensitivity of financial markets is the structure of the capital accounts of the countries. Financial links connect international financial systems, transmitting assets between countries. Real economic links, usually associated with international trade, reflect fundamental economic relationship among economies. Finally, social and political links can lead to clustering of shocks within political clubs or groups.
However, the excess correlation within financial systems, which can not be explained by macroeconomic fluctuations, could be observed during crisis periods. For example, the Thailand crisis of 1997, induced by massive speculative attacks on the local financial market,has spread to South Korea, Indonesia and other countries of the region.
Empirical evidence showed that market shocks can be transmitted between regions with very short lags, and the change in the structure of trade-related capital flows can not perfectly account for this excess relation, as it requires much longer period of time to be affected. Since the factors mentioned are unlikely to cause the observed excess relationship, the notion of the financial contagion was introduced by Pindyck and Rotemberg in 1990. This phenomenon is associated with the evidence of "excess co-movement" in stock and commodity prices between interdependent financial markets built on very different industry and idiosyncratic fundamentals. A more restrictive definition, allowing testing for the presence of this effect, was maintained by the World Bank: contagion occurs when cross-country correlations increase during "crisis times" relative to correlations during "tranquil times." From the researcher's point of view, each financial crisis can be considered as an opportunity to estimate contagion effects between specific countries. The latter description motivates the exploration of contagion effect during the financial crisis of 2007–2009, as the clear evidence of global instability can be observed on the world financial markets.
The most frequently used method of empirical analysis of this phenomenon, following from the definition, is based on the investigation of correlation coefficients of stock return during volatile (crisis) periods compared to relatively stable periods. There are two key points in this approach. First issue is related to defining of the timing of crisis period beginning in order to divide the historical data into crisis and control samples. Secondly, observing crises on many markets at the same time does not necessary imply that they were caused by contagion from one system to another. Instead, they could be caused by similar underlying problems that would have affected each country individually even in the absence of linkages. The natural endogeneity of cross-country time series, which was described above, complicates the estimation of excess correlation as we need to divide the connection between markets into two components: global changes in business activity and new local linkages created after beginning of unstable period.
The global financial crisis of 2007-2009 provides possibility for extensive empirical research on contagion, as itgives the chance to check for existence of excess negative spillover effects between almost all countries of the world. Dramatic movements in the US stock market could have a powerful impact on markets of very different sizes and structures worldwide. In case a significant increase in cross-market correlation coefficients after a shock to one country (or a group of countries) occurred, this could be interpreted as a presence of contagion effect.
This paper proposes the estimation of the dependency between pairs of stock markets and its dynamics during the crisis period. The response of seven stock markets to movements in daily return rates before and during the active pattern of the financial crisis of 2007-2009 is examined. This paper engages the assumption that major moves in equity markets are driven by macroeconomic variables in the long run. Therefore, lagged variables of market returns may be used to explain the trend, as they represent natural historical tendency of specific market. However, in the short run and during crisis periods excess fluctuations are mostly driven by local and global financial environment factors.
Contagion is tested within pairs of countries separately, due to the specificity of bivariate probability density functions applied for estimation. A set of time series includes developed and emerging markets such as USA, UK, Germany, Japan, Brazil, Russia and China. The selected countries represent different economic and geographical areas of the world.
The main task of this paper is to estimate the cross impact between stock markets and examine how the strength of the effect depends on volatility and direction of trend, both before and during the crisis of 2007-2009. Moreover, the paper is the first attempt to analyze financial contagion during the recent financial crisis.
Results obtained in this research can be applied for the purposes of risk management of internationally diversified portfolios. Investment portfolio construction is commonly based on the assumption that values of correlation between assets are known. Thus, non-stable correlation may induce unexpected losses for an investor. International and domestic supervisory institutions are also interested in studies on contagion effect, as the latter can change preferences of financial prudential supervision policy. For example, the crisis on one market can cause additional underestimated consequences influencing the liquidity of financial institutions on other market, thus, decreasing stability. Supervision institutions can predict such co-movement using previous data about the strength of contagion effect and take measures to minimize the crisis impact by means of controlling the capital flows in short-run.
Chapter 2
Literature Review
Increases in cross-market correlations have been documented during numerous market crises. Still, there is widespread disagreement about how contagion should be defined and empirically tested. Nevertheless, all of the previous papers on the topic have detected the evidence of contagion during the periods of high market volatility.
Different papers point toward different directions while answering the question about causes of contagion. A group of researchers claim that contagion is explained by real links, including:
- trade patterns and arrangements (Ades and Chua, 1993);
- technological factors and political instability (Chua, 1993; Easterly and Levine, 1994).
Other authors provide a financial explanation, or argue that herding behavior is the key element to understand the recent contagious episodes, relating contagion to one of the following issues:
- highly integrated capital markets, when shortages from the large country may be transmitted to small countries through trade in assets (Hoffmaister and Végh, 1994; Talvi, 1994);
- "bandwagon" effects, in which investor’s sentiment does not discriminate among different macroeconomic fundamentals across countries (Eichengreen, Rose and Wyplosz, 1995);
- regional preferences of foreign investors, implying that investors first select the larger, usually better known, countries as a place to invest (Calvo and Reinhart, 1996).
Calvo and Reinhart enriched the set of theoretical sources of contagion. They have examined “spillover” or “contagion” effect in light of the Mexican crisis in December 1994 and the effect that this event has had on other emerging market economies. The authors pointed out that institutional practices also could be a channel of spillovers. For instance, mutual funds, while expecting an increasing amount of redemptions, may sell off their holdings of equity in several emerging markets in an effort to raise cash.
Four main methods of testing are most often used in recent research on the topic: cross-market correlation analysis, GARCH analysis, long-term cointegration, and probit models.
Historically, studies examining cross-market correlation constitute the most numerous group, due to simplicity of estimation and easy interpretation of results. The method itself was developed after 1987 crisis when stock markets around the world crashed following the shock in Hong Kong. Afterwards, this method has been used for estimation of every new crisis occurring. The main idea of this method lies in testing whether the difference in correlation coefficients between highly volatile periods (crises) and stable periods is significant. The findings of changes of dependence between stable and crisis periods would suggest that the crises drive higher market dependence. This is empirically supported by the fact that while different economies follow different financial cycles, there are obvious linkages between stock markets during crisis periods (Samitas, Kenourgios and Paltalidis, 2007).
For instance, King and Wadhwani (1990) investigate why in October 1987 almost all stock markets in US, UK and Japan fell, despite difference in economic circumstances. The authors define contagion as a “mistakes-transmitting channel” which appears as the result of attempts by rational agents to infer information from price changes in other markets. Lee and Kim (1993) estimated the correlations between the stock markets for the same event across 12 largest stock indices and got similar result, which maintains the presence of contagion.
Hoffmaister and Végh (1994) and Talvi (1994) define speculative attacks or crises as large movements in exchange rates, interest rates, and international reserves, and test for heterogeneity of fundamental variables in 22 countries between 1967 and 1992. They conclude that for a number of countries there were significant differences in the behavior of key macroeconomic variables between crisis and non-crisis periods, which can be interpreted as contagion by definition.
Contagion effects also have been found by Pindyck and Rotemberg (1990, 1993). They interpret residual co-movement across stocks having very different industry and idiosyncratic fundamentals as the evidence of "excess co-movement" in stock and commodity prices.
However, Forbes and Rigobon (2002) show that unadjusted cross-market correlation coefficients are conditional on market volatility, and thus test of difference between stable and unstable periods are biased due to specific volatility dynamics structure of returns (see Methodology part for details). After correcting for this bias, the authors conclude that there is no empirical evidence of contagion during all crises analyzed (1987 US stock collapse, 1994 Mexican crisis, 1998 East Asian collapse). In other words, the hypothesis of co-integration during financial instability periods is rejected. In the authors’ opinion, interdependence existing in all states of the world acts as the cause of strong cross-market linkages.
Bartram and Wang (2004) argue upon the results obtained by Forbes and Rigobon (2002). Exploring the impact of volatility on market dependence by means of simulated and empirical time series analysis, Bartram and Wang show that “market dependence is not generally conditional on volatility regimes” and that “a bias in dependence measures occurs only for particular assumptions about the time-series dynamics”. In general, the authors argue that due to the latter fact the correction of correlation coefficients estimated during high volatility periods may be unnecessary. As a consequence, contagion can be tested using correlation coefficients approach.
Another method of testing for contagion is ARCH-GARCH variance-covariance transmission modeling, which allows estimating the effect of shocks influencing a certain market on volatility of another one. The case of 1987 US crisis was analyzed in this framework by Hamao (1990) and Chou (1994). The authors have concluded that there has been no evidence of contagion between countries, and that interdependence between markets exists constantly as stable linkage. Edwards (1998) estimated augmented GARCH model for Mexican bonds during the crisis of 1994 and detected the presence of capital transmission effect from Mexico to Chile. According to the paper, no changes in the effect have occurred during the crisis.
Long-term co-integration relationship analysis is another method used by numerous researchers. This approach is based on the assumption that cross-market relationship is constant over the observed period. As a consequence, testing for contagion becomes impossible during short crisis periods. Longin and Slonik (1995), for example, considered market returns data between 1960 and 1990 and found that correlation of the US markets with other countries increased by 0.36 over 30 years. However, the results of the research do not support hypothesis of non-stable relationship during the oil crisis in 1980s and US stock crash in late 1980s. Therefore, the method applied does not allow producing any significant result in finding the evidence of change in cross-market relations.
Several authors also include exogenous explanatory variables (for instance, news, events, and bankruptcies) into the analysis and estimate probit models to test for change in correlation coefficients. These papers also have found cross-country linkages between financial markets. For example, Baig and Goldfajn (1998) state that daily news have had a cross-country influence during 1997-1998 East Asian crisis. Forbes (1999) finds that country specific effects influenced the transmission mechanisms between individual companies during the same crisis period. Eichengreen, Rose and Wyplosz (1998) prove that crisis probability in one country depends on exogenous speculative attacks in other countries in ERM. Kaminski and Reinhart (1998) argue that conditional probability of financial downturn for the country under study increases if other countries in the region have already suffered.