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Globalization, Financial Crisis and Contagion: Time - Dynamic Evidence from Financial Markets of Developing Countries

Globalization, Financial Crisis and Contagion: Time - Dynamic Evidence from Financial Markets of... Long-term Capital Management Whereas the Indian current acunt has been opened fully though gradually in the 1990s, a more calibrated approach has been followed in the opening of the capital acunt and subsequently the 131 emerging financial markets. The rest of the paper is organized as follows. Section 2 thoroughly reviews related literature. Data and methodology for measuring ntagion are presented and outlined respectively in Section 3. Empirical analysis and discussion are vered in Section 4. We nclude with Section 5. 2. Related literature 2.1. Effects of financial market integration Financial integration between enomies is believed to have two main positive impacts: the improvement of capital allocation efficiency and diversification of risks (Demyanyk, and Volosovych 2008; ulibaly 2009; Kose et al. 2011). However, the recent global financial crisis which is viewed by many analysts and policy makers as worst since the Great Depression has cast a dark shadow on the ntagious effect of financial integration; despite its advantages. There is an extensive enomics and finance literature that addresses the potential benefits of financial integration. From a theoretical standpoint financial globalization should facilitate efficient international allocation of capital and improve international risks sharing (Kose et al. 2011). Kose et al. (2011) posit that the benefits are much greater for developing untries because they are relatively scare in capital and rich in labor sources. In effect, access to foreign capital should help them grow faster through new sources of investment. They further profess that since developing untries have more volatile output growth than advanced industrial enomies; their potential welfare gains from international risk sharing are much greater. Their findings reveal that with certain identifiable thresholds in variables such as financial depth and institutional quality, the st-benefit trade-off from financial openness improves significantly once the threshold nditions are met. Much earlier Demyanyk, and Volosovych (2008) in analyzing the benefits of financial integration (resulting from international risk sharing) among 25 European Union (EU) untries presented a case for diversification of risk across EU member states if the risks are fully shared. In a nutshell they stressed that the 10 new members joining the EU would have higher gains than the long standing 15 members. The most striking indication of financial integration benefits is the case of South Africa, a untry that has experienced financial autarky as a result of the embargo imposed in 1985 and removed in 1993. With respect to ulibaly (2009), there was a significant decrease in the rates of investment, capital and output during the embargo period in South Africa as mpared to pre-embargo and post - embargo periods. During the embargo South Africa uld benefit from financial isolation in event of a global financial meltdown. This implies untries in relative financial autarky as less exposed to international financial shocks. Though a prime advantage of financial integration is risk diversification, paradoxically increased financial globalization can reduce the spe for risk diversification because integrated markets tend to be more interdependent and highly rrelated. Another disadvantage of financial integration uld be linked to threshold factors pointed out earlier by Kose et al. (2011). Their study reveals that untries with low levels of financial depth and institutional quality do not stand to benefit from financial integration. This perspective is shared by Schmukler (2004) who stresses the importance of sound financial fundamentals and strong macroenomic institutions, the presence of which should enable more effective management of crises and lower the probability of crises and ntagion. Therefore financial globalization uld itself be a source of crises. 2.2. Linkages between financial integration (globalization) and crises We have seen that financial globalization has several potential benefits. However the recent stream of financial crises and ntagion owing to growing liberalization of financial systems and integration of financial markets around the world, might lead some to suggest that globalization breeds financial volatility and crises. Though domestic factors are mostly at the origin of crises, there are different channels via which financial globalization uld be related to crises. Firstly, as pointed out by Schmukler (2004) when a untry's financial system is liberalized, it bemes an object of market discipline exercised by both foreign and domestic investors. In a closed enomy, only domestic financial sector. This approach is nsistent with the weight of available empirical evidence on the benefits of capital acunt liberalization for acceleration of enomic growth, particularly in emerging enomies. Evidence suggests that the greatest gains are obtained from openness to foreign direct investment followed by portfolio investment. Benefits resulting from external debt flows are questionable until greater domestic financial market development has taken place (Henry 2007). investors monitor and react to unsound fundamentals, whereas in an open domestic and foreign investors might prompt the untry to achieve sound fundamentals. As elucidated earlier, the absence of sound macroenomic, financial and institutional fundamentals uld increase the probability of crises. It logically follows that antagonistic interests and views between investors (domestic and foreign) on key fundamentals might precipitate crises and reduce the ability to effectively monitor and manage them. Sendly, even with sound domestic fundamentals and quality institutions, international financial market imperfections uld also lead to crises. Among other things, these uld lead to herding behavior, irrational behavior, speculative attacks, bubbles, and crashes. To put this point plainer, regardless of market fundamentals investors uld speculate against a currency if they believe that the exchange rate is unsustainable; this uld lead to self-fulfilling balance ­ of -payments. This thesis illustrated by Obstfeld (1986) has been purported by Schmukler (2004); amongst others. Thirdly, even in the presence of sound fundamentals and absence of imperfections in international capital markets, crises might still arise owing to external factors (Schmukler 2004) such as determinants of capital flows (Calvo et al. 1996) and foreign interest rates (Frankel, and Rose 1996). For instance if a untry bemes dependent on foreign capital, shifts in foreign capital flows uld create financial issues and enomic downturns. Frankel, and Rose (1996) clearly point-out the role foreign interest rates play in determining the likelihood of financial crises in developing untries. Fourthly, still borrowing from Schmukler (2004) financial globalization uld lead to financial crises by ntagion, namely by shocks through real links, financial links and herding - behavior or unexplained high rrelations. We shall focus on this fourth example3 within our research framework; the elucidation and definition of which are worthwhile. 2.3. Definitions and channels of ntagion 2.3.1. Definitions of ntagion As yet, there is no established definition of ntagion by enomists. Acrding to the World Bank, there are three main definitions of the phenomenon. Firstly, from a broad perspective ntagion uld be identified with the general process of stock transmission across untries. Therefore, it is worthwhile understanding that this definition does enmpass both negative shocks and positive spillover effects. Sendly, ntagion uld be nceived as the propagation of shocks between two untries in excess of what should be expected, based on the fundamentals after nsidering -movements triggered by mmon shocks. This send definition is somewhat restrictive only to shocks and presupposes the mastery of what nstitutes the underlying fundamentals, without which an appraisal of excess -movements is not possible. The last and more restrictive definition nsiders the phenomenon as the change in the transmission mechanisms that take place during a period of turmoil and uld be appreciated by a significant increase in cross-market rrelations. Within the framework of this study, we shall be restricted to the third definition because: (1) our study aims to investigate the global financial crisis which is a negative shock and not a positive spill-over (as opposed to the first definition); and (2) we do not master what nstitutes underlying fundamentals of - movements we are about to study (in antagonism to the send definition). Empirically, the third definition was first proposed by Forbes, and Rigobon (2002). They assessed ntagion as a significant increase in market -movements after a shock occurred in one untry. With respect to this definition, the ndition for ntagion is a significant increase in -movements as a result of a shock in one market. This implies, if two markets display a high degree of -movements during the stability period, even if they are highly rrelated during a crisis, if this crisis-rrelation is not significant it does not amount to ntagion. In the absence of a significant rrelation during the crisis - period, the term `interdependence' is used to qualify the situation between the two markets. 2.3.2. Channels of ntagion Borrowing from Schmukler (2004), three mains channels of ntagion have been identified in the literature. (1) Real links which are often associated with trade links. For example if two untries are trading together and mpete in the same external market, a devaluation of the exchange rate of one untry deteriorates the other untry's mpetitive advantage. In a bid to rebalance its external sectors, the loosing untry would want to devaluate its own currency; such is the nature of Chino-American mmercial relations today. (2) Financial links Example on the link between financial integration and crises. me in when two enomies are nnected through the international financial system. For instance, let's nsider leverage institutions facing margin calls. Should the value of the llateral fall as a result of a negative shock in one untry, in a bid to increase their initial stock these institutions will sell some of their holdings in untries not yet affected by the shock. This gives birth to a mechanism that ripples the shocks to other untries. (3) Finally, due to herding behaviors or panics resulting from asymmetric information, financial markets might transmit shocks across markets. We shall not elaborate on the mechanics of this third type because of obvious reasons (mmon sense). 2.4. Measuring ntagion Many methods of measuring ntagion have been proposed in the literature to appreciate the spreading of international shocks across untries. The most widely used are cross - market rrelation efficients procedures (King, and Wadhwani 1990; Forbes, and Rigobon 2002; llins, and Biekpe 2003; Lee et al. 2007; Asongu 2011a b), cross - market - integration vectors changing techniques (Kanas 1998), and volatility analysis based on ARCH and GARCH models (King et al. 1994) and direct estimation of specific transmission mechanisms (Forbes 2000). With respect to our restrictive definition of ntagion we shall adopt Forbes, and Rigobon (2002) in the ntext of llins, and Biekpe (2003)4. 3. Data and Methodology 3.1. Data The object of this study is to investigate rrelations between the returns of the USA stock index and stock indexes of emerging untries. Taking the Dow Jones Industrial Average as the base criterion, we analyze if movements between the base criterion and afore mentioned financial markets were significantly strengthened during the recent global financial crisis. In et al. (2008), MacAndrews (2008), Taylor, and William (2008) and more recently Ji, and In (2010) all use the August 9th 2007 date as the start of the financial crisis5. The sample period is divided into two categories: a 14 month pre-crisis period also known as the tranquil or stable period and a 15 month crisis or turmoil period. In a bid to make our findings robust, the turmoil period is further divided into three sections6: the short-run or four month crisis-period (August 09, 2007 to December 06, 2007); the medium-term or eight months crisis period (August 09, 2007 to April 10, 2008) and the long - term or 15 month's crisis - period (August 09, 2007 to November 13, 2008). Weekly data used in the study is obtained from Bloomberg's database. We use local currency index return because Forbes and Rigobon (2002) have shown that using dollar or local indices will produce similar outmes. 3.2. Methodology ntagion is defined by Forbes, and Rigobon (2002) as a significant increase in market -movements after a shock occurred in one untry7. The rrelation efficient is defined as: The hypothesis testing in llins, and Biekpe (2003) is slightly different from that of Forbes, and Rigobon (2002) in that, the test statistics to determine ntagion is not calculated using estimated sample variances. Their test statistics (llins, and Biekpe, 2003) uses exact student statistics based on actual sample rrelation efficients. ntagion is then measured by the significance of increase in adjusted rrelation efficients during the crisis period as mpared with the stable period. 5 Date at which, BNP Paribas announced the closure of its funds that held US subprime debts. 6 From empirical literature, the tranquil period is always longer than the turmoil period. For instance it is longer by a year, ten and a half months and nine months in Forbes, and Rigobon (2002), llins, and Biekpe (2003) and Lee et al. (2007) respectively. With respect to this definition, the presence of high rrelation between two markets during the stable period and eventually ntinuous increase in the high degree of cross market -movements at the turmoil period does not amount to ntagion. Therefore ntagion acrding to this definition is the presence of significant increase in -movements after a shock. On the other hand, if the high rrelation degree is not significant, the term `interdependence' is used to describe the event. 134 xy x y (1) where: `x' is the base criterion while `y' is an emerging equity market. Borrowing from Forbes, and Rigobon (2002), the rrelation efficient is adjusted in the following manner: 1 [1 ( ) 2 ] (2) where: h xx 1 which appreciates the change in high - period volatility against low -period volatility. l xx The crisis - period is used as the high volatility period and the tranquil period as the low volatility period in the calculation of this rrelation efficient adjuster. ntagion is subsequently measured as the significance of adjusted rrelation efficients in time- varying turmoil periods versus the stability period. In empirical literature, llins, and Biekpe (2003), and Lee et al. (2007) have applied both the and F-test respectively for the significance of difference in rrelations. When only one efficient is to be estimated, both tests have the same implications. Following the t-statistics, the significance of increase in rrelations during the turmoil period (t) with respect to the stable(s) period is defined by: t ( t s ) where nt ns 4 1 ( t s ) 2 (3) t( 0.01, nt ns 4 ) with, nt (ns) indicating actual observed weeks during the turmoil (stable) period. The following hypothesis is then put to test: H o : 1 2 0 versus H 1 : 1 2 0 Where H o is the null hypothesis of no ntagion and H 1 is the alternative hypothesis for the presence of ntagion 4. Empirical Results 4.1. Presentation of results Empirical results are presented below in Tables 1 at page no. 136 and Table 2 at page no. 137. 4.2. Discussion of results As shown in Tables 1 and 2, ntagion results based on significant shifts in nditional (unadjusted) rrelation efficients are robust to adjusted (unnditional) rrelations. From a broad point of view the following effects of the financial crisis uld be observed: (1) with the exceptions of India and Dhaka, Asian markets were worst hit; (2) but for Peru, Venezuela and lumbia, Latin American untries were least affected; (3) Africa and Middle East emerging markets were averagely ntaminated with the exceptions of Kenya, Namibia, Nigeria, Moroc, Dubai, Jordan, Israel, Oman, Saudi Arabia and Lebanon. The somewhat immunity of Latin American untries to the recent global financial meltdown is not unexpected. Given its history of financial crises, this ntinent was the most prepared. Current nditions show that Latin America has improved since the Russian crisis, which gave untries in the ntinent some leeway (particularly in monetary policy) to implement measures that attenuate crisis effect. Latin America and the Caribbean untries have built up to 400 billion dollars in international reserves and they have substantially reduced their dollar - denominated debt, especially within the banking system. For instance, lower levels of debt dollarization has allowed Brazil to loosen monetary policy amid the credit crunch in ways that many untries uld not in the post Russian crisis era. In the wake of the financial crisis, Latin American untries swiftly depreciated their currencies without entering the turmoil. From a fiscal perspective, many of these untries saved a nsiderable amount of their tax inme on extra revenue from mmodity bonanza at the turn of the century. For instance, Chile spent only 34% and kept the rest of increased tax llected in a special fund. Therefore even if the crisis had affected these untries, they still had the leeway of increasing spending while lowering taxes, so as to easily rever from recession. Results from Africa are entirely not unexpected. But for Kenya, Namibia, Nigeria and Moroc, African stock markets are ntaminated in at least one time horizon. This reflects the increasing nnection of African markets with global capital flows. As a matter of fact, African markets are growing in size, liquidity and degree of foreign participation. Though it may be misleading to equate ntagion to integration, a logical extension of results uld make a case for African equity markets global integration. DOI: 10.2478/v10259-012-0008-9 Table1. International stock indexes returns nditional (unadjusted) rrelation efficient s in 2007 financial crisis Stable period Short-term turmoil period Medium-term turmoil period Long-term turmoil period Regions untries Full period Botswana -0.040 0.015 0.024 0.014 0.573 0.010 5.641*** Y 0.197 0.008 1.675* Y -0.188 0.013 -2.419** N Egypt 0.336 0.045 0.196 0.034 0.419 0.028 1.968* Y 0.212 0.028 0.154 N 0.353 0.051 1.757* Y Kenya 0.083 0.034 0.008 0.028 0.049 0.030 0.494 N -0.178 0.038 -1.656 N 0.079 0.038 0.970 N Mauritius 0.302 0.030 0.003 0.028 0.001 0.024 0.039 N -0.099 0.027 -0.922 N 0.382 0.031 4.636*** Y Moroc 0.059 0.024 0.024 0.025 0.022 0.019 -0.014 N -0.109 0.019 -1.288 N 0.051 0.021 0.294 N Africa Namibia 0.376 0.037 0.417 0.024 0.558 0.034 1.219 N 0.111 0.043 -3.093*** N 0.342 0.045 -0.845 N Nigeria 0.027 0.038 0.095 0.032 -0.457 0.027 -5.710*** N -0.410 0.026 -5.617*** N -0.060 0.040 -1.743* N South A 0.435 0.030 0.380 0.021 0.674 0.024 2.641** Y 0.238 0.031 -1.378 N 0.428 0.036 0.522 N Tunisia 0.258 0.016 0.129 0.014 0.183 0.009 0.462 N 0.165 0.018 0.343 N 0.341 0.018 2.405** Y A Dhabi -0.069 0.030 -0.053 0.021 0.246 0.024 2.706*** Y -0.133 0.025 -0.761 N -0.086 0.037 -0.356 N Bahrain 0.017 0.015 -0.031 0.013 0.477 0.013 5.069*** Y 0.173 0.012 1.998** Y -0.004 0.017 0.297 N Dubai -0.085 0.039 -0.027 0.027 -0.160 0.031 -1.146 N -0.173 0.030 -1.410 N -0.126 0.048 -1.089 N Israel 0.264 0.028 0.531 0.023 0.697 0.019 1.444 N 0.287 0.025 -2.411** N 0.089 0.032 -5.462*** N Middle Jordan 0.015 0.031 0.044 0.020 0.148 0.016 0.893 N 0.034 0.020 -0.105 N 0.011 0.040 -0.381 N East Kuwait -0.085 0.026 n.a n.a 0.681 0.014 n.a 0.106 0.013 n.a -0.085 0.026 n.a Lebanon 0.200 0.033 0.226 0.023 0.145 0.023 -0.710 N 0.181 0.021 -0.441 N 0.213 0.040 -0.155 N Oman -0.217 0.031 0.112 0.016 0.013 0.019 -0.865 N -0.261 0.028 -3.867*** N -0.306 0.040 -5.112*** N Qatar -0.133 0.040 -0.032 0.030 0.186 0.027 1.930* Y -0.101 0.037 -0.653 N -0.175 0.047 -1.595 N Saudi A 0.012 0.047 0.059 0.041 -0.302 0.027 -3.339*** N -0.113 0.053 -1.681* N -0.002 0.052 0.522 N China 0.073 0.056 0.071 0.048 0.528 0.045 4.507*** Y 0.071 0.048 0.064 N 0.063 0.012 -0.582 N Dhaka 0.047 0.024 -0.275 0.020 -0.462 0.022 -6.698*** N -0.275 0.020 -4.539*** N -0.132 0.020 -3.289*** Y India 0.264 0.038 0.252 0.044 0.400 0.042 0.574 N 0.252 0.044 -0.778 N 0.212 0.048 -1.355 N Indonesia 0.057 0.040 0.394 0.054 0.773 0.055 5.268*** Y 0.394 0.054 1.389 N -0.031 0.052 -3.263*** N Malaysia 0.100 0.026 0.457 0.036 0.838 0.034 6.045*** Y 0.457 0.036 1.903* Y 0.015 0.031 -2.832*** N Mongolia 0.062 0.046 -0.093 0.044 -0.175 0.056 0.665 N -0.093 0.044 1.538 N 0.049 0.038 3.499*** Y Asia Pakistan 0.021 0.037 0.330 0.028 0.338 0.033 2.584** Y 0.330 0.028 2.798*** Y -0.031 0.042 -0.898 N Philippines 0.361 0.040 0.621 0.045 0.855 0.053 7.127*** Y 0.621 0.045 4.229*** Y 0.373 0.048 1.749* Y S. Korea 0.469 0.034 0.640 0.041 0.822 0.047 10.324*** Y 0.640 0.041 6.945*** Y 0.502 0.042 5.562*** Y Sri Lanka 0.204 0.027 0.380 0.019 -0.100 0.021 -0.828 N 0.380 0.019 3.997*** Y 0.288 0.027 3.390*** Y Taiwan 0.429 0.035 0.415 0.040 0.836 0.041 18.401*** Y 0.415 0.040 5.315*** Y 0.482 0.043 7.331*** Y Thailand 0.355 0.037 0.422 0.039 0.715 0.035 5.908*** Y 0.422 0.039 2.722*** Y 0.385 0.046 2.698*** Y Vietnam 0.204 0.060 0.319 0.056 0.524 0.032 3.842*** Y 0.319 0.056 1.985* Y 0.195 0.068 0.876 N Argentina 0.543 0.041 0.644 0.026 0.752 0.045 0.934 N 0.630 0.037 -0.136 N 0.505 0.051 -1.556 N Brazil 0.773 0.043 0.797 0.027 0.831 0.043 0.290 N 0.720 0.042 -0.744 N 0.765 0.052 -0.358 N Chile 0.690 0.034 0.588 0.020 0.721 0.040 1.154 N 0.710 0.040 1.178 N 0.703 0.043 1.281 N lumbia 0.475 0.032 0.336 0.026 0.381 0.030 0.386 N 0.616 0.034 2.802*** Y 0.504 0.036 1.896* Y Latin sta Rica -0.020 0.028 -0.085 0.031 -0.088 0.019 -0.025 N -0.203 0.023 -1.140 N -0.083 0.021 0.023 N America Ecuador 0.030 0.029 0.085 0.015 0.010 0.005 -0.648 N 0.040 0.049 -0.431 N 0.016 0.037 -0.773 N Mexi 0.774 0.037 0.721 0.026 0.814 0.037 0.800 N 0.865 0.037 1.391 N 0.784 0.044 0.692 N Peru 0.422 0.052 -0.066 0.029 0.907 0.063 35.962*** Y 0.693 0.059 11.16*** Y 0.478 0.065 7.185*** Y Venezuela 0.119 0.034 0.035 0.038 0.193 0.027 1.379 N 0.269 0.034 2.313** Y 0.159 0.030 1.385 N The table shows the nditional (unadjusted) cross market rrelation efficients () and standard deviations for the US and other stock markets. Test statistics is obtained from t-transformations. The stable period is defined as the 14-month pre-crisis period (June 08, 2006 to August 09, 2007). The short-term turmoil period is defined as the four-month crisis period (August 09, 2007 to December 06, 2007). The medium-term turmoil period is defined as the eight months crisis period (August 09, 2007 to April 10, 2008). The long-term turmoil period is defined the fifteen months crisis period (August 09, 2007 to November 13, 2008). The full period is the stable period plus the long-term turmoil period (June 08, 2006 to November 13, 2008). ntagion () occurs (Y) when the test statistics is greater than the critical values. No ntagion (N) occurs when the test statistics is less than or equal to the critical value. *, **, ***: represent significance at 10%, 5% and 1% respectively. (nt+ns-4) degrees of freedom for the t-statistics are (66+61-4); (35+61-4);(17+61-4) for the long, medium and short terms respectively. : represents the standard deviation. Table 2. International stock indexes returns unnditional (adjusted) rrelation efficient in 2007 financial crisis Medium-term turmoil Regions untries Full period Stable period Short-term turmoil period Long-term turmoil period period *stp *mtp *ltp * * * Botswana -0.040 0.015 0.030 0.034 0.026 0.647 -0.321 6.747*** Y 0.265 -0.466 2.278** Y -0.197 -0.090 -2.538** N Egypt 0.336 0.045 0.219 0.217 0.163 0.459 -0.202 2.133** Y 0.234 -0.189 0.168 N 0.296 0.475 1.498 N Kenya 0.083 0.034 -0.008 -0.007 -0.007 0.048 0.062 0.479 N -0.155 0.339 -1.432 N 0.069 0.317 0.845 N Mauritius 0.302 0.030 -0.004 -0.003 -0.003 0.001 -0.163 0.043 N -0.102 -0.057 -0.949 N 0.373 0.060 4.502*** Y Moroc 0.059 0.024 0.028 0.028 0.026 0.026 -0.250 -0.016 N -0.126 -0.250 -1.489 N 0.055 -0.160 0.320 N Africa Namibia 0.376 0.037 0.366 0.329 0.323 0.499 0.362 1.152 N 0.084 0.745 -2.419** N 0.261 0.809 -0.694 N Nigeria 0.027 0.038 0.105 0.106 0.086 -0.492 -0.171 -6.40*** N -0.448 -0.195 -6.38*** N -0.054 0.225 -1.573 N South A 0.435 0.030 0.358 0.321 0.302 0.648 0.151 2.604** Y 0.198 0.471 -1.188 N 0.342 0.688 0.446 N Tunisia 0.258 0.016 0.166 0.117 0.117 0.233 -0.399 0.582 N 0.150 0.221 0.312 N 0.311 0.228 2.198** Y A Dhabi -0.069 0.030 -0.051 -0.050 -0.041 0.235 0.107 2.566** Y -0.124 0.145 -0.713 N -0.066 0.686 -0.275 N Bahrain 0.017 0.015 -0.032 -0.033 -0.028 0.483 -0.033 5.160*** Y 0.181 -0.089 2.095** Y -0.004 0.235 0.268 N Dubai -0.085 0.039 -0.027 -0.166 -0.021 -0.152 0.110 -1.089 N -0.166 0.094 -0.002 N -0.096 0.727 -0.830 N Israel 0.264 0.028 0.569 0.522 0.477 0.731 -0.180 1.414 N 0.281 0.052 -2.380** N 0.077 0.338 -4.829 Y Jordan 0.015 0.031 0.050 0.045 0.032 0.166 -0.204 0.998 N 0.034 -0.017 -0.106 N 0.007 1.009 -0.269 N Middle Kuwait -0.085 0.026 n.a -0.007 n.a n.a n.a n.a n.a n.a n.a n.a n.a n.a East Lebanon 0.200 0.033 0.233 0.239 0.178 0.148 -0.051 -0.727 N 0.191 -0.106 -0.463 N 0.167 0.653 -0.124 N Oman -0.217 0.031 0.104 0.087 0.072 0.012 0.181 -0.796 N -0.204 0.680 -2.92*** N -0.201 1.453 -3.14*** N Qatar -0.133 0.040 -0.035 -0.030 -0.026 0.198 -0.123 2.063** Y -0.092 0.196 -0.598 N -0.142 0.540 -1.289 N Saudi A 0.012 0.047 0.074 0.052 0.053 -0.366 -0.351 -4.21*** N -0.099 0.294 -1.474 N -0.002 0.267 -0.606 N China 0.073 0.056 0.058 0.059 0.052 0.488 0.112 4.108*** Y 0.065 0.165 0.060 N 0.009 0.533 -0.470 N Dhaka 0.047 0.024 0.178 0.173 0.171 -0.510 -0.121 -8.05*** N -0.309 -0.224 -5.27*** N -0.148 -0.210 -3.732 Y India 0.264 0.038 0.223 0.266 0.256 0.272 0.559 0.426 N 0.200 0.637 -0.639 N 0.161 0.773 -1.067 N Indonesia 0.057 0.040 0.107 0.165 0.169 0.490 1.441 3.566*** Y 0.267 1.392 0.983 N -0.021 1.287 -2.142** N Malaysia 0.100 0.026 0.152 0.195 0.208 0.679 0.780 5.338*** Y 0.352 0.872 1.521 N 0.012 0.632 2.222** Y Mongolia 0.062 0.046 -0.203 -0.253 -0.270 -0.140 0.258 0.543 N -0.094 -0.009 1.544 N 0.053 -0.138 3.782*** Y Pakistan 0.021 0.037 0.047 0.052 0.043 0.318 0.072 2.420** Y 0.342 -0.077 2.906*** Y -0.026 0.382 -0.764 N Asia Philippines 0.361 0.040 0.122 0.176 0.171 0.701 0.817 6.113*** Y 0.537 0.545 3.712*** Y 0.299 0.650 1.432 N S. Korea 0.469 0.034 0.020 0.035 0.034 0.527 1.724 5.060*** Y 0.477 1.348 4.734*** Y 0.350 1.410 3.687*** Y Sri Lanka 0.204 0.027 -0.006 -0.005 -0.004 -0.127 -0.216 -1.056 N 0.434 -0.271 4.687*** Y 0.286 0.017 3.362*** Y Taiwan 0.429 0.035 -0.034 -0.050 -0.048 0.639 1.028 7.839*** Y 0.311 0.945 3.711*** Y 0.355 1.105 4.876*** Y Thailand 0.355 0.037 0.109 0.121 0.111 0.605 0.374 4.908*** Y 0.353 0.527 2.282** Y 0.296 0.815 2.087** Y Vietnam 0.204 0.060 0.172 0.109 0.098 0.687 -0.327 5.169*** Y 0.299 0.155 1.862* Y 0.165 0.416 0.744 N Argentina 0.543 0.041 0.538 0.579 0.410 0.654 0.746 1.006 N 0.565 0.407 -0.139 N 0.293 0.976 -1.312 N Brazil 0.773 0.043 0.724 0.728 0.601 0.765 0.586 0.352 N 0.640 0.550 -0.843 N 0.557 0.900 -0.482 N Chile 0.690 0.034 0.453 0.454 0.326 0.589 1.044 1.174 N 0.577 1.035 1.189 N 0.434 1.228 1.198 N lumbia 0.475 0.032 0.316 0.300 0.252 0.359 0.142 0.370 N 0.567 0.289 2.665*** Y 0.394 0.377 1.591 N Latin sta Rica -0.020 0.028 -0.108 -0.097 -0.123 -0.111 -0.376 -0.031 N -0.231 -0.235 -1.294 N -0.120 -0.309 0.033 N America Ecuador 0.030 0.029 0.145 0.047 0.034 0.017 -0.659 -1.106 N 0.022 2.360 -0.236 N 0.006 1.517 -0.308 N Mexi 0.774 0.037 0.657 0.655 0.537 0.761 0.430 0.898 N 0.820 0.442 1.607 N 0.614 0.715 0.857 N Peru 0.422 0.052 -0.045 -0.046 -0.029 0.824 1.184 15.092*** Y 0.555 1.072 7.210*** Y 0.242 1.268 3.117*** Y Venezuela 0.119 0.034 0.042 0.037 0.044 0.229 -0.301 1.642 N 0.285 -0.114 2.452** Y 0.201 -0.216 1.760* Y The table shows the unnditional (adjusted) cross market rrelation efficients () and standard deviations for the US and other stock markets. Test statistics is obtained from t-transformations. The stable period is defined as the 14-month pre-crisis period (June 08, 2006 to August 09, 2007). The short-term turmoil period is defined as the four-month crisis period (August 09, 2007 to December 06, 2007). The medium-term turmoil period is defined as the eight months crisis period (August 09, 2007 to April 10, 2008). The long-term turmoil period is defined the fifteen months crisis period (August 09, 2007 to November 13, 2008). The full period is the stable period plus the long-term turmoil period (June 08, 2006 to November 13, 2008). ntagion () occurs (Y) when the test statistics is greater than the critical values. No ntagion (N) occurs when the test statistics is less than or equal to the critical value. *, **, ***: represent significance at 10%, 5% and 1% respectively. (nt+ns-4) degrees of freedom for the t-statistics are (66+61-4); (35+61-4);(17+61-4) for the long, medium and short terms respectively. : represents the standard deviation. *stp, *mtp, *ltp denote adjusted rrelation efficients for the short, medium and long term periods respectively. : rrelation efficient adjuster. Looking at the Middle East, with the exceptions of Israel, Oman and Saudi Arabia, oil exporting untries (Bahrain and Qatar) were ntaminated while but for Abu Dhabi non producing states (Dubai, Jordan, Lebanon) remained unaffected. Borrowing from Anoruo, and Mustafa (2007) on the relation between oil and stock prices, where causality runs from the Dow Jones Industrial Average (DJIA) to oil prices and not vice versa; the DJIA which is our base criterion in this study negatively affected oil prices which in - turn had a toll on stock markets of oil exporting untries. While Dhaka and India in Asia remained unntaminated, China and Mongolia were affected only in the short and long horizons respectively. Other emerging markets were ntaminated at least in two time-horizons each. The unexpected speed and force with which the global financial crisis affected Asian enomies uld be explained from trade channels. The region has deep enomic integration with the rest of the world, especially developments in the United States. A case in point is the loss in export volume growth in Western Asia from 6.4% in 2006 to -0.6 in 2007. nversely, the fact that India was unaffected is not unexpected. This is because, India has a mpletely different approach to financial globalization. Whereas, the Indian current acunt was fully opened on a gradual basis in the 90s, a more calibrated approach has been followed to the opening of the capital acunt and subsequently the financial sector. This approach is nsistent with the weight of available empirical evidence on the benefits of capital acunt liberalization for acceleration of enomic growth, particularly in emerging enomies. Evidence suggests that the greatest gains are obtained from openness to foreign direct investment followed by portfolio investment. Benefits resulting from external debt flows are questionable until greater domestic financial market development has taken place (Henry 2007). nclusion Financial integration among enomies has the benefit of improving allocation efficiency and diversifying risk. However the recent global financial crisis, nsidered as the worst since the Great Depression has re ignited the fierce debate about the merits of financial globalization and its implications for growth especially in developing untries. This paper has examined whether equity markets in emerging untries were vulnerable to ntagion during the recent global financial meltdown. Findings show: (1) with the exceptions of India and Dhaka, Asian markets were worst hit; (2) but for Peru, Venezuela and lumbia, Latin American untries were least affected; (3) Africa and Middle East emerging markets were averagely ntaminated with the exceptions of Kenya, Namibia, Nigeria, Moroc, Dubai, Jordan, Israel, Oman, Saudi Arabia and Lebanon. Results have two important policy implications. Firstly, we nfirm that Latin America was most prepared to brace the financial crisis, implying their fiscal and monetary policies are desirous of examination and imitation. Sendly, we have nfirmed that strategic opening of the current and capital acunts based on empirical evidence for a given region/untry as practiced by India is a caution against global enomic and financial shocks. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Advanced Studies in Finance de Gruyter

Globalization, Financial Crisis and Contagion: Time - Dynamic Evidence from Financial Markets of Developing Countries

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10.2478/v10259-012-0008-9
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Abstract

Long-term Capital Management Whereas the Indian current acunt has been opened fully though gradually in the 1990s, a more calibrated approach has been followed in the opening of the capital acunt and subsequently the 131 emerging financial markets. The rest of the paper is organized as follows. Section 2 thoroughly reviews related literature. Data and methodology for measuring ntagion are presented and outlined respectively in Section 3. Empirical analysis and discussion are vered in Section 4. We nclude with Section 5. 2. Related literature 2.1. Effects of financial market integration Financial integration between enomies is believed to have two main positive impacts: the improvement of capital allocation efficiency and diversification of risks (Demyanyk, and Volosovych 2008; ulibaly 2009; Kose et al. 2011). However, the recent global financial crisis which is viewed by many analysts and policy makers as worst since the Great Depression has cast a dark shadow on the ntagious effect of financial integration; despite its advantages. There is an extensive enomics and finance literature that addresses the potential benefits of financial integration. From a theoretical standpoint financial globalization should facilitate efficient international allocation of capital and improve international risks sharing (Kose et al. 2011). Kose et al. (2011) posit that the benefits are much greater for developing untries because they are relatively scare in capital and rich in labor sources. In effect, access to foreign capital should help them grow faster through new sources of investment. They further profess that since developing untries have more volatile output growth than advanced industrial enomies; their potential welfare gains from international risk sharing are much greater. Their findings reveal that with certain identifiable thresholds in variables such as financial depth and institutional quality, the st-benefit trade-off from financial openness improves significantly once the threshold nditions are met. Much earlier Demyanyk, and Volosovych (2008) in analyzing the benefits of financial integration (resulting from international risk sharing) among 25 European Union (EU) untries presented a case for diversification of risk across EU member states if the risks are fully shared. In a nutshell they stressed that the 10 new members joining the EU would have higher gains than the long standing 15 members. The most striking indication of financial integration benefits is the case of South Africa, a untry that has experienced financial autarky as a result of the embargo imposed in 1985 and removed in 1993. With respect to ulibaly (2009), there was a significant decrease in the rates of investment, capital and output during the embargo period in South Africa as mpared to pre-embargo and post - embargo periods. During the embargo South Africa uld benefit from financial isolation in event of a global financial meltdown. This implies untries in relative financial autarky as less exposed to international financial shocks. Though a prime advantage of financial integration is risk diversification, paradoxically increased financial globalization can reduce the spe for risk diversification because integrated markets tend to be more interdependent and highly rrelated. Another disadvantage of financial integration uld be linked to threshold factors pointed out earlier by Kose et al. (2011). Their study reveals that untries with low levels of financial depth and institutional quality do not stand to benefit from financial integration. This perspective is shared by Schmukler (2004) who stresses the importance of sound financial fundamentals and strong macroenomic institutions, the presence of which should enable more effective management of crises and lower the probability of crises and ntagion. Therefore financial globalization uld itself be a source of crises. 2.2. Linkages between financial integration (globalization) and crises We have seen that financial globalization has several potential benefits. However the recent stream of financial crises and ntagion owing to growing liberalization of financial systems and integration of financial markets around the world, might lead some to suggest that globalization breeds financial volatility and crises. Though domestic factors are mostly at the origin of crises, there are different channels via which financial globalization uld be related to crises. Firstly, as pointed out by Schmukler (2004) when a untry's financial system is liberalized, it bemes an object of market discipline exercised by both foreign and domestic investors. In a closed enomy, only domestic financial sector. This approach is nsistent with the weight of available empirical evidence on the benefits of capital acunt liberalization for acceleration of enomic growth, particularly in emerging enomies. Evidence suggests that the greatest gains are obtained from openness to foreign direct investment followed by portfolio investment. Benefits resulting from external debt flows are questionable until greater domestic financial market development has taken place (Henry 2007). investors monitor and react to unsound fundamentals, whereas in an open domestic and foreign investors might prompt the untry to achieve sound fundamentals. As elucidated earlier, the absence of sound macroenomic, financial and institutional fundamentals uld increase the probability of crises. It logically follows that antagonistic interests and views between investors (domestic and foreign) on key fundamentals might precipitate crises and reduce the ability to effectively monitor and manage them. Sendly, even with sound domestic fundamentals and quality institutions, international financial market imperfections uld also lead to crises. Among other things, these uld lead to herding behavior, irrational behavior, speculative attacks, bubbles, and crashes. To put this point plainer, regardless of market fundamentals investors uld speculate against a currency if they believe that the exchange rate is unsustainable; this uld lead to self-fulfilling balance ­ of -payments. This thesis illustrated by Obstfeld (1986) has been purported by Schmukler (2004); amongst others. Thirdly, even in the presence of sound fundamentals and absence of imperfections in international capital markets, crises might still arise owing to external factors (Schmukler 2004) such as determinants of capital flows (Calvo et al. 1996) and foreign interest rates (Frankel, and Rose 1996). For instance if a untry bemes dependent on foreign capital, shifts in foreign capital flows uld create financial issues and enomic downturns. Frankel, and Rose (1996) clearly point-out the role foreign interest rates play in determining the likelihood of financial crises in developing untries. Fourthly, still borrowing from Schmukler (2004) financial globalization uld lead to financial crises by ntagion, namely by shocks through real links, financial links and herding - behavior or unexplained high rrelations. We shall focus on this fourth example3 within our research framework; the elucidation and definition of which are worthwhile. 2.3. Definitions and channels of ntagion 2.3.1. Definitions of ntagion As yet, there is no established definition of ntagion by enomists. Acrding to the World Bank, there are three main definitions of the phenomenon. Firstly, from a broad perspective ntagion uld be identified with the general process of stock transmission across untries. Therefore, it is worthwhile understanding that this definition does enmpass both negative shocks and positive spillover effects. Sendly, ntagion uld be nceived as the propagation of shocks between two untries in excess of what should be expected, based on the fundamentals after nsidering -movements triggered by mmon shocks. This send definition is somewhat restrictive only to shocks and presupposes the mastery of what nstitutes the underlying fundamentals, without which an appraisal of excess -movements is not possible. The last and more restrictive definition nsiders the phenomenon as the change in the transmission mechanisms that take place during a period of turmoil and uld be appreciated by a significant increase in cross-market rrelations. Within the framework of this study, we shall be restricted to the third definition because: (1) our study aims to investigate the global financial crisis which is a negative shock and not a positive spill-over (as opposed to the first definition); and (2) we do not master what nstitutes underlying fundamentals of - movements we are about to study (in antagonism to the send definition). Empirically, the third definition was first proposed by Forbes, and Rigobon (2002). They assessed ntagion as a significant increase in market -movements after a shock occurred in one untry. With respect to this definition, the ndition for ntagion is a significant increase in -movements as a result of a shock in one market. This implies, if two markets display a high degree of -movements during the stability period, even if they are highly rrelated during a crisis, if this crisis-rrelation is not significant it does not amount to ntagion. In the absence of a significant rrelation during the crisis - period, the term `interdependence' is used to qualify the situation between the two markets. 2.3.2. Channels of ntagion Borrowing from Schmukler (2004), three mains channels of ntagion have been identified in the literature. (1) Real links which are often associated with trade links. For example if two untries are trading together and mpete in the same external market, a devaluation of the exchange rate of one untry deteriorates the other untry's mpetitive advantage. In a bid to rebalance its external sectors, the loosing untry would want to devaluate its own currency; such is the nature of Chino-American mmercial relations today. (2) Financial links Example on the link between financial integration and crises. me in when two enomies are nnected through the international financial system. For instance, let's nsider leverage institutions facing margin calls. Should the value of the llateral fall as a result of a negative shock in one untry, in a bid to increase their initial stock these institutions will sell some of their holdings in untries not yet affected by the shock. This gives birth to a mechanism that ripples the shocks to other untries. (3) Finally, due to herding behaviors or panics resulting from asymmetric information, financial markets might transmit shocks across markets. We shall not elaborate on the mechanics of this third type because of obvious reasons (mmon sense). 2.4. Measuring ntagion Many methods of measuring ntagion have been proposed in the literature to appreciate the spreading of international shocks across untries. The most widely used are cross - market rrelation efficients procedures (King, and Wadhwani 1990; Forbes, and Rigobon 2002; llins, and Biekpe 2003; Lee et al. 2007; Asongu 2011a b), cross - market - integration vectors changing techniques (Kanas 1998), and volatility analysis based on ARCH and GARCH models (King et al. 1994) and direct estimation of specific transmission mechanisms (Forbes 2000). With respect to our restrictive definition of ntagion we shall adopt Forbes, and Rigobon (2002) in the ntext of llins, and Biekpe (2003)4. 3. Data and Methodology 3.1. Data The object of this study is to investigate rrelations between the returns of the USA stock index and stock indexes of emerging untries. Taking the Dow Jones Industrial Average as the base criterion, we analyze if movements between the base criterion and afore mentioned financial markets were significantly strengthened during the recent global financial crisis. In et al. (2008), MacAndrews (2008), Taylor, and William (2008) and more recently Ji, and In (2010) all use the August 9th 2007 date as the start of the financial crisis5. The sample period is divided into two categories: a 14 month pre-crisis period also known as the tranquil or stable period and a 15 month crisis or turmoil period. In a bid to make our findings robust, the turmoil period is further divided into three sections6: the short-run or four month crisis-period (August 09, 2007 to December 06, 2007); the medium-term or eight months crisis period (August 09, 2007 to April 10, 2008) and the long - term or 15 month's crisis - period (August 09, 2007 to November 13, 2008). Weekly data used in the study is obtained from Bloomberg's database. We use local currency index return because Forbes and Rigobon (2002) have shown that using dollar or local indices will produce similar outmes. 3.2. Methodology ntagion is defined by Forbes, and Rigobon (2002) as a significant increase in market -movements after a shock occurred in one untry7. The rrelation efficient is defined as: The hypothesis testing in llins, and Biekpe (2003) is slightly different from that of Forbes, and Rigobon (2002) in that, the test statistics to determine ntagion is not calculated using estimated sample variances. Their test statistics (llins, and Biekpe, 2003) uses exact student statistics based on actual sample rrelation efficients. ntagion is then measured by the significance of increase in adjusted rrelation efficients during the crisis period as mpared with the stable period. 5 Date at which, BNP Paribas announced the closure of its funds that held US subprime debts. 6 From empirical literature, the tranquil period is always longer than the turmoil period. For instance it is longer by a year, ten and a half months and nine months in Forbes, and Rigobon (2002), llins, and Biekpe (2003) and Lee et al. (2007) respectively. With respect to this definition, the presence of high rrelation between two markets during the stable period and eventually ntinuous increase in the high degree of cross market -movements at the turmoil period does not amount to ntagion. Therefore ntagion acrding to this definition is the presence of significant increase in -movements after a shock. On the other hand, if the high rrelation degree is not significant, the term `interdependence' is used to describe the event. 134 xy x y (1) where: `x' is the base criterion while `y' is an emerging equity market. Borrowing from Forbes, and Rigobon (2002), the rrelation efficient is adjusted in the following manner: 1 [1 ( ) 2 ] (2) where: h xx 1 which appreciates the change in high - period volatility against low -period volatility. l xx The crisis - period is used as the high volatility period and the tranquil period as the low volatility period in the calculation of this rrelation efficient adjuster. ntagion is subsequently measured as the significance of adjusted rrelation efficients in time- varying turmoil periods versus the stability period. In empirical literature, llins, and Biekpe (2003), and Lee et al. (2007) have applied both the and F-test respectively for the significance of difference in rrelations. When only one efficient is to be estimated, both tests have the same implications. Following the t-statistics, the significance of increase in rrelations during the turmoil period (t) with respect to the stable(s) period is defined by: t ( t s ) where nt ns 4 1 ( t s ) 2 (3) t( 0.01, nt ns 4 ) with, nt (ns) indicating actual observed weeks during the turmoil (stable) period. The following hypothesis is then put to test: H o : 1 2 0 versus H 1 : 1 2 0 Where H o is the null hypothesis of no ntagion and H 1 is the alternative hypothesis for the presence of ntagion 4. Empirical Results 4.1. Presentation of results Empirical results are presented below in Tables 1 at page no. 136 and Table 2 at page no. 137. 4.2. Discussion of results As shown in Tables 1 and 2, ntagion results based on significant shifts in nditional (unadjusted) rrelation efficients are robust to adjusted (unnditional) rrelations. From a broad point of view the following effects of the financial crisis uld be observed: (1) with the exceptions of India and Dhaka, Asian markets were worst hit; (2) but for Peru, Venezuela and lumbia, Latin American untries were least affected; (3) Africa and Middle East emerging markets were averagely ntaminated with the exceptions of Kenya, Namibia, Nigeria, Moroc, Dubai, Jordan, Israel, Oman, Saudi Arabia and Lebanon. The somewhat immunity of Latin American untries to the recent global financial meltdown is not unexpected. Given its history of financial crises, this ntinent was the most prepared. Current nditions show that Latin America has improved since the Russian crisis, which gave untries in the ntinent some leeway (particularly in monetary policy) to implement measures that attenuate crisis effect. Latin America and the Caribbean untries have built up to 400 billion dollars in international reserves and they have substantially reduced their dollar - denominated debt, especially within the banking system. For instance, lower levels of debt dollarization has allowed Brazil to loosen monetary policy amid the credit crunch in ways that many untries uld not in the post Russian crisis era. In the wake of the financial crisis, Latin American untries swiftly depreciated their currencies without entering the turmoil. From a fiscal perspective, many of these untries saved a nsiderable amount of their tax inme on extra revenue from mmodity bonanza at the turn of the century. For instance, Chile spent only 34% and kept the rest of increased tax llected in a special fund. Therefore even if the crisis had affected these untries, they still had the leeway of increasing spending while lowering taxes, so as to easily rever from recession. Results from Africa are entirely not unexpected. But for Kenya, Namibia, Nigeria and Moroc, African stock markets are ntaminated in at least one time horizon. This reflects the increasing nnection of African markets with global capital flows. As a matter of fact, African markets are growing in size, liquidity and degree of foreign participation. Though it may be misleading to equate ntagion to integration, a logical extension of results uld make a case for African equity markets global integration. DOI: 10.2478/v10259-012-0008-9 Table1. International stock indexes returns nditional (unadjusted) rrelation efficient s in 2007 financial crisis Stable period Short-term turmoil period Medium-term turmoil period Long-term turmoil period Regions untries Full period Botswana -0.040 0.015 0.024 0.014 0.573 0.010 5.641*** Y 0.197 0.008 1.675* Y -0.188 0.013 -2.419** N Egypt 0.336 0.045 0.196 0.034 0.419 0.028 1.968* Y 0.212 0.028 0.154 N 0.353 0.051 1.757* Y Kenya 0.083 0.034 0.008 0.028 0.049 0.030 0.494 N -0.178 0.038 -1.656 N 0.079 0.038 0.970 N Mauritius 0.302 0.030 0.003 0.028 0.001 0.024 0.039 N -0.099 0.027 -0.922 N 0.382 0.031 4.636*** Y Moroc 0.059 0.024 0.024 0.025 0.022 0.019 -0.014 N -0.109 0.019 -1.288 N 0.051 0.021 0.294 N Africa Namibia 0.376 0.037 0.417 0.024 0.558 0.034 1.219 N 0.111 0.043 -3.093*** N 0.342 0.045 -0.845 N Nigeria 0.027 0.038 0.095 0.032 -0.457 0.027 -5.710*** N -0.410 0.026 -5.617*** N -0.060 0.040 -1.743* N South A 0.435 0.030 0.380 0.021 0.674 0.024 2.641** Y 0.238 0.031 -1.378 N 0.428 0.036 0.522 N Tunisia 0.258 0.016 0.129 0.014 0.183 0.009 0.462 N 0.165 0.018 0.343 N 0.341 0.018 2.405** Y A Dhabi -0.069 0.030 -0.053 0.021 0.246 0.024 2.706*** Y -0.133 0.025 -0.761 N -0.086 0.037 -0.356 N Bahrain 0.017 0.015 -0.031 0.013 0.477 0.013 5.069*** Y 0.173 0.012 1.998** Y -0.004 0.017 0.297 N Dubai -0.085 0.039 -0.027 0.027 -0.160 0.031 -1.146 N -0.173 0.030 -1.410 N -0.126 0.048 -1.089 N Israel 0.264 0.028 0.531 0.023 0.697 0.019 1.444 N 0.287 0.025 -2.411** N 0.089 0.032 -5.462*** N Middle Jordan 0.015 0.031 0.044 0.020 0.148 0.016 0.893 N 0.034 0.020 -0.105 N 0.011 0.040 -0.381 N East Kuwait -0.085 0.026 n.a n.a 0.681 0.014 n.a 0.106 0.013 n.a -0.085 0.026 n.a Lebanon 0.200 0.033 0.226 0.023 0.145 0.023 -0.710 N 0.181 0.021 -0.441 N 0.213 0.040 -0.155 N Oman -0.217 0.031 0.112 0.016 0.013 0.019 -0.865 N -0.261 0.028 -3.867*** N -0.306 0.040 -5.112*** N Qatar -0.133 0.040 -0.032 0.030 0.186 0.027 1.930* Y -0.101 0.037 -0.653 N -0.175 0.047 -1.595 N Saudi A 0.012 0.047 0.059 0.041 -0.302 0.027 -3.339*** N -0.113 0.053 -1.681* N -0.002 0.052 0.522 N China 0.073 0.056 0.071 0.048 0.528 0.045 4.507*** Y 0.071 0.048 0.064 N 0.063 0.012 -0.582 N Dhaka 0.047 0.024 -0.275 0.020 -0.462 0.022 -6.698*** N -0.275 0.020 -4.539*** N -0.132 0.020 -3.289*** Y India 0.264 0.038 0.252 0.044 0.400 0.042 0.574 N 0.252 0.044 -0.778 N 0.212 0.048 -1.355 N Indonesia 0.057 0.040 0.394 0.054 0.773 0.055 5.268*** Y 0.394 0.054 1.389 N -0.031 0.052 -3.263*** N Malaysia 0.100 0.026 0.457 0.036 0.838 0.034 6.045*** Y 0.457 0.036 1.903* Y 0.015 0.031 -2.832*** N Mongolia 0.062 0.046 -0.093 0.044 -0.175 0.056 0.665 N -0.093 0.044 1.538 N 0.049 0.038 3.499*** Y Asia Pakistan 0.021 0.037 0.330 0.028 0.338 0.033 2.584** Y 0.330 0.028 2.798*** Y -0.031 0.042 -0.898 N Philippines 0.361 0.040 0.621 0.045 0.855 0.053 7.127*** Y 0.621 0.045 4.229*** Y 0.373 0.048 1.749* Y S. Korea 0.469 0.034 0.640 0.041 0.822 0.047 10.324*** Y 0.640 0.041 6.945*** Y 0.502 0.042 5.562*** Y Sri Lanka 0.204 0.027 0.380 0.019 -0.100 0.021 -0.828 N 0.380 0.019 3.997*** Y 0.288 0.027 3.390*** Y Taiwan 0.429 0.035 0.415 0.040 0.836 0.041 18.401*** Y 0.415 0.040 5.315*** Y 0.482 0.043 7.331*** Y Thailand 0.355 0.037 0.422 0.039 0.715 0.035 5.908*** Y 0.422 0.039 2.722*** Y 0.385 0.046 2.698*** Y Vietnam 0.204 0.060 0.319 0.056 0.524 0.032 3.842*** Y 0.319 0.056 1.985* Y 0.195 0.068 0.876 N Argentina 0.543 0.041 0.644 0.026 0.752 0.045 0.934 N 0.630 0.037 -0.136 N 0.505 0.051 -1.556 N Brazil 0.773 0.043 0.797 0.027 0.831 0.043 0.290 N 0.720 0.042 -0.744 N 0.765 0.052 -0.358 N Chile 0.690 0.034 0.588 0.020 0.721 0.040 1.154 N 0.710 0.040 1.178 N 0.703 0.043 1.281 N lumbia 0.475 0.032 0.336 0.026 0.381 0.030 0.386 N 0.616 0.034 2.802*** Y 0.504 0.036 1.896* Y Latin sta Rica -0.020 0.028 -0.085 0.031 -0.088 0.019 -0.025 N -0.203 0.023 -1.140 N -0.083 0.021 0.023 N America Ecuador 0.030 0.029 0.085 0.015 0.010 0.005 -0.648 N 0.040 0.049 -0.431 N 0.016 0.037 -0.773 N Mexi 0.774 0.037 0.721 0.026 0.814 0.037 0.800 N 0.865 0.037 1.391 N 0.784 0.044 0.692 N Peru 0.422 0.052 -0.066 0.029 0.907 0.063 35.962*** Y 0.693 0.059 11.16*** Y 0.478 0.065 7.185*** Y Venezuela 0.119 0.034 0.035 0.038 0.193 0.027 1.379 N 0.269 0.034 2.313** Y 0.159 0.030 1.385 N The table shows the nditional (unadjusted) cross market rrelation efficients () and standard deviations for the US and other stock markets. Test statistics is obtained from t-transformations. The stable period is defined as the 14-month pre-crisis period (June 08, 2006 to August 09, 2007). The short-term turmoil period is defined as the four-month crisis period (August 09, 2007 to December 06, 2007). The medium-term turmoil period is defined as the eight months crisis period (August 09, 2007 to April 10, 2008). The long-term turmoil period is defined the fifteen months crisis period (August 09, 2007 to November 13, 2008). The full period is the stable period plus the long-term turmoil period (June 08, 2006 to November 13, 2008). ntagion () occurs (Y) when the test statistics is greater than the critical values. No ntagion (N) occurs when the test statistics is less than or equal to the critical value. *, **, ***: represent significance at 10%, 5% and 1% respectively. (nt+ns-4) degrees of freedom for the t-statistics are (66+61-4); (35+61-4);(17+61-4) for the long, medium and short terms respectively. : represents the standard deviation. Table 2. International stock indexes returns unnditional (adjusted) rrelation efficient in 2007 financial crisis Medium-term turmoil Regions untries Full period Stable period Short-term turmoil period Long-term turmoil period period *stp *mtp *ltp * * * Botswana -0.040 0.015 0.030 0.034 0.026 0.647 -0.321 6.747*** Y 0.265 -0.466 2.278** Y -0.197 -0.090 -2.538** N Egypt 0.336 0.045 0.219 0.217 0.163 0.459 -0.202 2.133** Y 0.234 -0.189 0.168 N 0.296 0.475 1.498 N Kenya 0.083 0.034 -0.008 -0.007 -0.007 0.048 0.062 0.479 N -0.155 0.339 -1.432 N 0.069 0.317 0.845 N Mauritius 0.302 0.030 -0.004 -0.003 -0.003 0.001 -0.163 0.043 N -0.102 -0.057 -0.949 N 0.373 0.060 4.502*** Y Moroc 0.059 0.024 0.028 0.028 0.026 0.026 -0.250 -0.016 N -0.126 -0.250 -1.489 N 0.055 -0.160 0.320 N Africa Namibia 0.376 0.037 0.366 0.329 0.323 0.499 0.362 1.152 N 0.084 0.745 -2.419** N 0.261 0.809 -0.694 N Nigeria 0.027 0.038 0.105 0.106 0.086 -0.492 -0.171 -6.40*** N -0.448 -0.195 -6.38*** N -0.054 0.225 -1.573 N South A 0.435 0.030 0.358 0.321 0.302 0.648 0.151 2.604** Y 0.198 0.471 -1.188 N 0.342 0.688 0.446 N Tunisia 0.258 0.016 0.166 0.117 0.117 0.233 -0.399 0.582 N 0.150 0.221 0.312 N 0.311 0.228 2.198** Y A Dhabi -0.069 0.030 -0.051 -0.050 -0.041 0.235 0.107 2.566** Y -0.124 0.145 -0.713 N -0.066 0.686 -0.275 N Bahrain 0.017 0.015 -0.032 -0.033 -0.028 0.483 -0.033 5.160*** Y 0.181 -0.089 2.095** Y -0.004 0.235 0.268 N Dubai -0.085 0.039 -0.027 -0.166 -0.021 -0.152 0.110 -1.089 N -0.166 0.094 -0.002 N -0.096 0.727 -0.830 N Israel 0.264 0.028 0.569 0.522 0.477 0.731 -0.180 1.414 N 0.281 0.052 -2.380** N 0.077 0.338 -4.829 Y Jordan 0.015 0.031 0.050 0.045 0.032 0.166 -0.204 0.998 N 0.034 -0.017 -0.106 N 0.007 1.009 -0.269 N Middle Kuwait -0.085 0.026 n.a -0.007 n.a n.a n.a n.a n.a n.a n.a n.a n.a n.a East Lebanon 0.200 0.033 0.233 0.239 0.178 0.148 -0.051 -0.727 N 0.191 -0.106 -0.463 N 0.167 0.653 -0.124 N Oman -0.217 0.031 0.104 0.087 0.072 0.012 0.181 -0.796 N -0.204 0.680 -2.92*** N -0.201 1.453 -3.14*** N Qatar -0.133 0.040 -0.035 -0.030 -0.026 0.198 -0.123 2.063** Y -0.092 0.196 -0.598 N -0.142 0.540 -1.289 N Saudi A 0.012 0.047 0.074 0.052 0.053 -0.366 -0.351 -4.21*** N -0.099 0.294 -1.474 N -0.002 0.267 -0.606 N China 0.073 0.056 0.058 0.059 0.052 0.488 0.112 4.108*** Y 0.065 0.165 0.060 N 0.009 0.533 -0.470 N Dhaka 0.047 0.024 0.178 0.173 0.171 -0.510 -0.121 -8.05*** N -0.309 -0.224 -5.27*** N -0.148 -0.210 -3.732 Y India 0.264 0.038 0.223 0.266 0.256 0.272 0.559 0.426 N 0.200 0.637 -0.639 N 0.161 0.773 -1.067 N Indonesia 0.057 0.040 0.107 0.165 0.169 0.490 1.441 3.566*** Y 0.267 1.392 0.983 N -0.021 1.287 -2.142** N Malaysia 0.100 0.026 0.152 0.195 0.208 0.679 0.780 5.338*** Y 0.352 0.872 1.521 N 0.012 0.632 2.222** Y Mongolia 0.062 0.046 -0.203 -0.253 -0.270 -0.140 0.258 0.543 N -0.094 -0.009 1.544 N 0.053 -0.138 3.782*** Y Pakistan 0.021 0.037 0.047 0.052 0.043 0.318 0.072 2.420** Y 0.342 -0.077 2.906*** Y -0.026 0.382 -0.764 N Asia Philippines 0.361 0.040 0.122 0.176 0.171 0.701 0.817 6.113*** Y 0.537 0.545 3.712*** Y 0.299 0.650 1.432 N S. Korea 0.469 0.034 0.020 0.035 0.034 0.527 1.724 5.060*** Y 0.477 1.348 4.734*** Y 0.350 1.410 3.687*** Y Sri Lanka 0.204 0.027 -0.006 -0.005 -0.004 -0.127 -0.216 -1.056 N 0.434 -0.271 4.687*** Y 0.286 0.017 3.362*** Y Taiwan 0.429 0.035 -0.034 -0.050 -0.048 0.639 1.028 7.839*** Y 0.311 0.945 3.711*** Y 0.355 1.105 4.876*** Y Thailand 0.355 0.037 0.109 0.121 0.111 0.605 0.374 4.908*** Y 0.353 0.527 2.282** Y 0.296 0.815 2.087** Y Vietnam 0.204 0.060 0.172 0.109 0.098 0.687 -0.327 5.169*** Y 0.299 0.155 1.862* Y 0.165 0.416 0.744 N Argentina 0.543 0.041 0.538 0.579 0.410 0.654 0.746 1.006 N 0.565 0.407 -0.139 N 0.293 0.976 -1.312 N Brazil 0.773 0.043 0.724 0.728 0.601 0.765 0.586 0.352 N 0.640 0.550 -0.843 N 0.557 0.900 -0.482 N Chile 0.690 0.034 0.453 0.454 0.326 0.589 1.044 1.174 N 0.577 1.035 1.189 N 0.434 1.228 1.198 N lumbia 0.475 0.032 0.316 0.300 0.252 0.359 0.142 0.370 N 0.567 0.289 2.665*** Y 0.394 0.377 1.591 N Latin sta Rica -0.020 0.028 -0.108 -0.097 -0.123 -0.111 -0.376 -0.031 N -0.231 -0.235 -1.294 N -0.120 -0.309 0.033 N America Ecuador 0.030 0.029 0.145 0.047 0.034 0.017 -0.659 -1.106 N 0.022 2.360 -0.236 N 0.006 1.517 -0.308 N Mexi 0.774 0.037 0.657 0.655 0.537 0.761 0.430 0.898 N 0.820 0.442 1.607 N 0.614 0.715 0.857 N Peru 0.422 0.052 -0.045 -0.046 -0.029 0.824 1.184 15.092*** Y 0.555 1.072 7.210*** Y 0.242 1.268 3.117*** Y Venezuela 0.119 0.034 0.042 0.037 0.044 0.229 -0.301 1.642 N 0.285 -0.114 2.452** Y 0.201 -0.216 1.760* Y The table shows the unnditional (adjusted) cross market rrelation efficients () and standard deviations for the US and other stock markets. Test statistics is obtained from t-transformations. The stable period is defined as the 14-month pre-crisis period (June 08, 2006 to August 09, 2007). The short-term turmoil period is defined as the four-month crisis period (August 09, 2007 to December 06, 2007). The medium-term turmoil period is defined as the eight months crisis period (August 09, 2007 to April 10, 2008). The long-term turmoil period is defined the fifteen months crisis period (August 09, 2007 to November 13, 2008). The full period is the stable period plus the long-term turmoil period (June 08, 2006 to November 13, 2008). ntagion () occurs (Y) when the test statistics is greater than the critical values. No ntagion (N) occurs when the test statistics is less than or equal to the critical value. *, **, ***: represent significance at 10%, 5% and 1% respectively. (nt+ns-4) degrees of freedom for the t-statistics are (66+61-4); (35+61-4);(17+61-4) for the long, medium and short terms respectively. : represents the standard deviation. *stp, *mtp, *ltp denote adjusted rrelation efficients for the short, medium and long term periods respectively. : rrelation efficient adjuster. Looking at the Middle East, with the exceptions of Israel, Oman and Saudi Arabia, oil exporting untries (Bahrain and Qatar) were ntaminated while but for Abu Dhabi non producing states (Dubai, Jordan, Lebanon) remained unaffected. Borrowing from Anoruo, and Mustafa (2007) on the relation between oil and stock prices, where causality runs from the Dow Jones Industrial Average (DJIA) to oil prices and not vice versa; the DJIA which is our base criterion in this study negatively affected oil prices which in - turn had a toll on stock markets of oil exporting untries. While Dhaka and India in Asia remained unntaminated, China and Mongolia were affected only in the short and long horizons respectively. Other emerging markets were ntaminated at least in two time-horizons each. The unexpected speed and force with which the global financial crisis affected Asian enomies uld be explained from trade channels. The region has deep enomic integration with the rest of the world, especially developments in the United States. A case in point is the loss in export volume growth in Western Asia from 6.4% in 2006 to -0.6 in 2007. nversely, the fact that India was unaffected is not unexpected. This is because, India has a mpletely different approach to financial globalization. Whereas, the Indian current acunt was fully opened on a gradual basis in the 90s, a more calibrated approach has been followed to the opening of the capital acunt and subsequently the financial sector. This approach is nsistent with the weight of available empirical evidence on the benefits of capital acunt liberalization for acceleration of enomic growth, particularly in emerging enomies. Evidence suggests that the greatest gains are obtained from openness to foreign direct investment followed by portfolio investment. Benefits resulting from external debt flows are questionable until greater domestic financial market development has taken place (Henry 2007). nclusion Financial integration among enomies has the benefit of improving allocation efficiency and diversifying risk. However the recent global financial crisis, nsidered as the worst since the Great Depression has re ignited the fierce debate about the merits of financial globalization and its implications for growth especially in developing untries. This paper has examined whether equity markets in emerging untries were vulnerable to ntagion during the recent global financial meltdown. Findings show: (1) with the exceptions of India and Dhaka, Asian markets were worst hit; (2) but for Peru, Venezuela and lumbia, Latin American untries were least affected; (3) Africa and Middle East emerging markets were averagely ntaminated with the exceptions of Kenya, Namibia, Nigeria, Moroc, Dubai, Jordan, Israel, Oman, Saudi Arabia and Lebanon. Results have two important policy implications. Firstly, we nfirm that Latin America was most prepared to brace the financial crisis, implying their fiscal and monetary policies are desirous of examination and imitation. Sendly, we have nfirmed that strategic opening of the current and capital acunts based on empirical evidence for a given region/untry as practiced by India is a caution against global enomic and financial shocks.

Journal

Journal of Advanced Studies in Finance – de Gruyter

Published: Dec 1, 2012

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