Next Article in Journal
Calisthenics with Words: The Effect of Readability and Investor Sophistication on Investors’ Performance Judgment
Previous Article in Journal
The Effect of Corporate Governance Elements on Corporate Social Responsibility (CSR) Disclosure: An Empirical Evidence from Listed Companies at KSE Pakistan
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJFS
Volume 3
Issue 4
10.3390/ijfs3040557
ijfs-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Do Markets Cointegrate after Financial Crises? Evidence from G-20 Stock Markets

by
Mahfuzul Haque
1 and
Hannarong Shamsub
2,*
1
Department of Accounting, Finance, Insurance and Risk Management, Scott College of Business, Indiana State University, Terre Haute, IN 47809, USA
2
Thailand Institute of Nuclear Technology (Public Organization), Ministry of Science and Technology, 16 Vibhavadi Rangsit Rd, Ladyao, Chatuchak, Bangkok 10900, Thailand
*
Author to whom correspondence should be addressed.
Int. J. Financial Stud. 2015, 3(4), 557-586; https://doi.org/10.3390/ijfs3040557
Submission received: 22 August 2015 / Revised: 26 October 2015 / Accepted: 6 November 2015 / Published: 10 December 2015
Versions Notes

Abstract

:
The results of the single-equation cointegration tests indicate that patterns of cointegration in the two main and four sub-periods are not homogeneous. Two key findings emerge from the study. First, fewer stock markets cointegrated with S&P 500 during the crisis period than they did during the pre-crisis. In other words, as the 2008 financial crisis deepened, S&P 500 and G-20 stock indices moved towards less cointegration. The decreasing number of cointegrating relationships implies that the U.S. stock markets and other G-20 markets have experienced different driving forces since the start of the U.S. crisis. Second, among those markets that are cointegrated with S&P 500, they happened to be deeply affected by S&P and the shocks emerging from it. The 2007–2009 financial crises can be considered a structural break in the long-run relationship and may have resulted from effective joint intervention/responses taken by members of G-20 nations.

1. Introduction

The rising number of financial crises that happened in recent times and studies looking at these events from various perspectives has enriched the literature on financial crises. The world witnessed the dreadful events of 11 September 2001, the attack on the Twin Towers in New York, USA, which caused the stock markets to plunge in the USA. The aftermath of the tragedy was visible worldwide as its impact was felt in major equity markets, which suffered sharp declines, signifying that market participants perceived the event as a global shock. The 2001 event was followed by another crisis of greater magnitude in the United States, namely the housing bubble. The subprime mortgage crisis of 2007–2009, in which the housing market collapsed, causing the values of securities connected to housing prices to tumble thereafter, damaged major financial institutions. In recent years, due to the increase in the degree of integration of world capital markets, financial crises originating from one country have had a worldwide impact. The tragedy of 11 September 2001 and the financial crisis that followed affected more economies than the world has ever seen. Several other crises followed, such as the 2008–2009 Russian financial crises, the 2008–2012 Icelandic financial crises and the 2008–2010 Ireland banking crisis, and the news of the European sovereign debt (Euro) crisis followed, shattering investors’ confidence and causing the global stock markets to plummet.
Since the seminal work of King and Wadhwani (1990) [1], international finance literature has examined how shocks are spread across the borders 1. Despite the fact that much of the literature studies the cointegration between the U.S. stock markets and other countries, very little has explored the co-movement of the U.S. markets and the rest of the G-20 markets. Our paper joins this crisis transmission literature and investigates the transmission of shocks from the U.S. market (S&P) to those of the G-20 nations. The U.S. financial crisis had global implications and brought about a fundamental change in the global economic governance, with the G-20 taking over the leadership of the world economy from the G-7. The G-20 was formed as a group in 1999 after the Asian crisis of 1997, and is an international forum of finance ministers and central bank governors from the twenty most economically developed countries that meet annually to discuss the critical issues affecting the global economy. The G-20 countries, which constitute over three-quarters of the global GDP (on a market exchange rate basis) and over two-thirds of the world’s population, became the de facto major global grouping of countries that is pushing responses to the crisis. The G-20’s work only gained importance in recent years, especially after the Pittsburgh summit in September 2009, though the diplomatic unanimity was formed at the London summit in April 2009. To ease the 2007–2009 financial crises the leaders of G-20 agreed on an action plan, which included reinforcing international cooperation, reforming the international financial institutions and ensuring that the IMF, World Bank and other multilateral development banks have sufficient resources to continue playing their role in overcoming the crisis 2. Building the resilience of the financial sector has been at the heart of the G-20’s work since the global financial crisis. To a large degree, the actions of the G20 economies helped to reverse the direction of the crisis and our findings lends credence to that fact.
In this study, we investigated if any cointegration exists between the G-20 markets with the U.S. after the stabilizing measures put into action by the G-20 countries during the global financial crisis of 2007–2009 3. We also attempted to identify whether the G-20 markets moved toward more or toward less integration after the financial crisis of 2007 4. Our findings provide evidence of the patterns of cointegration and of the effectiveness of G-20 intervention/responses to the crisis. We applied the following methodologies: (1) Cointegration (CI); (2) Vector Auto regression (VAR); (3) Granger Causality (GC) and (4) Variance decomposition (VC) to perform two levels of analysis: bivariate analyses, using the U.S. (S&P) and each individual country, and multivariate analyses, using regional cointegration.
The paper is organized as follows. Section 2 presents the main contributions of the literature. Section 3 discusses the data and the sample, while Section 4 deals with the methodology. Section 5 reports and discusses the empirical results, while Section 6 concludes the study.

2. Literature Review

Market contagion and co-integration/co-movement related to financial crises and their responses to the market are issues of enormous interest in the literature. Bekaert, Harvey and Ng (2005) [13] have identified contagion in equity markets. Papers have been written proposing quantitative measures of contagion (Karolyi (2003) [14], Dungey et al. (2004) [15]) and developing theories to explain it (Allen and Gale (2000) [16]). Concerning the U.S. financial crisis, Wei and Hui (2011) [17] found that the average decline in stock prices during the crisis in a sample of 4000 firms in 24 emerging countries was more severe for those firms intrinsically more dependent on external finance (in particular on bank lending and portfolio flows). Hau and Lai (2011) [18] state that stocks with a high share of equity funds ownership performed relatively well during the crisis, whereas stocks with ownership links to funds that were heavily affected by portfolio losses in financial stocks severely underperformed. Yang et al. (2003) [19] examined whether long-run integration between the United States and many international stock markets has strengthened over time. Their results show that there is no long-run relationship between most of these markets and the United States 5.
Many researchers have also pointed out the increased vulnerability to crises that comes with financial and economic integration 6. Bekaert et al. (2011) [13], using the 2007–2009 financial crisis as a laboratory case, analyzed the transmission of crises to country-industry equity portfolios in 55 countries. They find statistically significant evidence of contagion from U.S. markets and from the global financial sector, but the effects are economically small. By contrast, there has been substantial contagion from domestic equity markets to individual domestic equity portfolios, with its severity inversely related to the quality of countries’ economic fundamentals and policies. Their findings confirm the old “wake-up call” hypothesis, with markets and investors focusing substantially more on country-specific characteristics during the crisis. Slimane et al. (2013) [28] found that the spread of the global financial crisis of 2008/2009 was rapid and affected the functioning and the performance of financial markets. Their paper investigates the patterns of linkage dynamics among three European stock markets, France, Germany and the U.K., during the global financial crisis by analyzing the intra-day dynamics of linkages among these markets during both calm and turmoil phases and applying a VAR-EGARCH framework to high frequency five-minute intra-day returns on selected representative stock indices. It found evidence that the interrelationship among European markets increased substantially during the period of crisis, pointing to an amplification of spillovers. Furthermore, during this period, French and U.K. markets herded around the German market, possibly due to the behavior factors influencing the stock markets on or near dates of extreme events 7. Wasim et al. (2014) [37] examined the contagion effects of the stock markets of Greece, Ireland, Portugal, Spain and Italy (GIPSI), as well as the U.S. stock markets, on seven Eurozone and six non-Eurozone stock markets. Empirical results suggest that among GIPSI stock markets, Spain, Italy, Portugal and Ireland appear to be most contagious for Eurozone and non-Eurozone markets. Their study found that the Eurozone countries of France, Belgium, Austria and Germany, as well as the non-Eurozone countries of UK, Sweden and Denmark, were strongly hit by the contagion shock. Prorokowski (2013) [38] combined quantitative and qualitative research methods and painted the picture of the contemporary European financial markets with particular attention paid to the existing cross-market linkages, vulnerabilities, systemic risks and flawed regulations that altogether constituted a group of factors propagating the financial crisis contagion. Thakor (2015) [39] reviewed the literature on the 2007–2009 crises and discusses the pre-crisis conditions, the crisis triggers, the crisis events, the real effects and the policy responses to the crisis. The author states that the pre-crisis conditions contributed to the housing price bubble and the subsequent price decline that led to a counterparty-risk crisis in which liquidity shrank due to insolvency concerns. The policy responses were influenced both by the initial belief that it was a market-wide liquidity crunch and the subsequent learning that insolvency risk was a major driver. Gennaioli et al. (2015) [40] modeled financial markets in which investor beliefs are shaped by representativeness. The authors express that the investors overreact to a series of good news because such a series is representative of a good state. A little bad news does not change the minds of investors because the good state is still representative, but enough bad news leads to a radical change in beliefs and a financial crisis. The model generates debt over-issuance, “this-time-is-different” beliefs, neglect-of-tail risks, and under- and over-reaction to information, boom-bust cycles and excessive volatility of prices in a unified psychological model of expectations. Reinhart et al. (2014) [41] examined the evolution of real per capita GDP around 100 systemic banking crises. Part of the costs of these crises is due to the protracted nature of recovery. On average, it takes about 8 years to reach the pre-crisis level of income; the median is about 6.5 years. Five to six years after the onset of the crisis, only Germany and the United States (out of 12 systemic cases) have reached their 2007–2008 peaks in real income. Forty-five percent of the episodes recorded double dips. Post-war business cycles are not the relevant comparator for the recent crises in advanced economies.

3. Data and Sample

The indices (equity daily price indices (PI)) in U.S. dollars (USD) or conversions to USD are used in the study and include Australia, Brazil, India, France, Germany, UK, Italy, Indonesia, South Korea, Argentina, Mexico, Japan, Russia, Canada, China, and South Africa 8. After matching the sample periods for each time series, a common sample period from 1 January 2000 to 30 April 2013, with the number of daily observations for each panel, is selected mainly from Yahoo Finance, the Federal Reserve St. Louis database, and Quandl. This period encompasses the three major events that have occurred since the advent of the 21st century—11 September 2001 (hereafter referred as 9/11), the 2007–2009 subprime mortgage crises in United States, and the burst of Europe’s sovereign debt (Euro) crisis in 2010. The purpose was to conduct extensive empirical research on the three events and to compare the impact these events had on the major economies. The sample period has been divided as follows: Two (2) main periods, four (4) subsample periods, and one (1) overall period. Main Period 1 ranges from 1 January 2000 to 31 December 2008. This period coincides with U.S. President George W. Bush’s two terms in office, which also coincided with 9/11 and the start of the subprime mortgage crises in the United States. Main Period 2 spans from 1 January 2009 to the end of our sample period, i.e., 30 April 2013, and includes the post U.S. financial crises and Europe’s sovereign debt (Euro) crisis. Subsample Period 1 covers 1 January 2000 to 10 September 2001 and is labeled as “Pre-9/11.” Subsample Period 2 extends from 15 September 2001 to 31 December 2006. U.S. stock markets were closed for a few days immediately following 9/11. This subsample period is labeled as “Post 9/11 and Pre-Financial Crisis.” Subsample Period 3, which extends from 1 January 2007 to 31 December 2009, is labeled as “Peak Financial Crises,” while the last subsample, Period 4, covers 1 January 2010 to 30 April 2013 and is labeled as “Post U.S. Crisis and Euro Crisis.” The overall period of the data sample is from 1 January 2000 to 30 April 2013.

4. Methodology

Engle & Granger’s (1987) [42] residual-based single-equation of cointegration was employed to analyze the data and estimated the following long-run equilibrium equation:
y t = α t + β t . X t + e t
where yt represents S&P 500 and Xt are individual stock market indices of the G-20 nations.
The augmented Dickey-Fuller (ADF) was used to check whether our time series data are I (1). For a variable to be I (1), the variable must be non-stationary at its level and become stationary after the first difference. We estimated ADF in Equation (2), shown below,
Δ y t = β ′ . D t + π . y t − 1 +   ∑ j = 1 p φ j . Δ y t − 1 + ε t
in which Dt is a vector of deterministic terms.
The single-equation technique was preferred over Johansen cointegration because of its intuitive interpretability. While the Johansen methodology is suitable for a system that involves more than two variables, Engle-Granger cointegration has an advantage when performing bivariate testing (Alexander, 1999 [43]). In this study, we performed bivariate testing between S&P 500 and the stock market of each G-20 country. In addition to the cointegration test, VAR and innovation accounting was also applied to analyze the series that are cointegrated with S&P 500. Regarding the choice of U.S. stock markets, we used S&P 500 instead of the Dow Jones Industrial Average (DJIA) because S&P 500 is a broader measure of market movements than DJIA.

5. Empirical Results and Discussion

The unit root test was conducted on all the periods. The results in Table 1 show that the time series process of all stock indices in all periods are non-stationary at their levels, except British and German Indices in Sub-Period 1, Chinese Index in Sub-Period 2 and South African Index in Sub-Period 4. Their first differences are stationary in all periods.
Table
Table 1. Unit Root Test Results.
Table 1. Unit Root Test Results.
CountriesMain Period 1Main Period 2Sub-period 1Sub-period 2Sub-period 3Sub-period 4
Level of SignificanceLagLevel of SignificanceLagLevel of SignificanceLagLevel of SignificanceLagLevel of SignificanceLagLevel of SignificanceLag
Level1st DifferenceLevel1st DifferenceLevel1st DifferenceLevel1st DifferenceLevel1st DifferenceLevel1st Difference
Australia0.99230.0000 ***10.47090.0000 ***10.20750.0000 ***10.54680.0000 ***00.93780.0000 ***00.24520.0000 ***1
Brazil0.93160.0000 ***10.44260.0000 ***00.44000.0000 ***00.55740.0000 ***10.93850.0000 ***00.11440.0000 ***2
India0.89650.0000 ***30.85380.0000 ***10.19020.0000 ***00.95730.0000 ***00.89340.0000 ***30.71860.0000 ***1
France0.75760.0000 ***00.42620.0000 ***10.10620.0000 ***00.49420.0000 ***00.88510.0000 ***00.33980.0000 ***1
Germany0.7430.0000 ***00.23230.0000 ***10.0329 **0.0000 ***00.75040.0000 ***00.82250.0000 ***00.18270.0000 ***1
UK0.75330.0000 ***40.28610.0000 ***10.0124 **0.0000 ***20.43180.0000 ***00.80990.0000 ***00.18640.0000 ***0
Italy0.99140.0000 ***00.49190.0000 ***1NANANA0.41580.0000 ***00.93740.0000 ***00.36170.0000 ***1
Indonesia0.96110.0000 ***10.48250.0000 ***00.49680.0000 ***10.85730.0000 ***10.99080.0000 ***30.29010.0000 ***0
South Korea0.93400.0000 ***00.70690.0000 ***10.26400.0000 ***00.85090.0000 ***00.93950.0000 ***10.45580.0000 ***1
Argentina0.99580.0000 ***00.84960.0000 ***00.72380.0000 ***00.14690.0000 ***00.99530.0000 ***00.96760.0000 ***0
Mexico0.82110.0000 ***10.32950.0000 ***30.14380.0000 ***10.96050.0000 ***10.94620.0000 ***10.06170.0000 ***3
Japan0.52140.0000 ***00.37280.0000 ***00.3340.0000 ***00.54220.0000 ***00.99520.0000 ***10.49270.0000 ***1
Russia0.99150.0000 ***160.61720.0000 ***10.38180.0000 ***00.98760.0000 ***00.93520.0000 ***00.67820.0000 ***1
Canada0.97540.0000 ***10.60430.0000 ***10.35010.0000 ***00.37780.0000 ***10.92230.0000 ***00.41970.0000 ***1
China0.85070.0000 ***50.0370**0.0000 ***00.67620.0000 ***00.99880.0000 ***00.73600.0000 ***00.2320.0000 ***0
South Africa0.94390.0000 ***20.26350.0000 ***1NANANA0.2644 0.0000 ***00.7845 0.0000 ***10.0312 **0.0000 ***0
Note: *, ** and *** denote levels of significance at 10%, 5% and 1%. NA = Data not available. Time Periods: Main Period 1 (01/01/2000–12/31/2008), Main Period 2 (01/01/2009–04/30/2013), Sub-Period 1 (01/01/2000–09/10/2001), Sub-Period 2 (09/15/2001–12/31/2006), Sub-Period 3 (01/01/2007–12/31/2009), Sub-Period 4 (01/01/2012–04/30/2013).
Table 2 presents the Pearson’s Correlation. The average correlations with S&P 500 of Periods 1 and 2 are very similar, with the correlation coefficients of 0.92 and 0.87 respectively. Among the four sub-periods, Sub-Period 4 shows lowest average correlation of 0.32. The average correlation of Sub-Period 1 is 0.65, while those of Sub-Period 2 and 3 are 0.92 and 0.87 respectively. During the Sub-Period 1, the stock markets of France, Japan and Canada show highest correlations with S&P 500. The markets that show highest correlation in Sub-Period 2 are Germany, the United Kingdom and France. The French and Italian markets exhibit the highest correlations with S&P 500 in Sub-Period 3. We find the markets that are highly correlated with S&P 500 during Sub-Periods 1 to 4 are in Europe and Asia. In the next section, we perform the bivariate cointegration test between the stock market in the G-20 country with S&P 500.
Table
Table 2. Pearson’s Correlation between S&P 500 and Individual G-20 Market.
Table 2. Pearson’s Correlation between S&P 500 and Individual G-20 Market.
IndexMain Period 1Main Period 2Sub-Period 1Sub-period 2Sub-period 3Sub-period 4
NIKKEI_225_JAP_USD0.918812470.7982682840.8483020.91946430.97204580.493656953
AORD_AUS_USD0.9104173910.8785344710.70965610.97040260.94207390.677888613
BOVESPA_BRAZIL_USD0.7127416760.3943799430.84666740.94272730.4551339−0.423957789
BSE_SENSEX_INDIA_USD0.823562430.5079934020.71463410.95460120.7582756−0.282360057
CAC_40_FRANCE_USD0.9505426310.2175922540.9149450.97821750.9898299−0.033576787
DAX_GERMANY_USD0.814849410.8870612050.82764810.97581260.93382450.672849902
FTSE_100_UK_USD0.9477523060.8901540930.87656460.96858310.97474340.773895442
FTSE_MIB_ITALY_USD0.918584634−0.285123418NA0.91783870.9906913−0.413138549
JAKARTA_COMPOSITE_USD0.8126147770.9272600230.62171970.95121230.69511860.787976012
KOPSI_COMPOSITE_SK__USD0.9094942380.8937804390.74397950.93554210.92706820.666669139
MERVAL_ARG_USD0.930897460.7881282980.65404380.9514060.83701850.389796718
MEXABOL_MEX_USD0.8404137020.9611429260.35766830.96253620.92308260.898493919
MSCI_SOUTH_AFRICA___USD0.8924045240.921946026NA0.93374270.83375030.765137815
S_P_TSX_CANADA_USD0.8643821840.8302910920.91201190.95122830.89574940.440639497
SSE_COMPOSITE_CHINA_USD0.674345803−0.089640613−0.43212850.30527620.6672256−0.400101793
RTSI_RUSSIA_USD0.5115925650.7380901390.30943960.87294180.91994620.185714189
Note: NA = Data not available. Time Periods: Main Period 1 (01/01/2000–12/31/2008), Main Period 2 (01/01/2009–04/30/2013), Sub-Period 1 (01/01/2000–09/10/2001), Sub-Period 2 (09/15/2001–12/31/2006), Sub-Period 3 (01/01/2007–12/31/2009), Sub-Period 4 (01/01/2012–04/30/2013).
Table 3 shows the results of single-equation cointegration tests. Nikkei is the only index that cointegrates with S&P during the Main Period 1. In Main Period 2, only Mexico’s Mexabol cointegrates. Testing cointegration during sub-periods, we find that three stock markets namely Canada, Japan and France are cointegrated with S&P 500 in Sub-Period 1. Like Sub-Period 1, three markets cointegrate with S&P 500 in Sub-Period 2; including Germany, UK and Italy. The number of cointregating relationships reduces in Sub-Periods 3 and 4. Only two markets, France and Italy, are cointegrated with S&P 500 during Sub-Period 3. Sub-Period 4 shows no evidence of cointegration. In addition to performing single-equation cointegration tests, we also used the Johansen methodology 9 to estimate the same data set to obtain the results presented in Table A6, Table A7, Table A8, Table A9, Table A10 and Table A11 of the Appendix section. The results of the Johansen estimation indicate that no markets are cointegrated with S&P during the two main periods and four sub-periods, except French and UK Indices, which cointegrate with S&P in Sub-Period 3. Regarding the choice of using either the Engle-Granger or the Johansen methodology to estimate the bivariate cointegration relationships discussed in the methodology section, we decided to adhere to the cointegration tests performed using the Engle-Granger methodology. The overall result shows that there are more stock markets of the G-20 nations that are cointegrated with S&P 500 during the pre-crisis era than during the post-crisis periods. The results of the cointegration analysis in Table 3 reveal two observations.
First, it shows that the markets cointegrated with S&P 500 were deeply affected by S&P 500 and shocks emanating from it. This finding is robust, as the summary of the impulse-response analyses shown in Table 4 and the vector auto regression analyses shown in Table 5 and Table A1 and Table A2 in the Appendix confirm, and is consistent with the study of Slimane et al. (2013) [28]. Second, in general, an increasing number of cointegration relationships indicate that stock markets become more integrated over time because they are being driven by the same common stochastic trends (Rangvid 2001 [21]). However, our results show the opposite. Fewer stock markets cointegrate with S&P 500 during the crisis than during the pre-crisis. In other words, S&P 500 and G-20 stock indices moved toward less cointegration after the 2008 global financial crisis. The findings are similar to those of Bekaert et al. [13], which found weak evidence of contagion from U.S. markets to equity markets globally. The decreasing number of cointegration relationships in our findings may imply that the U.S. stock markets and other G-20 markets have experienced different driving forces since the start of the U.S. crisis. It may also imply that the 2008 financial crisis can be considered a structural break in the long-run relationship. For the sake of brevity, the sub-periods’ tables (Table A1, Table A2, Table A3, Table A4 and Table A5) are not reported here.
Next, we tested to see if the regional market cointegration exhibits the same pattern as that of each individual market and S&P 500. We classify G-20 markets into Asia, Europe, and Latin America. The results in Table 5 below illustrate that only Sub-Period 1 European markets exhibit regional cointegration. Sub-periods 2, 3, and 4 show no cointegration 10. This implies that, as the crisis deepened, fewer G-20 markets were cointegrated with S&P, and no regional markets were integrated.
Table
Table 3. Single Equation Cointegration Test.
Table 3. Single Equation Cointegration Test.
CountriesIndexMain Period 1Main Period 2Sub-period 1Sub-period 2Sub-period 3Sub-period 4
Z-statisticsLagZ-statisticsLagZ-statisticsLagZ-statisticsLagZ-statisticsLagZ-statisticsLag
AustraliaS&P500−7.2000942−3.3175213−15.567850−12.374861−13.831722−2.6910713
AORD_AUS_USD−3.9752262−6.1216263−7.918131−10.600151−13.817552−14.615370
BrazilS&P500−1.77383220.6635131−14.101220−14.178641−0.3773352−10.781812
BOVESPA_BRAZIL_USD−2.8196990−3.7959680−11.242090−11.453431−2.8348781−16.299292
IndiaS&P500−7.88391330.9196741−5.9026592−11.913480−4.225591−5.2829542
BSE_SENSEX_INDIA_USD−4.3280073−3.941620−9.5336530−10.004440−6.5197190−7.0528861
FranceS&P500−11.6725420.0959371−30.83013 ***0−14.740012−30.09887 *2−2.325850
CAC_40_FRANCE_USD−9.3764433−9.4718181−28.98695 ***0−13.037032−30.42807 *2−11.588581
GermanyS&P500−3.5771582−8.5925874−13.411440−20.17685 *1−8.4390931−4.9300924
DAX_GERMANY_USD−2.3937522−15.405440−11.11030−18.53736 *1−9.2855991−14.540540
UKS&P500−10.087262−10.849751−11.425963−19.23137 *1−11.508932−8.585151
FTSE_100_UK_USD−8.051283−14.21151−12.052653−17.89519 *1−11.723942−21.265070
ItalyS&P500−13.447622−3.9843920NANA−18.67027 *0−32.93658 ***2−6.1414640
FTSE_MIB_ITALY_USD−16.063142−8.8299841NANA−19.61084 *0−32.62543 ***2−9.6999140
IndonesiaS&P500−8.0442712−15.670271−5.8050990−18.438660−1.5197162−11.058011
JAKARTA_COMPOSITE_USD−3.6101412−16.963031−5.6465010−14.106190−3.3669070−9.5827083
South KoreaS&P500−6.3084432−6.229481−11.013580−11.695721−12.313771−2.6125141
KOPSI_COMPOSITE_SK__USD−2.9795172−9.1345651−14.859280−8.4338191−11.722361−8.7019361
ArgentinaS&P500−6.7021950−2.4051250−7.1692740−7.1361140−1.97185400.6955962
MERVAL_ARG_USD−3.4948240−3.611430−4.3613130−6.4450010−1.7544970−1.8285160
MexicoS&P500−5.8251492−19.17152 *0−2.4252580−11.302570−5.3614112−13.681780
MEXABOL_MEX_USD−2.1553010−19.79081 *0−11.164621−7.3855630−6.8677090−15.031550
South Africa S&P500−3.3049640−3.8196780NANA−17.671980−5.895090−2.3534850
MSCI_SOUTH_AFRICA___USD−3.7418440−9.423480NANA−16.480670−8.4676311−17.01690
JapanS&P500−22.91169 **1−10.034181−20.27009 *0−16.206371−12.335122−5.2680251
NIKKEI_225_JAP_USD−24.77195 **1−12.785741−17.22679 *0−15.635151−13.403942−15.502361
RussiaS&P500−5.7925652−1.4039941NANA−9.0214171−10.538981−2.9488223
RTSI_RUSSIA_USD−4.4662031−4.7799080NANA−5.4669241−10.997631−6.2672711
Canada S&P500−6.27924112.2170430−21.25206**1−10.606761−7.86644601.7424091
S_P_TSX_CANADA_USD−3.1023291−3.7193260−20.01905*1−9.5789881−7.0766330−8.7721551
ChinaS&P500−6.2180432−3.0416410−3.87912309.3712420−5.1801912−10.422310
SSE_COMPOSITE_CHINA_USD−6.1922474−11.362110−7.952130−2.1235580−6.7977841−5.1109960
Note: Since the results of Tau and Z-statistics do not contradict, we report only Z statistics. *, ** and *** represent levels of significance at 10%, 5% and 1%. NA = Data not available. Time Periods: Main Period 1 (01/01/2000–12/31/2008), Main Period 2 (01/01/2009–04/30/2013), Sub-Period 1 (01/01/2000–09/10/2001), Sub-Period 2 (09/15/2001–12/31/2006), Sub-Period 3 (01/01/2007–12/31/2009), Sub-Period 4 (01/01/2012–04/30/2013).
Table
Table 4. Impulse Response Analysis.
Table 4. Impulse Response Analysis.
PeriodResponse of:To a One-Standard Deviation Shock in:Average Response *
Sub-Period 1FranceS&P50
SPFrance1
JapanS&P1.1
S&PJapan1
CanadaS&P80
S&PCanada−1
Sub-Period 2GermanyS&P51
S&PGermany0.2
UKS&P56
S&PUK0.15
ItalyS&P200
S&PItaly0.05
Sub-Period 3FranceS&P85
S&PFrance0.1
ItalyS&P450
S&PItaly1.02
Note: * Average 10-period response to a one standard shock. The table shows that the response of the markets to the shock that emanated from S&P is substantially greater than that of the stock markets to S&P.
Table
Table 5. Test for Possible Regional Cointegration.
Table 5. Test for Possible Regional Cointegration.
RegionMarketsSub-period 1Sub-period 2Sub-period 3Sub-period 4
Dependentz-statisticProb.Max lagz-statisticProb.Max lagz-statisticProb.Max lagz-statisticProb.Max lag
AsiaBSE_SENSEX_INDIA_USD−23.268140.488715−25.889650.404820NANANA−18.5430.546018
SSE_COMPOSITE_CHINA_USD−17.482180.7432150.6726631.000020NANANA−15.3220.699418
JAKARTA_COMPOSITE_USD−15.506210.820515−17.326840.761720−11.014450.744518−6.21320.980118
KOPSI_COMPOSITE_SK__USD−15.640150.815615−20.95710.609520−22.798660.206618−9.72710.914518
NIKKEI_225_JAP_USD−0.2613551.000015−12.053760.927220−17.621670.402018NA NANA
Latin AmericaBOVESPA_BRAZIL_USD−10.459480.575916−17.277550.236021−9.3032610.652519−9.30330.652519
MERVAL_ARG_USD−11.724750.497716−5.8036020.859921−22.421960.099819−22.4220.099819
MEXABOL_MEX_USD−11.179710.530916−15.430340.309721−6.6802010.813019−6.68020.813019
EuropeCAC_40_FRANCE_USD−52.487350.0029 **16−45.125520.034420−47.704210.022619−27.5130.342919
DAX_GERMANY_USD−47.333190.0080 ***16−46.997620.025820−27.51330.342919−20.0230.648119
FTSE_100_UK_USD−39.610410.0316 **16−41.426070.060120−20.023020.648119−22.1070.558319
FTSE_MIB_ITALY_USDNANANA−31.290390.228220−22.106580.558319−47.7040.022619
BRICBOVESPA_BRAZIL_USD−20.350260.281416−29.639160.075422−11.022590.745118−3.40150.986918
RTSI_RUSSIA_USD−7.9346390.884216−12.089340.690222−5.8528530.948618−4.09370.979718
BSE_SENSEX_INDIA_USD−24.755490.150216−28.842510.085422−19.18220.333618−4.69260.971318
SSE_COMPOSITE_CHINA_USD−17.49232 0.401916 0.585733 0.999622−16.60899 0.450418−9.1886 0.833418
Note: *,**, and *** denote levels of significance at 10%, 5%, and 1%. Maximum lag automatically selected based on Schwrz criteria. NA = Data Not Available. Only European G-20 markets are cointegrated. The cointegration occurs in sub-period 1. BRIC is not a regional trading block. Time Periods: Main Period 1 (01/01/2000–12/31/2008), Main Period 2 (01/01/2009–04/30/2013), Sub-Period 1 (01/01/2000–09/10/2001), Sub-Period 2 (09/15/2001–12/31/2006), Sub-Period 3 (01/01/2007–12/31/2009), Sub-Period 4 (01/01/2012–04/30/2013).

6. Conclusions

The results of the single-equation cointegration tests indicate that patterns of cointegration in all main and sub-periods are not homogeneous. Two major findings emerge from the study. First, fewer stock markets cointegrated with S&P 500 during the crisis period than they did during the pre-crisis period. As the 2007 financial crisis deepened, S&P 500 and G-20 stock indices moved toward less cointegration. The decreasing number of cointegrating relationships may indicate that the U.S. stock markets and other G-20 markets have experienced different driving forces since the start of the U.S. crisis. Second, among those markets that were cointegrated with S&P 500, they happened to have been deeply affected by S&P and the shocks that emerged from it. The 2007–2009 financial crises can be considered a structural break in the long-run relationship and may have resulted from effective joint intervention/responses taken by members of G-20 nations. For international investors, findings suggest that, in the long run, there were probable rewards, which may have been acquired by smart investors through portfolio diversification. While the global financial markets were being assimilated, with the economies turning out to be more interdependent, the instantaneous outcomes of the markets may not have been associated to the rising ability of information-processing by the financial markets. Our results for the sample periods, including the sub-periods, support the findings of Bekaert et al. (2011) [13], which pointed out that, during most of the global crisis, the market’s external exposure played a very small role in determining its equity market performance. Though Prorokowski (2011) [38] states that the role of the USA in propagating the financial crisis was far more important, his study considers the financial crisis contagion in Europe only and recommends a future study that would investigate the role of the USA in propagating the global financial crisis. We believe that the findings from our study fill the gap and contribute to the literature. It is apparent from the study that, as the crisis deepened, the G-20 markets moved toward less cointegration with the U.S. market. G-20 markets perhaps should be investigated more intensely in a future study to determine whether the degree of contagion was lessened by a single country’s domestic intervention or by the G-20’s joint international responses to the crisis.

Acknowledgments

The authors’ gratefully acknowledge the comments and suggestions from the three anonymous referees and thank the editor for their helpful suggestions. The usual disclaimer applies.

Author Contributions

Mahfuzul Haque collected the data and wrote the Introduction, Literature Review, Data and Sample, and Conclusion sections.
Hannarong Shamsub analyzed the data and wrote the Methodology, Empirical Results and Discussion, and Conclusion sections.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix

Table
Table A1. Vector Autoregression Estimates for Sub-period 1.
Table A1. Vector Autoregression Estimates for Sub-period 1.
Sub-period 1FranceJapanCanada
S&P 500CAC 40S&P 500NIKKEIS&P 500S&P TSX
C21.09390 *−2.63159735.25743 ***−1.43934214.09819−67.46486
(12.3560)(55.4936)(15.0272)(1.58807)(13.7162)(72.0172)
S&P 500 (−1)0.969790 ***1.290730 ***0.979064 ***0.051899 ***1.031769 ***0.945515 ***
(0.05439)(0.24428)(0.05182)(0.00548)(0.06780)(0.35597)
S&P 500 (−2)−0.024779−1.243955 ***−0.019607−0.050389 ***−0.0498960.757188 ***
(0.05460)(0.24523)(0.05245)(0.00554)(0.06740)(0.35389)
CAC 40 (−1)−0.0021130.915999 ***
(0.01182)(0.05310)
CAC 40 (−2)0.0118080.072035
(0.01187)(0.05333)
NIKKEI (−1)0.1644431.016849 ***
(0.44289)(0.04680)
NIKKEI (−2)−0.025792−0.022785
(0.43976)(0.04647)
S&P TSX (−1)−0.0146760.975012 ***
(0.01294)(0.06793)
S&P TSX (−2)0.016280−0.006405
(0.01272)(0.06680)
Adj. R-squared0.9745170.9836900.9749090.9961090.9730610.984757
F-statistic3815.5946017.1933634.00423,939.973567.9356380.843
Log likelihood−1723.254−2324.106−1615.849−773.0942−1716.373−2373.069
Akaike AIC8.64126911.645538.6445274.1498368.69380212.01045
Schwarz SC8.69116211.695428.6968864.2021958.74407312.06072
Determinant resid covariance (dof adj.)1,791,9491194.8731,749,841
Determinant resid covariance1,747,4311163.2221,705,932
Log likelihood−4009.882−2387.757−3965.024
Akaike information criterion20.0994112.7880420.07588
Schwarz criterion 20.19920 12.89275 20.17642
Note: *, **, and *** denote levels of significance at 10%, 5%, and 1%.
Table
Table A2. Vector Autoregression Estimates for Sub-period 2.
Table A2. Vector Autoregression Estimates for Sub-period 2.
Sub-period 2GermanyUKItaly
S&P 500DAXS&P 500FTSE 100S&P 500FTSE MIB
C6.202999−59.02332 *7.145254 ***−1.974516.218217−963.6439 ***
(4.92074)(33.1596)(3.20529)(25.9995)(5.49276)(469.551)
S&P500 (−1)0.963801 ***1.621340 ***0.979557 ***2.613688 ***0.9427797.992102 ***
(0.03430)(0.23115)(0.03164)(0.25665)(0.03414)(2.91824)
S&P500 (−2)0.026923−1.527808 ***0.003943−2.59485 ***0.051119−6.650008 ***
(0.03440)(0.23179 )(0.03168)(0.25697 )(0.03436)(2.93754)
DAX (−1)0.0017540.842618 ***
(0.00501)(0.03377)
DAX (−2)−0.0008780.148926 ***
(0.00499)(0.03362)
FTSE 100 (−1)−0.0027880.842054 ***
(0.00377)(0.03054)
FTSE 100 (−2)0.0041890.155980 ***
(0.00377)(0.03054)
FTSE MIB (−1)−4.68 × 10−50.953878 ***
(0.00040)(0.03407)
FTSE MIB (−2)8.79 × 10−50.030525
(0.00040)(0.03387)
Adj. R-squared0.9946700.9975600.9947690.9973270.9942440.995992
F-statistic59672.42130748.659860.98117417.838907.4255971.73
Log likelihood−4814.257−7256.335−4732.428−7369.954−3150.578−7162.986
Akaike AIC7.53008911.345847.51972711.706286.99684715.89354
Schwarz SC 7.550224 11.36597 7.540119 11.72667 7.023480 15.92017
Note: *, **, and *** denote levels of significance at 10%, 5%, and 1%.
Table
Table A3. Vector Autoregression Estimates for Sub-period 3.
Table A3. Vector Autoregression Estimates for Sub-period 3.
Sub-period 3FranceItaly
S&P 500CAC 40S&P500FTSE MIB
C4.920230−54.97064 ***19.03720 **−567.558 *
(5.33725)(27.4212)(9.64657)(315.142)
S&P500 (−1)0.885546 ***2.409731 ***0.850657 ***12.67524 ***
(0.04544)(0.23344)(0.04490)(1.46669)
S&P500 (−2)0.099719 ***−2.101178 ***0.104169 ***−11.24646 ***
(0.04628)(0.23778)(0.04545)(1.48472)
CAC 40 (−1)−0.0135790.713610 ***
(0.00848)(0.04356)
CAC 40 (−2)0.015531 *0.235352 ***
(0.00827)(0.04251)
FTSE MIB (−1)−0.0009370.850992 ***
(0.00133)(0.04340)
FTSE MIB (−2)0.0017670.120711 ***
(0.00129)(0.04217)
R-squared0.9939480.9958830.9940370.997661
Adj. R-squared0.9939150.9958600.9940030.997648
F-statistic29769.5643839.9929462.8275386.49
Log likelihood−3208.44−4403.164−3127.208−5609.539
Akaike AIC8.80394612.077168.79833715.77118
Schwarz SC 8.835405 12.10862 8.830416 15.80326
Note: *, **, and *** denote levels of significance at 10%, 5%, and 1%.
Table
Table A4. Granger Causality Tests.
Table A4. Granger Causality Tests.
Country Null Hypothesis:ObsF-StatisticProb.
Sub-period 1
FranceCAC_40_FRANCE_USD does not Granger Cause S_P_500_USD 400 3.850820.0221
S_P_500_USD does not Granger Cause CAC_40_FRANCE_USD 14.04271 × 10−6
JapanNIKKEI_225_JAP_USD does not Granger Cause S_P_500_USD 375 2.862070.0584
S_P_500_USD does not Granger Cause NIKKEI_225_JAP_USD 44.92363 × 10−18
CanadaS_P_TSX_CANADA_USD does not Granger Cause S_P_500_USD 396 0.943860.39
S_P_500_USD does not Granger Cause S_P_TSX_CANADA_USD 4.395300.013
Sub-period 2
Germany DAX_GERMANY_USD does not Granger Cause S_P_500_USD 1280 0.686810.5034
S_P_500_USD does not Granger Cause DAX_GERMANY_USD 25.25032 × 10−11
UK FTSE_100_UK_USD does not Granger Cause S_P_500_USD 1260 3.293140.0375
S_P_500_USD does not Granger Cause FTSE_100_UK_USD 52.07422 × 10−22
Italy FTSE_MIB_ITALY_USD does not Granger Cause S_P_500_USD 902 0.240990.7859
S_P_500_USD does not Granger Cause FTSE_MIB_ITALY_USD 6.438810.0017
Sub-period 3
FranceCAC 40 France does not Granger Cause S&P 5007301.812060.1641
S&P 500 does not Granger Cause CAC 40 France53.70032 × 10−22
ItalyFTSE MIB Italy does not Granger Cause S&P 5007122.559810.078
S&P 500 does not Granger Cause FTSE MIB Italy 37.34414 × 10−16
Note: Among eight pairs of cointegration from Period 1 to 3, S&P 500 Granger Causes all markets; while only four markets Granger Cause S&P. Time Periods are as follows: Sub-Period 1 (01/01/2000–09/10/2001); Sub-Period 2 (09/15/2001–12/31/2006); Sub-Period 3 (01/01/2007–12/31/2009).
Table
Table A5. Impulse Response Functions.
Table A5. Impulse Response Functions.
Panel A
France 1Japan 1
Response of S_P_500_USD:Response of CAC_40_FRANCE_USD:Response of S_P_500_USD:Response of NIKKEI_225_JAP_USD:
PeriodS_P_500_USDCAC_40_FRANCE_USDS_P_500_USDCAC_40_FRANCE_USDS_P_500_USDNIKKEI_225_JAP_USDS_P_500_USDNIKKEI_225_JAP_USD
118.092280.00000033.5894073.9894118.114330.0000000.1520251.908265
217.47474-0.15632154.1201067.7742017.760090.3138011.0947051.940417
316.780780.57886852.0427467.2091617.209200.5771021.1186511.945917
416.369911.22352651.4913167.3872516.656410.8288121.1107811.948630
515.965281.82344351.1694767.4272116.124081.0703951.1013021.951060
615.577222.39128050.8237967.4490515.612381.3023141.0920701.953323
715.207842.92752350.4864367.4585915.120551.5249501.0831651.955432
814.855873.43373350.1584767.4546914.647831.7386671.0745801.957393
914.520403.91148449.8389467.4381214.193471.9438121.0663021.959213
10 14.20058 4.362247 49.52745 67.40960 13.75674 2.140720 1.058319 1.960896
Panel B
Canada 1Germany 2
Response of S_P_500_USD: Response of S_P_TSX_CANADA_USD: Response of S_P_500_USD: Response of DAX_GERMANY_USD:
PeriodS_P_500_USDS_P_TSX_CANADA_USDS_P_500_USDS_P_TSX_CANADA_USDS_P_500_USDDAX_GERMANY_USDS_P_500_USDDAX_GERMANY_USD
118.572640.00000066.6068471.2238710.424480.00000041.8896056.39172
218.18517−1.0452682.5031769.4441110.120600.09890752.1985447.51667
317.70975−0.9380983.1462966.2643010.089690.12918250.7042248.59696
417.48791−0.7576883.5153864.0681410.040060.17070651.3945148.08347
517.28779−0.5964184.0213762.036679.9939140.20969451.7203747.83276
617.09097−0.4449684.4911160.085909.9480680.24840052.0987547.54480
716.89923−0.3011984.9113158.217979.9030170.28646952.4622847.26802
816.71270−0.1647685.2857256.430489.8586680.32396952.8219546.99450
916.53115−0.0353485.6169154.719759.8150230.36090053.1760746.72544
10 16.35438 0.087387 85.90700 53.08229 9.772069 0.397272 53.52502 46.46058
Panel C
UK 2Italy 2
Response of S_P_500_USD: Response of FTSE_100_UK_USD: Response of S_P_500_USD: Response of FTSE_MIB_ITALY_USD:
PeriodS_P_500_USDFTSE_100_UK_USDS_P_500_USDFTSE_100_UK_USDS_P_500_USDFTSE_MIB_ITALY_USDS_P_500_USDFTSE_MIB_ITALY_USD
110.370290.00000040.5053873.72337 7.978265 0.000000 148.9598 665.5597
210.04538−0.20550961.2124162.07906 7.514778-0.031115 205.8526 634.8629
39.879973−0.06550657.2083563.23618 7.496091-0.000499 207.9087 625.6496
49.8145780.01881857.4773763.29344 7.459686 0.024506 214.5395 616.3758
59.7323440.10665757.3376263.37925 7.424280 0.049270 220.7605 607.2447
69.6530490.19303557.2166763.47122 7.389332 0.073506 226.8561 598.2834
79.5748020.27809657.0991563.55988 7.354835 0.097237 232.8167 589.4856
89.4976640.36189656.9825763.64707 7.320782 0.120473 238.6451 580.8485
99.4216260.44444856.8675063.73263 7.287167 0.143223 244.3439 572.3692
10 9.346671 0.525769 56.75385 63.81659 7.253980 0.165495 249.9155 564.0446
Panel D
France 3Italy 3
Response of S_P_500_USD:Response of CAC_40_FRANCE_USD:Response of S_P_500_USD:Response of FTSE_MIB_ITALY_USD:
PeriodS_P_500_USDCAC_40_FRANCE_USDS_P_500_USDCAC_40_FRANCE_USDS_P_500_USDFTSE_MIB_ITALY_USDS_P_500_USDFTSE_MIB_ITALY_USD
119.680370.00000065.4687477.0550519.623570.000000389.9819508.8169
216.53887-1.04631694.1435354.9872616.32768-0.476545580.6048432.9991
316.34689-0.47644581.0922454.8531716.078630.088026527.4267423.8582
416.48620-0.4170684.6655953.1355215.910030.393253539.0931419.4429
516.33920-0.28640384.8830150.8239315.635790.699693543.2658412.1015
616.27546-0.16006885.2322048.9601815.401640.991239546.6450405.7725
716.20299-0.04575985.6878447.1160415.178101.264123550.1409399.7494
816.131690.06416086.0544745.3714214.966251.521083553.3236394.0399
916.063430.16794486.4038043.7171014.765951.762801556.2829388.6421
10 15.99682 0.266178 86.72468 42.14510 14.57635 1.990155 559.0291 383.5334
Note: 1 = cointegrated in Sub-period 1; 2 = cointegrated in Sub-period 2; 3 = cointegrated in Sub-period 3.
Table
Table A6. Test for Johansen Cointegrtion in Main Period 1.
Table A6. Test for Johansen Cointegrtion in Main Period 1.
MarketsHypothesizedEigenvalueTrace0.05Prob.Max-Eigen0.05Prob.
No. of CE(s)StatisticCritical ValueStatisticCritical Value
AustraliaNone0.0020845.05700015.494710.80274.18729914.264600.8391
At most 10.0004330.8697013.8414660.35100.8697013.8414660.3510
BrazilNone0.00711313.6635915.494710.092612.4486014.264600.0949
At most 10.0006961.2149963.8414660.27031.2149963.8414660.2703
IndiaNone0.0041118.37061215.494710.42667.18371914.264600.4676
At most 10.0006801.1868943.8414660.27601.1868943.8414660.2760
FranceNone0.0035529.13247915.494710.35337.47924414.264600.4341
At most 10.0007861.6532353.8414660.19851.6532353.8414660.1985
GermanyNone0.0020546.51183715.494710.63504.31457314.264600.8247
At most 10.0010472.1972643.8414660.13832.1972643.8414660.1383
UKNone0.0028277.72778615.494710.49505.72899614.264600.6482
At most 10.0009871.9987903.8414660.15741.9987903.8414660.1574
ItalyNone0.00804711.1893715.494710.200110.9392414.264600.1573
At most 10.0001850.2501303.8414660.61700.2501303.8414660.6170
SpainNone0.0012303.37873615.494710.94712.45167714.264600.9764
At most 10.0004650.9270593.8414660.33560.9270593.8414660.3356
IndonesiaNone0.0047528.59894115.494710.40388.07796714.264600.3708
At most 10.0003070.5209743.8414660.47040.5209743.8414660.4704
South KoreaNone0.0017333.63272615.494710.93092.94174214.264600.9506
At most 10.0004070.6909843.8414660.40580.6909843.8414660.4058
ArgentinaNone0.0034187.87516015.494710.47886.01843714.264600.6108
At most 10.0010561.8567223.8414660.17301.8567223.8414660.1730
MexicoNone0.0032017.06405115.494710.57026.03984014.264600.6081
At most 10.0005431.0242103.8414660.31151.0242103.8414660.3115
South AfricaNone0.0058118.42000815.494710.42168.19369114.264600.3593
At most 10.0001610.2263173.8414660.63430.2263173.8414660.6343
JapanNone0.0041627.99035215.494710.46646.96094914.264600.4938
At most 10.0006171.0294033.8414660.31031.0294033.8414660.3103
CanadaNone0.0023234.62299915.494710.84754.45749814.264600.8082
At most 18.63 × 10−50.1655013.8414660.68410.1655013.8414660.6841
ChinaNone0.00514113.7395715.494710.090411.1846514.264600.1452
At most 10.0011772.5549243.8414660.10992.5549243.8414660.1099
Note: Prob. = Probability based on MacKinnon-Haug-Michelis (1999) [44] p-values.
Table
Table A7. Test for Johansen Cointegrtion in Main Period 2.
Table A7. Test for Johansen Cointegrtion in Main Period 2.
MarketsHypothesizedEigenvalueTrace0.05Prob.Max-Eigen0.05Prob.
No. of CE(s)StatisticCritical ValueStatisticCritical Value
AustraliaNone0.0082708.10230315.494710.45457.89750114.264600.3892
At most 10.0002150.2048023.8414660.65090.2048023.8414660.6509
BrazilNone0.01435112.3463815.494710.141112.0701214.264600.1080
At most 10.0003310.2762583.8414660.59920.2762583.8414660.5992
IndiaNone0.0119899.79537115.494710.29689.79416814.264600.2258
At most 11.48 × 10−60.0012033.8414660.97190.0012033.8414660.9719
FranceNone0.0074737.81643915.494710.48527.76325114.264600.4033
At most 15.14 × 10−50.0531883.8414660.81760.0531883.8414660.8176
GermanyNone0.0068527.35073615.494710.53727.29470614.264600.4549
At most 15.28 × 10−50.0560313.8414660.81290.0560313.8414660.8129
UKNone0.01211912.9085315.494710.118211.8271214.264600.1173
At most 10.0011141.0814013.8414660.29841.0814013.8414660.2984
ItalyNone0.0073567.34691015.494710.53766.93266314.264600.4971
At most 10.0004410.4142473.8414660.51980.4142473.8414660.5198
SpainNone0.0083578.61624115.494710.40218.55168314.264600.3254
At most 16.34 × 10−50.0645593.8414660.79940.0645593.8414660.7994
IndonesiaNone0.01312911.0132415.494710.210710.8631314.264600.1612
At most 10.0001830.1501073.8414660.69840.1501073.8414660.6984
South KoreaNone0.0029042.54475615.494710.98372.53305214.264600.9729
At most 11.34 × 10−50.0117043.8414660.91360.0117043.8414660.9136
ArgentinaNone0.0018811.61940515.494710.99841.46693514.264600.9983
At most 10.0001960.1524703.8414660.69620.1524703.8414660.6962
MexicoNone0.0082377.54018615.494710.51587.51035714.264600.4307
At most 13.29E-050.0298293.8414660.86280.0298293.8414660.8628
South AfricaNone0.0073188.11785815.494710.45297.95443414.264600.3833
At most 10.0001510.1634243.8414660.68600.1634243.8414660.6860
JapanNone0.0077158.22323615.494710.44186.11043314.264600.5991
At most 10.0026742.1128023.8414660.14612.1128023.8414660.1461
RussiaNone0.0069696.80333415.494710.60066.21053214.264600.5863
At most 10.0006670.5928023.8414660.44130.5928023.8414660.4413
CanadaNone0.0059615.80266715.494710.71875.54792114.264600.6716
At most 10.0002740.2547463.8414660.61380.2547463.8414660.6138
ChinaNone0.01385213.6488015.494710.093113.4187114.264600.0677
At most 1 0.000239 0.230089 3.841466 0.6315 0.230089 3.841466 0.6315
Note: Prob. = Probability based on MacKinnon-Haug-Michelis (1999) [44] p-values.
Table
Table A8. Test for Johansen Cointegration in Sub-period 1.
Table A8. Test for Johansen Cointegration in Sub-period 1.
MarketsHypothesizedEigenvalueTrace0.05Prob.Max-Eigen0.05Prob.
No. of CE(s)StatisticCritical ValueStatisticCritical Value
AustraliaNone0.0165326.84910115.494710.59536.30131614.264600.5749
At most 10.0014480.5477843.8414660.45920.5477843.8414660.4592
BrizilNone0.0145294.84770115.494710.82484.78590514.264600.7686
At most 10.0001890.0617963.8414660.80370.0617963.8414660.8037
IndiaNone0.0073793.48776915.494710.94042.46625414.264600.9758
At most 10.0030631.0215153.8414660.31221.0215153.8414660.3122
FranceNone0.0154376.00503815.494710.69505.81858814.264600.6366
At most 10.0004980.1864503.8414660.66590.1864503.8414660.6659
GermanyNone0.0088393.66027815.494710.92903.42697414.264600.9143
At most 10.0006040.2333033.8414660.62910.2333033.8414660.6291
UKNone0.0211358.89869515.494710.37488.11750814.264600.3669
At most 10.0020540.7811873.8414660.37680.7811873.8414660.3768
SpainNone0.0064652.71259115.494710.97842.34810714.264600.9804
At most 10.0010060.3644833.8414660.54600.3644833.8414660.5460
IndonesiaNone0.03616212.0697515.494710.153610.4233614.264600.1856
At most 10.0058011.6463843.8414660.19951.6463843.8414660.1995
South KoreaNone0.0303019.20511915.494710.34689.20007614.264600.2699
At most 11.69 × 10−50.0050433.8414660.94240.0050433.8414660.9424
ArgentinaNone0.0052641.71985415.494710.99781.71525714.264600.9958
At most 11.41 × 10−50.0045973.8414660.94500.0045973.8414660.9450
MexicoNone0.03050510.1432915.494710.269910.1306114.264600.2035
At most 13.88 × 10−50.0126783.8414660.91010.0126783.8414660.9101
JapanNone0.0148655.06548115.494710.80184.98712614.264600.7435
At most 10.0002350.0783553.8414660.77950.0783553.8414660.7795
RussiaNone0.0150176.48752115.494710.63796.37012614.264600.5662
At most 10.0002790.1173963.8414660.73190.1173963.8414660.7319
CanadaNone0.03243012.7816915.494710.123112.1648314.264600.1046
At most 10.0016700.6168663.8414660.43220.6168663.8414660.4322
ChinaNone0.0133416.22302415.494710.66925.65427714.264600.6579
At most 1 0.001350 0.568748 3.841466 0.4508 0.568748 3.841466 0.4508
Note: Prob. = Probability based on MacKinnon-Haug-Michelis (1999) [44] p-values.
Table
Table A9. Test for Johansen Cointegration in Sub-period 2.
Table A9. Test for Johansen Cointegration in Sub-period 2.
MarketsHypothesizedEigenvalueTrace0.05Prob.Max-Eigen0.05Prob.
No. of CE(s)StatisticCritical ValueStatisticCritical Value
AustraliaNone0.0042445.09054215.494710.79915.02724614.264600.7384
At most 15.35 × 10−50.0632963.8414660.80130.0632963.8414660.8013
BrazilNone0.0071007.38195715.494710.53377.35323014.264600.4482
At most 12.78 × 10−50.0287273.8414660.86540.0287273.8414660.8654
IndiaNone0.0020833.08018115.494710.96322.16246614.264600.9865
At most 10.0008850.9177153.8414660.33810.9177153.8414660.3381
FranceNone0.0076479.63655215.494710.30979.63428914.264600.2371
At most 11.80 × 10−60.0022623.8414660.96010.0022623.8414660.9601
GermanyNone0.0064378.01710215.494710.46368.00822114.264600.3779
At most 17.16 × 10−60.0088813.8414660.92460.0088813.8414660.9246
UKNone0.0047686.27909015.494710.66255.69700514.264600.6524
At most 10.0004880.5820853.8414660.44550.5820853.8414660.4455
ItalyNone0.0093369.01344115.494710.36418.43219714.264600.3365
At most 10.0006460.5812453.8414660.44580.5812453.8414660.4458
SpainNone0.0030394.66922915.494710.84293.53662414.264600.9047
At most 10.0009741.1326053.8414660.28721.1326053.8414660.2872
IndonesiaNone0.0077588.91529815.494710.37337.87384914.264600.3916
At most 10.0010301.0414493.8414660.30751.0414493.8414660.3075
South KoreaNone0.01254912.7045815.494710.126112.7040614.264600.0869
At most 15.17 × 10−70.0005203.8414660.98380.0005203.8414660.9838
ArgentinaNone0.0030163.83028315.494710.91673.13849014.264600.9371
At most 10.0006660.6917933.8414660.40560.6917933.8414660.4056
MexicoNone0.0072889.37186115.494710.33218.25828114.264600.3530
At most 10.0009861.1135803.8414660.29131.1135803.8414660.2913
South AfricaNone0.0083908.02013815.494710.46337.59927714.264600.4209
At most 10.0004660.4208603.8414660.51650.4208603.8414660.5165
JapanNone0.01230812.1001315.494710.152212.0129614.264600.1102
At most 18.99 × 10−50.0871713.8414660.76780.0871713.8414660.7678
RussiaNone0.0036205.45516715.494710.75874.79447414.264600.7675
At most 10.0005000.6606933.8414660.41630.6606933.8414660.4163
CanadaNone0.0028213.13809615.494710.96033.13809614.264600.9371
At most 17.45 × 10−118.28 × 10−83.8414660.99978.28E-083.8414660.9997
ChinaNone ***0.01533420.5871915.494710.007820.5212214.264600.0045
At most 1 4.97 × 10−5 0.065966 3.841466 0.7973 0.065966 3.841466 0.7973
Note: Prob. = Probability based on MacKinnon-Haug-Michelis (1999) [44] p-values. ***denotes significance at 1%.
Table
Table A10. Test for Johansen Cointegration in Sub-period 3.
Table A10. Test for Johansen Cointegration in Sub-period 3.
MarketsHypothesizedEigenvalueTrace0.05Prob.Max-Eigen0.05Prob.
No. of CE(s)StatisticCritical ValueStatisticCritical Value
AustraliaNone0.0085207.22041015.494710.55225.92963214.264600.6223
At most 10.0018611.2907783.8414660.25591.2907783.8414660.2559
BrazilNone0.0085675.03463415.494710.80515.01635014.264600.7398
At most 13.14 × 10−50.0182843.8414660.89230.0182843.8414660.8923
IndiaNone0.0078597.22967915.494710.55114.35528014.264600.8201
At most 10.0051942.8743993.8414660.09002.8743993.8414660.0900
FranceNone **0.02156915.8481615.494710.044215.3289814.264600.0338
At most 10.0007380.5191843.8414660.47120.5191843.8414660.4712
GermanyNone0.01434912.5603015.494710.132010.2038914.264600.1989
At most 10.0033322.3564053.8414660.12482.3564053.8414660.1248
UKNone ***0.02812420.5574915.494710.007919.4267114.264600.0070
At most 10.0016591.1307833.8414660.28761.1307833.8414660.2876
ItalyNone0.01762112.5097215.494710.134112.0893214.264600.1073
At most 10.0006180.4203983.8414660.51670.4203983.8414660.5167
SpainNone0.0106437.86795115.494710.47967.38327914.264600.4448
At most 10.0007020.4846723.8414660.48630.4846723.8414660.4863
IndonesiaNone0.0031041.79983815.494710.99711.79706814.264600.9946
At most 14.79 × 10−60.0027703.8414660.95550.0027703.8414660.9555
South KoreaNone0.0090076.39861015.494710.64845.47421214.264600.6812
At most 10.0015270.9243983.8414660.33630.9243983.8414660.3363
ArgentinaNone0.0040633.36981015.494710.94762.34528714.264600.9805
At most 10.0017771.0245233.8414660.31141.0245233.8414660.3114
MexicoNone0.0080725.09040815.494710.79915.08983314.264600.7304
At most 19.17 × 10−70.0005763.8414660.98260.0005763.8414660.9826
South AfricaNone0.0097798.53136415.494710.41057.38030814.264600.4452
At most 10.0015321.1510563.8414660.28331.1510563.8414660.2833
JapanNone0.0094807.32037515.494710.54075.14343114.264600.7236
At most 10.0040232.1769443.8414660.14012.1769443.8414660.1401
RussiaNone0.01584811.0027015.494710.211310.2398614.264600.1967
At most 10.0011890.7628433.8414660.38240.7628433.8414660.3824
CanadaNone0.0044284.04414715.494710.89992.86221314.264600.9555
At most 10.0018311.1819343.8414660.27701.1819343.8414660.2770
China None0.0076126.17181215.494710.67525.19600714.264600.7169
At most 1 0.001434 0.975805 3.841466 0.3232 0.975805 3.841466 0.3232
Note: Prob. = Probability based on MacKinnon-Haug-Michelis (1999) [44] p-values; ** denotes significance at 5%;*** denotes significance at 1%.
Table
Table A11. Test for Johansen Cointegration in Sub-period 4.
Table A11. Test for Johansen Cointegration in Sub-period 4.
MarketsHypothesizedEigenvalueTrace0.05Prob.Max-Eigen0.05Prob.
No. of CE(s)StatisticCritical ValueStatisticCritical Value
AustraliaNone0.0051653.74691215.494710.92293.74392214.264600.8853
At most 14.13 × 10−60.0029903.8414660.95480.0029903.8414660.9548
BrazilNone0.01642611.3511615.494710.190810.6328614.264600.1736
At most 10.0011180.7183053.8414660.39670.7183053.8414660.3967
IndiaNone0.0133828.78596215.494710.38568.70344114.264600.3117
At most 10.0001280.0825213.8414660.77390.0825213.8414660.7739
FranceNone0.0108968.80411715.494710.38388.71025814.264600.3111
At most 10.0001180.0938593.8414660.75930.0938593.8414660.7593
GermanyNone0.0104048.74138215.494710.38998.56524114.264600.3241
At most 10.0002150.1761423.8414660.67470.1761423.8414660.6747
UKNone0.0117508.88308015.494710.37638.77016414.264600.3058
At most 10.0001520.1129163.8414660.73680.1129163.8414660.7368
ItalyNone0.0120648.60518615.494710.40328.60513614.264600.3205
At most 17.14 × 10−85.06 × 10−53.8414660.99675.06E-053.8414660.9967
SpainNone0.01405411.3186115.494710.192611.1675714.264600.1460
At most 10.0001910.1510393.8414660.69750.1510393.8414660.6975
IndonesiaNone0.01347910.0437415.494710.27748.68532014.264600.3133
At most 10.0021201.3584163.8414660.24381.3584163.8414660.2438
South KoreaNone0.0026872.36129315.494710.98851.75427614.264600.9953
At most 10.0009310.6070173.8414660.43590.6070173.8414660.4359
ArgentinaNone0.0033772.56621915.494710.98312.01945514.264600.9903
At most 10.0009150.5467643.8414660.45960.5467643.8414660.4596
MexicoNone0.0138509.75895015.494710.29979.73472114.264600.2299
At most 13.47 × 10−50.0242283.8414660.87620.0242283.8414660.8762
South AfricaNone0.0112589.42691115.494710.32749.40830314.264600.2537
At most 12.24 × 10−50.0186073.8414660.89140.0186073.8414660.8914
JapanNone0.0119418.40205915.494710.42347.32812814.264600.4511
At most 10.0017591.0739303.8414660.30011.0739303.8414660.3001
RussiaNone0.0063144.40948315.494710.86784.09154014.264600.8496
At most 10.0004920.3179443.8414660.57280.3179443.8414660.5728
CanadaNone0.0032703.78003515.494710.92052.35182114.264600.9803
At most 10.0019871.4282133.8414660.23211.4282133.8414660.2321
ChinaNone0.0055634.08975015.494710.89623.99984614.264600.8593
At most 1 0.000125 0.089904 3.841466 0.7643 0.089904 3.841466 0.7643
Note: Prob. = Probability based on MacKinnon-Haug-Michelis (1999) [44] p-values.
Table
Table A12. Test for Possible Regional Cointegration in Asia.
Table A12. Test for Possible Regional Cointegration in Asia.
Sub-periodHypothesizedEigenvalueTrace0.05Prob.Max-Eigen0.05Prob.
No. of CE(s)StatisticCritical ValueStatisticCritical Value
1None0.20966861.3935869.818890.195036.7072033.876870.0223
At most 10.08513624.6863947.856130.926813.8808527.584340.8308
At most 20.04946110.8055429.797070.96577.91324021.131620.9089
At most 30.0149672.89229515.494710.97162.35242614.264600.9803
At most 40.0034550.5398693.8414660.46250.5398693.8414660.4625
2None0.05646258.3642469.818890.289029.9891333.876870.1359
At most 10.03003028.3751147.856130.797215.7326827.584340.6883
At most 20.01994412.6424429.797070.907210.3950521.131620.7070
At most 30.0039942.24738315.494710.99092.06499314.264600.9892
At most 40.0003530.1823893.8414660.66930.1823893.8414660.6693
3None0.06069850.2633069.818890.626319.9750833.876870.7584
At most 10.04456630.2882247.856130.704214.5430227.584340.7834
At most 20.02882215.7452029.797070.73019.32919721.131620.8050
At most 30.0164166.41600215.494710.64635.28005514.264600.7062
At most 40.0035551.1359473.8414660.28651.1359473.8414660.2865
4None0.07439947.0759369.818890.758022.2658933.876870.5870
At most 10.03546324.8100347.856130.923610.3989127.584340.9785
At most 20.03091814.4111229.797070.81689.04482021.131620.8288
At most 30.0135215.36629815.494710.76873.92064114.264600.8676
At most 4 0.005007 1.445657 3.841466 0.2292 1.445657 3.841466 0.2292
Note: Prob. = Probability based on MacKinnon-Haug-Michelis (1999) [44] p-values.
Table
Table A13. Test for Possible Regional Cointegration in Latin America.
Table A13. Test for Possible Regional Cointegration in Latin America.
Sub-periodHypothesizedEigenvalueTrace0.05Prob.Max-Eigen0.05Prob.
No. of CE(s)StatisticCritical ValueStatisticCritical Value
1None0.04565212.3444129.797070.919210.3733921.131620.7090
At most 10.0082251.97101315.494710.99541.83349714.264600.9940
At most 20.0006190.1375163.8414660.71080.1375163.8414660.7108
2None0.02309620.7720029.797070.372017.4787621.131620.1506
At most 10.0043583.29323915.494710.95203.26660214.264600.9274
At most 23.56 × 10−50.0266363.8414660.87030.0266363.8414660.8703
3None0.02827216.4581829.797070.679712.0166721.131620.5460
At most 10.0098904.44150515.494710.86494.16451914.264600.8416
At most 20.0006610.2769863.8414660.59870.2769863.8414660.5987
4None0.03611718.6610929.797070.517516.1118921.131620.2184
At most 10.0048422.54919815.494710.98362.12576814.264600.9876
At most 2 0.000966 0.423430 3.841466 0.5152 0.423430 3.841466 0.5152
Note: Prob. = Probability based on MacKinnon-Haug-Michelis (1999) [44] p-values.
Table
Table A14. Test for Possible Regional Cointegration in Europe.
Table A14. Test for Possible Regional Cointegration in Europe.
Sub-periodHypothesizedEigenvalueTrace0.05Prob.Max-Eigen0.05Prob.
No. of CE(s)StatisticCritical ValueStatisticCritical Value
1None0.05194522.2069229.797070.287218.7766721.131620.1035
At most 10.0095123.43025115.494710.94403.36433114.264600.9196
At most 20.0001870.0659203.8414660.79740.0659203.8414660.7974
2None **0.03957957.0513847.856130.005432.6298427.584340.0103
At most 10.02240524.4215429.797070.183218.3088621.131620.1187
At most 20.0071596.11267915.494710.68225.80562414.264600.6383
At most 30.0003800.3070553.8414660.57950.3070553.8414660.5795
3None *0.04057458.4046447.856130.003826.6328727.584340.0658
At most 1 *0.03139731.7717729.797070.029220.5121421.131620.0608
At most 20.01631711.2596415.494710.196010.5785614.264600.1766
At most 30.0010590.6810733.8414660.40920.6810733.8414660.4092
4None0.03121337.7449847.856130.313221.0876827.584340.2709
At most 10.01520116.6573029.797070.665210.1865721.131620.7269
At most 20.0096836.47072215.494710.63996.47036214.264600.5536
At most 3 5.42 × 10−7 0.000360 3.841466 0.9869 0.000360 3.841466 0.9869
Note: Prob. = Probability based on MacKinnon-Haug-Michelis (1999) [44] p-values; * denotes significance at 10%;** denotes significance at 5%.
Table
Table A15. Test for Possible Regional Cointegration in BRIC.
Table A15. Test for Possible Regional Cointegration in BRIC.
Sub-periodHypothesized EigenvalueTrace0.05Prob.Max-Eigen0.05Prob.
No. of CE(s)StatisticCritical ValueStatisticCritical Value
1None0.07013432.4793747.856130.585719.1239127.584340.4050
At most 10.02703513.3554529.797070.87477.20819021.131620.9453
At most 20.0143886.14726515.494710.67813.81151814.264600.8787
At most 30.0088422.3357463.8414660.12642.3357463.8414660.1264
2None0.02519042.4009847.856130.147821.0987027.584340.2703
At most 10.01584221.3022929.797070.339113.2063021.131620.4335
At most 20.0072168.09598715.494710.45525.98961314.264600.6145
At most 30.0025442.1063743.8414660.14672.1063743.8414660.1467
3None0.05458643.8303647.856130.113620.5445327.584340.3047
At most 10.03228423.2858329.797070.232312.0107321.131620.5466
At most 20.02217111.2751015.494710.19518.20598314.264600.3581
At most 30.0083503.0691133.8414660.07983.0691133.8414660.0798
4None0.01820613.2254547.856131.00006.70638127.584340.9999
At most 10.0101086.51907029.797070.99963.70808521.131620.9997
At most 20.0069812.81098515.494710.97482.55687514.264600.9718
At most 3 0.000696 0.254110 3.841466 0.6142 0.254110 3.841466 0.6142
Note: Prob. = Probability based on MacKinnon-Haug-Michelis (1999) [44] p-values. BRIC is not a trading block.

References

  1. M.A. King, and S. Wadhwani. “Transmission of Volatility between Stock Markets.” Rev. Financ. Stud. 3 (1990): 5–33. [Google Scholar] [CrossRef]
  2. M.P. Taylor, and I. Tonks. “The internationalization of stock markets and abolition of UK exchange control.” Rev. Econ. Stat. 71 (1989): 332–336. [Google Scholar] [CrossRef]
  3. K. Kasa. “Common stochastic trends in international stock markets.” J. Monet. Econ. 29 (1992): 95–124. [Google Scholar] [CrossRef]
  4. A.M. Masih, and R. Masih. “Dynamic linkages and the propagation mechanism driving major international markets: An analysis of the pre- and post-crash areas.” Q. Rev. Econ. Financ. 37 (1997): 859–885. [Google Scholar] [CrossRef]
  5. A.R. Chowdhry. “Stock market interdependencies: Evidence from the Asian NIEs.” J. Macroecon. 16 (1994): 629–651. [Google Scholar] [CrossRef]
  6. T. Chowdhry, L. Lu, and K. Peng. “Common stochastic trends among Far Eastern stock prices: Effects of Asian financial crisis.” Int. Rev. Financ. Anal. 16 (2007): 242–261. [Google Scholar] [CrossRef]
  7. B.S.R. Rao, and U. Naik. “Inter-relatedness of stock market spectral investigation of USA, Japan and Indian markets note.” Artha Vignana 32 (1990): 309–321. [Google Scholar]
  8. K.C. Chan, B.E. Gup, and M.S. Pan. “International stock market efficiency and integration: A study of eighteen nations.” J. Bus. Financ. Account. 24 (1997): 803–813. [Google Scholar] [CrossRef]
  9. A.C.C. Kwan, A.B. Sim, and J.A. Cotsomitis. “The causal relationships between equity indices on world exchanges.” Appl. Econ. 27 (1995): 33–37. [Google Scholar] [CrossRef]
  10. “The Australian Government Treasury. ” Available online: http://www.treasury.gov.au/PublicationsAndMedia/Publications/2011/Australia-and-the-IFIs/Australia-and-the-IFIs/Section-1-Responding-to-the-global-economic-crisis (accessed on 12 September 2015).
  11. I. Angeloni, and J. Pisany-Ferry. “Wanted: A stronger and better G20 for the global economy.” Available online: http://bruegel.org/2011/10/wanted-a-stronger-and-better-g20-for-the-global-economy/ (accessed on 9 June 2015).
  12. M.L. Duca, and L. Stracca. The Effect of G20 Summits on Global Financial Markets. Working Paper Series, No. 1668; Frankfurt, Germany: European Central Bank, 2014. [Google Scholar]
  13. G. Bekaert, C.R. Harvey, and A. Ng. “Market integration and contagion.” J. Bus. 78 (2005): 39–70. [Google Scholar] [CrossRef]
  14. A. Karolyi. “Does international finance contagion really exist? ” Int. Financ. 6 (2003): 179–199. [Google Scholar] [CrossRef]
  15. M. Dungey, R. Fry, B. Gonzalez-Hermosillo, and V.L. Martin. “Empirical modeling of contagion. A review of methodologies.” Quant. Financ. 5 (2004): 9–24. [Google Scholar] [CrossRef]
  16. F. Allen, and D. Gale. “Financial contagion.” J. Political Econ. 108 (2000): 1–33. [Google Scholar] [CrossRef]
  17. S.-J. Wei, and T. Hui. “The Composition Matters: Capital Inflows and Liquidity Crunch during a Global Economic Crisis.” Rev. Financ. Stud. 24 (2011): 2023–2052. [Google Scholar]
  18. H. Hau, and S. Lai. “The Role of Equity Funds in the Financial Crisis Propagation.” Available online: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1742065 (accessed on 17 January 2015).
  19. J. Yang, M.M. Khan, and L. Pointe. “Increasing Integration Between the United States and Other International Stock Markets, A Recursive Cointegration Analysis.” Emerg. Mark. Financ. Trade 39 (2003): 39–53. [Google Scholar]
  20. R. Dornbush, Y.C. Park, and S. Claessens. “Contagion: Understanding how it spreads.” World Bank Res. Obs. 15 (2000): 177–197. [Google Scholar] [CrossRef]
  21. M. Pritsker. “The Channels for Financial Contagion.” In International Financial Contagion. Edited by S. Claessens and K. Forbes. Boston, MA, USA: Kluwer Academic Publishers, 2001, pp. 67–97. [Google Scholar]
  22. J. Rangvid. “Increasing Convergence among European Stock Markets?: A Recursive Common Stochastic Trends Analysis.” Econ. Letters 71 (2001): 383–389. [Google Scholar] [CrossRef]
  23. E. Mendoza, and V. Quadrini. “Financial Globalization, Financial Crises and Contagion.” J. Monetary Econ. 57 (2010): 24–39. [Google Scholar] [CrossRef]
  24. M. Fratzscher. “Capital Flows, Push versus Pull Factors and the Global Financial Crisis forthcoming.” J. Int. Econ. 88 forthcoming. (2012). [Google Scholar] [CrossRef]
  25. T.A. Khan. “Cointegration of International Stock Markets: An Investigation of Diversification Opportunities.” Available online: http://digitalcommons.iwu.edu/cgi/viewcontent.cgi?article=1153&context=uer (accessed on 2 May 2015).
  26. S. Johansen. “Statistical Analysis of Cointegration Vectors.” J. Econ. Dyn. Control 12 (1988): 231–254. [Google Scholar] [CrossRef]
  27. A. Gregory, and B. Hansen. “Residual-based tests for cointegration in models with regime shifts.” J. Econ. 70 (1996): 99–126. [Google Scholar] [CrossRef]
  28. F.B. Slimane, M. Mehanaoui, and I.A. Kazi. “How Does the Financial Crisis Affect Volatility Behavior and Transmission Among European Stock Markets.” Int. J. Financ. Stud. 1 (2013): 81–101. [Google Scholar] [CrossRef] [Green Version]
  29. H.C. Sheng, and A.H. Tu. “A study of cointegration and variance decomposition among national equity indices before and during the period of the Asian financial crisis.” J. Multinatl. Financ. Manag. 10 (2000): 345–365. [Google Scholar] [CrossRef]
  30. P. Masson. “Contagion: Macroeconomic models with multiple equilibria.” J. Int. Money Financ. 18 (1998): 587–602. [Google Scholar] [CrossRef]
  31. P. Masson. “Contagion: Monsoonal effects, spillovers, and jumps between multiple equilibria.” In The Asian Financial Crisis: Causes, Contagion and Consequences. Edited by P.R. Agenor, M. Miller, D. Vines and A. Weber. Cambridge, UK: Cambridge University Press, 1999. [Google Scholar]
  32. S. Calvo, and C. Reinhart. “Capital Inflows to Latin America: Is There Evidence of Contagion Effects? ” Available online: https://mpra.ub.uni-muenchen.de/7124/1/MPRA_paper_7124.pdf (accessed on 13 June 2015).
  33. K. Forbes, and R. Rigobon. “No contagion, only interdependence: Measuring stock market co-movement.” J. Financ. 57 (2002): 2223–2261. [Google Scholar] [CrossRef]
  34. M.H. Pesaran, and A. Pick. Econometric Issues in the Analysis of Contagion. Working Paper 1176; Cambridge, UK: University of Cambridge, 2003. [Google Scholar]
  35. M. Pericoli, and M. Sbracia. “A primer on financial contagion.” J. Econ. Surv. 17 (2003): 571–608. [Google Scholar] [CrossRef]
  36. G. Corsetti, P. Pesenti, and N. Roubini. “What caused the Asian currency and financial crisis? ” Jpn. World Econ. 11 (1999): 305–373. [Google Scholar] [CrossRef]
  37. A. Wasim, N.R. Bhanumurthy, and S. Sehgal. “The Eurozone crisis and its contagion effects on the European stock markets.” Stud. Econ. Financ. 31 (2014): 325–352. [Google Scholar]
  38. L. Prorokowski. “Recovery from the current banking crisis: Insights into costs and effectiveness of response regulations.” Qualit. Res. Financ. Mark. 3 (2011): 193–223. [Google Scholar] [CrossRef]
  39. A.V. Thakor. “The Financial Crisis of 2007–2009: Why Did It Happen and What Did We Learn? ” Rev. Corp. Financ. Stud. 4 (2015): 155–205. [Google Scholar] [CrossRef]
  40. N. Gennaioli, A. Shleifer, and R. Vishny. “Neglected Risks: The Psychology of Financial Crises.” Am. Econ. Rev. 105 (2015): 310–314. [Google Scholar] [CrossRef]
  41. C.M. Reinhart, and K.S. Rogoff. “Recovery from Financial Crises: Evidence from 100 Episodes.” Am. Econ. Rev. 104 (2014): 50–55. [Google Scholar] [CrossRef]
  42. R.F. Engle, and C.W.J. Granger. “Co-integration and Error Correction: Representation, Estimation and Testing.” Econometrica 55 (1987): 251–276. [Google Scholar] [CrossRef]
  43. C. Alexander. “Optimal Hedging Using Cointegration.” Philos. Trans. R. Soc. Lond. 357 (1999): 2039–2058. [Google Scholar] [CrossRef]
  44. J.G. MacKinnon, A.A. Haug, and L. Michelis. “Numerical Distribution Functions of Likelihood Ratio Tests for Cointegration.” J. Appl. Econom. 14 (1999): 563–577. [Google Scholar] [CrossRef]
  • 1Taylor and Tonks (1989) [2], Kasa (1992) [3] and, subsequently, Masih and Masih (1997) [4], Chowdhry (1994) [5] and Chowdhry et al. (2007) [6], among several others, have used the cointegration hypothesis to assess the international integration of financial markets. Rao and Naik (1990) [7], Chan et al. (1997) [8], Kasa [3] and Kwan et al. (1995) [9] have examined the integration of financial markets before the Asian economic crisis. The second group of studies examined the effects of the economic crisis on the financial integration after the Asian crisis.
  • 2The Australian Government Treasury (2015) [10].
  • 3Angeloni, I. and J. Pisany-Ferry (2015) [11]. The G20 acted as a crisis manager when global financial markets were under threat in 2008 and 2009, and contributed to a positive outcome.
  • 4Duca and Stracca (2014) [12] ran an event study to test whether G20 meetings at ministerial and leaders level have had an impact on global financial markets. By focusing on the period from 2007 to 2013, looking at equity returns, bond yields and measures of market risk such as implied volatility, skewness and kurtosis. They found that G20 summits have not had a strong, consistent and durable effect on any of the markets that we consider, suggesting that the information and decision content of G20 summits is of limited relevance for market participants.
  • 5Dornbush, Park and Classens (2000) [20] adopt the definition of contagion as being the dissemination of market disturbances, most of the time with negative consequences, from one market to another, while Pritsker (2001) [21] also defines contagion as the occurrence of a shock in one or more markets, countries or institutions that spread to other markets, countries or institutions. Rangvid (2001) [22] investigates the degree of convergence among three major European stock markets, and is analyzed within the framework of a recursive common stochastic trends analysis. The results point towards a decreasing number of common stochastic trends influencing the stock markets, i.e., the degree of convergence among European stock markets has been increased during the recent two decades.
  • 6Mendoza and Quadrini (2010) [23] for a theoretical analysis, and Fratzscher (2012) [24] for empirical evidence during the 2007–2009 crises. Khan Taimur A. (2011) [25], paper examines the long-run convergence of the United States and 22 other developed and developing countries. Using daily data to run the Johansen (1988) [26] and the Gregory and Hansen (1996) [27] test, and find stock markets of most countries have become cointegrated by 2010. Also using the relative risk of each country (the CAPM model) to measure performance of each country over the recession of the 2000s and finds that the relative risk of a country is a good predictor of country performance in a recession.
  • 7In addition to testing for cointegration, researchers have also examined causality among international indices. Sheng and Tu (2000) [29] have found evidence suggesting that the U.S. market still causes some Asian markets (such as Hong Kong and South Korea) during the period of the financial crisis and conclude that the results reflect the U.S. market’s dominant role. Masson (1998) [30] and (1999) [31], Calvo and Reinhart (1995) [32], Forbes and Rigobon (2002) [33], Pesaran and Pick (2003) [34], Dornbush et al. (2000) [19], Pritsker (2001) [20], Pericoli and Sbracia (2003) [35] and Corsetti et al. (1999) [36], however, assert that an excessive increase in correlation occurs between the country causing the crisis and all other countries where contagion prevails.
  • 8Due to non-availability, data for Saudi Arabia and Turkey (to match our main or even the smaller sub periods) was not included in the G20 sample.
  • 9As suggested by the anonymous referee and the editor.
  • 10Estimation using the Johansen methodology reveals slightly different results. European markets were cointegrated in Sub-Periods 2 and 3. See Table A12, Table A13, Table A14 and Table A15 in the Appendix.

Share and Cite

MDPI and ACS Style

Haque, M.; Shamsub, H. Do Markets Cointegrate after Financial Crises? Evidence from G-20 Stock Markets. Int. J. Financial Stud. 2015, 3, 557-586. https://doi.org/10.3390/ijfs3040557

AMA Style

Haque M, Shamsub H. Do Markets Cointegrate after Financial Crises? Evidence from G-20 Stock Markets. International Journal of Financial Studies. 2015; 3(4):557-586. https://doi.org/10.3390/ijfs3040557

Chicago/Turabian Style

Haque, Mahfuzul, and Hannarong Shamsub. 2015. "Do Markets Cointegrate after Financial Crises? Evidence from G-20 Stock Markets" International Journal of Financial Studies 3, no. 4: 557-586. https://doi.org/10.3390/ijfs3040557

Article Metrics

Back to TopTop