Do markets cointegrate after financial crisis? Evidence from G-20 stock markets

Haque, Mahfuzul; Shamsub, Hannarong Article Do markets cointegrate after financial crisis? Evidence from G-20 stock markets International Journal of Financial Studies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Haque, Mahfuzul; Shamsub, Hannarong (2015) : Do markets cointegrate after financial crisis? Evidence from G-20 stock markets, International Journal of Financial Studies, ISSN 2227-7072, MDPI, Basel, Vol. 3, Iss. 4, pp. 557-586, https://doi.org/10.3390/ijfs3040557 This Version is available at: https://hdl.handle.net/10419/167798 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Evidence from G-20 Stock Markets 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; E-Mail: [email protected] 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; E-Mail: [email protected]; Tel.: +66-2-579-0230. Academic Editor: Kuan Min Wang Received: 22 August 2015 / Accepted: 6 November 2015 / Published: 10 December 2015 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. Keywords: financial crises; euro crises; stock markets-developed and developing; cointegration; vector auto regression; granger causality and variance decomposition JEL Classification: C5; G01; F3; F36 OPEN ACCESS Int. J. Financial Stud. 2015, 3 558 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 1 Taylor 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. 2 The Australian Government Treasury (2015) [10]. Int. J. Financial Stud. 2015, 3 559 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 . 3 Angeloni, 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. 4 Duca 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. 5 Dornbush, 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 Int. J. Financial Stud. 2015, 3 560 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 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. 6 Mendoza 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. 7 In 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. Int. J. Financial Stud. 2015, 3 561 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 8 Due 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. Int. J. Financial Stud. 2015, 3 562 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: 𝑦𝑡=α𝑡+β𝑡.𝑋𝑡+𝑒𝑡 (1) 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, ∆𝑦𝑡=β′.𝐷𝑡+π.𝑦𝑡−1 + ∑φ𝑗 𝑝 𝑗=1 .∆𝑦𝑡−1 +ε𝑡 (2) 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. Int. J. Financial Stud. 2015, 3 563 Table 1. Unit Root Test Results. Countries Main Period 1 Main Period 2 Sub-period 1 Sub-period 2 Sub-period 3 Sub-period 4 Level of Significance Lag Level of Significance Lag Level of Significance Lag Level of Significance Lag Level of Significance Lag Level of Significance Lag Level 1st Difference Level 1st Difference Level 1st Difference Level 1st Difference Level 1st Difference Level 1st Difference Australia 0.9923 0.0000 *** 1 0.4709 0.0000 *** 1 0.2075 0.0000 *** 1 0.5468 0.0000 *** 0 0.9378 0.0000 *** 0 0.2452 0.0000 *** 1 Brazil 0.9316 0.0000 *** 1 0.4426 0.0000 *** 0 0.4400 0.0000 *** 0 0.5574 0.0000 *** 1 0.9385 0.0000 *** 0 0.1144 0.0000 *** 2 India 0.8965 0.0000 *** 3 0.8538 0.0000 *** 1 0.1902 0.0000 *** 0 0.9573 0.0000 *** 0 0.8934 0.0000 *** 3 0.7186 0.0000 *** 1 France 0.7576 0.0000 *** 0 0.4262 0.0000 *** 1 0.1062 0.0000 *** 0 0.4942 0.0000 *** 0 0.8851 0.0000 *** 0 0.3398 0.0000 *** 1 Germany 0.743 0.0000 *** 0 0.2323 0.0000 *** 1 0.0329 ** 0.0000 *** 0 0.7504 0.0000 *** 0 0.8225 0.0000 *** 0 0.1827 0.0000 *** 1 UK 0.7533 0.0000 *** 4 0.2861 0.0000 *** 1 0.0124 ** 0.0000 *** 2 0.4318 0.0000 *** 0 0.8099 0.0000 *** 0 0.1864 0.0000 *** 0 Italy 0.9914 0.0000 *** 0 0.4919 0.0000 *** 1 NA NA NA 0.4158 0.0000 *** 0 0.9374 0.0000 *** 0 0.3617 0.0000 *** 1 Indonesia 0.9611 0.0000 *** 1 0.4825 0.0000 *** 0 0.4968 0.0000 *** 1 0.8573 0.0000 *** 1 0.9908 0.0000 *** 3 0.2901 0.0000 *** 0 South Korea 0.9340 0.0000 *** 0 0.7069 0.0000 *** 1 0.2640 0.0000 *** 0 0.8509 0.0000 *** 0 0.9395 0.0000 *** 1 0.4558 0.0000 *** 1 Argentina 0.9958 0.0000 *** 0 0.8496 0.0000 *** 0 0.7238 0.0000 *** 0 0.1469 0.0000 *** 0 0.9953 0.0000 *** 0 0.9676 0.0000 *** 0 Mexico 0.8211 0.0000 *** 1 0.3295 0.0000 *** 3 0.1438 0.0000 *** 1 0.9605 0.0000 *** 1 0.9462 0.0000 *** 1 0.0617 0.0000 *** 3 Japan 0.5214 0.0000 *** 0 0.3728 0.0000 *** 0 0.334 0.0000 *** 0 0.5422 0.0000 *** 0 0.9952 0.0000 *** 1 0.4927 0.0000 *** 1 Russia 0.9915 0.0000 *** 16 0.6172 0.0000 *** 1 0.3818 0.0000 *** 0 0.9876 0.0000 *** 0 0.9352 0.0000 *** 0 0.6782 0.0000 *** 1 Canada 0.9754 0.0000 *** 1 0.6043 0.0000 *** 1 0.3501 0.0000 *** 0 0.3778 0.0000 *** 1 0.9223 0.0000 *** 0 0.4197 0.0000 *** 1 China 0.8507 0.0000 *** 5 0.0370** 0.0000 *** 0 0.6762 0.0000 *** 0 0.9988 0.0000 *** 0 0.7360 0.0000 *** 0 0.232 0.0000 *** 0 South Africa 0.9439 0.0000 *** 2 0.2635 0.0000 *** 1 NA NA NA 0.2644 0.0000 *** 0 0.7845 0.0000 *** 1 0.0312 ** 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). Int. J. Financial Stud. 2015, 3 564 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 2. Pearson’s Correlation between S&P 500 and Individual G-20 Market. Index Main Period 1 Main Period 2 Sub-Period 1 Sub-period 2 Sub-period 3 Sub-period 4 NIKKEI_225_JAP_USD 0.91881247 0.798268284 0.848302 0.9194643 0.9720458 0.493656953 AORD_AUS_USD 0.910417391 0.878534471 0.7096561 0.9704026 0.9420739 0.677888613 BOVESPA_BRAZIL_USD 0.712741676 0.394379943 0.8466674 0.9427273 0.4551339 −0.423957789 BSE_SENSEX_INDIA_USD 0.82356243 0.507993402 0.7146341 0.9546012 0.7582756 −0.282360057 CAC_40_FRANCE_USD 0.950542631 0.217592254 0.914945 0.9782175 0.9898299 −0.033576787 DAX_GERMANY_USD 0.81484941 0.887061205 0.8276481 0.9758126 0.9338245 0.672849902 FTSE_100_UK_USD 0.947752306 0.890154093 0.8765646 0.9685831 0.9747434 0.773895442 FTSE_MIB_ITALY_USD 0.918584634 −0.285123418 NA 0.9178387 0.9906913 −0.413138549 JAKARTA_COMPOSITE_USD 0.812614777 0.927260023 0.6217197 0.9512123 0.6951186 0.787976012 KOPSI_COMPOSITE_SK__USD 0.909494238 0.893780439 0.7439795 0.9355421 0.9270682 0.666669139 MERVAL_ARG_USD 0.93089746 0.788128298 0.6540438 0.951406 0.8370185 0.389796718 MEXABOL_MEX_USD 0.840413702 0.961142926 0.3576683 0.9625362 0.9230826 0.898493919 MSCI_SOUTH_AFRICA___USD 0.892404524 0.921946026 NA 0.9337427 0.8337503 0.765137815 S_P_TSX_CANADA_USD 0.864382184 0.830291092 0.9120119 0.9512283 0.8957494 0.440639497 SSE_COMPOSITE_CHINA_USD 0.674345803 −0.089640613 −0.4321285 0.3052762 0.6672256 −0.400101793 RTSI_RUSSIA_USD 0.511592565 0.738090139 0.3094396 0.8729418 0.9199462 0.185714189 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 Tables A6–A11 of the Appendix section. 9 As suggested by the anonymous referee and the editor. Int. J. Financial Stud. 2015, 3 571 Appendix Table A1. Vector Autoregression Estimates for Sub-period 1. Sub-period 1 France Japan Canada S&P 500 CAC 40 S&P 500 NIKKEI S&P 500 S&P TSX C 21.09390 * −2.631597 35.25743 *** −1.439342 14.09819 −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 *** −0.049896 0.757188 *** (0.05460) (0.24523) (0.05245) (0.00554) (0.06740) (0.35389) CAC 40 (−1) −0.002113 0.915999 *** (0.01182) (0.05310) CAC 40 (−2) 0.011808 0.072035 (0.01187) (0.05333) NIKKEI (−1) 0.164443 1.016849 *** (0.44289) (0.04680) NIKKEI (−2) −0.025792 −0.022785 (0.43976) (0.04647) S&P TSX (−1) −0.014676 0.975012 *** (0.01294) (0.06793) S&P TSX (−2) 0.016280 −0.006405 (0.01272) (0.06680) Adj. R-squared 0.974517 0.983690 0.974909 0.996109 0.973061 0.984757 F-statistic 3815.594 6017.193 3634.004 23,939.97 3567.935 6380.843 Log likelihood −1723.254 −2324.106 −1615.849 −773.0942 −1716.373 −2373.069 Akaike AIC 8.641269 11.64553 8.644527 4.149836 8.693802 12.01045 Schwarz SC 8.691162 11.69542 8.696886 4.202195 8.744073 12.06072 Determinant resid covariance (dof adj.) 1,791,949 1194.873 1,749,841 Determinant resid covariance 1,747,431 1163.222 1,705,932 Log likelihood −4009.882 −2387.757 −3965.024 Akaike information criterion 20.09941 12.78804 20.07588 Schwarz criterion 20.19920 12.89275 20.17642 Note: *, **, and *** denote levels of significance at 10%, 5%, and 1%. Int. J. Financial Stud. 2015, 3 572 Table A2. Vector Autoregression Estimates for Sub-period 2. Sub-period 2 Germany UK Italy S&P 500 DAX S&P 500 FTSE 100 S&P 500 FTSE MIB C 6.202999 −59.02332 * 7.145254 *** −1.97451 6.218217 −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 *** 0.942779 7.992102 *** (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) 0.001754 0.842618 *** (0.00501) (0.03377) DAX (−2) −0.000878 0.148926 *** (0.00499) (0.03362) FTSE 100 (−1) −0.002788 0.842054 *** (0.00377) (0.03054) FTSE 100 (−2) 0.004189 0.155980 *** (0.00377) (0.03054) FTSE MIB (−1) −4.68 × 10−5 0.953878 *** (0.00040) (0.03407) FTSE MIB (−2) 8.79 × 10−5 0.030525 (0.00040) (0.03387) Adj. R-squared 0.994670 0.997560 0.994769 0.997327 0.994244 0.995992 F-statistic 59672.42 130748.6 59860.98 117417.8 38907.42 55971.73 Log likelihood −4814.257 −7256.335 −4732.428 −7369.954 −3150.578 −7162.986 Akaike AIC 7.530089 11.34584 7.519727 11.70628 6.996847 15.89354 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 A3. Vector Autoregression Estimates for Sub-period 3. Sub-period 3 France Italy S&P 500 CAC 40 S&P500 FTSE MIB C 4.920230 −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) −0.013579 0.713610 *** (0.00848) (0.04356) CAC 40 (−2) 0.015531 * 0.235352 *** (0.00827) (0.04251) FTSE MIB (−1) −0.000937 0.850992 *** (0.00133) (0.04340) FTSE MIB (−2) 0.001767 0.120711 *** (0.00129) (0.04217) Int. J. Financial Stud. 2015, 3 573 Table A3. Cont. Sub-period 3 France Italy S&P 500 CAC 40 S&P500 FTSE MIB R-squared 0.993948 0.995883 0.994037 0.997661 Adj. R-squared 0.993915 0.995860 0.994003 0.997648 F-statistic 29769.56 43839.99 29462.82 75386.49 Log likelihood −3208.44 −4403.164 −3127.208 −5609.539 Akaike AIC 8.803946 12.07716 8.798337 15.77118 Schwarz SC 8.835405 12.10862 8.830416 15.80326 Note: *, **, and *** denote levels of significance at 10%, 5%, and 1%. Table A4. Granger Causality Tests. Country Null Hypothesis: Obs F-Statistic Prob. Sub-period 1 France CAC_40_FRANCE_USD does not Granger Cause S_P_500_USD 400 3.85082 0.0221 S_P_500_USD does not Granger Cause CAC_40_FRANCE_USD 14.0427 1 × 10−6 Japan NIKKEI_225_JAP_USD does not Granger Cause S_P_500_USD 375 2.86207 0.0584 S_P_500_USD does not Granger Cause NIKKEI_225_JAP_USD 44.9236 3 × 10−18 Canada S_P_TSX_CANADA_USD does not Granger Cause S_P_500_USD 396 0.94386 0.39 S_P_500_USD does not Granger Cause S_P_TSX_CANADA_USD 4.39530 0.013 Sub-period 2 Germany DAX_GERMANY_USD does not Granger Cause S_P_500_USD 1280 0.68681 0.5034 S_P_500_USD does not Granger Cause DAX_GERMANY_USD 25.2503 2 × 10−11 UK FTSE_100_UK_USD does not Granger Cause S_P_500_USD 1260 3.29314 0.0375 S_P_500_USD does not Granger Cause FTSE_100_UK_USD 52.0742 2 × 10−22 Italy FTSE_MIB_ITALY_USD does not Granger Cause S_P_500_USD 902 0.24099 0.7859 S_P_500_USD does not Granger Cause FTSE_MIB_ITALY_USD 6.43881 0.0017 Sub-period 3 France CAC 40 France does not Granger Cause S&P 500 730 1.81206 0.1641 S&P 500 does not Granger Cause CAC 40 France 53.7003 2 × 10−22 Italy FTSE MIB Italy does not Granger Cause S&P 500 712 2.55981 0.078 S&P 500 does not Granger Cause FTSE MIB Italy 37.3441 4 × 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). Int. J. Financial Stud. 2015, 3 574 Table A5. Impulse Response Functions. Panel A France 1 Japan 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: Period S_P_500_USD CAC_40_FRANCE_USD S_P_500_USD CAC_40_FRANCE_USD S_P_500_USD NIKKEI_225_JAP_USD S_P_500_USD NIKKEI_225_JAP_USD 1 18.09228 0.000000 33.58940 73.98941 18.11433 0.000000 0.152025 1.908265 2 17.47474 -0.156321 54.12010 67.77420 17.76009 0.313801 1.094705 1.940417 3 16.78078 0.578868 52.04274 67.20916 17.20920 0.577102 1.118651 1.945917 4 16.36991 1.223526 51.49131 67.38725 16.65641 0.828812 1.110781 1.948630 5 15.96528 1.823443 51.16947 67.42721 16.12408 1.070395 1.101302 1.951060 6 15.57722 2.391280 50.82379 67.44905 15.61238 1.302314 1.092070 1.953323 7 15.20784 2.927523 50.48643 67.45859 15.12055 1.524950 1.083165 1.955432 8 14.85587 3.433733 50.15847 67.45469 14.64783 1.738667 1.074580 1.957393 9 14.52040 3.911484 49.83894 67.43812 14.19347 1.943812 1.066302 1.959213 10 14.20058 4.362247 49.52745 67.40960 13.75674 2.140720 1.058319 1.960896 Panel B Canada 1 Germany 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: Period S_P_500_USD S_P_TSX_CANADA_USD S_P_500_USD S_P_TSX_CANADA_USD S_P_500_USD DAX_GERMANY_USD S_P_500_USD DAX_GERMANY_USD 1 18.57264 0.000000 66.60684 71.22387 10.42448 0.000000 41.88960 56.39172 2 18.18517 −1.04526 82.50317 69.44411 10.12060 0.098907 52.19854 47.51667 3 17.70975 −0.93809 83.14629 66.26430 10.08969 0.129182 50.70422 48.59696 4 17.48791 −0.75768 83.51538 64.06814 10.04006 0.170706 51.39451 48.08347 5 17.28779 −0.59641 84.02137 62.03667 9.993914 0.209694 51.72037 47.83276 6 17.09097 −0.44496 84.49111 60.08590 9.948068 0.248400 52.09875 47.54480 7 16.89923 −0.30119 84.91131 58.21797 9.903017 0.286469 52.46228 47.26802 8 16.71270 −0.16476 85.28572 56.43048 9.858668 0.323969 52.82195 46.99450 9 16.53115 −0.03534 85.61691 54.71975 9.815023 0.360900 53.17607 46.72544 10 16.35438 0.087387 85.90700 53.08229 9.772069 0.397272 53.52502 46.46058 Int. J. Financial Stud. 2015, 3 575 Table A5. Cont. Panel C UK 2 Italy 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: Period S_P_500_USD FTSE_100_UK_USD S_P_500_USD FTSE_100_UK_USD S_P_500_USD FTSE_MIB_ITALY_USD S_P_500_USD FTSE_MIB_ITALY_USD 1 10.37029 0.000000 40.50538 73.72337 7.978265 0.000000 148.9598 665.5597 2 10.04538 −0.205509 61.21241 62.07906 7.514778 -0.031115 205.8526 634.8629 3 9.879973 −0.065506 57.20835 63.23618 7.496091 -0.000499 207.9087 625.6496 4 9.814578 0.018818 57.47737 63.29344 7.459686 0.024506 214.5395 616.3758 5 9.732344 0.106657 57.33762 63.37925 7.424280 0.049270 220.7605 607.2447 6 9.653049 0.193035 57.21667 63.47122 7.389332 0.073506 226.8561 598.2834 7 9.574802 0.278096 57.09915 63.55988 7.354835 0.097237 232.8167 589.4856 8 9.497664 0.361896 56.98257 63.64707 7.320782 0.120473 238.6451 580.8485 9 9.421626 0.444448 56.86750 63.73263 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 3 Italy 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: Period S_P_500_USD CAC_40_FRANCE_USD S_P_500_USD CAC_40_FRANCE_USD S_P_500_USD FTSE_MIB_ITALY_USD S_P_500_USD FTSE_MIB_ITALY_USD 1 19.68037 0.000000 65.46874 77.05505 19.62357 0.000000 389.9819 508.8169 2 16.53887 -1.046316 94.14353 54.98726 16.32768 -0.476545 580.6048 432.9991 3 16.34689 -0.476445 81.09224 54.85317 16.07863 0.088026 527.4267 423.8582 4 16.48620 -0.41706 84.66559 53.13552 15.91003 0.393253 539.0931 419.4429 5 16.33920 -0.286403 84.88301 50.82393 15.63579 0.699693 543.2658 412.1015 6 16.27546 -0.160068 85.23220 48.96018 15.40164 0.991239 546.6450 405.7725 7 16.20299 -0.045759 85.68784 47.11604 15.17810 1.264123 550.1409 399.7494 8 16.13169 0.064160 86.05447 45.37142 14.96625 1.521083 553.3236 394.0399 9 16.06343 0.167944 86.40380 43.71710 14.76595 1.762801 556.2829 388.6421 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. Int. J. Financial Stud. 2015, 3 576 Table A6. Test for Johansen Cointegrtion in Main Period 1. Markets Hypothesized Eigenvalue Trace 0.05 Prob. Max-Eigen 0.05 Prob. No. of CE(s) Statistic Critical Value Statistic Critical Value Australia None 0.002084 5.057000 15.49471 0.8027 4.187299 14.26460 0.8391 At most 1 0.000433 0.869701 3.841466 0.3510 0.869701 3.841466 0.3510 Brazil None 0.007113 13.66359 15.49471 0.0926 12.44860 14.26460 0.0949 At most 1 0.000696 1.214996 3.841466 0.2703 1.214996 3.841466 0.2703 India None 0.004111 8.370612 15.49471 0.4266 7.183719 14.26460 0.4676 At most 1 0.000680 1.186894 3.841466 0.2760 1.186894 3.841466 0.2760 France None 0.003552 9.132479 15.49471 0.3533 7.479244 14.26460 0.4341 At most 1 0.000786 1.653235 3.841466 0.1985 1.653235 3.841466 0.1985 Germany None 0.002054 6.511837 15.49471 0.6350 4.314573 14.26460 0.8247 At most 1 0.001047 2.197264 3.841466 0.1383 2.197264 3.841466 0.1383 UK None 0.002827 7.727786 15.49471 0.4950 5.728996 14.26460 0.6482 At most 1 0.000987 1.998790 3.841466 0.1574 1.998790 3.841466 0.1574 Italy None 0.008047 11.18937 15.49471 0.2001 10.93924 14.26460 0.1573 At most 1 0.000185 0.250130 3.841466 0.6170 0.250130 3.841466 0.6170 Spain None 0.001230 3.378736 15.49471 0.9471 2.451677 14.26460 0.9764 At most 1 0.000465 0.927059 3.841466 0.3356 0.927059 3.841466 0.3356 Indonesia None 0.004752 8.598941 15.49471 0.4038 8.077967 14.26460 0.3708 At most 1 0.000307 0.520974 3.841466 0.4704 0.520974 3.841466 0.4704 South Korea None 0.001733 3.632726 15.49471 0.9309 2.941742 14.26460 0.9506 At most 1 0.000407 0.690984 3.841466 0.4058 0.690984 3.841466 0.4058 Argentina None 0.003418 7.875160 15.49471 0.4788 6.018437 14.26460 0.6108 At most 1 0.001056 1.856722 3.841466 0.1730 1.856722 3.841466 0.1730 Mexico None 0.003201 7.064051 15.49471 0.5702 6.039840 14.26460 0.6081 At most 1 0.000543 1.024210 3.841466 0.3115 1.024210 3.841466 0.3115 South Africa None 0.005811 8.420008 15.49471 0.4216 8.193691 14.26460 0.3593 At most 1 0.000161 0.226317 3.841466 0.6343 0.226317 3.841466 0.6343 Japan None 0.004162 7.990352 15.49471 0.4664 6.960949 14.26460 0.4938 At most 1 0.000617 1.029403 3.841466 0.3103 1.029403 3.841466 0.3103 Canada None 0.002323 4.622999 15.49471 0.8475 4.457498 14.26460 0.8082 At most 1 8.63 × 10−5 0.165501 3.841466 0.6841 0.165501 3.841466 0.6841 China None 0.005141 13.73957 15.49471 0.0904 11.18465 14.26460 0.1452 At most 1 0.001177 2.554924 3.841466 0.1099 2.554924 3.841466 0.1099 Note: Prob. = Probability based on MacKinnon-Haug-Michelis (1999) [44] p-values. Int. J. Financial Stud. 2015, 3 577 Table A7. Test for Johansen Cointegrtion in Main Period 2. Markets Hypothesized Eigenvalue Trace 0.05 Prob. Max-Eigen 0.05 Prob. No. of CE(s) Statistic Critical Value Statistic Critical Value Australia None 0.008270 8.102303 15.49471 0.4545 7.897501 14.26460 0.3892 At most 1 0.000215 0.204802 3.841466 0.6509 0.204802 3.841466 0.6509 Brazil None 0.014351 12.34638 15.49471 0.1411 12.07012 14.26460 0.1080 At most 1 0.000331 0.276258 3.841466 0.5992 0.276258 3.841466 0.5992 India None 0.011989 9.795371 15.49471 0.2968 9.794168 14.26460 0.2258 At most 1 1.48 × 10−6 0.001203 3.841466 0.9719 0.001203 3.841466 0.9719 France None 0.007473 7.816439 15.49471 0.4852 7.763251 14.26460 0.4033 At most 1 5.14 × 10−5 0.053188 3.841466 0.8176 0.053188 3.841466 0.8176 Germany None 0.006852 7.350736 15.49471 0.5372 7.294706 14.26460 0.4549 At most 1 5.28 × 10−5 0.056031 3.841466 0.8129 0.056031 3.841466 0.8129 UK None 0.012119 12.90853 15.49471 0.1182 11.82712 14.26460 0.1173 At most 1 0.001114 1.081401 3.841466 0.2984 1.081401 3.841466 0.2984 Italy None 0.007356 7.346910 15.49471 0.5376 6.932663 14.26460 0.4971 At most 1 0.000441 0.414247 3.841466 0.5198 0.414247 3.841466 0.5198 Spain None 0.008357 8.616241 15.49471 0.4021 8.551683 14.26460 0.3254 At most 1 6.34 × 10−5 0.064559 3.841466 0.7994 0.064559 3.841466 0.7994 Indonesia None 0.013129 11.01324 15.49471 0.2107 10.86313 14.26460 0.1612 At most 1 0.000183 0.150107 3.841466 0.6984 0.150107 3.841466 0.6984 South Korea None 0.002904 2.544756 15.49471 0.9837 2.533052 14.26460 0.9729 At most 1 1.34 × 10−5 0.011704 3.841466 0.9136 0.011704 3.841466 0.9136 Argentina None 0.001881 1.619405 15.49471 0.9984 1.466935 14.26460 0.9983 At most 1 0.000196 0.152470 3.841466 0.6962 0.152470 3.841466 0.6962 Mexico None 0.008237 7.540186 15.49471 0.5158 7.510357 14.26460 0.4307 At most 1 3.29E-05 0.029829 3.841466 0.8628 0.029829 3.841466 0.8628 South Africa None 0.007318 8.117858 15.49471 0.4529 7.954434 14.26460 0.3833 At most 1 0.000151 0.163424 3.841466 0.6860 0.163424 3.841466 0.6860 Japan None 0.007715 8.223236 15.49471 0.4418 6.110433 14.26460 0.5991 At most 1 0.002674 2.112802 3.841466 0.1461 2.112802 3.841466 0.1461 Russia None 0.006969 6.803334 15.49471 0.6006 6.210532 14.26460 0.5863 At most 1 0.000667 0.592802 3.841466 0.4413 0.592802 3.841466 0.4413 Canada None 0.005961 5.802667 15.49471 0.7187 5.547921 14.26460 0.6716 At most 1 0.000274 0.254746 3.841466 0.6138 0.254746 3.841466 0.6138 China None 0.013852 13.64880 15.49471 0.0931 13.41871 14.26460 0.0677 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. Int. J. Financial Stud. 2015, 3 578 Table A8. Test for Johansen Cointegration in Sub-period 1. Markets Hypothesized Eigenvalue Trace 0.05 Prob. Max-Eigen 0.05 Prob. No. of CE(s) Statistic Critical Value Statistic Critical Value Australia None 0.016532 6.849101 15.49471 0.5953 6.301316 14.26460 0.5749 At most 1 0.001448 0.547784 3.841466 0.4592 0.547784 3.841466 0.4592 Brizil None 0.014529 4.847701 15.49471 0.8248 4.785905 14.26460 0.7686 At most 1 0.000189 0.061796 3.841466 0.8037 0.061796 3.841466 0.8037 India None 0.007379 3.487769 15.49471 0.9404 2.466254 14.26460 0.9758 At most 1 0.003063 1.021515 3.841466 0.3122 1.021515 3.841466 0.3122 France None 0.015437 6.005038 15.49471 0.6950 5.818588 14.26460 0.6366 At most 1 0.000498 0.186450 3.841466 0.6659 0.186450 3.841466 0.6659 Germany None 0.008839 3.660278 15.49471 0.9290 3.426974 14.26460 0.9143 At most 1 0.000604 0.233303 3.841466 0.6291 0.233303 3.841466 0.6291 UK None 0.021135 8.898695 15.49471 0.3748 8.117508 14.26460 0.3669 At most 1 0.002054 0.781187 3.841466 0.3768 0.781187 3.841466 0.3768 Spain None 0.006465 2.712591 15.49471 0.9784 2.348107 14.26460 0.9804 At most 1 0.001006 0.364483 3.841466 0.5460 0.364483 3.841466 0.5460 Indonesia None 0.036162 12.06975 15.49471 0.1536 10.42336 14.26460 0.1856 At most 1 0.005801 1.646384 3.841466 0.1995 1.646384 3.841466 0.1995 South Korea None 0.030301 9.205119 15.49471 0.3468 9.200076 14.26460 0.2699 At most 1 1.69 × 10−5 0.005043 3.841466 0.9424 0.005043 3.841466 0.9424 Argentina None 0.005264 1.719854 15.49471 0.9978 1.715257 14.26460 0.9958 At most 1 1.41 × 10−5 0.004597 3.841466 0.9450 0.004597 3.841466 0.9450 Mexico None 0.030505 10.14329 15.49471 0.2699 10.13061 14.26460 0.2035 At most 1 3.88 × 10−5 0.012678 3.841466 0.9101 0.012678 3.841466 0.9101 Japan None 0.014865 5.065481 15.49471 0.8018 4.987126 14.26460 0.7435 At most 1 0.000235 0.078355 3.841466 0.7795 0.078355 3.841466 0.7795 Russia None 0.015017 6.487521 15.49471 0.6379 6.370126 14.26460 0.5662 At most 1 0.000279 0.117396 3.841466 0.7319 0.117396 3.841466 0.7319 Canada None 0.032430 12.78169 15.49471 0.1231 12.16483 14.26460 0.1046 At most 1 0.001670 0.616866 3.841466 0.4322 0.616866 3.841466 0.4322 China None 0.013341 6.223024 15.49471 0.6692 5.654277 14.26460 0.6579 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. Int. J. Financial Stud. 2015, 3 579 Table A9. Test for Johansen Cointegration in Sub-period 2. Markets Hypothesized Eigenvalue Trace 0.05 Prob. Max-Eigen 0.05 Prob. No. of CE(s) Statistic Critical Value Statistic Critical Value Australia None 0.004244 5.090542 15.49471 0.7991 5.027246 14.26460 0.7384 At most 1 5.35 × 10−5 0.063296 3.841466 0.8013 0.063296 3.841466 0.8013 Brazil None 0.007100 7.381957 15.49471 0.5337 7.353230 14.26460 0.4482 At most 1 2.78 × 10−5 0.028727 3.841466 0.8654 0.028727 3.841466 0.8654 India None 0.002083 3.080181 15.49471 0.9632 2.162466 14.26460 0.9865 At most 1 0.000885 0.917715 3.841466 0.3381 0.917715 3.841466 0.3381 France None 0.007647 9.636552 15.49471 0.3097 9.634289 14.26460 0.2371 At most 1 1.80 × 10−6 0.002262 3.841466 0.9601 0.002262 3.841466 0.9601 Germany None 0.006437 8.017102 15.49471 0.4636 8.008221 14.26460 0.3779 At most 1 7.16 × 10−6 0.008881 3.841466 0.9246 0.008881 3.841466 0.9246 UK None 0.004768 6.279090 15.49471 0.6625 5.697005 14.26460 0.6524 At most 1 0.000488 0.582085 3.841466 0.4455 0.582085 3.841466 0.4455 Italy None 0.009336 9.013441 15.49471 0.3641 8.432197 14.26460 0.3365 At most 1 0.000646 0.581245 3.841466 0.4458 0.581245 3.841466 0.4458 Spain None 0.003039 4.669229 15.49471 0.8429 3.536624 14.26460 0.9047 At most 1 0.000974 1.132605 3.841466 0.2872 1.132605 3.841466 0.2872 Indonesia None 0.007758 8.915298 15.49471 0.3733 7.873849 14.26460 0.3916 At most 1 0.001030 1.041449 3.841466 0.3075 1.041449 3.841466 0.3075 South Korea None 0.012549 12.70458 15.49471 0.1261 12.70406 14.26460 0.0869 At most 1 5.17 × 10−7 0.000520 3.841466 0.9838 0.000520 3.841466 0.9838 Argentina None 0.003016 3.830283 15.49471 0.9167 3.138490 14.26460 0.9371 At most 1 0.000666 0.691793 3.841466 0.4056 0.691793 3.841466 0.4056 Mexico None 0.007288 9.371861 15.49471 0.3321 8.258281 14.26460 0.3530 At most 1 0.000986 1.113580 3.841466 0.2913 1.113580 3.841466 0.2913 South Africa None 0.008390 8.020138 15.49471 0.4633 7.599277 14.26460 0.4209 At most 1 0.000466 0.420860 3.841466 0.5165 0.420860 3.841466 0.5165 Japan None 0.012308 12.10013 15.49471 0.1522 12.01296 14.26460 0.1102 At most 1 8.99 × 10−5 0.087171 3.841466 0.7678 0.087171 3.841466 0.7678 Russia None 0.003620 5.455167 15.49471 0.7587 4.794474 14.26460 0.7675 At most 1 0.000500 0.660693 3.841466 0.4163 0.660693 3.841466 0.4163 Canada None 0.002821 3.138096 15.49471 0.9603 3.138096 14.26460 0.9371 At most 1 7.45 × 10−11 8.28 × 10−8 3.841466 0.9997 8.28E-08 3.841466 0.9997 China None *** 0.015334 20.58719 15.49471 0.0078 20.52122 14.26460 0.0045 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%. Int. J. Financial Stud. 2015, 3 580 Table A10. Test for Johansen Cointegration in Sub-period 3. Markets Hypothesized Eigenvalue Trace 0.05 Prob. Max-Eigen 0.05 Prob. No. of CE(s) Statistic Critical Value Statistic Critical Value Australia None 0.008520 7.220410 15.49471 0.5522 5.929632 14.26460 0.6223 At most 1 0.001861 1.290778 3.841466 0.2559 1.290778 3.841466 0.2559 Brazil None 0.008567 5.034634 15.49471 0.8051 5.016350 14.26460 0.7398 At most 1 3.14 × 10−5 0.018284 3.841466 0.8923 0.018284 3.841466 0.8923 India None 0.007859 7.229679 15.49471 0.5511 4.355280 14.26460 0.8201 At most 1 0.005194 2.874399 3.841466 0.0900 2.874399 3.841466 0.0900 France None ** 0.021569 15.84816 15.49471 0.0442 15.32898 14.26460 0.0338 At most 1 0.000738 0.519184 3.841466 0.4712 0.519184 3.841466 0.4712 Germany None 0.014349 12.56030 15.49471 0.1320 10.20389 14.26460 0.1989 At most 1 0.003332 2.356405 3.841466 0.1248 2.356405 3.841466 0.1248 UK None *** 0.028124 20.55749 15.49471 0.0079 19.42671 14.26460 0.0070 At most 1 0.001659 1.130783 3.841466 0.2876 1.130783 3.841466 0.2876 Italy None 0.017621 12.50972 15.49471 0.1341 12.08932 14.26460 0.1073 At most 1 0.000618 0.420398 3.841466 0.5167 0.420398 3.841466 0.5167 Spain None 0.010643 7.867951 15.49471 0.4796 7.383279 14.26460 0.4448 At most 1 0.000702 0.484672 3.841466 0.4863 0.484672 3.841466 0.4863 Indonesia None 0.003104 1.799838 15.49471 0.9971 1.797068 14.26460 0.9946 At most 1 4.79 × 10−6 0.002770 3.841466 0.9555 0.002770 3.841466 0.9555 South Korea None 0.009007 6.398610 15.49471 0.6484 5.474212 14.26460 0.6812 At most 1 0.001527 0.924398 3.841466 0.3363 0.924398 3.841466 0.3363 Argentina None 0.004063 3.369810 15.49471 0.9476 2.345287 14.26460 0.9805 At most 1 0.001777 1.024523 3.841466 0.3114 1.024523 3.841466 0.3114 Mexico None 0.008072 5.090408 15.49471 0.7991 5.089833 14.26460 0.7304 At most 1 9.17 × 10−7 0.000576 3.841466 0.9826 0.000576 3.841466 0.9826 South Africa None 0.009779 8.531364 15.49471 0.4105 7.380308 14.26460 0.4452 At most 1 0.001532 1.151056 3.841466 0.2833 1.151056 3.841466 0.2833 Japan None 0.009480 7.320375 15.49471 0.5407 5.143431 14.26460 0.7236 At most 1 0.004023 2.176944 3.841466 0.1401 2.176944 3.841466 0.1401 Russia None 0.015848 11.00270 15.49471 0.2113 10.23986 14.26460 0.1967 At most 1 0.001189 0.762843 3.841466 0.3824 0.762843 3.841466 0.3824 Canada None 0.004428 4.044147 15.49471 0.8999 2.862213 14.26460 0.9555 At most 1 0.001831 1.181934 3.841466 0.2770 1.181934 3.841466 0.2770 China None 0.007612 6.171812 15.49471 0.6752 5.196007 14.26460 0.7169 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%.