Return and volatility spillovers among CIVETS stock markets

Emerging Markets Review 13 (2012) 230–252

Contents lists available at SciVerse ScienceDirect

Emerging Markets Review journal homepage: www.elsevier.com/locate/emr

Return and volatility spillovers among CIVETS stock markets Turhan Korkmaz a,⁎, Emrah İ. Çevik a, Erdal Atukeren b, c, d a

Zonguldak Karaelmas University, Turkey ETH Zurich, KOF Swiss Economic Institute, CH-8092 Zurich, Switzerland SBS Swiss Business School, Balz-Zimmermannstr. 34, CH-8302 Kloten, Switzerland d BSL Business School Lausanne, Rte. de la Maladiére 21, PO Box 73, CH-1022 Chavannes, Switzerland b c

a r t i c l e

i n f o

Article history: Received 11 October 2011 Received in revised form 2 March 2012 Accepted 6 March 2012 Available online 13 March 2012 JEL Classification: C32 F30 G11 G15 O16

a b s t r a c t Coined in 2009, the CIVETS refers to Colombia, Indonesia, Vietnam, Egypt, Turkey, and South Africa as a new group of frontier emerging markets with young and growing populations and dynamic economies. We provide a first look into the return and volatility spillovers between the CIVETS countries by employing causality-in-mean and causality-in-variance tests. The empirical results indicate that the contemporaneous spillover effects are generally low. Nevertheless, CIVETS stock markets may exhibit higher degrees of co-movements at times. The structure of the causal relationships further suggests the presence of intra-regional and inter-regional return and volatility interdependence effects. © 2012 Elsevier B.V. All rights reserved.

Keywords: CIVETS Stock markets Spillovers Causality-in-variance Volatility breaks Emerging markets

1. Introduction In a report published by the global bank Goldman Sachs, Wilson and Purushothaman (2003) introduced the term BRICs envisaging that the combined gross domestic products of Brazil, Russia, India, and China would exceed that of the U.S, Japan, the U.K, Germany, France, and Italy by 2050. Not only the term BRICs held up well in the global business arena but it has also become a discussion and research topic in the academia as well.1

⁎ Corresponding author at: Zonguldak Karaelmas Universitesi, IIBF, Isletme Bolumu, Zonguldak, Turkey. Tel.: +90 372 257 4010 1687. E-mail addresses: [email protected] (T. Korkmaz), [email protected] (E.İ. Çevik), [email protected] (E. Atukeren) 1 See Bell (2011) for a review of the Goldman Sachs report and the current status of the BRICs. 1566-0141/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.ememar.2012.03.003

T. Korkmaz et al. / Emerging Markets Review 13 (2012) 230–252

231

The search for new acronyms continues.2 In 2009, the Economist Intelligence Unit (EIU) came up with another one, CIVETS, to refer to Colombia, Indonesia, Vietnam, Egypt, Turkey, and South Africa as the new stardom candidates among emerging markets.3 A civet is indeed a wild cat-like mammal that lives in some of the CIVETS countries. As such, the term captures the dynamism of these countries. For instance, the CIVETS' population amounts to about 600 million in 2010 with an average age of 27. Despite some disparities in their level of development, the six CIVETS countries share common elements such as young populations, political stability, diversified domestic economic structure, not much reliance on commodity exports, relatively developed financial markets, and the potential for outperforming returns.4 Looking for returns in the global economy, international financial investors and institutions are quick to catch on with CIVETS. In May 2011, HSBC's Global Asset Management created a CIVETS fund with an initial investment of US$ 5 billion.5 6 Again in May 2011, the Standard & Poors (S&P) launched the CIVETS 60 index, which is composed of the ten most liquid stocks in each of the CIVETS stock markets.7 The Fact Sheet prepared by the Standard & Poors for the CIVETS 60 Index recognizes these countries as the “…second generation emerging markets characterized by dynamic, rapidly changing economies and young growing populations”.8 The historical performance of the S&P's CIVETS 60 index appears to have outperformed that of the S&P's BRIC 40 and S&P Emerging BMI indices especially since December 2009.9 There is plenty of academic research on the economies and the financial markets of individual CIVETS countries. Nevertheless, since the coining of the CIVETS as a group is quite new, academic research on the inter-linkages between the CIVETS per se as a group is not existent at the moment to the best of our knowledge. 10 This paper provides a comprehensive look at the return and volatility spillovers among the stock markets of the CIVETS countries. We use weekly data and examine the correlational and causal relationships for the period between 24 July 2002 and 29 December 2010. In testing for the causal relationships, we employ Hong's (2001) version of Cheung and Ng's (1996) causality-in-mean and causality-invariance tests. Our analysis takes into account the influence of common third factors, such as the developments in the US and Japanese stock markets, the effects of possible breaks in the variance of the series, and the causality-in-mean effects in the testing for causality-in-variance. Our findings indicate that the contemporaneous correlations obtained after filtering out ARCH effects and common factors are rather volatile, generally low, and might turn into negative at times. The causality patterns suggest the presence of some degree of intra- and inter-regional causal return and volatility spillover effects and interdependence among the CIVETS countries. These findings also contribute to the literature on the contagion versus interdependence hypothesis in international financial markets. In the context of CIVETS, we find interdependence effects rather than contagion after removing the common factors and the breaks in the variance of the series and controlling for causality-in-mean effects. The rest of the paper is organized as follows. Section 2 reviews the literature on the financial market inter-relationships between the individual CIVETS countries. In Section 3, we discuss the methodology employed in our paper. Section 4 presents the data characteristics and the results from correlation analysis. The empirical analysis of the causal relationships is carried out in Section 5. Section 6 concludes.

2 Witold Henisz, a professor at the University of Pennsylvania, states that “…a catchy name and new focus may give multinationals and investors more incentive to look toward these lesser-known countries.” (Knowledge@Wharton, 2011). 3 Actually, Goldman Sachs also issued another acronym the “N11”, which stands for the Next Eleven. The N11 consists of four of the CIVETS (Egypt, Indonesia, Turkey, Vietnam) plus Mexico, Iran, Bangladesh, Nigeria, Pakistan, the Phillippines, and South Korea. 4 Knowledge@Wharton (2011) for the background on and the press coverage of CIVETS. 5 See http://www.assetmanagement.hsbc.com/displayArticle?cd_doc_path=/uk/press/2011/jan-jun/civets_fund. html&siab_microsite= uk. 6 The percentages allocated to each country as of December 2010 were: Colombia (16%), Indonesia (25%), Vietnam (1.5%), Egypt (7.5%), Turkey (25%), South Africa (25%). 7 As of April 29, 2011, the country weights were: Colombia (12.4%), Indonesia (28.3%), Vietnam (1%), Egypt (4.9%), Turkey (21.8%), and South Africa (31.6%). The Bloomberg and the Reuters tickers for total returns is SPCIVETT and .SPCIVETT, respectively. 8 See http://www.standardandpoors.com/indices/sp-civets-60/en/us/?indexId=spgbbmcv60usdw—p-rcv—. 9 The one-year (imputed) annualized return is given as 21.29% while the three year (imputed) annualized return is 9.10% according to the S&P's CIVETS 60 Fact Sheet. 10 A search made on http://www.repec.org with the keyword “CIVETS” did not return any hits as of 28.02.2012.

232

T. Korkmaz et al. / Emerging Markets Review 13 (2012) 230–252

2. CIVETS economies and the international integration of their stock markets 2.1. CIVETS: business community views and economic performance indicators After the acronym CIVETS spurred interest in the international business and financial community, University of Pennsylvania's Wharton School and Fleishman-Hillard firm conducted a survey of 153 corporate and business leaders for their opinion on the economic and financial potential of CIVETS. More than 80% of the survey participants have stated Indonesia, South Africa, and Turkey as offering a “great deal of opportunity” or “some opportunity”. These figures are 77% for Vietnam, 61% for Egypt, and 56% for Colombia (Knowledge@Wharton, 2011). The value and the innovativeness of the products & services produced, the GDP growth potential, and their financial position were given as the top reasons contributing the competitiveness of the companies in CIVETS (Knowledge@Wharton, 2011). Nevertheless, the lack of visibility in the international markets, lack of transparency and governance, lack of marketing, branding, communication, and global thinking are considered as the areas to improve for many companies in the CIVETS group (Knowledge@Wharton, 2011). Schiller (2011) also reviews the inherent dynamics of the CIVETS countries and suggests that they “…have demographic, resource and business-environment characteristics that make them worthwhile long-term bets” (Schiller, 2011: 27). Table 1 presents some key indicators on the CIVETS economies. Note that we use the Penn World Tables 7.0 data since it offers the most compatible basis for inter-country comparisons. According to the population figures in Table 1, the total population of the CIVETS as a group totals 577.3 million in 2009, up from 509.5 million in 2000. The average per capita income (after allowing for terms of trade changes and PPP adjustments) increased from 4699 international dollars (I$, in Heston et al.'s definition) to 6163 I$ in 2009. The openness level of the CIVETS countries also increased as a group over the last decade. Vietnam has the highest openness ratio, which now exceeds 160%. A convergence towards the US per capita income levels in all CIVETS countries is also seen in the last two columns of Table 1. These observations are in line with the dynamism associated with the CIVETS in the international business and financial community. 2.2. Recent literature on the international integration of CIVETS stock markets The academic research on the CIVETS is limited to works on their individual domestic economies, financial markets, or in some cases, to their international linkages. However, at the moment, there is no dedicated study of all CIVETS countries' financial markets. In what follows, we focus on some of the studies in the literature that provide insights about the international financial market integration of the CIVETS stock markets. Colombia is generally found to be well integrated with the Latin American and the US stock markets (Chen et al., 2002; Choudhry, 1997; Christofi and Pericli, 1999). The studies on Colombia employ causality Table 1 Selected economic indicators on the CIVETS countries. Population (in millions)

Colombia Indonesia Vietnam Egypt Turkey South Africa

2009 43.7 240.3 88.6 78.9 76.8 49.1

2000 38.9 213.8 79.2 65.2 67.3 45.1

Terms of trade and PPP-adjusted per capita GDP

Openness (X + M) as % of PPP-adjusted GDP in 2005 constant prices

Ratio of PPPadjusted per capita GDP to USA (in 2005 constant prices)

2009 7714 3957 2934 4722 9833 7816

2009 40.6 56.9 161.2 63.6 45.3 48.4

2009 18.7 9.6 7.1 11.5 23.9 19.0

2000 5664 3071 1683 3734 8278 5762

2000 34.5 54.0 110.0 47.4 40.0 51.9

2000 14.4 7.8 4.3 9.5 21.1 14.7

Note: All figures are obtained from Heston et al. (2011). The variable codes in the PWT 7.0 database are as follows. Population: POP; Terms of Trade and PPP-adjusted per capita GDP: RGDPTT; Openness as % of PPP-adjusted GDP in 2005 constant prices: OPENK; the ratio of PPP-adjusted per capita GDP to the corresponding US per capita GDP: Y.

T. Korkmaz et al. / Emerging Markets Review 13 (2012) 230–252

233

and cointegration tests and examine the presence of common stochastic trends with other Latin American and the US stock markets. The extent of volatility spillovers is also investigated (Christofi and Pericli, 1999). The empirical analysis results indicate that the Colombian stock market is cointegrated with other Latin American markets and this finding remains robust even after taking the crisis periods into account (Chen et al., 2002). Hence, there is only limited potential for diversification benefits from investing in a portfolio of Latin American countries that includes Colombia (Chen et al., 2002). Colombia is the only Latin American country in the CIVETS group. Then, the question is whether there can be diversification benefits from combining Colombian stocks with a portfolio of non-Latin American stocks, or whether this is not the case due to inter-regional spillovers. Indonesia and Vietnam represent Asia in the CIVETS portfolio. Indonesia is a member of the original ASEAN-5 area while Vietnam is also now included in the ASEAN. The recent research on the international linkages of the Indonesian stock market includes Click and Plummer (2005), Chancharoechai and Dibooglu (2006), Henry et al. (2007), Wang (2007), Wang and Lee (2009), Abdul Karim et al. (2009), and Mulyadi (2009). Click and Plummer (2005) investigate the Asian financial crisis of 1997–1998 for its implications on the degree of stock market integration in the ASEAN-5 region. For the period from July 1998 to December 2002, it is found that the ASEAN-5 stock markets (Indonesia, Malaysia, the Philippines, Singapore, and Thailand) are cointegrated, but it is far from being complete. Therefore, there is still some room for portfolio diversification possibilities for international investors. 11 Chancharoechai and Dibooglu (2006) investigate the volatility spillovers and contagion during the Asian financial crisis using multivariate GARCH models. Their findings for Indonesia indicate that Indonesia is indirectly affected by Malaysia while there are direct volatility transmissions from Japan and the USA. Henry et al. (2007) uses Cheung and Ng's (1996) causality-in-mean and causality in variance tests to examine the stock market return and volatility spillovers amongst eight South East Asian countries, namely, Hong Kong, Indonesia, Japan, South Korea, Malaysia, the Philippines, Singapore, and Thailand. The study uses weekly data from 2 April 1990 to 19 June 2006. The causality-in-mean results indicate that Indonesian stock returns are caused by Hong Kong, Malaysia, the Philippines, Singapore, and Thailand while Indonesian stock returns do not cause any other stock returns in the mean. The causality-in-variance tests show that there are contemporaneous pair-wise volatility spillovers effects on Indonesia from South Korea, Singapore, the Philippines, and Thailand. Furthermore, the Philippines and Malaysia are also found to bring volatility spillovers into the Indonesian stock market. These findings indicate that the Indonesian stock market is well integrated with the Asian stock markets and not immune to shocks occurring in the region. Henry et al.'s (2007) study is particularly interesting in the context of our paper since it uses a similar methodology. Using autoregressive distributed lags (ARDL) approach to cointegration, Abdul Karim et al. (2009) find that Indonesia's stock market is cointegrated with the stock markets of its major trading partners (Japan, the US, Singapore, and China). The sample uses weekly data covering the period from July 1998 to December 2007. It is argued that portfolio diversification possibilities are hence limited. Mulyadi (2009) uses daily data for the period from January 2004 to December 2008 to investigate the stock market volatility spillovers between Indonesia, the US and Japan. It is found that volatility spillovers run unidirectionally from the US to Indonesia while there is a bidirectional (feedback) link between Indonesia and Japan. Overall, the Indonesian stock market is also generally found to be integrated with the stocks markets in its region and with the US. As in the case of Colombia, the question is whether the geographically diverse CIVETS portfolio might help bring portfolio diversification benefits. Vietnam's young stock market received recent attention in the academic literature. Chang et al. (2009), Chang and Su (2010), Loc et al. (2010), Thuan and Yuan (2011), and Tuyen (2011) analyse various aspects of the stock returns in Vietnam. Loc et al. (2010) provide a review of the developments in Vietnam's Ho Chi Minh City stock exchange since its start in 2000. Loc et al. (2010) find that the stock market in Vietnam is not efficient in the weak form. It should, however, be noted that Luc et al.'s analysis covers the period until 2005, that is before major changes occurred in the organization of the Vietnamese stock market in 2006. Thuan and Yuan (2011) test for the influence of the US stock market developments on the Vietnamese 11 See also Sharma and Bodla (2010) for a review of the literature on global financial market interlinkages with a focus on Asian countries.

234

T. Korkmaz et al. / Emerging Markets Review 13 (2012) 230–252

stock returns using daily data for the period 2003–2009. The results indicate that both the S&P 500 and the Dow Jones indices affect the mean stock returns in Vietnam strongly and positively. However, there is no evidence of volatility spillover effects. Chang and Su (2010) employ the nonlinear threshold error correction model (TECM) and bivariate BJR_GARCH models to examine the relationship between the Vietnam stock market and its major trading partners (the USA, Japan, Singapore, and China). The study uses daily closing prices and covers the period from March 2002 to February 2007. The results indicate that the developments in Japan and Singapore stock markets influence the Vietnam stock market. An asymmetric volatility effect is also detected: a negative shock leads to larger volatility changes than a positive one. The research on the international linkages of the Egyptian stock market (Cairo Stock Exchange) is found in several contexts: linkages with the developed country stock markets (Maneschiold, 2005; Neaime, 2006), linkages within the Middle East North Africa (MENA) region (Alkulaib et al., 2009; Lagoarde-Segot and Lucey, 2007; Maneschiold, 2005; Neaime, 2006; Soofi, 2008; and Yu and Hassan, 2008), as an African emerging market (Alagidede and Panagiotidis, 2009), and as a member of the Organization of the Islamic Conference (OIC) (Hassan and Yu, 2007). Maneschiold (2005) examines the presence of international diversification benefits between subsector indices for the US, Turkey and Egypt stock markets via cointegration analysis and Granger causality test. Cointegration analysis results indicated the evidence of long-run relation between general and some sub-indices. Neaime (2006) investigates the causal relation among the emerging stock markets of the Middle East and North Africa (MENA) region. In addition to this, the relationship between each MENA stock markets and stock markets of Europe and United states are also examined by using GARCH and VAR models. The empirical results suggest that stock returns in Egypt lead to changes in the stock markets of Jordan, Turkey and Morocco. Neaime (2006) also finds that information giving rise to volatilities in a given market is transmitted more rapidly among markets that are located closer geographically and among markets that are more efficiently organized and more open like those of Egypt, Jordan, Morocco and Turkey. Moreover, the US, UK and Egyptian stock markets are found to have significant influence on other MENA markets. Yu and Hassan (2008) investigate the presence of financial integration in the MENA region (Bahrain, Oman, Saudi Arabia, Egypt, Jordan, Morocco, Turkey) by means of a multivariate GARCH model. Analysis results indicate the US stock market Granger-cause stock market developments in non-Gulf Cooperation Council (GCC) countries that include Egypt and Turkey. Furthermore, the US stock market is found to have significant effects on future volatility in the most of the MENA countries. The international integration of the Turkish stock market is recently investigated within the context of emerging Europe and transition economies (Kenourigios and Samitas, 2011; Mandaci and Torun, 2007), within the MENA region (Maneschiold, 2005; Neaime, 2006; Soofi, 2008; Yu and Hassan, 2008), and for its linkages with other major developing and developed countries (Kasman et al., 2009; Mandaci and Torun, 2007). Kenourigios and Samitas (2011) investigate the long-run relation among five Balkan emerging stock markets (Turkey, Romania, Bulgaria, Croatia and Serbia), the US, the UK, Germany and Greece by using regime-switching cointegration tests and multivariate GARCH model. Cointegration test result provides evidence in favor of regional and global equity market integration. Maneschiold (2005) also finds that the Turkish stock returns are Granger-caused by the US stock market developments. This is also supported by findings of Soofi (2008) who uses cointegration and Granger-causality tests to examine the degree of financial integration of a number of Middle East North Africa (MENA) countries with the US and the US markets. Soofi (2008) concludes that only the Turkish market is cointegrated with the S&P 500 and the UK's FTSE indices. S&P 500 and FTSE are Granger-cause the Turkish market. In addition, it is found that, except for Turkey and Tunisia, there is only a limited degree of financial integration between the MENA stock markets and the S&P 500 and the FTSE. Yu and Hassan (2008) further add to these conclusions that the Turkish stock market is very sensitive and over-reacts to the US stock market developments. Mandaci and Torun (2007) test for the stock market integration between Turkey, Russia, Brazil, Korea, South Africa, and Poland for the period between January 1996 and August 2006 by means of cointegration and Granger-causality tests. The Turkish stock market is not found to be pair-wise cointegrated with other stock markets in the sample. South Africa is found to be pair-wise cointegrated with Russia. Grangercausality test results indicate that South Africa causes Brazil; Poland causes South Africa and Turkey; and Brazil causes Turkey. Kasman et al. (2009) emphasizes the importance of taking structural breaks

T. Korkmaz et al. / Emerging Markets Review 13 (2012) 230–252

235

into account in testing for integration between the financial markets. It is found that the Turkish market is cointegrated with the stock markets in Germany, France, the UK, the US, Japan, Korea, Thailand, China and Brazil once structural breaks are accounted for. 12 The most recent work on the behavior and the international linkages of the South African stock market include Alagidede and Panagiotidis (2009) and Goldberg and Veitch (2010). Alagidede and Panagiotidis (2009) study the behavior of stock returns in Egypt, Kenya, Morocco, Nigeria, South Africa, Tunisia, and Zimbabwe. The random walk hypothesis is rejected for all countries. However, the GARCH estimates imply that the weak efficiency hypothesis cannot be rejected in the case of Egypt and Nigeria. 13 Goldberg and Veitch (2010) employ a time-varying beta model of country-risk for South Africa covering the period 1993–2008. The sample is divided into pre- and post-financial integration periods, the year 1998 being dated as the turning point. In a global CAPM model, South Africa's beta is found to be low and dependent on exchange rates and gold prices during the pre-integration period while the value of the beta is higher and economic fundamentals no longer play a significant explanatory role. This implies that the South African (Johannesburg Securities Exchange) stock market is integrated into the global financial markets. It should still be recognized that any grouping of countries involves some degree of arbitrariness. In the case of BRICs, the country and population size coupled with economic growth potential act as a common framework. In the case of CIVETS, it is the young population, economic dynamism, and less dependence on external markets. One critical question that comes to mind is: what relationships are there between Egypt and Vietnam or Turkey and Colombia? One way to address this question is to look at the channels of international business cycle transmission. It is argued in the literature that financial and trade linkages, coordinated policies, and business and consumer confidence spillovers might lead to co-movements of economic cycles across different countries (Anderton, et al., 2004). At first sight, one may argue that there is perhaps not much correlation among the CIVETS economies. From an international investor's point of view, this is actually good news as investing in CIVETS countries might lead to diversification benefits. On the other hand, international asset allocation decisions establish a financial linkage element. As we have seen in the above review of the literature CIVETS stock markets are integrated at least at the regional level and they are also affected by the developments in the capital markets of developed countries. Hence, contrary to first impressions, it may not be surprising to find that the developments in Colombia or Vietnam affect the volatility of Turkish or Egyptian stock returns. An important question in the international finance literature is whether the presence of return and volatility spillover effects across countries can be attributed to contagion or whether it is due to interdependence (Aloui, et al., 2011; Baur and Fry, 2009; Edwards and Susmel, 2001; Forbes and Rigobon, 2002; Samarakoon, 2011). We address this question by means of rolling correlation analysis and causality-in-mean and causality-in-variance tests after controlling for a number of common factors and other statistical artifacts that might otherwise lead to the non-rejection of the contagion hypothesis too often. The investigation of the contagion versus interdependency hypothesis in the context of CIVETS economies is interesting since the linkages among the CIVETS stem rather from international financial flows as opposed to the countries that are economically interdependent or that are all members of a formal economic grouping (the EU, NAFTA, ASEAN, etc). Our findings suggest that the stock returns and volatility spillovers between individual CIVETS countries arise due to interdependence rather than contagion effects. As such, our study contributes to the empirical finance literature by shedding further light into the testing of contagion versus interdependency hypothesis. 3. Econometric framework Causal relationships between returns in different financial markets have been widely examined in the literature using Granger's (1969) causality test and its variants. Granger (1969) defines causality in terms of predictability and states that a stochastic variable X causes another distinct stochastic variable Y if and 12 Using four types of cointegration tests, Lagoarde-Segot and Lucey (2007), however, reject the cointegration of Turkish and Egyptian capital market with those of MENA, European Monetary Union, and the World overall. This suggests the presence of portfolio diversification possibilities. 13 Alagidede and Panagiotidis (2009) quote The Economist's (29.07.2007) Market View article entitled: “Into Africa: the investors eye globalisation's final frontier”. “But this is the age of globalisation, when investors feel free to boldly go where they had not gone before. After all, places that were previously regarded as exotic, from Bulgaria to Vietnam, are integrated into the global economy. Now it may be Africa's turn.”

236

T. Korkmaz et al. / Emerging Markets Review 13 (2012) 230–252

only if X contains unique information about Y. In operational terms, Granger's definition of causality requires that if the inclusion of the history of X in an information set containing all available information in the universe including the history of Y improves the forecast error variance of Y, then X is said to Grange-cause Y. While this definition is general with respect to the moments of the series involved, the tests of Granger-causality have traditionally been conducted in the conditional means of the variables in question. Cheung and Ng (1996) extend the notion of Granger-causality to the conditional variance of the series. This is important especially for financial series as it enables the detection of causal relationships in volatility transmission between financial assets or financial markets in general. In addition, the tests of causality-in-mean may indeed suffer from specification error if there is causality-in-variance between the variables involved. For instance, Mantalos and Shukur (2010) use Monte-Carlo simulations and demonstrate that the Wald test based on a VAR model over-rejects the null hypothesis of non-causality when there are volatility spillover effects. In this context, Cheung and Ng (1996) extend the nation of Granger-causality to the second moments of the variables as follows. A stationary and ergodic stochastic variable is said to cause another distinct, stationary, and ergodic stochastic variable Y in variance if 

2 2 E X tþ1 −μ x;tþ1 jIt g≠E X tþ1 −μ x;tþ1 jJ t g ð1Þ where It and Jt are two information sets defined by It = {Xt − j; j ≥ 0} and Jt = {Xt − j, Yt − j ; j ≥ 0}. The notion of causality-in-variance provides additional insights about the temporal dynamics of economic and financial time series as the changes in variance reflect the arrival of information and the extent to which the market evaluates the value of new information. Cheung and Ng (1996) use the following procedure in order to operationalise their causality-in-variance test. In the first step, a GARCH model is fitted to each series (X and Y) in question. Next, a cross-correlation function is estimated between the squared residuals from the respective GARCH models for X and Y variables. Then, Cheung and Ng (1996) derive an S-statistic which follows a chi-square distribution to determine the existence or lack of causality-invariance between the variables of interest. In this test, the null hypothesis is no causality-in-variance at all lags and the alternate hypothesis is the presence of causality-in-variance at some lag. Cheung and Ng's (1996) set up is flexible enough to allow for testing for causality-in-mean in a similar manner to Pierce and Haugh (1977). 14 Furthermore, the problem of a common cause for both X and Y (such as a Z variable) can be handled in Cheung and Ng's approach by including the Z variable in the mean and/or variance equations of the respective GARCH models. 15 In subsequent research, it was argued that Cheung and Ng's (1996) S-statistic may not be fully efficient when a large lag order (M) is used because it gives equal weighting to each of the M sample cross-correlations. In practice, it has been argued that this drawback need not be very severe since the cross-correlation between financial assets usually decays to zero as the lag order is increased (Gebka and Serwa, 2007). In order to overcome the equal-weighting problem found in Cheung and Ng's test, Hong (2001) modifies Cheung and Ng's (1996) S-statistic by employing a non-uniform kernel weighting function. The new test statistic is found to perform better than the S-test in Monte Carlo simulations. Hong (2001) defines two statistics Q1 and Q2 to test for causality-in-mean and causality-in-variance, respectively. The Q1-statistic for the causality-in-mean test is defined as: T

T−1 P l¼1

k

2 l  2 ^ M ρ ξi ξj ðlÞ−C 1T ðkÞ

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð2Þ 2D1T ðkÞ o n −1=2 ^ 2ξ ξ ðlÞ ¼ C^ ξ ξ ð0ÞC^ ξ ξ ð0Þ C^ ξi ξj ðlÞ, C^ ξi ξi ð0Þ ¼ T −1 ∑Tt¼1 ξ^i;t , C^ ξj ξj ð0Þ ¼ T −1 ∑Tt¼1 ξ^j;t and ξ^i;t and ξ^j;t where ρ i i j j Q1 ¼ i j

14 Causality-in-mean in Cheung and Ng's (1996) context is conducted by using the residuals from the GARCH models instead of the squared resisuals. 15 Another approach to testing for causality-in-variance is based on a dynamic specification of multivariate GARCH (MGARCH) model where the notion of causality-in-variance can be represented in terms of specific parameter restrictions. Nevertheless, Hafner and Herwartz (2006) indicate that the likelihood-based tests within multivariate dynamic models typically suffer from a curse of dimensionality. Therefore, we employ causality-in-mean and causality-in-variance tests based on estimating of univariate GARCH models.

T. Korkmaz et al. / Emerging Markets Review 13 (2012) 230–252

237

are standardized residuals derived from GARCH model. In Eq. (2), k(l/M) is a weight function, for which the Barlett kernel is 16:  kðl=M Þ ¼

1−jl=ðM þ 1Þj if k=ðM þ 1Þ≤1 0 otherwise

ð3Þ

^ ξ ξ ðlÞ is the sample cross-correlation between the centered standardized residuals from the In Eq. (2), ρ i j GARCH models for X and Y. The terms C1T(k)and D1T(k)in the numerator and the denominator of Eq. (2) is obtained from T−1

2

T−1

4

C 1T ðkÞ ¼ ∑l¼1 ð1−jlj=T Þk ðl=MÞ and D1T ðkÞ ¼ ∑l¼1 ð1−jlj=T Þf1−ðjlj þ 1Þ=T gk ðl=MÞ: where M is a predetermined lag order. Hong's (2001) Q2-statistic for testing for the causal link between the variances of return series is obtained similarly by using the squares of the standardized residuals ξ^i;t and ξ^j;t in Eq. (2). The sample ^ ξ ξ ðlÞ is then calculated between the squares of the centered standardized cross-correlation term ρ i j GARCH residuals. Both Q1 and Q2 statistics are one-sided and the upper tailed normal distribution critical values should be used. The asymptotic critical value at the 5% level is 1.645. If the computed Q1 (or Q2) statistic is larger than the asymptotic critical value at the desired confidence level, then the null hypothesis of no causalityin-mean (no causality-in-variance) at all lags is rejected. The results of Cheung and Ng's (1996) and Hong's (2001) causality tests depend on the correct specification of the GARCH models and the unbiased estimation of the GARCH parameters. In this context, the effects of possible structural breaks in the variances of the series on GARCH parameter estimates and hence their effects on Cheung and Ng's and Hong's causality tests have received further attention. It has been shown that the presence of structural breaks in the unconditional variance of the series lead to the overestimation of the size of the GARCH parameters. Using Monte-Carlo simulations, Hillebrand (2005) shows that parameter regime changes in GARCH models that are not accounted for in global estimations cause the sum of estimated GARCH parameters to convergence to one. This effect is termed as “spurious almost-integration”. Galeano and Tsay (2010) further argue that high excess kurtosis and volatility persistence can indeed not be adequately explained even by the GARCH processes with the heavy tails. Hence, the shifts in volatility brought about by financial crises might lead to changes in the parameters of the GARCH model. The implications of the breaks in volatility on the tests of causality-in-variance tests are examined by Van Dijk, et al. (2005) and Rodrigues and Rubia (2007). It is found that severe size distortions in causality-in-variance tests occur when there are structural breaks in the variance of series. Thus, the presence of structural breaks in the unconditional variance must be examined before testing for causality-invariance. Inclan and Tiao (1994) propose a test procedure that is based on “Iterative Cumulative Sum of Squares” (ICSS) to detect structural breaks in the unconditional variance of a stochastic process. In order to test null hypothesis of constant unconditional variance against the alternative hypothesis of a break in the unconditional variance, Inclan and Tiao (1994) propose using the statistic given by: pffiffiffiffiffiffiffiffi IT ¼ T=2Dk ð4Þ where Dk = (Ck/CT) − (k/T) and Ck = ∑tk= 1rt2 be the cumulative sum of squares of a series of uncorrelated 2 random ffiffiffiffiffiffiffiffi  with mean 0 and variance σt , for t = 1, 2, …,T. The value of k (k = 1, …, T) that maxipvariables mizes  T=2Dk  is the estimate of the break date. Under the variance homogeneity the IT-statistic behaves asymptotically like a Brownian bridge. At the 5% significance level, the critical value computed by Inclan and Tiao (1994) is C0.05 = 1.358. The most serious drawback of the IT-statistic is that it assumes independently and identically distributed random variables. Andreou and Ghysels (2002) and Sanso et al. (2004) show that the IT-statistic generates oversized results when dependent variable exhibits conditional heteroskedasticity. In this context, Fernandez (2006) presents evidence that the IT-statistic fails to find the effect of terrorist attacks of September 11 on the volatility of world stock markets. Sanso et al. (2004) develop a modified version of the 16

We use the Barlett kernel since Hong (2001) shows that similar results are obtained from several non-uniform kernels.

238

T. Korkmaz et al. / Emerging Markets Review 13 (2012) 230–252

IT-statistic and show by means of Monte Carlo simulations that these new test statistics outperform the ITstatistic. Two cases are considered. The first one is the kappa-1(κ1) statistic which is appropriate for the case where only the normality assumption does not hold. The κ1statistic is given by:    −1=2  κ1 ¼ sup T Bk 

ð5Þ

  C − Tk C T −1 T 4 4 −1 ; where η^ 4 ¼ T ∑t¼1 εt and σ^ ¼ T CT Bk ¼ qkffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 4 ^ ^ η 4−σ

ð6Þ

k

Sanso et al's (2004) second statistic is the kappa-2 (κ2) statistic, which is best suited to the cases where both the normality assumption does not hold and there are ARCH effects in addition. The modified IT test statistic given by:    −1=2  ð7Þ Gk  κ2 ¼ sup T k  −1=2  k ^4 ^ 4 is a consistent estimator of ω4. A non-parametric estimator ofω4is where Gk ¼ ω C k − T C T and ω given by: ^4 ¼ ω

T
m T
 
 X 1X 2X 2 2 2 2 2 2 2 r t−1 −σ^ r t −σ^ þ ωðl; mÞ r t −σ^ T t¼1 T l¼1 t¼lþ1

ð8Þ

where ω(l, m) is a lag window, such as the Barlett, defined as ω(l, m) = 1 − l/(m + 1), or the quadratic spectral. Note that the Bk and Gk functions would provide a satisfactory procedure if there is only one break point in the series. However, in the case of multiple changes or break points in the series, the usefulness of the Bk and Gk functions becomes questionable because of masking effects. In this case, the determination of multiple break points is achieved by following the iterative procedure suggested by Inclan and Tiao (1994) which requires the successive calculation of the Bk and Gk functions for the segments of the series divided consecutively after a possible change point is found. 4. Data and empirical results The aim of this study is to examine the presence of return and volatility spillovers among the CIVETS countries. We use weekly data in which Wednesday stock market prices are collected from the Bloomberg covering the period from July 24, 2002 to December 29, 2010. 17 The total number of observations per country is 440. The logarithmic return series are obtained by using the rt = 100 x ln (Pt/Pt − 1) formula. The descriptive statistics for all return series are presented in Table 2. The weekly mean of all return series varies between 0.205% and 0.584%. The highest mean return (excluding dividend payments) occurs in the Colombian stock market. The Vietnamese stock market, on the other hand, yields the lowest mean returns during the sample periods. Furthermore, Vietnamese stock return series exhibit higher volatility. All return series show evidence of strong negative skewness and excess kurtosis which indicate that they are leptokurtic. The Jarque–Bera normality test also rejects the normality of the stock returns in the CIVETS countries. The Ljung–Box Q statistics indicate the presence of serial correlation in the returns and in the squared returns series. Finally, we examine the existence of unit roots by means of the augmented Dickey and Fuller (1979) (ADF), Phillips and Perron (1988) (PP) and the Kwiatkowski, Phillips, Schmidt and Shin (1992) (KPSS) unit root tests. All unit root tests results strongly suggest that all return series are stationary. 17 The stock indices used in the study and their Bloomberg codes are as follows. Colombia: Bolsa de Valores de Colombia (BVC); Indonesia: Jakarta Stock Exchange Composite Index (JCI:IND); Vietnam: Hi Chi Minh Stock Index/VN Index (VNINDEX:IND); Egypt: Egyptian Financial Group Hermes Stock Market Index (HERMES:IND); Turkey: Istanbul Stock Exchange National 100 Index (XU100: IND); and South Africa: (FTSE/JSE Africa Top 40 Tradable Index (TOP40:IND). The stock market capitalization to GDP ratios for the CIVETS countries in 2010 are as follows. Colombia (72.3%); Indonesia (51%); Vietnam (27.4%); Egypt (38.5%); Turkey (41.8%), and South Africa (254.3%).

T. Korkmaz et al. / Emerging Markets Review 13 (2012) 230–252

239

Table 2 CIVETS stock returns: descriptive statistics.

N Mean Std. dev. Skewness Kurtosis Jarque–Bera ARCH (5) Q (20) Qs (20) ADF PP KPSS

Colombia

Indonesia

Vietnam

Egypt

Turkey

S. Africa

440 0.584 3.453 − 1.357 13.375 2108.6 [0.000] 33.893 [0.000] 19.711 [0.476] 229.619 [0.000] − 20.001*** − 20.122*** 0.087***

440 0.464 3.716 − 1.165 10.997 1272.1 [0.000] 31.928 [0.000] 57.176 [0.000] 205.778 [0.000] − 10.096*** − 21.320*** 0.076***

440 0.205 4.456 − 0.127 4.621 49.369 [0.000] 19.569 [0.000] 59.067 [0.000] 234.693 [0.000] − 16.858*** − 17.529*** 0.131***

440 0.561 4.039 − 0.801 5.444 156.63 [0.000] 10.684 [0.000] 48.060 [0.000] 128.343 [0.000] − 19.224*** − 19.680*** 0.075***

440 0.420 4.349 − 0.546 4.478 61.953 [0.000] 2.985 [0.011] 27.819 [0.113] 41.025 [0.003] − 20.409*** − 20.449*** 0.077***

440 0.243 3.179 − 0.040 4.700 53.126 [0.000] 15.527 [0.000] 94.820 [0.000] 216.939 [0.000] − 24.046*** − 24.063*** 0.131***

Notes: The figures in square brackets show the probability (p-values) of rejecting the null hypothesis. ARCH (5) indicates LM conditional variance test. Q(20) and Qs(20) indicate Ljung–Box serial correlation test for return and squared return series respectively. *** indicate that the series in question is stationary at the 1% significance level.

4.1. Tests for volatility breaks We examine the presence of breaks in the variances CIVETS stock returns by means of Inclan and Tiao (1994) and Sanso et al.'s (2004) iterative procedures as explained in Section 3. Table 3 presents the results from the variance break tests. Most notably, Inclan and Tiao's (1994) IT test indicates the presence of

Table 3 CIVETS stock returns: variance break tests. Inclan and Tiao (1994)

Sanso et al. (2004) Kappa-1

Sanso et al. (2004) Kappa-2

No. of breaks

Break dates

No. of breaks

Break dates

No. of breaks

Colombia

5

1

12.10.2008

0

Indonesia

4

14.01.2004 10.05.2006 12.07.2006 27.08.2008 10.12.2008 18.07.2007 03.09.2008 19.11.2008 09.09.2009 27.11.2002 05.11.2003 14.04.2004 25.08.2004 03.08.2005 15.02.2006 13.01.2010 19.01.2005 02.08.2006 06.08.2008 04.02.2009 28.07.2010 10.09.2008 14.01.2009 03.03.2010 25.06.2003 12.10.2005 27.08.2008 20.05.2009

Vietnam

7

Egypt

5

Turkey

3

South Africa

4

2

Break dates

0 18.07.2007 09.09.2009

7

1

05.11.2003 14.04.2004 15.02.2006 17.01.2007 16.01.2008 12.11.2008 13.01.2010 19.01.2005

0

4

1

0

0

25.06.2003 12.10.2005 27.08.2008 20.05.2009

0

15.02.2006

240

T. Korkmaz et al. / Emerging Markets Review 13 (2012) 230–252

breaks in the stock return volatilities of all countries. Most of the breaks are found to occur after 2007: 11 out of 28 breaks are in 2008 and 2009 and four in 2010. Nevertheless, the IT test has its drawbacks and may tend to over-detect volatility breaks when the underlying series are not normally distributed and do not follow i.i.d. processes, which is the case in the CIVETS stock return series. The modified IT-tests developed by Sanso et al. (2004) to tackle this problem draw a different picture of the variance breaks in CIVETS stock returns. The kappa-1 statistics, which is suited to the cases of nonnormality still identifies 15 volatility breaks, seven of which are after 2007. However, the kappa-2 statistic which also takes the ARCH effects into account detects only one break point and that is for Vietnam on 15.02.2006. 18 Having been established in July 2000, the Vietnamese stock market is rather young compared to other CIVETS countries. In essence, our finding of a volatility break in Vietnamese stock returns is in line with McMillan and Wohar (2011) who study the volatility breaks in the sectoral stock returns in the UK and present evidence that newer sectors exhibit more breaks than the traditional sectors. On the other hand, the modified IT statistic cannot determine any structural breaks in the variance of the other countries. These results are important on their own as they provide a comparison of the performance of the different tests for breaks in the variance of financial time series. Our results also illustrate that the Inclan and Tiao (1994) test over-rejects the null hypothesis of no break in variance when the series do not follow normal distribution and further exhibit ARCH effects 4.2. The GARCH model estimates Since the causality-in-variance test relies on estimating the univariate GARCH model for each return series, we first employ the following GARCH model:

rt ¼ μ þ

k X ρi r t−i þ δ1 SP500t þ δ2 Nikkeit þ εt i¼1

ð9Þ

σ 2t ¼ ω þ αε2t−i þ βσ 2t−1

In Eq. (9), rt indicates stock market index returns and εt is an error term that follows a generalized error distribution (GED). In the GARCH model, when ω > 0, α and β ≥ 0, the positive conditional variance condition is satisfied. The GARCH model in Eq. (9) is a modified version of Bollerslev (1986) and includes the US Standard & Poor's 500 (S&P500) and Japan's Nikkei 225 (Nikkei) indices. The effects of the US stock markets on emerging markets have been widely examined in the literature and results of these studies suggest the presence of significant spillovers effect (Özdemir et al., 2009). Also, most of Asian countries' stock markets such as China, South Korea, Indonesia and Vietnam are affected from the Japanese stock market. 19 Hence, we account for possible common international spillovers effects (or common third factor effects) on the return and volatility spillovers on CIVETS stock markets by controlling for them in the GARCH equation. Based on the results from the breaks in variance tests, we constructed a dummy variable to eliminate the effects of structural breaks in the variance of Vietnam stock return series. This is in line with the practice in the literature. See, for instance, Lamoureux and Lastrapes (1990), Aggarwal et al. (1999), AragoManzana and Fernandez-Izquierdo (2007), Wang and Thi (2007), Rapach and Strauss (2008), and Ewing and Malik (2010). We have also tested whether the dummy variables corresponding to the times of volatility breaks indicated by the Inclan and Tiao (1994) IT test for other countries enter their respective GARCH models significantly. This was not found to be case. Furthermore the GARCH models without these dummy variables 18 The Ho Chi Minh city stock market was established on 20.07.2000, initially with two stocks listed. In September 2005, the allowed ownership foreign ownership share in companies was raised from 30% to 49%. In 2006, the number of companies listed almost tripled compared to 2006 and the market became much more liquid. The volatility change indicated by the formal tests as well as Fig. 1 should be capturing this phenomenon. 19 See De Gooijer and Sivarajasingham (2008), Royfaizal et al. (2009), and Wang and Lee (2009) for a review and investigation of the stock market impulse transmission linkages in Asia.

T. Korkmaz et al. / Emerging Markets Review 13 (2012) 230–252

241

are found to have higher log-likelihood values than the GARCH models with dummy variables. These results support the findings from Sanso et al.'s (2004) kappa-2 test. 20 Next, we estimate univariate GARCH model in Eq. (9) for each return series and find the GARCH (1,1) model to be sufficient for adequately modeling the return volatility. 21 The optimal lag lengths for the autoregressive parameters in the mean equation are determined by the Akaike information criterion (AIC). The selected optimal lags that render the residuals white noise are two for South Africa, three for Indonesia, and four for Vietnam. According to the GARCH model results in Table 4 the S&P 500 and the Nikkei index returns are found to be statistically significant at 1% level for all countries except for Vietnam. The effect of the Nikkei index return on Vietnamese stock market turns out to be statistically significant at the 10% level. These findings suggest the presence of spillovers effects from developed countries to the emerging markets. The statistical implication is that not accounting for these common effects might have led to spurious causality results (Lütkepohl, 1982). The alpha parameter is found to be statistically significant at the 10% level for Indonesia and Turkey, at the 5% level for Colombia and at the 1% level for Vietnam, Egypt and South Africa. On the other hand, the estimates of beta parameter are statistically significant at the 1% level for all countries. Also, the sum of the alpha and beta parameters, which indicates the degree of persistence in volatility, is found to vary between 0.780 (Colombia) and 0.980 (Turkey). It can be said that the shocks are more persistent in the Turkish, Egyptian and the South African stock markets. The volatility break dummy variable for the Vietnamese stock market is found to be statistically significant at the 1% level – demonstrating that volatility in the Vietnam stock market has increased significantly after 2006. 4.3. Rolling correlation analysis The correlation structure between stock returns is widely used in finance and financial management literature and it is employed in drawing efficient frontiers of portfolio holdings. A recent study of correlations and co-movements between 33 developed and developing country stock market indices (including only Indonesia and Turkey among the CIVETS) is provided by Evans and McMillan (2009). 22 The problem with many simple Pearson correlation figures is that they can only capture linear relationships and that they may be spurious. Even if the latter is not the case, they may exhibit time-varying behavior. In this section, we provide a rolling-correlation analysis of the stock returns in CIVETS countries. The rolling-correlation analysis addresses the possible time-varying nature of the correlation coefficients. We use a 16-week rolling-window over the sample period. 23 We conduct our analysis not only on the simple stock return series, but also on the residuals and squared residuals from the GARCH models estimated in Section 4.2. This procedure is aimed at addressing the effects of common factors and ARCH effects on the correlation coefficients. This has also implication about the interpretation of the return and volatility correlations as contagion or interdependence. Forbes and Rigobon (2002), for instance, argue that ARCH effects lead to an upward bias in simple correlation coefficients. Thus, during times of heightened volatility (or crisis periods), higher simple correlation coefficients are observed and often incorrectly interpreted as contagion across financial markets. Filtering out the ARCH effects remove this bias and any remaining correlation which does not represent a significant increase over non-crisis periods can then be interpreted as interdependence (see also Edwards and Susmel, 2001). 20

The test results are available upon request. GARCH (1,1) model is a parsimonious representation of the ARCH (∞) model. We also implemented the EGARCH and GJR-GARCH models to determine the presence of leverage effect in the volatility of returns series. However, EGARCH and GJR-GARCH models are not found to outperform the GARCH (1,1) model. 22 Angelidis (2010) examine the correlation between idiosynratic risk in 25 emerging markets using monthly data from December 1994 to May 2007.The idiosyncratic risk derived as the residuals from a regression of individual stock returns on a valule-weigthed market return. The sample of countries in Angelidis's (2010) study includes all CIVETS countries with the exception of Vietnam. The correlation between the idiosyncratic risk for the CIVETS group is reported to be as follows (Angelidis, 2010: 1061, Table 2): Colombia and Egypt: 0.10, Colombia and Indonesia: 0.41, Colombia and South Africa: 0.40, Colombia and Turkey: 0.19, Egypt and Indonesia: 0.12, Egypt and South Africa: 0.09, Egypt and Turkey: − 0.13, Indonesia and South Africa: 0.70, Indonesia and Turkey: 0.33, and Turkey and South Africa: 0.37. 23 Edwards and Susmel (2001) find in their study of volatility dependence and contagion among Argentina, Brazil, Chile, Mexico, and Hong Kong that high volatility periods are rather short-lived and last between two to 12 weeks. Hence, our choice of a 16 week horizon should be a reasonable time-window for the calculation of rolling correlation coefficients. 21

242

T. Korkmaz et al. / Emerging Markets Review 13 (2012) 230–252

Table 4 GARCH model results. Colombia Mean equation μ 0.598*** (0.114) ρ1 – ρ2 – ρ3 – ρ4 – δ1 0.199*** (0.051) δ2 0.131*** (0.042) Variance equation ω 1.879** (0.860) α 0.228** (0.091) β 0.562*** (0.155) d – ν 1.288*** (0.074) α+β 0.780 L− 1064.281 Likelihood Q (20) 24.934 [0.204] Qs (20) 11.630 [0.928]

Indonesia

Vietnam

Egypt

Turkey

South Africa

0.665*** (0.108) − 0.070 (0.135) − 0.106** (0.020) 0.108** (0.045) – 0.245*** (0.051) 0.539*** (0.000)

− 0.080 (0.164) 0.206*** (0.055) 0.041 (0.055) 0.012 (0.050) 0.081 (0.049) 0.093* (0.050) 0.056 (0.042)

0.588*** (0.139) – – – – 0.150*** (0.053) 0.400*** (0.048)

0.489*** (0.169) – – – – 0.530*** (0.079) 0.363*** (0.068)

0.254*** (0.072) − 0.178*** (0.051) − 0.122*** (0.050) – – 0.585*** (0.047) 0.258*** (0.037)

1.492 (1.015) 0.097* (0.057) 0.718*** (0.162) – 1.233*** (0.162) 0.805 − 1054.049

0.432** (0.172) 0.318*** (0.097) 0.602*** (0.085) 3.385*** (1.097) 1.446*** (0.116) 0.920 − 1140.490

0.346 (0.229) 0.111*** (0.038) 0.867*** (0.040) – 1.427*** (0.139) 0.978 − 1146.638

0.229 (0.309) 0.038* (0.022) 0.942*** (0.040) – 1.564*** (0.149) 0.980 − 1193.675

0.227 (0.145) 0.118*** (0.042) 0.840*** (0.059) – 1.588*** 0.958 − 957.454

13.866 [0.737] 17.586 [0.415]

19.193 [0.259] 14.560 [0.557]

33.131 [0.033] 20.116 [0.451]

18.418 [0.560] 11.172 [0.942]

16.783 [0.538] 21.920 [0.236]

Notes: The figures in brackets and square brackets show the standard errors and p-values respectively. v is GED parameter and d is dummy variable corresponding to structural break. Q(20) and Qs(20) indicate Ljung–Box serial correlation test for return and squared return series respectively. *, ** and *** indicates statistically significance at the 1%, 5% and 10% level respectively.

We first calculate the simple Pearson correlation coefficients between CIVETS stock returns. 24 They are all found to be positive. They range between 0.072 (Vietnam and Colombia) and 0.492 (South Africa and Indonesia. However, when the correlations are calculated in a rolling window, a different picture emerges. Fig. 1 suggests that the correlation coefficients are time-varying and include some periods where they are negative, which is a desired feature for portfolio diversification. Table 5 provides further information on the descriptive statistics of the simple rolling correlation coefficients. The range of the simple correlation coefficients is very wide. For instance, the maximum correlation between stock returns in Colombia and South Africa is as high as 0.914 but the minimum figure is a negative 0.437. The median of the correlation coefficients ranges from 0.039 (Colombia and Vietnam) to 0.485 (Indonesia and South Africa). The correlation structure from the standardized GARCH residuals that eliminate the influence of common factors such as the US and the Japanese stock market and the individual ARCH effects lead to lower figures for the overall period (Table 6). This is in line with Forbes and Rigobon's (2002) argument. The maximum correlation observed is 0.291 (Colombia and Turkey). Furthermore, the Vietnamese stock returns are found not be contemporaneously correlated with any of the other CIVETS stock markets. Fig. 2 presents the evolution of the 16-week rolling correlations between standardized GARCH residuals over the sample period and Table 7 presents the corresponding descriptive statistics. The range of the correlation coefficients is again wide. The median correlations are now negative between Colombia and Vietnam, Vietnam and Turkey, and Vietnam and South Africa. The maximum correlation between Turkey and Colombia is 0.816 (observed in 2010), but its minimum is a negative 0.481 (observed in 2009). Next, we examine the development of the correlation coefficients between squared standardized GARCH residuals, which provides insights into contemporaneous volatility spillovers between CIVETS stocks markets. Table 8 presents the correlation coefficients for the overall sample. Strikingly, statistically significant volatility spillovers are observed only between South Africa and Colombia and Turkey and Egypt. In both cases, the figures are rather low (0.111 and 0.120, respectively). Rolling correlation analysis presented in Fig. 3 shows that the contemporaneous volatility spillovers also vary widely over time. They can at times be as high as 0.965 (between Colombia and South Africa)

24

The results are available upon request.

T. Korkmaz et al. / Emerging Markets Review 13 (2012) 230–252 1.0

COLOMBIA and INDONESIA

1.0

0.5

COLOMBIA and VIETNAM

1.0

COLOMBIA and EGYPT

1.0

COLOMBIA and TURKEY

1.0

0.0

0.0

0.0

0.0

0.0

-0.5

-0.5

-0.5

-0.5

-0.5

-1.0

-1.0

1.0

-1.0

02 03 04 05 06 07 08 09

02 03 04 05 06 07 08 09

02 03 04 05 06 07 08 09

INDONESIA and VIETNAM

INDONESIA and EGYPT

INDONESIA and TURKEY

1.0

0.5

0.5

1.0

-1.0

1.0

0.5

0.0

0.0

0.0

0.0

-0.5

-0.5

-0.5

-1.0

1.0

-1.0

02 03 04 05 06 07 08 09 VIETNAM and TURKEY

1.0

-1.0

VIETNAM and S.AFRICA

1.0

-1.0

EGYPT and TURKEY 1.0

0.5

0.5

0.5

02 03 04 05 06 07 08 09

02 03 04 05 06 07 08 09

-1.0 02 03 04 05 06 07 08 09

02 03 04 05 06 07 08 09

INDONESIA and S.AFRICA 1.0

0.5

-0.5

0.0 -0.5 02 03 04 05 06 07 08 09

-1.0 02 03 04 05 06 07 08 09

EGYPT and S.AFRICA 1.0

0.5

0.0

0.0

0.0

0.0

-0.5

-0.5

-0.5

-0.5

-1.0

-1.0 02 03 04 05 06 07 08 09

-1.0 02 03 04 05 06 07 08 09

TURKEY and S.AFRICA

0.5

0.0

02 03 04 05 06 07 08 09

VIETNAM and EGYPT

0.5

-0.5 -1.0

COLOMBIA and S.AFRICA

0.5

0.5

0.5

0.5

243

02 03 04 05 06 07 08 09

-1.0 02 03 04 05 06 07 08 09

Fig. 1. Rolling correlation coefficients among CIVETS countries for stock returns.

and as low as − 0.710 (between Indonesia and Turkey). However, the median pair-wise correlation coefficients are all near zero and indeed negative in most cases. These findings indicate that there are, in general, only limited contemporaneous volatility spillovers between CIVETS markets, but the time-varying nature of the correlations should also be kept in mind. Hence, we have tested the equality of the means of the correlation between the country pair-wise return and volatility series for the pre-and post-2008 periods by means of t-tests. Table 9 displays the means of the return and volatility series before and after 2008 and the corresponding t- and p-values for the equality of the means test. First, it is seen that there is an increase in the correlation of stock returns across all CIVETS countries after 2008 compared to the pre-2008 or global financial crisis period. However, most of this correlation might be due to an upward bias due to common factors and ARCH effects and indeed it disappears or is greatly reduced after these effects are accounted for. An increase in the correlation between the return spillovers measured by the standardized returns (GARCH residuals) is observed only between Indonesia and Vietnam, Indonesia and South Africa, and Vietnam and South Africa. However, the average post2008 correlation figure is still negative (−0.025) between Vietnam and South Africa, rather low between Indonesia and Vietnam (0.148). Only between Indonesia and Vietnam, a modest increase is observed

Table 5 Descriptive statistics for rolling correlation coefficients.

Colombia and Indonesia Colombia and Vietnam Colombia and Egypt Colombia and Turkey Colombia and S. Africa Indonesia and Vietnam Indonesia and Egypt Indonesia and Turkey Indonesia and S. Africa Vietnam and Egypt Vietnam and Turkey Vietnam and S. Africa Egypt and Turkey Egypt and S. Africa Turkey and S. Africa

Mean

Median

Maximum

Minimum

Std. dev.

0.302 0.056 0.239 0.365 0.326 0.119 0.299 0.365 0.447 0.078 0.070 0.058 0.306 0.221 0.405

0.284 0.039 0.240 0.368 0.347 0.122 0.309 0.387 0.485 0.080 0.079 0.093 0.345 0.233 0.410

0.740 0.751 0.749 0.882 0.914 0.758 0.814 0.892 0.887 0.642 0.734 0.715 0.802 0.703 0.929

− 0.305 − 0.610 − 0.333 − 0.649 − 0.437 − 0.529 − 0.460 − 0.575 − 0.264 − 0.667 − 0.578 − 0.630 − 0.321 − 0.457 − 0.353

0.244 0.273 0.233 0.281 0.295 0.282 0.251 0.280 0.257 0.271 0.281 0.285 0.268 0.251 0.282

244

T. Korkmaz et al. / Emerging Markets Review 13 (2012) 230–252

Table 6 Correlation coefficients among CIVETS countries for standardized residuals. Correlation

Colombia

Indonesia

Vietnam

Egypt

Turkey

S. Africa

Colombia Indonesia Vietnam Egypt Turkey S. Africa

1 0.182*** 0.025 0.177*** 0.291*** 0.174***

1 0.069 0.175*** 0.184*** 0.228***

1 0.072 0.042 − 0.023

1 0.214*** 0.093

1 0.223***

1

(0.364 in the post-2008 period versus 0.212 before 2008). This might also be due to the growth and the increasing regional integration of Vietnam. Regarding the correlation between volatility spillovers as measured by the squared GARCH residuals, a statistically significant increase at 5% level after 2008 is observed between Indonesia and Vietnam, Indonesia and South Africa, and Egypt and South Africa. The average correlation level after 2008 in these cases is however, only 0.03 in the first case, 0.087 in the second case, and 0.192 in the last case. Furthermore, we also find that the correlation between volatility spillovers statistically significantly decreased at the 5% level between Indonesia and Egypt, Vietnam and South Africa, Egypt and Turkey, and Turkey and South Africa. Overall, the results from the (rolling-) correlation analysis (standardized and squared standardized residuals) between the CIVETS stock markets suggest some interdependence rather than contagion effects. A well-known caveat about all correlation analysis is that correlation does not imply causality. We address the question of the causal relationships between CIVETS stock markets in the next section. 5. Granger-Cheung-Ng-Hong (GCNH) causality tests for the CIVETS stock markets 5.1. Causality-in-mean tests In order to test for the causal links among the stock return spillovers (causality-in-mean) of the CIVETS countries, the standardized residuals are derived from the respective GARCH models and the test procedure described in Eqs. (3) and (4) in Section 3 is employed. The test results are presented in Table 10.

COLOMBIA and INDONESIA

COLOMBIA and VIETNAM

COLOMBIA and EGYPT

1.0

.8

.8

0.5

.4

.4

0.0

.0

.0

-0.5

-.4

-.4

0.50 0.25

.0

0.00

-.4

-0.25 -0.50

-.8

02 03 04 05 06 07 08 09

02 03 04 05 06 07 08 09

02 03 04 05 06 07 08 09

INDONESIA and VIETNAM

INDONESIA and EGYPT

INDONESIA and TURKEY

INDONESIA and S.AFRICA

.4

.8

1.2

.6

0.8

.4

.0

0.0

.0

-0.4

-.2 02 03 04 05 06 07 08 09

-.4

VIETNAM and TURKEY

-0.8

02 03 04 05 06 07 08 09 VIETNAM and EGYPT

.8

.6

.6

.4

.4

0.4

.2

-.4 -.8

.4

02 03 04 05 06 07 08 09

.8

COLOMBIA and S.AFRICA .8

0.75

-.8

-.8

-1.0

COLOMBIA and TURKEY 1.00

.2

.2

.0

.0

-.2

-.2

-.4

-.4

02 03 04 05 06 07 08 09

02 03 04 05 06 07 08 09

02 03 04 05 06 07 08 09

VIETNAM and S.AFRICA

EGYPT and TURKEY

EGYPT and S.AFRICA

-.6 02 03 04 05 06 07 08 09 TURKEY and S.AFRICA

.8

.8

.8

.8

.8

.4

.4

.4

.4

.4

.0

.0

.0

.0

.0

-.4

-.4

-.4

-.4

-.4

-.8 02 03 04 05 06 07 08 09

-.8

-.8

-.8 02 03 04 05 06 07 08 09

02 03 04 05 06 07 08 09

-.8 02 03 04 05 06 07 08 09

02 03 04 05 06 07 08 09

Fig. 2. Rolling correlation coefficients among CIVETS countries for standardized residuals.

T. Korkmaz et al. / Emerging Markets Review 13 (2012) 230–252

245

Table 7 Descriptive statistics for rolling correlation coefficients.

Colombia and Indonesia Colombia and Vietnam Colombia and Egypt Colombia and Turkey Colombia and S. Africa Indonesia and Vietnam Indonesia and Egypt Indonesia and Turkey Indonesia and S. Africa Vietnam and Egypt Vietnam and Turkey Vietnam and S. Africa Egypt and Turkey Egypt and S. Africa Turkey and S. Africa

Mean

Median

Maximum

Minimum

Std. dev.

0.179 − 0.018 0.138 0.257 0.151 0.044 0.143 0.188 0.253 0.065 0.004 − 0.069 0.191 0.082 0.195

0.174 − 0.022 0.151 0.277 0.159 0.032 0.184 0.158 0.292 0.073 − 0.014 − 0.094 0.253 0.054 0.212

0.764 0.644 0.660 0.816 0.695 0.722 0.621 0.857 0.742 0.560 0.656 0.739 0.753 0.784 0.780

− 0.424 − 0.658 − 0.507 − 0.481 − 0.535 − 0.597 − 0.372 − 0.646 − 0.381 − 0.488 − 0.589 − 0.587 − 0.412 − 0.438 − 0.550

0.223 0.264 0.218 0.255 0.298 0.269 0.202 0.310 0.252 0.226 0.264 0.245 0.292 0.239 0.297

An examination of the results presented in Table 10 leads to following observations. 1) Only ten of the possible 30 country pair-wise directional causal relationships are found to be significant. 2) The Colombian stock market is found to be a Granger-Cheung-Ng-Hong (GCNH)-cause of other CIVETS stock returns in-mean except for Vietnam. Interestingly, none of the other five CIVETS stock markets is the GCNH-cause of the Colombian stock market. Hence, there is evidence of the existence of a unidirectional causal link in-mean from the Colombian stock market returns to the other CIVETS stock returns. 3) No GCNH causal relationship in-mean from the Vietnamese stock market to other CIVETS stock markets is detected. Nevertheless, the Indonesian stock market is found to be a GCNH-cause of the Vietnamese stock returns. 4) We find a feedback (bidirectional causality) relationship between the Indonesian and the Egyptian stock markets. 5) The developments in the Turkish stock returns cause the Indonesian and the Egyptian stock markets unidirectionally in the Granger-Cheung-Ng-Hong sense. 6) The South African stock market is found to cause only the Turkish stock returns among the CIVETS. It must be recognized that the above linkages represent the results of pair-wise causal relationships between the CIVETS countries. There might still be possible indirect linkages as well. According to Hsiao (1982), even if there is no (direct) causal relationship between two stochastic variables X and Z, X might still be an indirect cause of Z if X causes another stochastic variable Y and Y in turn causes Z. That is, a causal chain from X to Z in the form of X → Y → Z can be established. In our context, for example, no causality is found from Colombia to Vietnam, but Colombia is found to cause Indonesia and Indonesia is found to cause Vietnam. In this case and in other similar cases, the presence of an indirect linkage can also come into question. Overall, the GCNH causality test results presented in Table 10 suggest the existence of bivariate (and possibly indirect) intra- and inter-regional causal return spillover relationships among CIVETS. For Table 8 Correlation coefficients among CIVETS countries for squared standardized residuals. Correlation

Colombia

Indonesia

Vietnam

Egypt

Turkey

S. Africa

Colombia Indonesia Vietnam Egypt Turkey S. Africa

1 0.012 0.011 0.006 0.089 0.120**

1 0.007 − 0.015 0.092 − 0.025

1 0.014 0.072 0.026

1 0.111** − 0.047

1 0.066

1

246

T. Korkmaz et al. / Emerging Markets Review 13 (2012) 230–252 COLOMBIA and INDONESIA

COLOMBIA and VIETNAM

COLOMBIA and EGYPT

COLOMBIA and TURKEY

COLOMBIA and S.AFRICA

1.0

1.0

1.0

1.0

1.0

0.5

0.5

0.5

0.5

0.5

0.0

0.0

0.0

0.0

0.0

-0.5

-0.5

-0.5

-0.5

-0.5

-1.0

-1.0

-1.0

-1.0

-1.0

02 03 04 05 06 07 08 09

02 03 04 05 06 07 08 09

02 03 04 05 06 07 08 09

02 03 04 05 06 07 08 09

INDONESIA and VIETNAM

INDONESIA and EGYPT

INDONESIA and TURKEY

INDONESIA and S.AFRICA

02 03 04 05 06 07 08 09 VIETNAM and EGYPT

1.0

1.0

1.0

1.0

1.0

0.5

0.5

0.5

0.5

0.5

0.0

0.0

0.0

0.0

0.0

-0.5

-0.5

-0.5

-0.5

-0.5

-1.0

-1.0

-1.0

-1.0

-1.0

02 03 04 05 06 07 08 09

02 03 04 05 06 07 08 09

02 03 04 05 06 07 08 09

02 03 04 05 06 07 08 09

VIETNAM and TURKEY

VIETNAM and S.AFRICA

EGYPT and TURKEY

EGYPT and S.AFRICA

TURKEY and S.AFRICA

1.0

1.0

1.0

1.0

1.0

0.5

0.5

0.5

0.5

0.5

0.0

0.0

0.0

0.0

0.0

-0.5

-0.5

-0.5

-0.5

-0.5

-1.0

-1.0 02 03 04 05 06 07 08 09

02 03 04 05 06 07 08 09

-1.0

-1.0 02 03 04 05 06 07 08 09

02 03 04 05 06 07 08 09

-1.0 02 03 04 05 06 07 08 09

02 03 04 05 06 07 08 09

Fig. 3. Rolling correlation coefficients among CIVETS countries for squared standardized residuals.

instance, a causal relationship is determined between Indonesia and Vietnam and among Turkey, Egypt and South Africa. Inter-regional causal links are also determined. The Colombian stock market is the GCNH- cause of Indonesian, Egyptian, Turkish and the South African stock markets. Vietnam appears to be the most segmented (or least integrated) market among the CIVETS.

5.2. Causality-in-variance tests The detection of a causal relationship between the second moments of stock return series indicates the existence of volatility contagion effects. As described in Section 3, Cheung and Ng's (1996) and Hong's (2001) causality tests aimed at detecting these effects. In principle, the squares of the standardized GARCH residuals are used to run Cheung and Ng's and Hong's causality-in-variance tests. However, a complication arises when there is a causal relationship in-mean between the series of interest. This has been noted by Cheung and Ng (1996) and further shown by Pantelidis and Pittis (2004) that the presence of causality-in-mean leads to severe size distortions in testing for causality-in-variance if such effects are not filtered out. In our study, we remove the causality-in-mean effects by including the lagged values of the stock returns series for country i which causes the stock returns of another country j in-mean in the mean equation of the GARCH model for country j. 25 Using the causality-in-mean results presented in Table 10, this approach leads to filtering-out of 10 causality-in-mean cases before running the causalityin-variance tests. The results of the GCNH causality-in-variance tests are displayed in Table 11. According to the test results in Table 11, only ten of the possible 30 country pair-wise directional causal relationships are found to be significant. In particular, the Colombian stock market has causality-invariance spillover effects on three other CIVETS stock markets. As in the causality-in-mean test, we find the presence of a unidirectional causal relation running from the Colombian market to the Indonesian, Egyptian and the Turkish stock markets. On the other hand, we cannot detect volatility spillovers effects from the Colombian market to the South African and the Vietnamese stock markets. The Indonesian stock market is found to cause volatility spillovers effects to the Vietnamese and the Turkish markets. Differently from the causality-in-mean test results, the causality-in-variance test results indicate that volatility in the Vietnamese stock market has affected the volatility in the Colombian, South African and Turkish stock markets. The Egyptian stock market causes the Vietnamese stock market. We cannot find any causal relation in variance running from the Turkish stock market to the other CIVETS. Finally, the South African stock market is found to be the Granger cause of the Indonesian and the Vietnamese stock markets. 25

Gebka and Serwa (2007) suggest using a similar approach for removing cointegrating relationships in-mean.

Returns

Colombia and Indonesia Colombia and Vietnam Colombia and Egypt Colombia and Turkey Colombia and S.Africa Indonesia and Vietnam Indonesia and Egypt Indonesia and Turkey Indonesia and S. Africa Vietnam and Egypt Vietnam and Turkey Vietnam and S. Africa Egypt and Turkey Egypt and S. Africa Turkey and S. Africa

Standardized residuals

Squared standardized residuals

Mean before 2008

Mean after 2008

t-statistic

p-value

Mean before 2008

Mean after 2008

t-statistic

p-value

Mean before 2008

Mean after 2008

t-statistic

p-value

0.246 0.033 0.212 0.320 0.283 0.032 0.255 0.341 0.387 0.004 0.004 − 0.002 0.350 0.178 0.263

0.459 0.121 0.314 0.488 0.447 0.362 0.422 0.434 0.615 0.283 0.251 0.224 0.555 0.339 0.425

− 10.031*** − 2.801*** − 4.492*** − 6.586*** − 6.162*** − 12.122*** − 6.192*** − 3.451*** − 11.961*** − 10.278*** − 10.047*** − 8.408*** − 6.464*** − 7.033*** − 7.520***

0.000 0.006 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.179 0.010 0.167 0.267 0.185 0.007 0.151 0.218 0.212 0.021 − 0.008 − 0.085 0.203 0.102 0.222

0.178 − 0.094 0.056 0.230 0.058 0.148 0.121 0.103 0.364 0.187 0.038 − 0.025 0.159 0.025 0.122

0.030 3.539*** 5.020*** 1.287 3.844*** − 4.331*** 1.532 3.339*** − 6.282*** − 7.621*** − 1.942* − 2.470** 1.332 3.274*** 2.577**

0.976 0.000 0.000 0.199 0.000 0.000 0.127 0.001 0.000 0.000 0.053 0.014 0.184 0.001 0.011

0.050 0.009 − 0.016 0.097 0.105 − 0.064 0.054 0.097 − 0.047 − 0.041 0.012 0.092 0.006 0.058 − 0.049

0.054 − 0.004 − 0.012 0.069 0.157 0.036 − 0.086 0.119 0.087 − 0.019 0.069 − 0.094 − 0.121 0.192 0.099

− 0.130 0.486 − 0.201 0.778 − 1.493 − 3.418*** 4.883*** − 0.696 − 5.259*** − 0.797 − 1.804* 7.206*** − 3.517*** 4.655*** − 4.479***

0.897 0.628 0.841 0.437 0.136 0.001 0.000 0.487 0.000 0.426 0.072 0.000 0.000 0.000 0.000

T. Korkmaz et al. / Emerging Markets Review 13 (2012) 230–252

Table 9 Student's t-tests for equality of means of rolling correlation coefficients before and after 2008.

247

248

T. Korkmaz et al. / Emerging Markets Review 13 (2012) 230–252

Table 10 Causality-in-mean test results for CIVETS stock returns. Causality direction

M=1

M=2

M=3

M=4

Colombia → Indonesia Colombia → Vietnam Colombia → Egypt Colombia → Turkey Colombia → South Africa Indonesia → Colombia Indonesia → Vietnam Indonesia → Egypt Indonesia → Turkey Indonesia → South Africa Vietnam → Colombia Vietnam → Indonesia Vietnam → Egypt Vietnam → Turkey Vietnam → South Africa Egypt → Colombia Egypt → Indonesia Egypt → Vietnam Egypt → Turkey Egypt → South Africa Turkey → Colombia Turkey → Indonesia Turkey → Vietnam Turkey → Egypt Turkey → South Africa South Africa → Colombia South Africa → Indonesia South Africa → Vietnam South Africa → Egypt South Africa → Turkey

2.772*** − 0.659 2.109** 1.427* 3.097*** − 0.521 2.205** 2.705*** − 0.599 − 0.645 0.975 − 0.693 − 0.662 − 0.702 − 0.670 − 0.700 1.968** − 0.367 1.054 − 0.708 − 0.640 1.815** 0.071 3.246*** − 0.451 − 0.451 − 0.245 − 0.554 0.994 2.502***

2.533*** − 0.785 2.017** 1.450* 2.891*** − 0.636 2.061** 3.140*** − 0.751 − 0.617 1.088 − 0.654 − 0.702 − 0.836 − 0.787 − 0.783 1.879** − 0.339 0.939 − 0.847 − 0.790 1.629* − 0.043 3.233*** − 0.463 − 0.379 0.289 − 0.709 0.804 2.338**

2.215** − 0.913 1.802** 1.342* 2.594*** − 0.509 1.842** 3.267*** − 0.878 − 0.571 1.122 − 0.670 − 0.770 − 0.951 − 0.805 − 0.881 1.715** 0.140 0.907 − 0.859 − 0.849 1.360* − 0.160 3.035*** − 0.477 − 0.382 0.628 − 0.796 0.680 2.166**

1.925** − 0.915 1.569* 1.275 2.315** − 0.353 1.625* 3.172*** − 0.968 − 0.563 1.179 − 0.740 − 0.860 − 1.052 − 0.791 − 0.851 1.582* 0.662 0.942 − 0.840 − 0.801 1.099 − 0.082 2.776*** − 0.521 − 0.200 0.855 − 0.689 0.649 2.066**

Notes: *, ** and *** indicates the existence of causal link at the 1%, 5% and 10% level respectively. M represents the maximum lag.

As discussed under the causality-in-mean test results, there might be further indirect causal linkages across CIVETS stock markets as well. For instance, There are no causal volatility spillovers from the Colombian stock market to the South African stock market according to the pair-wise (bivariate) tests. However, Indonesia is found to cause volatility spillover in Vietnam (intra-regional) and Vietnam in turn causes volatility spillovers in South Africa (inter-regional). Hence, a possible indirect inter-regional linkage may also arise from Indonesia to South Africa. In view of both bivariate and possible indirect linkages, it can be said that there exist intra-regional and inter-regional volatility spillovers effects amongst the CIVETS countries. Such spillover effects might arise due to the portfolio optimization processes of international investors. Wang (2007), for instance shows financial liberalization and the subsequent foreign equity trading have led to heightened market volatility in Indonesia and Thailand. A key question in studies that affirm the return and volatility spillovers effects across countries is whether this phenomenon can be considered as contagion or interdependence. In our study, we find that the rolling correlation coefficients after allowing for ARCH effects are much lower than the simple correlation coefficients and we also do not observe an increasing trend in ARCH-adjusted correlation coefficients. Furthermore, the results of the causality-in-variance tests do not suggest volatility transmission effects across all markets and only one case of a feedback relationship is found. 26 Hence, the presence of inter- and intra-regional linkages between CIVETS stock markets might rather be seen as interdependence rather than contagion.

26 Edwards and Susmel (2001) also qualify their findings of volatility transmission within Latin America and between Lation America and Asia (Hong Kong) as volatility interdependence rather than contagion.

T. Korkmaz et al. / Emerging Markets Review 13 (2012) 230–252

249

Table 11 Causality in variance test results. Causality direction

M=1

M=2

M=3

M=4

Colombia → Indonesia Colombia → Vietnam Colombia → Egypt Colombia → Turkey Colombia → South Africa Indonesia → Colombia Indonesia → Vietnam Indonesia → Egypt Indonesia → Turkey Indonesia → South Africa Vietnam → Colombia Vietnam → Indonesia Vietnam → Egypt Vietnam → Turkey Vietnam → South Africa Egypt → Colombia Egypt → Indonesia Egypt → Vietnam Egypt → Turkey Egypt → South Africa Turkey → Colombia Turkey → Indonesia Turkey → Vietnam Turkey → Egypt Turkey → South Africa South Africa → Colombia South Africa → Indonesia South Africa → Vietnam South Africa → Egypt South Africa → Turkey

7.730*** − 0.366 1.628* 7.658*** − 0.626 − 0.582 3.118** − 0.175 6.392*** − 0.383 8.853*** − 0.708 − 0.240 − 0.695 2.766*** 0.196 − 0.682 3.933*** − 0.534 0.417 0.555 − 0.479 0.336 − 0.669 0.029 − 0.642 76.635*** 2.181** − 0.630 − 0.646

7.342*** − 0.501 1.409* 7.439*** − 0.772 − 0.698 2.859*** − 0.306 6.232*** − 0.495 8.419*** − 0.398 − 0.131 0.979 2.622*** 0.029 − 0.833 3.767*** − 0.624 0.237 0.370 − 0.589 0.314 − 0.198 − 0.087 − 0.733 74.297*** 3.114*** − 0.699 − 0.582

6.697*** − 0.626 1.124 6.915*** − 0.915 − 0.821 2.497*** 0.271 5.907*** − 0.611 7.697*** − 0.163 − 0.086 2.057** 2.376*** − 0.149 − 0.976 3.459*** − 0.700 0.202 0.153 − 0.408 0.217 0.567 − 0.054 − 0.829 69.541*** 3.666*** − 0.789 − 0.540

6.094*** − 0.739 0.967 6.372*** − 1.037 − 0.877 2.161*** 0.929 5.553*** − 0.699 7.017*** 0.060 − 0.117 2.580*** 2.120** − 0.258 − 0.988 3.174*** − 0.759 0.209 − 0.046 − 0.143 0.505 0.669 − 0.004 − 0.932 64.955*** 3.835*** − 0.858 − 0.553

Notes: *, ** and *** indicate the existence of causal link at the 1%, 5% and 10% level respectively.

6. Conclusions In 2009, the international business community coined the acronym CIVETS to refer to Colombia, Indonesia, Vietnam, Egypt, Turkey, and South Africa as a group of countries with young and growing populations and dynamic and rather resilient economies which present a new set of frontier countries that may offer further business opportunities and financial returns in an increasingly borderless global business environment. In May 2010, the Standard & Poor's established a CIVETS 60 index that includes ten most liquid stocks in these six countries and the global bank HSBC also launched a CIVETS fund. Since the coining of CIVETS as a group is rather new, to the best of our knowledge, there is no dedicated study of the stock market interactions and integration among the CIVETS countries in the academic literature. Our study aims to fill this gap by providing a first look at the return and volatility spillovers between the CIVETS countries. In doing so, we employ Hong's (2001) version of Cheung and Ng's (1996) causalityin-mean and causality-in-variance tests. We use the current state-of-the-art methodology in running Hong's (2001) test by allowing for the effects of common factors, structural breaks in the variance of the series, and causality-in-mean effects where applicable. In addition, we make a rolling correlation analysis of the stock return and volatility series to provide insights on contemporaneous time-varying relationships. The empirical results indicate that the contemporaneous return and volatility spillover effects are generally low. Nevertheless, due to their time-varying nature, the CIVETS stock markets may exhibit higher degrees of co-movements at times. The causality-in-mean results suggest that out of thirty possible pair-wise causal relationships only ten of them turn out to be statistically significant. A feedback effect is detected only between Indonesia and Egypt. Still, the room for indirect linkages remains. A similar picture arises with respect to the causality-in-variance results. Out of thirty possible causal linkages, only 10

250

T. Korkmaz et al. / Emerging Markets Review 13 (2012) 230–252

are found to be statistically significant. The only pair-wise feedback relationship detected is between Vietnam and South Africa. Yet, the analysis of the structure of the statistically significant causal relationships along with the implied possible indirect relationships suggest the presence of both intra-regional and inter-regional return and volatility spillover effects among the CIVETS countries. These effects might, however, be seen as some evidence of interdependence rather than contagion. Overall, international investors need to consider the strength, the stability, and the causal dynamics of the spillover linkages among the CIVETS stock markets in making their portfolio decisions. References Abdul Karim, B., Abdul Majid, M.S., Abdul Karim, S.A., 2009. Financial integration between Indonesia and its major trading partners. MPRA Munich Personal RePEc Archive Paper No. 17277. http://mpra.ub.uni-muenchen.de/17277. Aggarwal, R., Inclan, C., Leal, R., 1999. Volatility in emerging markets. Journal of Financial and Quantitative Analysis 34, 33–55. Alagidede, P., Panagiotidis, T., 2009. Modelling stock returns in Africa's emerging equity markets. International Review of Financial Analysis 18, 1–18. Alkulaib, Y.A., Najand, M., Mashayekh, A., 2009. Dynamic linkages among equity markets in the Middle East and North African countries. Journal of Multinational Financial Management 19, 43–53. Aloui, R., Aïssa, M.S.B., Nguyen, D.K., 2011. Global financial crisis, extreme interdependences, and contagion effects: the role of economic structure? Journal of Banking and Finance 35 (1), 130–141. Anderton, R., di Mauro, F., Moneta, F., 2004. Understanding the impact of the external dimension on the Euro area: trade, capital flows and other international macroeconomic linkages. European Central Bank Occasional Paper Series, No. 12. (April 2004). Andreou, E., Ghysels, E., 2002. Detecting multiple breaks in financial market volatility dynamics. Journal of Applied Econometrics 17, 579–600. Angelidis, T., 2010. Idiosyncratic risk in emerging markets. The Financial Review 45, 1053–1078. Arago-Manzana, V., Fernandez-Izquierdo, M.A., 2007. Influence of structural changes in transmission of information between stock markets: a European empirical study. Journal of Multinational Financial Management 17, 112–124. Baur, D.G., Fry, R.A., 2009. Multivariate contagion and interdependence. Journal of Asian Economics 20 (4), 353–366. Bell, H.A., 2011. Status of the ‘BRICs’: an analysis of growth factors. International Research Journal of Finance and Economics 69, 19–25. Bollerslev, T., 1986. Generalized autoregressive conditional heteroscedasticity. Journal of Econometrics 31, 307–327. Chancharoechai, K., Dibooglu, S., 2006. Volatility spillovers and contagion during the Asian crisis. Emerging Markets Finance and Trade 42 (2), 4–17. Chang, H.-L., Su, C.-W., 2010. The relationship between the Vietnam stock market and its major trading partners – TECM with bivariate asymmetric Garch model. Applied Economics Letters 17, 1279–1283. Chang, H.-L., Su, C.-W., Lai, Y.-C., 2009. Asymmetric price transmissions between the exchange rate and stock market in Vietnam. International Research Journal of Finance and Economics 23, 104–113. Chen, G.-M., Firth, M., Rui, O.M., 2002. Stock market linkages: evidence from Latin America. Journal of Banking & Finance 26, 1113–1141. Cheung, Y.W., Ng, L.K., 1996. A causality-in-variance test and its application to financial market prices. Journal of Econometrics 72, 33–48. Choudhry, T., 1997. Stochastic trends in stock prices: evidence from Latin American markets. Journal of Macroeconomics 19 (2), 285–304. Christofi, A., Pericli, A., 1999. Correlation in price changes and volatility of major Latin American stock markets. Journal of Multinational Financial Management 9, 79–93. Click, R.W., Plummer, M.G., 2005. Stock market integration in ASEAN after the Asian financial crisis. Journal of Asian Economics 16, 5–28. De Gooijer, J.G., Sivarajasingham, S., 2008. Parametric and nonparametric Granger-causality testing: linkages between international stock markets. Physica A: Applications of Statistical Mechanics 38, 2547–2560. Dickey, D.A., Fuller, W.A., 1979. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association 74, 427–431. Edwards, S., Susmel, R., 2001. Volatility dependence and contagion in emerging equity markets. Journal of Development Economics 66, 505–532. Evans, T., McMillan, D.G., 2009. Financial co-movement and correlation: evidence from 33 international stock market indices. International Journal of Banking Accounting, and Finance 1 (3), 215–241. Ewing, B.T., Malik, F., 2010. Estimating volatility persistence in oil prices under structural breaks. The Financial Review 45, 1011–1023. Fernandez, V., 2006. The impact of major global events on volatility shifts: evidence from the Asian crisis and 9/11. Economic Systems 30, 79–97. Forbes, K.J., Rigobon, R., 2002. No contagion, only interdependence: measuring stock market comovements. Journal of Finance 57 (5), 2223–2261. Galeano, P., Tsay, R.S., 2010. Shifts in individual parameters of a Garch model. Journal of Financial Econometrics 8 (1), 122–153. Gebka, B., Serwa, D., 2007. Intra- and inter-regional spillovers between emerging capital markets around the world. Research in International Business and Finance 21, 203–221. Goldberg, C.S., Veitch, J.M., 2010. Country risk and financial integration – a case study of South Africa. Research in International Business and Finance 24, 138–145. Granger, C.W.J., 1969. Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37, 424–438. Hafner, C.M., Herwartz, H., 2006. A lagrange multiplier test for causality in variance. Economics Letters 93, 137–141.

T. Korkmaz et al. / Emerging Markets Review 13 (2012) 230–252

251

Hassan, K.M., Yu, J.-S., 2007. Stock exchanges alliances in organization of the Islamic conference countries. Review of Islamic Economics 11, 123–148. Henry, O.T., Olekalns, N., Lakshman, R.W.D., 2007. Identifying interdependencies between south-east Asian stock markets: a nonlinear approach. Australian Economic Papers 46 (2), 122–135. Heston, A., Summers, R., Aten, B., 2011. Penn world table version 7.0, center for international comparisons of production, income and prices at the University of Pennsylvania. Hillebrand, E., 2005. Neglecting parameter changes in Garch models. Journal of Econometrics 129, 121–138. Hong, Y., 2001. A test for volatility spillover with application to exchange rates. Journal of Econometrics 103, 183–224. Hsiao, C., 1982. Autoregressive modeling and causal ordering of economic variables. Journal of Economic Dynamics and Control 4, 243–259. Inclan, C., Tiao, G.C., 1994. Use of cumulative sums of squares for retrospective detection of changes in variance. Journal of the American Statistic Association 89, 913–923. Kasman, A., Vardar, G., Okan, B., Aksoy, G., 2009. The Turkish stock market integration with developed and emerging countries' stock markets: evidence from cointegration tests with and without regime shifts. Review of Middle East Economics and Finance 5 (1) Article 2. Kenourigios, D., Samitas, A., 2011. Equity market integration in emerging Balkan markets. Research in International Business and Finance 25, 296–307. Knowledge@Wharton, 2011. The new BRICS on the block: which emerging markets are up and coming? http://knowledge.wharton. upenn.edu/article.cfm?articleid=2679. Kwiatkowski, D., Phillips, P.C.B., Schmidt, P., Shin, Y., 1992. Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics 54, 159–178. Lagoarde-Segot, T., Lucey, B.M., 2007. Capital market integration in the Middle East and North Africa. Emerging Markets Finance and Trade 43 (3), 34–57. Lamoureux, C.G., Lastrapes, W.D., 1990. Persistence in variance, structural change and the Garch model. Journal of Business and Economic Statistics 68, 225–234. Loc, T.D., Lanjouw, G., Lensink, R., 2010. Stock market efficiency in thin-trading markets: the case of the Vietnamese stock market. Applied Economics 42, 3519–3532. Lütkepohl, H., 1982. Non-causality due to omitted variables. Journal of Econometrics 19, 367–378. Mandaci, P.E., Torun, E., 2007. Testing integration between the major emerging markets. Central Bank Review (Turkey) 1, 1–12. Maneschiold, P.-O., 2005. International diversification benefits between US, Turkish and Egyptian stock markets. Review of Middle East Economics and Finance 3 (2), 115–133. Mantalos, P., Shukur, G., 2010. The effect of spillover on the Granger causality test. Journal of Applied Statistics 37, 1473–1486. McMillan, D.G., Wohar, M.E., 2011. Structural breaks in volatility: the case of UK sector returns. Applied Financial Economics 21 (15), 1079–1093. Mulyadi, M.S., 2009. Volatility spillover in Indonesia, USA, and Japan capital market. MPRA Munich Personal RePEc Archive Paper No. 16914. http://mpra.ub.uni-muenchen.de/16914. Neaime, S., 2006. Volatilities in emerging MENA stock markets. Thunderbird International Business Review 48 (4), 455–484. Özdemir, Z.A., Olgun, H., Saracoglu, B., 2009. Dynamic linkages between the center and periphery in international stock markets. Research in International Business and Finance 23, 46–53. Pantelidis, T., Pittis, N., 2004. Testing for Granger causality in variance in the presence of causality in mean. Economics Letters 85, 201–207. Phillips, P.C.B., Perron, P., 1988. Testing for a unit root in time series regression. Biometrika 75, 335–346. Pierce, D.A., Haugh, L.D., 1977. Causality in temporal systems: characterizations and a survey. Journal of Econometrics 5, 265–293. Rapach, D.E., Strauss, J.K., 2008. Structural breaks and GARCH models of exchange rate volatility. Journal of Applied Econometrics 23, 65–90. Rodrigues, P.M.M., Rubia, A., 2007. Testing for causality in variance under nonstationarity in variance. Economics Letters 97, 133–137. Royfaizal, R.C., Lee, C., Azali, M., 2009. The linkages of Asian and the US Stock markets. The IUP Journal of Financial Economics 7 (2), 74–90. Samarakoon, L.P., 2011. Stock market interdependence, contagion, and the U.S. financial crisis: the case of emerging and frontier markets. Journal of International Financial Markets, Institutions & Money 21 (5), 724–742. Sanso, A., Arago, V., Carrion, J.L., 2004. Testing for change in the unconditional variance of financial time series. Revista de Economia Financiera 4, 32–53. Schiller, B., 2011. Beyond the BRICS: which countries are waiting in line to become the economic powerhorses of tomorrow? Financial Management (Chartered Institute of Management Accountants) 27–30 March issue. Sharma, G.D., Bodla, B.S., 2010. Are global stock markets inter-linked? evidence from the literature. Global Journal of Management and Business Research 10 (1), 29–40. Soofi, A.S., 2008. Global financial integration and the MENA countries: evidence from equity and money markets. Review of Middle East Economics and Finance 4 (2) Article 4. Thuan, L.T., Yuan, C., 2011. The relationship between the United States and Vietnam stock markets. International Journal of Business and Finance Research 5 (1), 77–89. Tuyen, T.M., 2011. Modeling volatility using GARCH models: evidence from Vietnam. Economics Bulletin 31 (3), 1935–1942. Van Dijk, D., Osborn, D.R., Sensier, M., 2005. Testing for causality in variance in the presence of breaks. Economics Letters 89, 193–199. Wang, J., 2007. Foreign equity trading and emerging market volatility: evidence from Indonesia and Thailand. Journal of Development Economics 84, 798–811. Wang, K.-M., Lee, Y.-M., 2009. The stock market spillover channels in the 1997 Asian financial crises. International Research Journal of Finance and Economics 26, 105–133.

252

T. Korkmaz et al. / Emerging Markets Review 13 (2012) 230–252

Wang, K.-M., Thi, T.-B.N., 2007. Testing for contagion under asymmetric dynamics: evidence from the stock markets between US and Taiwan. Physica A 376, 422–432. Wilson, D., Purushothaman, R., 2003. Dreaming with BRICs: the path to 2050. Global Economics Paper No: 9. http://www2. goldmansachs.com/ideas/brics/book/99-dreaming.pdf. Yu, J.-S., Hassan, K., 2008. Global and regional integration of the Middle East and North African (MENA) stock markets. The Quarterly Review of Economics and Finance 48, 482–504.