International Stock Market Interdependence: A South African Perspective

Transcription

1 International Stock Market Interdependence: A South African Perspective Owen Beelders Department of Economics Emory University March 14, 2002 Abstract The time-varying interdependence between South Africa and both the UK and US is modeled using a latent variable approach. We find little interdependence prior to March 1995 when exchange controls and a dual exchange rate regime were in place. After the unification of the exchange rates and the removal of exchange controls on nonresidents, the interdependence with the US increased considerably. Despite the fact that many large South African companies have their primary or secondary listing on the London Stock Exchange, there is little interdependence with the UK after March The increase in interdependence with the US is consistent with the increase in US purchases of South African equities after March JEL Classification Code: G15 I thank Hisham Foad and Francisco Parodi for the comments and Daniel English for his research assistance. All remaining errors are mine. Contact Information: Owen Beelders, Department of Economics, Emory University, Atlanta Ga ; tel.no.: (404) ; fax: (404) ; obeelde@emory.edu 1

2 1 Introduction The South African financial markets have been through a tumultuous time during the decade of the 1990 s. Following enormous political changes and an easing of foreign sentiment towards the country in the first half of the decade, it was reclassified as an emerging market and was subject to severely volatile portfolio flows in the latter half of the decade. The objective of this paper is to analyze the relationship between the South African (SA) stock market and the stock markets of the United Kingdom (UK) and United States (US) during this period. In the decade of the 1990 s, the most important economic event was the unification of the dual exchange rate on March 13, This was accompanied by the relaxation of exchange controls on residents and the removal of exchange controls on non-residents. The dual exchange rate regime and stringent exchange controls were originally introduced to insulate the local economy from the severe capital outflow due to political risk, but with the releaseofnelsonmandela(theimprisonedleaderoftheafricannational Congress) in 1990, the first democratic elections in April 1994, and an improvement in the level of foreign reserves, these measures could be removed. Despite all these positive changes, the Economist classified South Africa as an Emerging Market in its January 8, 1994 issue. This classification was also adopted by the global financial community and as a result, the South African stock market was severely affected by the emerging market crises in the latter part of the 1990 s. Ripley (1973) and Roll (1992) have documented that the strength of the correlation between stock markets is affected by the following factors. First, markets that are in the same times zone tend to be more strongly correlated. Trading times on the London Stock Exchange run from 9am to 5pm, Greenwich Mean Time (GMT), 9:30am to 4pm (GMT-5) on the New York Stock Exchange and 9am to 5pm (GMT+2) on the Johannesburg Stock Exchange (JSE). We expect to find a stronger correlation between SA and the UK because the trading times overlap by 6 hours as opposed to only 90 minutes during daylight savings time in the US. Second, the strength of the correlation is positively related to the number of stocks in each national index. To be consistent across the three countries, we choose the broadly based market indexes for this study. We use the JSE all share index for South Africa, the Standard and Poors 500 Index for the US and the Financial Times All share index for the UK. Third, differences in the industrial composition of 2

3 each country reduces the measured correlation. The South African economy has been resourced based for most of the past century and this has been reflected in the stock market where precious metals such as gold have played a very important role. To the extent that the US and UK have a greater manufacturing component and more recently a greater service sector, this may reduce the correlation with the US and UK. Fourth, the exchange rate regime matters. From 1985 to 1995, South Africa had a dual exchange rate regime: a commercial rate was used for trade flows and a financial rate for capital flows. The financial rate traded at a discount to the commercial rate and the discount was referred to as a political risk premium. Following the removal of the dual exchange rate regime in South Africa and the move to a freely floating exchange rate, we expect to find a stronger correlation with the US and UK after March Beside the research of Ripley (1973) and Roll (1992), Bekaert (1995) has also documented that the cross-listing of stocks helps to increase the correlation between markets. The fact that many South African companies are traded as American Depository Receipts (ADR s) in the US and have secondary listings on the London Stock Exchange should increase the correlation with both the US and UK. Finally, with the reduced cost of electronic trading, and the development of information media such as Bloomberg terminals and CNN that have expedited the transmission of news, we expect to see greater co-movement in the latter half of the decade. Within the broader macroeconomic and political changes in South Africa, there were also two positive changes on the JSE that may lead to an increase in its the correlation with the US and UK. First, brokerage fees on the JSE changed from fixed to flexible in November 1995 after the introduction of the new Stock Exchange Control Act. Second, the Johannesburg electronic trading (JET) system was phased in on the JSE from March to June of In addition, there has also been a positive change on the South African futures exchange. In 1995, the JSE indices underlying the futures contracts were redesigned to consist of a smaller basket of stocks. The All Share Index that consisted of over 400 stocks was replaced by an index consisting of 40 stocks, the ALSI40. Similarly, the gold and industrial indexes were replaced by the GLDI10 and INDI25 indexes, respectively, where the number at the end of each index denotes the number of stocks in the index. Each of these changes reduced the cost of trading and hedging, and made the markets more accessible to foreign investors. In 2000, 40% of the trading on the SAFEX was conducted by foreign investors. 3

4 However, despite all these positive changes, a number of large South AfricancompaniessuchasOldMutual, South African Breweries, etc. have moved their primary listing to the London Stock Exchange due to the greater access to global capital markets and possibly to avoid paying the sovereign risk spread associated with South Africa s status as an emerging market. As of September 2001, eight South African companies had their primary listing on the London Stock Exchange and 29 had a secondary listing. Given the removal of the dual exchange rate system, the reduction in exchange controls and the increase in the number of cross-listings of South African companies on the LSE we expect to find greater interdependence with both the US and the UK after March To test our hypothesis, we use the methodology of Rockinger and Urga (2001) who develop a timevarying parameter approach that is flexible enough to allow the degree of interdependence to change over time. We find that while South Africa s interdependence with the US has in fact increased over time, it has not increased with the UK. The latter result is surprising given the cross-listings and 6-hour overlap in trading times. The result also stands in contrast to Ripley (1973) who found that South Africa had a stronger relationship with the UK than the US during the period 1960 to The greater interdependence with the US is consistent with the increase in purchases of South African equities by US citizens after March 1995 and the fact that the US has become the dominant capital market in the world. The outline of the paper is as follows. In Section 2 we briefly document the history of exchange controls and the dual exchange rate in South Africa. In section 3 we outline the methodology of Rockinger and Urga (2001) and our motivation for using it. We discuss the empirical implementation of our model and the results in Section 4 and conclude with Section 5. 2 Dual exchange rates and Exchange Controls The purpose of this section is to emphasize the importance of the dual exchange rate regime and exchange controls in protecting the capital account from large capital outflows due to the episodes of political risk in South Africa s history. Exchange controls on South African residents have been in place since the 4

5 outbreak of the Second World War (Garner (1994), Kahn (1991)). Following the Sharpeville massacre in 1960, there was a sharp increase in the sale of South African securities by non-residents due to the increase in political uncertainty, and in December 1962, the rapid deterioration of South Africa s capital account led to the introduction of exchange controls on the sale of securities by non-residents. Before February 1976, the proceeds from selling capital assets and securities on the JSE by non-residents were designated as the blocked rand when approval to purchase foreign exchange was denied by the South African authorities. These funds were not directly transferable among non-residents, but could be used to buy long-term government bonds or quoted South African securities. The quoted Johannesburg securities could be transferred to London and sold, generally at a discount. This discount emerged due to the combination of a lack of demand from non-residents for South African securities and upward pressure on security prices on the JSE due to controls on residents capital outflows. In February 1976, the government introduced the securities rand which allowed the direct transfer between non-residents. On January 24, 1979 the securities rand was replaced by the financial rand and the dual exchange rate regime was put in place. The dual exchange rate regime consisted of a commercial rate and a financial rate. The financial rand served as the principal exchange control on non-resident equity capital flows into and out of South Africa with the purpose of insulating the South African current account from the volatility in the flow of non-resident equitycapital. Thecoreroleofthefinancial rand was to set a price at which equity capital was traded among non-residents while ensuring that the total stock of the non-resident investment in the South Africa remained constant. The financial rand in general applied to portfolio investments or approved direct investment by non-residents, while the commercial rand applied to foreigntradesaswellasforeignloansandcredits. On February 7, 1983 the South African government decided that the abolition of the financial rand was feasible because foreign reserves had improved substantially since mid-1982, the current account deficit had been reduced and a surplus was expected for the year of However, after renewed political turmoil, the imposition of economic sanctions by the UN and the refusal of a number of international banks to roll over short-term loans to South African borrowers, the South African government announced a state of emergency and re-introduced the financial rand mechanism on September 1, 1985, in essentially the same form as it existed before February 1983 (Kahn 5

6 (1991)). Both the commercial rate and the financial rate were freely floated, but with more government intervention in the commercial rate. During this period, the financial rate traded at an average discount of 30% to the commercial rand: this discount reflected a political risk premium required by foreign investors. After the first democratic elections in the country, the removal of sanctions and the stabilization of the South African economy, the dual exchange rates were unified on March 13, Since then the rand has been freely floating against foreign currencies. Figure 1A contains a time series plot of the commercial and financial exchange rates in dollars per rand from January 2, 1990 to December 29, Two features stand out in figure 1A: first, the financial rate always trades at a discount to the commercial rate, and second, the commercial rate has been depreciating against the dollar for the entire decade because of the inflation differential between the US and SA. Figure 1B contains a time series plot of the percentage discount of the financial exchange rate to the commercial exchange rate. The discount was clearly highly variable prior to the unification of the dual rates in March 1995 and was regarded as a political risk premium that discouraged foreign investors from investing in South Africa. In summary, South Africa imposed a dual exchange rate regime on two occasions: from January 24, 1979 to February 7, 1983, and from September 2, 1985 to March 10,1995. We are interested in the second regime because it covers our sample period. 3 A Model of Time-Varying Integration It has long been recognized that the interdependence between markets is not constant, but varies over time. Cumby and Khanthavit (1998) and Bekaert and Harvey (1995) have used switching models to capture the time-varying nature of the interdependence, but the drawback to their approach is that it only allows for two regimes. Recently, Rockinger and Urga (2001) introduced a model that allows for a time-varying relationship between a dominant market and a satellite market. We prefer the model of Rockinger and Urga because it can capture the time-varying nature of the South African political risk prior to March 1995 and the effect of the emerging market crises on South Africa after March Let the dominant country be indexed by D and denote the market returns 6

7 as r D,t = 100 ln(s D,t /S D,t 1 )wheres D,t is the closing price of the market index of country D at time t. We assume that the market returns evolve accordingtothefollowingmodel: with r D,t = α D,t x D,t + ² D,t (1) α D,t = ξ D α D,t 1 + η D,t (2) ² D,t = σ D,t z D,t (3) σ 2 D,t = β 0 D + β + D ² 2 D,t 11(² D,t 1 > 0) (4) +β D ² 2 D,t 11(² D,t 1 0) + β 1 D σ 2 D,t 1 and α D,t is a time-varying vector of parameters allowing for autoregressive behavior in the conditional mean. We assume that z D,t and η D,t are random noise that have a normal distribution with mean zero and variances of 1andQ D,t, respectively. Q D,t is a square matrix with dimension equal to the number of rows of α D,t. The vector x D,t corresponds to variables that are assumed to describe the conditional mean. These variables may include lagged returns, seasonal dummies etc. or simply an element equal to 1 that creates a latent factor. Finally, (4) allows for the presence of conditional heteroskedasticity in the returns where β + D and β D capture the different impacts of positive and negative shocks and β 1 D captures the serial dependence in the conditional variance. This form of the conditional variance is known as the threshold ARCH (TARCH) model where ARCH is an acronym for autoregressive conditionally heteroskedasticity (Zakoïan (1994) and Glosten, Jaganathan, and Runkle (1993)). For a given satellite country i we assume that the return dynamics can be modeled as follows: with r i,t = α (1) i,t x i,t + ² i,t ² i,t = α (2) i,t ² D,t 1 + e i,t e i,t = σ i,t z i,t σ 2 i,t = β 0 i + β + i ² 2 i,t 11(² i,t 1 > 0) +β i ² 2 i,t 11(² i,t 1 0) + β 1 i σ 2 i,t 1, 7

8 α i,t = ³ α (1) i,t, α(2) i,t 0, α i,t = ξ i α i,t + η i,t wherewealsoassumethatz i,t and η i,t have a normal distribution with mean zero and variances of 1 and Q i,t, respectively. The vector x i,t consists of variables that describe the conditional mean of country i, thetime-varying parameter α (1) i,t captures the degree of predictability in country i, α(2) i,t captures the time-varying degree of integration with the dominant country and we also allow for conditional heteroskedasticity. The advantage of this specification is that it demonstrates how foreign shocks affect local volatility. If E[² D,t e i,t ] = 0, then the conditional variance of country i is given by ³ E t 1 ² 2 i,t = α (2) 2 σ 2 D,t + σ 2 i,t, i,t where the condition requires that foreign shocks are uncorrelated with domestic shocks. Following Bekaert and Harvey (1997), we can measure the proportion of the local variance accounted for by the dominant market through thevarianceratio, VR it = ³ ³ α (2) i,t α (2) i,t 2 σ 2 D,t 2 σ 2 D,t + σ2 i,t and because E t 1 [² D,t ² i,t ]=α (2) i,t σ2 D,t, the correlation between country i and the dominant country D is given by ρ i,t = α (2) i,t σ D,t r ³ α (2) 2 i,t σ 2 D,t + σ2 i,t (5). (6) The correlation coefficient drives home two important points: first, when foreign volatility increases with respect to local volatility, then correlation increases across markets (Longin and Solnik (1995)), and second, the stronger the impact of the dominant market on the local market through α (2) i,t,the higher the correlation. To implement this model in the context of emerging markets we can allow x i,t to capture the macroeconomic variables. However, in the case of emerging 8

9 markets these variables are not very well measured so they may cause more problems than they resolve. In addition, it is difficult to measure political events and expectations (Cutler, Poterba and Summers (1989)). Thus we rely on a latent variable specification for the constant, time-varying serial correlation and time-varying market integration. Following these remarks we reformulate the system for country i as follows: with r i,t = α (0) i,t + α(1) i,t r i,t 1 + α (2) i,t ² D,t 1 + e i,t (7) e i,t = σ i,t z i,t (8) σ 2 i,t = β 0 i + β + i ² 2 i,t 11(² i,t 1 > 0) (9) +β i ² 2 i,t 11(² i,t 1 0) + β 1 i σ 2 i,t 1, α (j) i,t = α(j) i,t 1 + η(j) i,t, (10) is the measure of time- where α (1) i,t is the measure of predictability and α (2) it varying integration. 4 Empirical Implementation 4.1 Data We use the Standard and Poors 500 index (S&P500) and the Financial Times All Share Index (FTAS) to represent the two dominant markets of the US and UK, respectively, and the JSE All Share Index represents the satellite market. Daily data for the three indexes were obtained from Datastream for the period January 2, 1990 to December 29, The commercial and financial rand-dollar and rand-pound exchange rates were also obtained from DatastreamforthesampleperiodsothatwecanconverttheFTASandS&P500 indexes into rand equivalents, i.e. we are considering interdependence from a South African investor s perspective. 9

10 4.2 Estimation We assume that Q t, the covariance matrix of the time-varying parameters, is diagonal, i.e. the changes in the parameters occur independently of each other. To implement the Kalman filter we assume that the errors have a normal distribution and correct for non-normality following Bollerslev and Wooldridge (1992). We follow the same two-step method of Rockinger and Urga (2001): first, we estimate univariate asymmetric GARCH models for the US and UK and save the standardized residuals as, ² US,t 1 and ² UK,t 1, i.e. we estimate (1), (2), (3) and (4) where we assume that α D is constant and x t equals 1 for all t. In the second step we use the Kalman filter to estimate the model in (7), (8), (9) and (10). (We provide an outline of the Kalman filter in the next subsection.) To obtain starting values, we proceed step-wise, starting from a reduced model and adding subsequent variables. 4.3 The Kalman Filter The general form of the state space model (Harvey (1989), p ) can be written as follows: the measurement equation is y t = c t + z t α t + ζ t where E[ζ t ]=0andVar(ζ t )=H t,andthetransition equation is α t = d t + T t α t 1 + R t η t where E[η t ]=0andVar(η t )=Q t. We further assume that both ζ t and η t have a normal distribution. Let a t 1 denote the optimal estimator of α t 1 based on the observations up to and including y t 1. Let P t 1 denote the m m covariance matrix of the estimation error, i.e. P t 1 = E (α t 1 a t 1 )(α t 1 a t 1 ) 0. Given a t 1 and P t 1, the optimal estimator of α t is given by a t t 1 = T t a t 1 + c t, while the covariance matrix of the estimation error is P t t 1 = T t P t 1 T 0 t + R t Q t R 0 t,fort =1,..., n. 10

11 These two equations are known as the prediction equations. Once the new observation y t becomes available, the estimator of α t,a t t 1, can be updated. The updating equations are a t = a t t 1 + P t t 1 ZtF 0 t 1 yt Z t a t t 1 d t P t = P t t 1 P t t 1 ZtF 0 t 1 Z t P t t 1, where F t = Z t P t t 1 Z 0 t + H t, for t =1,..., n. We can use the fact that the condition distribution of the prediction errors, v t = y t Z t a t t 1 d t is Gaussian with mean zero and variance F t to construct a likelihood L = 1 2 nx t=1 ln (2π)+lnkFt k + v 0 tf 1 t v t. We maximize the likelihood with respect to the parameter vector to obtain the maximum likelihood estimates. Based on our specification in (7) through (10) we set y t = r i,t, z t =(1,r i,t 1,² D,t 1 ), ζ t = e i,t H t = σ 2 i,t ³ α t = α (0) 0 i,t, α(1) i,t, α(2) i,t, Tt = R t = I 3. ³ The parameter vector for country i is α (0) o, α (1) o, α (2) o,q 1,q 2,q 3, β 0 i, β + i, β i, β 1 i where α (j) 0 denotes the starting value for α (j) it for j =0, 1, Descriptive Statistics We report the descriptive statistics of the three indexes for the sample period January 2, 1990 to December 29, 1999 in Table 1. We use the GMM estimator to obtain the test statistics for skewness and kurtosis because its standard 11

12 errors are robust to conditional heteroskedasticity (Pagan (1996)). The common characteristics of the US and UK in their own currency are the small average returns, variances of less than 1% per day, no skewness, and excess kurtosis. When we convert these indices into rands, we find that the variance triples for the UK and doubles for the US, there is still no skewness and the excess kurtosis increases and remains statistically significant. The JSE is also characterized by a small average return, a variance of approximately 1% per day and both excess skewness and kurtosis. The excess kurtosis in the JSE returns can be attributed to conditional heteroskedasticity, political risk and the many emerging market crises at the end of the decade, e.g. the Asian crisis in October 1997 and the Russian crisis in October It is interesting to note that the variance of SA is smaller than the variance of the US and UK when denominated in rands. Based on the time series plot of the returns of the three indexes in rands in Figure 2, we notice that the US and UK indexes are a lot more variable before the unification of the dual exchange rate regime in March This observation is consistent with the dual rate serving as a mechanism for insulating the domestic market from foreign shocks due to political risk. To test this hypothesis we report the variance ratios of the indexes and the financial exchange rate in Table 1. For the own-currency returns, we find that the variance ratio of the pre- to post-march 1995 returns are less than one and statistically significant for all three indexes, i.e. SA, the US and UK were less volatile before March 1995 than after March When denominated in rands, we find that the variance ratios are greater than one and statistically significant for both the US and UK. Finally, the variance ratio for the exchange rates is 3.6 for the UK and greater than 4 for the US. These results confirm that the exchange rate was more variable in the pre-1995 period and served as buffer for the South African financial markets. In the last two panels of Table 1 we report tests for serial correlation and conditional heteroskedasticity. We find significant serial correlation for SA and the UK at the first lag in their own currency, and greater serial dependence for the UK when it is denominated in rands. For the US we find significant serial correlation at the third and fourth lags when denominated in dollars, and at the first lag when denominated in rands. Finally, using an LM test, we find significant evidence of conditional heteroskedasticity for all three indexes in their own currency and in rands. 12

13 4.5 Extreme Returns In Tables 2A and B we report the five largest positive and negative returns forsa,theukandtheus.intable2awereporttheextremereturnswhen the indexes are denominated in their own currency. First, it is interesting to note that all the extreme events for South Africa occur after March 13, 1995, after the dual exchange rates were unified. Four of the negative returns of SA occur at the end of August and end of October 1998 and correspond to twoeventsonthesamedatesintheusreturns. These dates coincide with the Russian crisis that affected many of the emerging markets. The UK also has a large negative return at the end of August, but beside that date, there is no common event for SA and the UK. For the positive returns, the end of October 1997 (Asian crisis) and middle of October 1998 (Russian Crisis) are associated with positive returns in both the US and SA, but there is no common event with the UK. Based on the own currency returns, we can draw three conclusions: first, co-movement in extreme events is greater after the March 1995; second, the US is the more important link for extreme events; finally, the extreme returns occur during crisis periods in other emerging markets. Turning to Table 2B, where we report the extreme events in rands, we find very little overlap between the three countries. All the large negative events in the UK returns occur before or during 1995, and there is no common event for SA and the US. The only common event for the US and SA occurs at the end of August 1998 (Russian Crisis). For the positive events, the US and the UK both experience the largest positive return on April 11, 1994: this event is associated with the first democratic elections in South Africa and is driven by an appreciation in the exchange rate and not by a movement on any of the three stock markets. There is no common event for SA and the UK and the only common event for SA and the US is at the end of October 1997 (Asian Crisis). We can conclude that in terms of extreme events, South Africa appears to have stronger ties with the US, especially after In addition, we notice that the dual exchange rate insulates the JSE from extreme movements during the pre-1995 period and makes the US and UK appear a lot more risky from the South African investor s perspective. 13

14 4.6 Interdependence with the UK We report the parameter estimates of (7), (8), (9) and (10) for SA and the UK in column 1 of Table 3. First, we find that the starting values of the time-varying parameters α (j) it are indistinguishable from zero. Second, the TARCH effects and asymmetry are significant. Finally, each of the variances of the time varying parameters is statistically significant thus confirming the importance of time-varying component of the parameters. The best way to analyze the model is to interpret Figures 3 and 4. In Figures 3A, B and C we plot the smoothed values of the time varying parameters and their 95% confidence intervals across the full sample period (January 1990 to December 1999) and in Figures 4A and B we plot the time varying correlation coefficient (6) and variance ratio (5). Based on Figure 3A, we can conclude that the conditional mean return of the JSE All Share Index is not statistically different from zero because the confidence interval of α (0) t straddles zero for the entire sample period. This result is consistent with market efficiency where (excess) returns should be a martingale difference sequence (Harrison and Kreps (1979)). Turning to Figure 3B, where we plotthetimevaryingcoefficient of the lagged returns, α (1) it,wenoticethat the confidence interval does not straddle zero for most of the sample except in 1996 and at the end of the sample period. In addition, the coefficient fluctuatesbetween0.1and0.2whichisconsistentwiththefirst order serial correlation coefficient of 0.15 in Table 1. Finally, in Figure 3C we plot the time-varying parameter that is the measure of interdependence with the UK, α (2) it : it is surprising to see that the confidence interval straddles zero for the entire sample period. There is a small upward movement in April 1994 after the South African elections that was caused by an appreciation in the exchange rate, but the general conclusion is that the UK has little effect on the South African financial markets. This is surprising given the number of cross listings between South Africa and the UK, and the 6-hour overlap in their trading hours. In Figure 4A we plot the time-varying correlation between SA and the UK. It is interesting to note there are a number of episodes where the correlation between the two countries is negative. For example, in 1997 the JSE was severely affected by the Asian crisis, but the UK was largely unaffected, hence the negative correlation. Following the 1994 elections, the correlation increases dramatically, but this increase does not persist beyond In fact, from 1997 onward the correlation has been negative. One caveat to 14

15 note is that the correlation is based on the time-varying parameter in Figure 3C, so it is unlikely to be statistically significant. Turning to the variance ratio in Figure 4B, we find that the largest percentage of variation explained by the UK is 12% in 1993 and thereafter the percentage is less than 6%. Once again this confirms the fact that the JSE and FTAS indexes are not closely linked at all despite the overlap in trading time, the cross-listing of companies and the improvements in the trading systems on the JSE and SAFEX. 4.7 Interdependence with the US We report the parameters estimates of (7), (8), (9) and (10) for SA and the US in column 2 of Table 3. First, we find that the starting values of the time-varying parameters α (j) it are indistinguishable from zero. Second, the TARCH effects and asymmetry are significant. Finally, each of the variances of the time-varying parameters is statistically significant thus confirming the importance of the time-varying parameters. The best way to analyze the results of the model is to consider Figures 5A, B and C where we plot the smoothed values of the α (j) it and their 95% confidence intervals. In Figures 5A and 5B we can draw similar conclusions to those obtained when considering interdependence with the UK. In Figure 5A we notice that the intercept of the conditional mean return is never statistically different from zero and is consistent with the results with the UK. In Figure 5B, the first order autocorrelation coefficient fluctuates between 0.1 and 0.2, which is consistent with the first order serial correlation coefficient of 0.15 in Table 1 and the confidence interval does not straddle zero except during the volatile periods of 1996 and Finally, in Figure 5C we plot the time-varying parameter that measures interdependence between SA and the US, α (2) it.theconfidence interval straddles zero from 1990 to the end of From 1994 onwards, interdependence between SA and the US increases, briefly dipsinthefirst half of 1997, reaches a maximum in 1998 and then it begins to decline. The greater degree of interdependence at the end of 1997 and in 1998 can be associated with the Asian and Russian crises that made this period very turbulent. This is even clearer in the next set of figures. In Figures 6A and B we plot the correlation and variance ratio of the JSE all share index and the S&P500. There is small increase in correlation between the S&P500 and the JSE index at the end of 1992 and then continues 15

16 to fluctuate between negative and positive values until In 1994, the correlation jumps up after the first free election in South Africa and increases from 0.1 to 0.5 at the end of 1997 and re-attains this value at the end of It is interesting to note that the spike in 1994 after the South African elections is followed by a period in which the correlation averages around 0.3 for the post-march 1995 period. In Figure 6B we see that the S&P 500 exlains less than 10% of the percentage of variation in the JSE up until Thereafter, the interdependence increases and the percentage of variation explained reaches a maximum of 35% in 1997 and 40% in The main conclusion of this analysis is that the strength of the interdependence between SA and the US has increased significantly following the positive macroeconomic and political changes in 1994 and This result is consistent with the increase in purchases of South African equities by US citizens in Figure 7. 1 From 1990 to 1994, the average monthly purchases (sales) of South African equities by US residents was $14.5 million ($6.95) and from the 1995 through 1999 the corresponding amount was $109 ($54). Figure 7 also provides a nice contrast with the financial rand discount in Figure 1B, i.e. after the unification of the exchange rates and the removal of the discount, US purchases and sales of South African securities increased dramatically. 5 Conclusion The objective of this paper was to analyze the interdependence between South Africa s stock market and those of the US and UK. Given the tremendous positive economic and political changes that have taken place in South Africa during the first half of the 1990 s, we expect to find greater integration with the US and UK in the latter half. This is indeed the case for the US: following the unification of the dual exchange rate regime and the removal of exchange controls on foreign investors in March 1995, the correlation with the US averages around 0.35 as opposed to fluctuating between -0.2 and 0.2 before March However, there is little increase in the interdependence with the UK despite the fact that many large South African companies are cross-listed on the LSE or have the LSE as their primary listing. These results contrast very strongly with those of Ripley (1973) who found that South 1 These data were obtained from the website of the US treasury, 16

17 Africa had a stronger relationship with the UK during the period 1960 to The strong correlation with the US may be due to the fact that the US has become the dominant capital market in the world. The interdependence with the US is also consistent with the increase in purchases of South African equities by US investors; unfortunately, we were unable to get similar data for the UK. References [1] Bekaert, G. (1995) Market Integration and Investment Barriers in Emerging Equity Markets, World Bank Economic Review, 9, [2] Bekaert, G. and C. Harvey (1997) Emerging Equity Market Volatility, Journal of Financial Economics, 43, 1, [3] Bollerslev, T. and J. Wooldridge (1992) Quasi-Maximum Likelihood Estimation and Inference in Dynamic Models with Time Varying Covariances, Econometric Reviews, 11, [4] Cutler, David M., James M. Poterba and Lawrence H. Summers (1989) What moves stock prices? The Journal of Portfolio Management, 15, 3, [5] Cumby, R. E. and A. Khanthavit (1998) A Markov Switching model of Market Integration, in Emerging Market Capital Flows, ed: Richard M.Levich,p [6] Harrison and Kreps (1979) Martingales and Arbitrage in Multiperiod Securities Markets, Journal of Economic Theory, 2, 3, [7] Garner, Jonathan (1994) An analysis of the Financial Rand Mechanism, Centre for Research into Economics and Finance in South Africa, Research Paper no. 9. [8] Harvey, Andrew C. (1989), Forecasting, Structural Time Series Models and the Kalman Filter, Cambridge University Press. [9] Khan, Brian (1991) Capital Flight and Exchange Controls in South Africa, Centre for the Study of the South African Economy and International Finance, Research Paper No

18 [10] Longin, F. and B. Solnik (1995) Is the Correlation in International Equity Returns Constant: ?, Journal of International Money and Finance, 14, 1, [11] Pagan, Adrian R. (1996) The Econometrics of Financial Markets, Journal of Empirical Finance, 3, [12] Ripley, Duncan M. (1973) Systematic Elements in the Linkage of National Stock Market Indices, Review of Economics and Statistics, 55, 3, p [13] Roll, R. (1992) The Industrial Structure and the Comparative Behavior of International Stock Market Indices, Journal of Finance, 47, [14] Rockinger, Michael and Giovanni Urga (2001), A Time-Varying Parameter Model to Test for Predictability and Integration in the Stock Markets of Transition Economies, Journal of Business and Economic Statistics, 19, 1,

19 Table 1: Descriptive Statistics of the Returns of the Three Indexes JSE All Share FT All Share S&P500 Rands Rands Pounds Rands Dollars Mean Variance Skewness (0.004) (0.706) (0.629) (0.482) (0.284) Kurtosis (0.027) (0.001) (0.000) (0.002) (0.001) Variance Ratios: Pre-March 13, 1995 to Post-March 13, 1995 Returns (0.000) (0.000) (0.051) (0.000) (0.000) Foreign Exchange (0.000) (0.000) The p-values are in parentheses below each statistic. The mean, standard deviation, skewness and kurtosis and their standard errors are estimated using the GMM estimator because it is robust to the presence of conditional heteroskedasticity. The sample period is January 2, 1990 to December 29, The variance ratio equals the variance of the returns prior to March 13, 1995 divided by the variance of the returns after March 13, There are 1274 (1206) returns before (after) March 13,

20 Table 1 (continued): Descriptive Statistics of the Returns of the Three Indexes. JSE All Share FT All Share S&P500 Rands Rands Pounds Rands Dollars Lag Serial Correlation in Returns c c a c b a a a Serial Correlation in Squared Returns (5 lags) LM test (χ 2 ) c c c c c We use the Newey-West estimator to compute the standard errors for each serial correlation coefficient so that it is robust to neglected heteroskedasticity and serial correlation. A superscript a, b, and c denotes significance at the 10%, 5% and 1% level, respectively. 20

21 Table2A:TheFiveLargestandSmallestReturnsintheThreeIndexes Own Currency South Africa UK US Negative Returns 10/28/ us 08/11/ /27/ sa 11/21/ /05/ /31/ /26/ us 12/01/ /22/ /27/ us 08/19/ /27/ sa 10/27/ us 08/27/ us,sa 11/15/ Positive Returns 10/29/ us 04/10/ /28/ sa 10/16/ us 09/17/ /08/ /06/ /12/ /15/ sa 10/31/ us 10/06/ /30/ /17/ /18/ /11/ The superscript sa, uk and us denotes the country that has an extreme return on the same date. 21

22 Table2B:TheFiveLargestandFiveSmallestReturnsoftheIndexes South African Rands South Africa UK US Negative Returns 10/28/ /19/ /20/ /21/ /11/ /31/ sa 08/26/ us 10/07/ /19/ /27/ us 10/09/ /12/ /27/ /19/ /06/ Positive Returns 10/29/ /11/ us 04/11/ uk 10/05/ /08/ /05/ /06/ /10/ /27/ /31/ us 12/17/ /22/ /17/ /19/ /28/ sa The superscript sa, uk and us denotes the country that has an extreme return on the same date. 22

23 Table 3: The Parameter Estimates of the Model where the Indexes are denominate in South African Rands Dominant Market Parameters FT All Share S&P 500 α (0) (0.482) (0.482) α (1) (0.463) (0.462) α (2) (0.413) (0.421) q (0.013) (0.011) q (0.053) (0.047) q (0.004) (0.000) β (0.192) (0.179) β (0.000) (0.000) β (0.000) (0.000) β (0.000) (0.000) Sample Size Mean Log-likelihood The p-values are in parentheses under the estimate of each parameter. 23

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