Given the dynamic nature of stock market integration, 24-week rolling regressions are estimated to establish whether the relationships identified in Table 4.6 above are stable over time.10 For each of these consecutive sample periods of 24 weeks the
estimated coefficients, corresponding t-statistics and variance shares are reported. Each variance share reported in Figure 4.4 therefore corresponds with the date at the end of each 24-week rolling window.
Figure 4.4: Estimated variance shares from rolling regressions across three samples 0.0 0.2 0.4 0.6 0.8 1.0 98 99 00 01 02 03 04 05 06 07 08 09 10 11 GlobalR2 DevelopedR2 EmergingR2
10 Previous studies dealing with integration used varying lengths of rolling periods: Yu, Fung and Tam
(2007) use 24 observations per rolling window, while Bruner, Li, Kritzman, Myrgren and Page (2008) and Frijns, Tourani-Rad and Indriawan (2012) include 36 observations. The rolling regressions are repeated over a 36-week rolling period – see Appendix 4.5. The graph in Appendix 4.5 points to the robustness of the findings regardless of the length of the rolling period.
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Figure 4.4 traces the integration of the South African stock market with global markets over the sample period of almost 13 years. These 13 years seem to be divided into two distinct periods. The first, more volatile period, runs from mid-1998 until mid-2004. The general trend is downward – indicating lower levels of integration over time. However, during this period of generally lower integration variance shares increased between 1999 and 2002. This increase can be attributed to certain crisis periods (see Duncan and Kabundi (2014) and Diebold and Yilmaz (2009)). The first was the Brazilian bonds crisis of 1999, whose main influence is reflected in a high variance share with the emerging market factors. Figure 2.1 depicts increased portfolio inflows during this period, after the liberalisation of the South African capital account, which corresponds with the notion of increased integration. The second crisis period includes the US dot-com crisis, when share prices of technology companies decreased, as well as the September 2001 terrorist attack on the US – see the portfolio outflows in Figure 2.1. The second distinguishable period, from mid- 2004 onwards, displays an increasing trend of variance shares. The South African stock market appears to be more integrated with global markets from mid-2004. The general increasing trend in integration is evident from the results of all three samples of country groupings.
Two distinct features of Figure 4.4 beg further explanation and analysis. The first question arises around the dominance of a specific group/sample for a certain 24- week period. For instance, what happened during those periods where the variance share based on the emerging markets common factors is so much higher than the variance shares of the other two regressions and vice versa? The second question relates to the periods of lesser integration: Would it be possible to identify certain country-specific (idiosyncratic) reasons why the performance of the South African stock market deviated from the global markets during specific periods?
Specific periods of dominance
Out of the 668 variance share values obtained from the rolling regressions, the variance share from the emerging markets sample is the highest in 284 weeks, the variance share from the developed markets sample is the highest in 278 weeks and the variance share from the overall sample is the highest in only 106 weeks. In an
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attempt to identify certain periods of clear dominance regarding the explanatory power of the extracted common factors, continuous periods of at least 18 weeks dominated by the same sample are identified (see Table 4.7).
Table 4.7: Periods of dominance
Emerging market factors Developed market factors Global factors 10/26/1998 - 2/22/1999 4/5/1999 - 8/23/1999 3/30/2009 - 9/28/2009 12/13/1999 - 6/19/2000 2/5/2001 - 6/18/2001 7/8/2002 - 2/24/2003 3/3/2003 - 6/16/2003 4/26/2004 - 9/27/2004 10/27/2003 - 4/19/2004 12/3/2007 - 6/2/2008 10/4/2004 - 3/7/2005 10/6/2008 - 3/23/2009 12/11/2006 - 7/9/2007 3/8/2010 - 11/1/2010 6/9/2008 - 9/29/2008 2/14/2011 - 8/8/2011 10/5/2009 - 3/1/2010
Table 4.7 thus indicates the starting and ending date of periods of at least 18 consecutive weeks where the variance share from a specific sample is the highest. There is a total of eight such periods in both the emerging markets and developed markets samples, while the global sample dominates for one single stretch only.
Table 4.8 summarises selected descriptive statistics for the weekly returns on the South African stock market as well as for three groups of countries. The statistics are reported for the whole sample period, as well as for the periods where one of the three country groupings dominated. During the 27 weeks of dominance by the global factors the mean returns and median returns of South Africa and all three country groupings were positive and much higher than the respective returns for the whole sample period. The standard deviations were also slightly higher than for the overall period.
The 176 weeks of developed market dominance appear to be a more subtle version of the period dominated by global factors. Again the mean returns are all positive, and all above the mean values for the whole sample period – but only slightly higher. However, this time around the standard deviations are all below the standard deviations for the whole period – indicating a more stable period with less volatility.
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Duncan and Kabundi (2014) describe the period 2001–2007 as one of reduced volatility co-movement in global equity markets in the absence of major financial crises. Notably most of the period of developed market dominance identified in Table 4.7 falls within this range of 2001–2007.
Table 4.8: Descriptive statistics for periods of dominance
South Africa Developed Developed- US Emerging Whole period Mean 0.00132 0.00032 0.00031 0.00157 Median 0.00463 0.00287 0.00301 0.00456 Maximum 0.11525 0.07075 0.08170 0.11866 Minimum -0.20365 -0.15287 -0.14558 -0.19707 Std. Dev. 0.03498 0.02160 0.02260 0.02759
Developed factors dominate
Mean 0.00296 0.00133 0.00130 0.00331
Median 0.00556 0.00415 0.00352 0.00564
Maximum 0.06144 0.05198 0.03735 0.05000
Minimum -0.08267 -0.07795 -0.08476 -0.08432
Std. Dev. 0.02756 0.01763 0.01866 0.02282
Emerging factors dominate
Mean -0.00033 -0.00214 -0.00284 -0.00170
Median 0.00453 0.00062 0.00090 0.00208
Maximum 0.11526 0.07075 0.08170 0.11866
Minimum -0.20365 -0.15287 -0.14558 -0.19707
Std. Dev. 0.04228 0.02827 0.02918 0.03374
Global factors dominate
Mean 0.01422 0.01136 0.01280 0.01692
Median 0.01835 0.01494 0.01867 0.01465
Maximum 0.07763 0.05604 0.05806 0.08560
Minimum -0.04600 -0.03375 -0.03281 -0.03438
Std. Dev. 0.03501 0.02311 0.02306 0.03075
The descriptive statistics for the 216 weeks of emerging market dominance make for interesting reading. All the mean returns are negative, all the median returns are below the medians for the whole period, all the standard deviations are higher compared to the whole period, all the standard deviations are the highest of all the sample periods and, last but not least, the periods of emerging market dominance include the respective minimum values and maximum values for all four
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countries/groups of countries. Therefore, the common factors extracted from the emerging markets sample best describe movements on the JSE during periods of overall negative returns and high volatility – as reflected in the high standard deviations and respective minimum and maximum values. This finding corresponds with that of De Beer and Pretorius (2012:9780), who conclude that the South African stock market “grow in tandem with developed markets and falls with emerging markets”. It is also in line with the correlation analysis in Section 4.5, where correlations between the JSE and emerging stock market returns increased during periods of emerging market crises.
Various studies have linked the level of financial integration with specific economic variables (see for instance Arfaoui and Abaoub (2010) and Forbes and Chinn (2004)). The variables identified as determinants of financial integration include economic growth, local inflation, trade openness, local investment as percentage of GDP, budget surplus/deficit, market capitalisation to GDP, domestic bank credit to GDP, domestic institutional and legal environment, and world interest rates. The high frequency of data employed in this empirical study rules out the empirical testing of these macroeconomic variables of lower frequency. In an attempt to look for a possible link between South Africa’s financial market integration and macroeconomic variables, graphical images portraying these variables are compared to the changing variance share in Figure 4.4 and similar figures of Chapters 5 and 6. Visual inspection suggests that South Africa’s stock market integration can be linked to the level of the real effective exchange rate.
Idiosyncratic behaviour
From Figure 4.4 a few periods can be identified during which the South African stock market was less integrated with the global markets. Various Annual Economic Reports, published by the South African Reserve Bank, provide reasons behind the nature of the idiosyncratic component of stock market behaviour in the Bank’s official summary and explanations of movements/developments on the JSE.
Towards the end of 1998 and during 1999: During this period the All Share Index on
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increasing. The Annual Economic Report of 1999 mentioned the “acquisition of offshore assets by South African banks” and later also the expansion of foreign interests by South African companies. Another reason for the poorer performance of the JSE during this time was uncertainty before the next general elections and concerns about the value of the rand (South African Reserve Bank 1999:36).
Mid-2001 – mid-2002: This was a turbulent period on the JSE, with high volatility. A
graphical comparison of movements of the JSE All Share Index and share prices of developed and emerging markets clearly portray how movements on the JSE were opposite to movements on global markets: the JSE All Share Index would grow while the general world trend would be downward – only for these trends to be reversed during the next period. One characteristic of this period was the outflow of portfolio investment – also reflected in Figure 2.1. This outflow can be attributed to the relaxation of exchange controls, allowing South Africa firms and individuals to invest more money abroad and to increase the annual amount spent on repaying foreign debt. Except for the potential impact of the behaviour of local citizens, foreigners were also net sellers of South African shares during this period. The SARB Quarterly Bulletin of September 2002 (2002:46) lists the selling of resource shares by foreigners as a reason for lower share prices on the JSE. These sales were partly in response to proposals around empowerment contained in the then draft Mining Charter of the Minerals and Petroleum Resources Development Bill. One last reason for lower share prices was a lower dollar price of gold at the time. The outflow of portfolio investment, as described above, led to a significant depreciation of the rand. High domestic food prices, together with the weaker rand, in turn fuelled a higher inflation rate.
Mid-2003 – mid-2004: During this period prices on the JSE experienced negative
growth, while developed and emerging markets stock prices were generally growing. Losses on the JSE were attributed to lower international commodity prices as well as a stronger rand that had a negative impact on export-orientated companies and dual- listed companies (SARB 2004). Share prices of mining companies in the gold industry fell by 38% and platinum by 28% after the Chinese government announced measures to limit China’s economic growth.
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End 2006: During the last quarter of 2006 share prices on the JSE dropped
significantly more than share prices for the emerging markets group, while share prices for developed markets increased. The reasons for the lower South African share prices were listed as declining resource share prices after a period of strong growth, higher interest rates and concerns about inflation together with a general negative sentiment towards emerging markets (SARB 2006).
2008: The world markets were in turmoil after the collapse of the Lehman Brothers in
2008 – explaining the temporary lower levels of integration. Although correlations increase during crisis periods, crises do have an impact on the explanatory power of common factors during periods of uncertainty. The higher levels of integration with the emerging markets compared to the developed markets can be explained by the recovery on the South African and emerging stock markets towards the end of 2008 – which was not the case for the developed markets. On the domestic front, South Africa was hit by rising food, oil and commodity prices – thus higher inflation – followed by a depreciation of the rand (SARB 2008:51).
End 2010 – start 2011: During this period integration with the developed markets
declined significantly and to a lesser degree than integration with the emerging markets. Turnover on the JSE increased after the European sovereign debt problems of mid-2010 and the total market capitalisation on the JSE reached a new highest level (SARB 2011). During this time the JSE outperformed (in US$ terms) the Morgan Stanley Capital International (MSCI) World Index.
As a summary the following reasons can explain why the JSE deviates from global stock markets – as is evident from lower variance shares obtained from the rolling regressions. Especially during the initial part of the study sample, certain country- specific factors are identified. Relaxed exchange controls led to an outflow of portfolio investments through the selling of South African shares and the purchase of foreign shares. This led to lower share prices – something that was not mirrored elsewhere in the world. Political uncertainty related to the second round of democratic elections in the country also had an impact on the JSE that could not be explained by global factors. The subsequent depreciation of the rand was not observed in other emerging market economies. Later in the study period the
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dependency on/sensitivity to movements in the share prices of resources led to lower levels of integration. The JSE would therefore react relatively more to developments in resource prices than global markets. During the last part of the study period weaknesses in developed stock markets (Lehman Brothers collapse, debt crisis in Europe to a lesser extent) resulted in foreigners preferring to buy emerging markets stocks. This also led to lower levels of integration with developed markets and greater synchronisation with emerging stock markets.
Estimated coefficients and t-stats of rolling regressions
Figure 4.5 traces the statistical significance of the three Factor 1s (measured in t- statistics) over time. The rolling regressions all include the same number of observations (24 weeks), the same dependent variable and the same number of explanatory variables (a constant and the two common factors). It is therefore, in this case, possible to compare t-values and draw conclusions about statistical significance from the calculated t-values. As usual, the higher the t-value, the more statistically significant is the estimated coefficient. For the sake of the interested reader, the critical value for 10% significance is 1.721, for 5% is 2.080 and for 1% it is 2.831. There are striking similarities – although not unexpected – between Figures 4.5 and 4.4.
Figure 4.5: Statistical significance of Factor 1 coefficients over time
0 4 8 12 16 20 24 28 98 99 00 01 02 03 04 05 06 07 08 09 10 11 t1Global t1Developed t1Emerging
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The two obvious outliers in Figure 4.5 indicate a marked increase in the explanatory power of the first factor extracted from the emerging markets sample. These periods, mid-2006 and the end of 2010, are described in the previous section on idiosyncratic behaviour. During both periods there is a divergence between developed and emerging markets – with the South African stock market following trends in the emerging markets. It is therefore not unexpected that the main factor extracted from the emerging markets sample is more significant in explaining movements on the JSE during these periods.
Lower explanatory power during 1999, 2002, 2003 and 2006 is also reflected in Figure 4.4 and explained in the previous section. During 2010 and 2011 the explanatory power of the main factor from the developed markets sample declines – a trend that is also observed in Figure 4.4. A graph similar to Figure 4.5 focusing on the explanatory power of factor 2 does not render significant results and is therefore excluded from this chapter.
The relationship between the variance shares of the rolling regressions and the t- statistics of the estimated coefficient of the first factor is highlighted for some periods in the previous paragraphs. Figure 4.6 provides, for all three samples, a graphical portrayal of the relationship between the variance shares of the rolling regressions and the t-statistics of the estimated coefficients of both factors (on the left-hand side) and the relationship between the variance shares of the rolling regressions and the size of the estimated coefficients of both factors (on the right-hand side).
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Figure 4.6: The relationship between variance shares, estimated coefficients and t-statistics 0 2 4 6 8 10 12 14 16 0.0 0.2 0.4 0.6 0.8 1.0 GLOBALR2 T1GLOBAL T2GLOBAL -.15 -.10 -.05 .00 .05 .10 .15 0.0 0.2 0.4 0.6 0.8 1.0 GLOBALR2 C1GLOBAL C2GLOBAL 0 4 8 12 16 20 0.0 0.2 0.4 0.6 0.8 1.0 DEVELOPEDR2 T1DEVELOPED T2DEVELOPED -.2 -.1 .0 .1 .2 .3 0.0 0.2 0.4 0.6 0.8 1.0 DEVELOPEDR2 C1DEVELOPED C2DEVELOPED 0 4 8 12 16 20 24 28 0.0 0.2 0.4 0.6 0.8 1.0 EMERGINGR2 T1EMERGING T2EMERGING -.10 -.05 .00 .05 .10 .15 .20 .25 0.0 0.2 0.4 0.6 0.8 1.0 EMERGINGR2 C1EMERGING C2EMERGING
* In each of the rows, the left-hand-side graph portrays the relationship between the variance shares and t-statistics of rolling regressions, while the right-hand-side graph portrays the relationship between the variance shares and estimated coefficients of the two common factors. The first row reports on the global sample, the second row on the developed market sample and the third row on the emerging market sample.
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From the left-hand-side panels it is clear that the major contribution of overall explanatory power of the regressions results from the statistical significance of the first factor. There is a clear positive relationship between the variance shares of the rolling regressions and the t-statistics of the estimated coefficients of the first factor. The potential relationship between the variance shares and the t-statistics of the estimated coefficients of the second factor is not very pronounced. On the right-hand side of the panels the relationship between the variance shares and the size of the estimated coefficients is not as strong. There is almost no relationship between the variance shares and the size of the estimated coefficients of the second factor.