As reflected in Table 5.5, the regressions over the whole sample period identified the second and third factor from the global sample as the most significant in explaining variations in South African bond yields. From the sample of emerging market countries, the first and third common factors are the most significant. Just as with the variance shares, reflected in Figure 5.4, the coefficients and t-statistics of the estimated coefficients from the rolling regressions were also recorded. Figure 5.5 reflects the t-statistics of the first factors from each sample of 24 weeks. The same dependent variable and the same number of explanatory variables (a constant and three common factors) are included in each of the rolling regressions. With the same degrees of freedom the calculated t-values can be compared. The higher the t-value, the more statistically significant is the estimated coefficient. In this case the critical value for 10% significance is 1.725, for 5% is 2.086 and for 1% it is 2.845.
The first factor from the emerging markets sample is the most significant explanatory variable over the whole sample period in terms of size. According to the values in Figure 5.5 it is also the most significant in terms of reported t-statistics over a rolling period. There is a striking similarity between Figures 5.4 and 5.5. Increased statistical significance of the first factor leads to higher variance shares. See for instance the periods where Developedtstat, the t-statistic of the first factor from the developed sample, rises above four, and that this corresponds with the periods where DevelopedR2 is the highest. In the same way the spikes in Emergingtstat
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correspond with periods where EmergingR2 increases. The one exception is the lack of similarity between the value of Globaltstat and GlobalR2.
Figure 5.5: Statistical significance of Factor 1 coefficients over time
Figure 5.6: Statistical significance of three factors from overall sample
In Figure 5.6 the statistical significance of the three factors from the global sample is compared over time. Only in the beginning of the sample period is the first factor the most important factor. From 2005 the second and third factors dominate. The spikes in GlobalR2 (Figure 5.4) correspond to higher levels of significance of the second and third global factors. Thus the rolling windows confirm the importance of the second and third global factors (linked to stock market returns in developed and
0 4 8 12 16 20 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Globaltstat Developedtstat Emergingtstat 0 2 4 6 8 10 12 14 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Global1tstat Global2tstat Global3tstat
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emerging markets and not by international bond yields) in explaining variations in South African bond yields observed over the whole sample period.
Figure 5.7: The relationship between variance shares, estimated coefficients and t-statistics 0 2 4 6 8 10 12 14 .0 .1 .2 .3 .4 .5 .6 .7 .8 .9 GlobalR2 Global1tstat Global2tstat Global3tstat -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 .0 .1 .2 .3 .4 .5 .6 .7 .8 .9 GlobalR2 Global1coef Global2coef Global3coef 0 2 4 6 8 10 12 .0 .1 .2 .3 .4 .5 .6 .7 .8 DevelopedR2 Developed1tstat Developed2tstat Developed3tstat -1 0 1 2 3 4 .0 .1 .2 .3 .4 .5 .6 .7 .8 DevelopedR2 Developed1coef Developed2coef Developed3coef 0 4 8 12 16 20 0.0 0.2 0.4 0.6 0.8 1.0 EmergingR2 Emerging1tstat Emerging2tstat Emerging3tstat -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 EmergingR2 Emerging1coef Emerging2coef Emerging3coef
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Through various scatter diagrams, Figure 5.7 provides a summary of the contributions of the respective coefficients and t-statistics to the recorded variance shares of the rolling regressions. The dominance of the second factor from the global sample is evident through increased variance shares linked with higher t-statistics and to a certain extent higher estimated coefficients. In the sample of developed markets the second and third factors dominate.
In the last panel of Figure 5.7, the dominance of the first factor from the emerging markets group is clear. There is a definite positive relationship between recorded variance shares and the statistical significance of EMERGING. The increased statistical significance of both EMERGING2 and EMERGING3 contribute to higher variance shares. In terms of the size of the estimated coefficients, an increase in EMERGING3 contributes relatively more than EMERGING2.
5.7 Conclusion
With a sample of 38 countries from the developed as well as emerging markets, this is one of the most comprehensive studies dealing with bond market integration. The results confirm that for the period April 2003 to October 2012 three common factors drive global nominal yields on 10-year government bonds, thus confirming the applicability of the APT to explaining changes in long-term bond yields. There is, however, a significant difference in the common factors driving global bond markets and those driving emerging ones. This is evident from the low correlations between extracted factors as well as correlations between economic variables and the extracted factors. Global bond market movement is correlated with long-term interest rates in Germany, while movements in emerging bond markets are correlated with rates in the US. Regardless of the sample, stock market returns are correlated with bond yields.
In recent years the South African bond market has benefited from international turmoil. As a high-yielding emerging market with sophisticated trading systems the
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country attracted investments during periods of international uncertainty – particularly the debt crisis in Europe. The South African bond market is integrated with global markets, and the level of integration increased during the sample period. The overall variance share of the common factors indicates that between 42% and 47% of variations in bond yields for the study period can be explained by factors common to international bond markets. Although these contributions may seem small, the actual picture becomes clear when changes over time are considered. At some periods 84% and 93% are explained by international factors – leaving only 16% and 7% to idiosyncratic factors. It is also evident that the South African bond market is much more integrated with emerging bond markets than with developed bond markets. This is confirmed by the values of the reported variance shares over time as well as the explanatory power of the common factors extracted from the two country groupings.
Although the sample periods differ, the results from this study are comparable with other studies that focused on South Africa. The variation explained by the common factors in the McGuire and Schrijvers studies is lower, but these authors focused on an earlier period. Bonga-Bonga also observed an increasing level of integration. Rabana concluded that there is no definite integration with the major developed markets – a conclusion shared by this study. The only results that are partly contradicted by this study are those of Dintwe, who reports a 91% variance share with a sample period ending in 2008. It is worth noting that none of these earlier South African studies focus on the time-varying nature of integration.
Valuable insights are gained from analysis in rolling periods, rolling regressions and rolling correlations. The varying nature of integration, as well as the changing economic variables correlated with common factors, is exploited. Examples include the varying correlation of long-term interest rates in Germany and the US, and periods where different factors momentarily show high correlation – for instance, European short-term interest rates, international oil prices and investor fears identified during and after the global financial crisis.
Out of the three financial markets under consideration in the study, the bond market stands out as the one where drivers of the developed and emerging bond markets
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are the most diverse. The South African bond market is much more integrated with emerging markets than with developed markets. From a policy perspective it is important to take note of the factors relating to emerging bond markets. US interest rates, rather than German rates, are correlated with emerging market yields. South African policy should therefore be mindful of the potential impact of US monetary policy and particularly the expected normalisation after quantitative easing. Measures should be in place to reduce the impact once it is implemented.
Furthermore, emerging bond markets are influenced impacted by developments in developed stock markets. Stock market volatility correlates with higher expected yields in emerging bond markets. Lower returns on developed stock markets also correlate with higher expected yields in emerging bond markets. During two crisis periods covered by the study period (the Asian and the global financial crisis) the South African bond market experienced record purchases by non-residents. South African bonds have been regarded as a viable diversification option as South African yields are higher than in developed markets and during times of decreasing stock market returns. The successful issuing of government bonds during 2009–2010 is an example of how this diversification option can be used to the advantage of the country. However, low levels of bond market integration highlight the importance of country-specific factors. Investors favour emerging market countries with a stable macroeconomic environment and sound macroeconomic policies.
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