The main aim of the study is to determine to what extent the South African financial markets are integrated with world markets. In doing so, three global samples representative of the stock market, bond market and currency market are compiled. From these samples an appropriate number of common factors are extracted through factor analysis. The extracted latent common factors are indicative of the global drivers of the financial markets. The international contribution of this study lies in the isolation of these common factors, but more so in the linking of these factors to economic variables. From a South African perspective, the contribution of the study lies in the analysis of the level of financial market integration.
Table 7.1 reports on the number of common factors present in the samples representative of the three financial markets. The number of factors is determined on the basis of the variance share criterion, which adds factors until the contribution of the last factor is more than 5%.
Table 7.1: Number of factors based on variance share
Stock market Bond market Currency market
Global sample 1 3 2
Developed sample 1 3 4
Emerging sample 2 10 5
Table 7.1 indicates that the variation in the global stock market is much more uniform and synchronised than in the other two financial markets. A single common factor drives the returns in both the global sample and the developed sample. In the emerging market sample two factors can explain the general variation. The currency market is more diverse – thus four factors are needed to explain developed market movements and five for the emerging markets compared to only two for the global sample. The least synchronised and more diverse financial market is the global market for government bond yields. Three factors are needed to explain global variation, while the emerging market sample needs at least 10. These findings are in accordance with earlier studies. As early as 1996 Solnik, Boucrelle and Le Fur report
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that stock and bond markets are not synchronised and that correlations between stock market indicators are double that of correlations in bond markets. Capiello, Engle and Sheppard (2006) find strong links in international equity markets, but less clear linkages in bond markets.
Table 7.2: Correlation between extracted factors
First factor Second factor Third factor Stock market Global-Developed 0.9904 -0.0901 Global-Emerging 0.9369 -0.4920 Developed-Emerging 0.8820 -0.0079 Bond market Global-Developed 0.9952 0.6955 0.3359 Global-Emerging 0.5221 0.3057 0.2701 Developed-Emerging 0.4455 0.1330 0.0586 Currency market Global-Developed 0.9770 0.8210 Global-Emerging 0.9610 -0.8910 Developed-Emerging 0.8830 -0.5460
In order to allow for the comparability of results, the empirical studies extract two common factors each for the stock and currency market and three for the bond market. Table 7.2 reports on the correlations between the common factors.
The most important factor driving the stock and currency markets is very similar, with correlations ranging from 0.995 to 0.882. The diversity in the bond market is evident from the relatively low correlations between the emerging market factor and those from the global and developed markets. It is clear that the emerging bond market is not moving in unison with the other bond markets.
The correlation analysis in the three empirical chapters has determined which economic variables are highly correlated with the common factors. The global stock market primarily co-moves with returns in the developed stock markets. The individual countries with the highest correlations are the UK and Germany. From the combined indices the highest correlation is with an index of developed markets without the US. Returns on emerging stock markets also have an impact. They show links with the second global factor as well as with the first factor from the emerging
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markets sample. Other variables with high correlations include industrial metal prices and energy prices. From the rolling correlations it is evident that the oil price suddenly renders high correlations with stock market returns in 2011, while US interest rates also become important during 2010 and 2011.
Global bond yields show the highest correlation with long-term government bond yields of advanced economies. Common factors from the developed market sample correlate with German yields and common factors from the emerging market sample with US yields. Again, there are specific economic variables that come to the fore during the last part of the sample period – covering the global financial crisis, but also the subsequent Euro-zone crisis. US government bond yield gains importance, as do equity returns (both developed and emerging), the VIX, the oil price and short- term interest in the Euro-zone (probably due to the euro crisis).
The global currency market co-moves with the euro. Other economic variables rendering high correlations with common factors from the currency market are the same factors that render high correlations with common factors from the stock market. Returns in the developed stock markets (excluding the US) are the most influential after the euro. Stock market returns in emerging markets also play a role together with commodity prices (industrial metal prices) and energy prices. Towards the end of the sample, movements in the stock markets of developed countries (excluding the USA) are more important than the euro.
Three general observations conclude the section. To a certain extent, stock and currency markets are driven by the same economic variables, while the bond market is driven by different variables. During the last turbulent period of the study different economic variables increase in importance – like the oil price, short-term interest rates and stock market returns. This study, including more recent data, challenges earlier findings that the US is the most important driver of global financial markets – see Eun and Shim (1989) as well as Hsin (2004). These earlier studies regard the US as the most influential stock market, or alternatively the leading market from the group of stock markets.
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The country-specific contribution of the current study is to determine whether the South African financial markets are integrated with world markets or not. Table 7.3 summarises the numeric indicators of integration.
The variance share indicates what percentage of the variation in the three different markets is explained by the extracted common factors over the entire sample period. The rest of the table refers to the estimated variance shares from the 24-week rolling regressions. For each sample the mean variance share (Mean) is reported together with the respective minimum (Minimum), maximum (Maximum) and standard deviation (Std dev).
It is evident that of the three financial markets the South African stock market is relatively more integrated with the world markets than the South African bond and currency markets.
Table 7.3: Indicators of financial market integration across three samples Global sample Developed sample Emerging sample
Stock market Variance share 0.6205 0.5964 0.6663 Mean 0.5973 0.5958 0.5990 Maximum 0.9035 0.8962 0.8982 Minimum 0.0252 0.0488 0.0232 Std dev 0.2068 0.1934 0.2167 Bond market* Variance share 0.4208 0.0925 0.4774 Mean 0.4377 0.3703 0.5185 Maximum 0.8414 0.7233 0.9270 Minimum 0.0029 0.0124 0.0562 Std dev 0.2083 0.1896 0.1977 Currency market Variance share 0.4000 0.4779 0.4041 Average 0.4536 0.5172 0.4577 Maximum 0.8711 0.8873 0.8641 Minimum 0.0007 0.0014 0.0026 Std dev 0.2373 0.1936 0.2410
*Three common factors are extracted for the bonds market, compared to the two for the stock and currency market. The variance shares from the bond market are therefore biased upward.
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Even though the variance share and mean variance shares from the rolling regressions are the highest in the stock market, the reported maximum variance shares from the other two financial markets are well above 0.70 and even as high as 0.93. This is indicative of local markets that are affected by international markets. It is therefore concluded that the South African financial market is integrated with the world markets.
To answer the question as to whether or not the South African market is more integrated with developed or emerging markets, the respective variance shares and mean variance share values are considered. Variations in the stock and bond markets are better explained by common factors from the emerging markets and it is therefore concluded that they are more integrated with the emerging markets. In the case of the currency market, however, the common factors from the developed markets explain a larger share of the variation in the South African market and it is thus concluded that the South African currency market is more integrated with the developed markets than with the emerging markets. The envisaged dualism in the South African financial markets – as described in Chapter 1 – has thus been confirmed.
The South African financial market is integrated with world markets and it is evident that the level of integration is increasing over time. The challenge to policy-makers relates to the dualistic nature of the South African markets: with elements of highly sophisticated, developed markets as well as elements of emerging markets, the stock, bond and currency markets are bound to react differently to international shocks as well as to domestic policy measures. The stock and bond markets are more inclined to follow the emerging markets, while the currency market is inclined to follow developed countries.
A few possible topics for future research can be identified from the current empirical study. In terms of the methodology, the question can be asked whether the extracted common factors would differ if the series were quoted in local currency compared to the common currency of the US dollar (in the case of the stock market). Other questions are: how much does the frequency matter (weekly observations compared
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to monthly) and does the size of the panel matter (for instance, including only the all share indices for the stock market compared to the sub-indices as well)? Another interesting question deals with the identification of the economic variables driving the common factors. The current study uses weekly data and therefore depends on data that is available on a daily basis. If the analysis is repeated in monthly frequency, more macroeconomic variables would be available to test as possible drivers of the global markets.
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