In their paper, Zivkov et al (2018) investigate the relationship between the major Eastern European stock indexes (Czechia, Hungary, Poland, Romania) and that of the US, Germany, and the UK. They estimate a strong correlation between Germany’s DAX and most Eastern European indexes (EEMs) Moreover, they make several observations with regards to the relationship of EEMs between themselves and other indexes.
Picture by Wance Paleri, Unsplash
How big are the Eastern European stock markets?
Among the Eastern European stock markets, Czechia’s (PX) is by far the largest in terms of market capitalisation, which averaged $703 bn in 2021. This was partially driven by a twelve-fold jump in value in 2018, after the inclusion of numerous medium-sized companies. Poland’s (WIG) exchange is the second largest, followed by Romania’s (BET) and Hungary’s (BUX). These stock exchanges are dwarfed compared to Germany’s (DAX) $2.5 tn market capitalisation, which is about 2.2 times larger than their combined value.
Figure 1: Germany’s stock exchange is over two times bigger than emerging Europe’s
DATA: Bonsai Economics, Prague Stock Exchange, Warsaw Stock Exchange, Budapest Stock Exchange, Bucharest Stock Exchange, Deutsche Börse Group
Most Eastern European indexes are strongly correlated to DAX
Zivkov et al (2018) investigate the extent to which Germany’s dominant position in the European economy leads a strong correlation between the DAX and EEM indexes. Using an econometrics model, they indeed estimate an average correlation of roughly 50% for Poland, Czechia and Hungary (see Figure 2, left-hand side). Romania was the only country not to exhibit a strong correlation, with a value of 24%. The authors attribute this result to Romania’s relatively less trade with Germany: their bilateral trade accounted for just 15% of Romanian GDP (see Figure 2, right-hand side). While the explanation makes sense, it is noteworthy that Poland also exhibits a similar degree of trade with Germany, but its index remains highly correlated to the DAX. The paper does not explain this phenomenon.
Figure 2: Less bilateral trade partially explains the Romanian index’s lower correlation to DAX
DATA: Bonsai Economics, Zivkov et al (2018), Observatory of Economic Complexity
… a few more interesting observations by Zivkov et al (2018)
In the paper, a number of other interesting empirical observations are made with regards to EEMs. More specifically:
Out of the main developed country indexes (S&P500, FTSE250, DAX), a shock on the S&P500 index had the largest impact on EEMs. This relates to the considerably larger size of the S&P500 compared to DAX and FTSE250.
During periods of crisis such as the global financial crisis of 2008 and the sovereign debt crisis of 2010, the correlation between EEMs increases, reaching almost 80%. The authors attribute this to so-called contagion effects when investors simultaneously move away from risky emerging market assets towards safer ones such as gold.
Previous shocks in EEMs have a stronger impact on their future volatility compared to the DAX index. In other words, there is a higher degree of shock persistency in emerging market stock indexes. This can be explained by the easier loss of investor trust in such markets.
While EEMs are too small to affect the S&P500, a shock in EEM stocks can affect the British FTSE250 and the German DAX index. This relates to the EEMs higher interconnectedness to these respective economies.
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