Are There Still Portfolio Diversification Benefits in Eastern Europe? Aggregate Versus Sectoral Stock Market Data*

Abstract

In this paper we measure the increase in stock integration between the three largest new European Union members (Hungary, the Czech Republic and Poland) and the Euro-zone using both country and industry level data. At the country market index level all three Eastern European markets show a considerable increase in correlations in 2006. At the industry level the dates and transition periods for the correlations differ and the correlations are lower, although also increasing. The results show that sectoral indices in Eastern European markets may provide larger diversification opportunities than the aggregate market.

Can we still benefit from international diversification? The case of the Czech and German stock markets

“…Syriopoulos (2004Syriopoulos ( , 2006 finds long-run cointegrating relationship and hence limited diversification opportunities between CEE markets and Germany. Aslanidis and Savva (2011) confirm these findings using more recent data. Égert and Kočenda (2007) analyze the intraday interdependence of Western European and Central and Eastern European markets using wide range of econometric techniques and find evidence of only short-term relationships among the CEE and Western European stock markets.…”

A three-tiered nested analytical approach to financial integration: The case of emerging and frontier equity markets

“…We base the choice of such an approach on considering that the integration among the markets under study might change as a gradual process. As pointed out in several studies (see, for instance, Chelley-Stelley 2005;Savva and Aslanidis, 2010;Aslanidis and Savva, 2011), the smooth transition analysis is an approach used to detect structural breaks denoted as smoothed transitions between regimes over time. Therefore, these models are suitable to measure structural changes in the level of integration among markets.…”

“…The parameter 𝛾 determines the smoothness of the change in 𝐺 𝑡 as a function of 𝑠 𝑡 . As 𝛾 → ∞ , 𝐺 𝑡 becomes a step function ( 𝐺 𝑡 = 0 if 𝑠 𝑡 < 𝑐 and 𝐺 𝑡 = 1 if 𝑠 𝑡 > 𝑐) and 𝐺 𝑡 = 1 so that the transition between the two extreme correlation states becomes 7 As Aslanidis and Savva (2011) pointed out, the rationale of using correlation coefficients as a measure of market integration is based on the consideration that higher integration among markets results in higher co-movements between their returns. It must be pointed out that a higher correlation among markets is a necessary but not a sufficient condition for greater market integration.…”

“…In the first level, we address the first research question. We consider the financial interrelations between the emerging and US stock markets, taken as a proxy of developed equity markets, and between the frontier and US equity markets, and, following Aslanidis and Savva (2011) as well as Wang and Moore (2008), we measure these interrelations by applying a smooth transition analytical approach. 4 In doing so, we take the view that although higher correlation is not per se a necessary or a sufficient condition for integration, its presence could be a strong indication of it 5 , and we accommodate nonlinearities by using a conditional perspective with timevarying correlations.…”

“…found that frontier markets were less correlated with either emerging or developed markets than developed and emerging were among themselves. Furthermore, by performing an asset allocation analysis,Gupta (2014) found that global investors can improve their portfolio's performance by including stocks of companies listed on the frontier markets.5 Aslanidis and Savva (2011) recognize this limitation of the time-varying correlation, justify its use because the more integrated the markets are, the higher the co-movements between their prices become.…”