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ABSTRACT
In this paper, we investigate spillovers from regional and global equity markets to sectoral equity indices for several different regions/countries. First, we investigate the connectedness of sectoral equity return spillovers and explore the different patterns and magnitudes of spillovers. Next, we look for the determinants of sectoral equity return spillovers. We find the regional and global markets spillovers on sector equity indices are highly dispersed across different markets. Novel to the literature, we examine the liquidity and financial positions of the sectors and find that sector positions are highly influential in explaining the extent of the spillovers. Particularly, our exploration evidence that regional and global spillovers to specific sector equity markets jump significantly when a sector has higher debt and lower interest expense coverage. Similarly, higher profit margins of the sector make it less vulnerable to global and regional shocks. We also find market capitalization of the sectors inversely affects the extent of the spillovers originating from global and regional markets.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
1 The Morgan Stanley Capital International Emerging Market Index (MSCI Index) contains the following countries: Brazil, Chile, China, Colombia, the Czech Republic, Egypt, Greece, Hungary, India, Indonesia, Korea, Malaysia, Mexico, Morocco, Qatar, Peru, the Philippines, Poland, Russia, South Africa, South Korea, Taiwan, Thailand, Turkey, and the United Arab Emirates. The purpose of the index is to track the market capitalization of each company listed on the stock markets of the included countries.
2 Established in 1992, the EU’s economic and monetary union (EMU) has played a significant role in driving forward economic integration. The main concerns of the EMU include coordination of fiscal and economic policies, a common currency (i.e., the euro), and a common monetary policy. Although each of the member states in the EU participates in the economic union, certain states increased their level of integration by adopting the euro. Collectively, these countries comprise the euro area.
3 In Burns, Engle, and Mezrich (Citation1998), it was noted that aggregation to weekly returns plays a key role in sidestepping the issues arising from non-overlapping trading hours. This study’s data have been screened to identify possible problems in this area. Initially, the question of whether it is possible to predict returns in one market based on lagged returns in markets closing later in the day was examined. This was rejected for every combination of countries and sectors. Additionally, despite the fact that the relationships based on monthly returns tended to be stronger than those based on weekly returns, this cannot be accounted for by referencing the non-overlapping trading hours issue. The disparity between the two correlations is relatively small for correlations involving Japan, which can be expected to be the most significantly affected by the issue. Conversely, the largest differences pertained to the correlations between the UK and Germany, where the issue is not significantly affecting the countries.
4 Monthly correlations present the same trending behaviour associated with weekly correlations. Based on the monthly returns observed between 1960 and 1990, Longin and Solnik (Citation1995) reported that correlations between the US stock market and several other stock markets had become stronger.
5 We also use the last price of the week (i.e. Wednesday) for the estimated weekly return. Any missing data on Wednesday are replaced with the closing prices of the last trading day. The results show no remarkable correlation.
6 More insights into Diebold and Yilmaz (Citation2009, Citation2012) methodology can consult generic papers.
7 We use 52-week (one-year) rolling window samples, following the methodology of Diebold and Yilmaz (Citation2012). The methodology is explained briefly in the econometric modelling framework later in section 4.