Long-Run and Short-Run Dynamics among the Sectoral Stock Indices: DataMore recently, Patra and Poshakwale provided empirical evidence on the short-term and long-term relationships among the major stock indices of the Athens stock exchange (ASE hereafter). They found that although the sector indices do not show a consistent and strong long-term relationship, the banking sector seems to have a strong influence on returns and volatility of other sectors at least in the short-run. Al-Fayoumi et al. investigated the dynamic interactions across four stock market indices in the Amman Stock Exchange. Similar to Wang et al.’s study, their findings indicated that investors who diversify their portfolios across sectors in ASE should expect portfolio advantages in the light of significant causality linkages and high correlations among sector returns. In this context, the service sector gives the best diversification opportunities since it is the least integrated with other sectors while the financial is the most influential sector in the ASE. Ahmed analyzed the long-run equilibrium relationships as well as the short-run dynamic linkages amongst the various sectors of the Egyptian stock market. These results are consistent with the economic intuition that the capital market sectors have a tendency to move towards the same direction, at least in the long run. read more
There are 20 sub-sector indices in the ISE. The sample data consists of daily closing price indices for the six sub-sectors of ISE; Chemistry, Petrol and Plastic (XKMYA), Basic Metal (XMANA), Trade (XTCRT), Telecommunication (XILTM), Banks (XBANK), Holding and Investment (XHOLD). The data set covers the period from January 2, 1997 through February 2, 2011, thereby providing a sample size of 3409 observations. All data were retrieved from the ISE database and transformed to natural logarithms for use in the analysis. The indices are weighted by market capitalization, containing the largest firms in each market. The main rationale behind including these six sub-sectors is that they account for approximately 83% of the total market capitalization in the ISE. Therefore, they dominate the market capitalization in the ISE. The remaining 14 sub-sectors had just 17% market capitalization. Table 1 indicates the weights of each sub-sector in the ISE-100 Index and their market capitalization.
The Banking sector leads all other sectors with the highest percentage (40.97%) of the total market capitalization. It is followed by Holding and Investment, Chemistry, Communication, Commerce and Basic Metal sectors respectively in terms of market capitalization.
The summary statistics of the daily stock index returns are presented in Table 2. The Basic Metal and Commerce sector outperform the other sectors in terms of daily index returns (0.0008) over the sample period, whereas Communication sector performs the worst with return averages of 0.0003. The Communication sector seems to be the most volatile amongst all six sectors with the highest standard deviation of 0.0326. All index returns are positively skewed except Basic Metal and Commerce sectors. The relatively large value of kurtosis suggests that the underlying data are leptokurtic or fat-tailed and sharply peaked about the mean when compared with the normal distribution. This finding is supported by the relevant Jarque-Bera test providing clear evidence to reject the null hypothesis of normality at 1% significance level.

Table-1: ISE Sector Indices (as of January 31, 2011)

Index Market Capitalization (million TL) Weight in ISE-100 Index(%)
XKMYA 9,350.8 8.68
XMANA 3,464.6 3.21
XTCRT 6,961.7 6.46
XILTM 8,698.8 8.08
XBANK 44,118.9 40.97
XHOLD 17,677.1 16.41

Table-2: Descriptive Statistics

XBANK XHOLD XILTM XKMYA XMANA XTCRT
Mean 0.0007 0.0004 0.0003 0.0006 0.0008 0.0008
Median 0.0009 0.0005 0.0000 0.0005 0.0007 0.0005
Maximum 0.1726 0.1795 0.1796 0.1871 0.1982 0.1781
Minimum -0.2117 -0.2017 -0.1961 -0.1856 -0.2075 -0.2037
Std. Dev. 0.0293 0.0268 0.0326 0.0243 0.0288 0.0235
Skewness 0.0706 -0.0310 0.0179 0.0610 -0.0728 -0.1377
Kurtosis 7.5316 8.4303 7.7351 10.0891 8.2931 12.6015
Jarque-Bera 2167.6 3110.2 2364.6 5301.4 2956.8 9730.0
Probability 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000