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Determinants of equity return correlations: a case study of the Amman Stock Exchange

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Abstract

This paper seeks to explain time-varying correlations among equity returns. The literature has shown that fundamental and economic factors can explain stock returns or the volatility of markets. Here, panel data analysis is employed to examine whether these factors can also explain the comovement of stock returns. Time-varying correlations among sectoral indexes are estimated using a restricted multivariate threshold GARCH model with dynamic conditional correlation controlling for the asymmetric effects of news and the influence of financial crises. The empirical results from this panel data analysis show that equity return correlations can be explained not only by macroeconomic variables but also by fundamentals within an industry.

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  1. Some of these studies focus on the transmission of shocks and volatility from the oil market to the stock markets of the Gulf Cooperation Council countries (Malik and Hammoudeh 2007; Fayyad and Daly 2011).

  2. Hassan and Malik (2007) used the daily close returns for the financial, industrial, consumer (services), health, energy (oil and gas), and technology sectors in their analysis. When they tried a four-variable GARCH model the system didn’t converge. Therefore, they estimated two trivariate BEKK-GARCH: one for the consumer, financial and technology sectors and the other for the energy, health and industrial sectors. They documented significant transmissions of shocks and volatility among consumer, financial and technology sectors and among energy, health and industrial sectors.

  3. By the end of 2006, the number of companies listed on the ASE had reached 227 indicating an increase in market depth as well as the diversity of investment opportunities provided (ASE Annual Report 2006). The rise in the prominence of the ASE has occurred at the same time as a number of regulatory changes and new listing requirements have been introduced (ASE Annual Report 2012). The ASE adopted a new sector classification that was in line with international standards and reflected a more “accurate” image of the listed companies to investors in terms of the nature of the work. The Standard and Poor’s classification has been adopted with some changes to accommodate the unique features of Jordanian companies. Listed companies are regrouped into three main sectors (financial, industrial and services sectors) with 23 sub-sectors.

  4. These sectoral equity indices are based on the free float shares, whereby the index is calculated using the market value of the free float shares of the companies and not the total number of listed shares of each company.

  5. Specifically, all of the 23 sectors in the ASE under the new industry group were ranked according to (1) their percentage of the total market capitalisation and (2) their number of constituent firms. Both rankings were jointly used to identify the top 10 most important sectors (by size) for the ASE.

  6. The ASE retroactively calculated sectoral equity indices of the new industry grouping for all sectors back to 2000 except for the telecommunication sector which was only calculated back to 2003.

  7. According to Miller and Blair (1985), the backward linkages of a sector indicate that an expansion in its production is valuable to the economy as it causes a rise in productive activities of other sectors. On the other hand, the forward linkages of a sector indicate that its production is sensitive to changes in other sectors’ output.

  8. For example, the APT developed by Ross (1976) asserts that asset returns are related in a linear fashion to k-different orthogonal risks, which arise from shocks to macroeconomic factors. Therefore, the k-different risk factors and their sensitivities can be the main source of correlation among returns.

  9. The Statistics and Publication Division, under the Research and International Relations Department of the ASE, calculated these ratios. The ratios are available at http://ase.com.jo/en/node/543.

  10. The Statistics and Publication Division calculates up to 16 financial ratios for different industries; however, these are not uniformly available across sectors. Only 10 financial ratios were common across all the sample industries (see Table 9 of Appendix).

  11. Imports of goods and non-factor services of Jordan were estimated at 72% of total consumption in 2011 (World Bank 2013).

  12. Huang et al. (2010) documented that the forecasting performance of the DCC-GARCH model is better than that of the GARCH-BEKK model. While the ADCC model of Cappiello et al. (2006) incorporates the leverage effect of shocks in the conditional correlation, Laurent et al. (2012) employing data for 10 stocks from five different sectors of the NYSE documented that the forecast of this ADCC model is not significantly better than that of the Engle’s (2002) DCC model with the leverage effect in the conditional variance.

  13. The results from VAR(1) in Table 11 of Appendix indicate that equity returns for the ASE sectors are mainly predictable from their own historical share prices changes; there are only a few cases where return changes from other sectors have an influence. An AR(1) process was therefore chosen for the mean equation instead of VAR(1). The reduction in parameters also helps getting a convergent estimation for the DCC-MTGARCH (1,1) for 10 sectors.

  14. The ASE market capitalization weighted index is calculated using the pre-2006 industry categories, but the ASE free float index is calculated using the new industry categories introduced in 2006. The Chow breakpoint test was therefore performed using the ASE free float index.

  15. The Chow breakpoint test was conducted to determine the dates of structural changes. The findings indicated structural changes at three points: November 8, 2005, December 17, 2006, and June 18, 2008.

  16. However, the correlation evolution equation in this paper (Eq. 5) follows the standard DCC model introduced by Engle (2002).

  17. Three panel data models, namely the pooled regression model, the fixed effect model and the random effect model, were first estimated. In order to determine which of the three models is the most appropriate for the analysis, the Hausman test was applied. The test result showed that the fixed effect model was the appropriate specification (see Sect. 5.4).

  18. Evidence suggesting that it is reasonable to use annual data when analysing time varying correlations is provided by several studies such as David and Simonovska (2016).

  19. The results of a correlation test indicated that the PCs of any two sectors are independent, so the products of PCs are used instead of the average values of PCs. The test results are available upon request from the authors.

  20. The likelihood ratio statistic in Panel C of Table 4 suggests that the DCC model used in this paper performs better than a CCC model.

  21. The conditional correlations of sectoral return seem to be associated with a number of common factors. One of these, for example, is inflation; there is a positive relationship between inflation and the conditional correlation among sectoral stock returns. The correlation values between these two variables are positive in 44 out of 45 instances; the only instance of a negative correlation is between the conditional correlation between BNK and MIX (ρ1,10) and inflation where a value of −0.10 is documented. For all other correlations, the values range from a low of 0.19 between inflation and the conditional correlation between INS and RES (ρ2,4) to a high of 0.69 between inflation and the conditional correlation between EDS and FOB (ρ5,9).

  22. In addition, we estimate the fixed effect model without interaction terms; the estimation result is shown as Model 4 in Table 7.

  23. Nonetheless, the likelihood ratio statistic reported in Table 8 suggests that the growth PC is an important factor that can explain conditional correlations between sectoral returns earned in the ASE.

  24. The likelihood ratio statistics reported in Table 8 confirm that macroeconomic variables are important determinants of the correlations of stock index returns and that there are significant interactions between sector-specific and economic factors.

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Acknowledgements

We would like to express our sincere thanks for helpful comments from those who attended the British Accounting and Finance Association Scottish area group conference held at the University of St Andrews, United Kingdom and the Southampton Business School Seminar series. The first author would also like to thank the German Jordanian University for funding which allowed him to undertake this research.

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Authors and Affiliations

  1. School of Management and Logistic Sciences, German Jordanian University, Amman Madaba Street, P.O. Box 35247, Amman, 11180, Jordan

    Mohammad Alomari

  2. School of Social Sciences, University of Dundee, Dundee, DD1 4HN, UK

    David. M. Power

  3. Southampton Business School, University of Southampton, Southampton, SO17 1BJ, UK

    Nongnuch Tantisantiwong

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  1. Mohammad Alomari
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Correspondence to Mohammad Alomari.

Appendix

Appendix

See Tables 9, 10 and 11.

Table 9 Definitions of the financial ratios
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Table 10 Definitions of the macroeconomic variables
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Table 11 Estimated coefficients of the var (1) model
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Alomari, M., Power, D.M. & Tantisantiwong, N. Determinants of equity return correlations: a case study of the Amman Stock Exchange. Rev Quant Finan Acc 50, 33–66 (2018). https://doi.org/10.1007/s11156-017-0622-4

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