Testing the linkages of Arab stock markets: a multivariate GARCH approach
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DOIhttp://dx.doi.org/10.21511/imfi.16(4).2019.17
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Article InfoVolume 16 2019, Issue #4, pp. 192-204
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The authors undertook to examine 720 monthly observations of activity in 15 Arab stock markets over four years (from 2014 to 2017) to identify the dynamic linkages among those markets. To achieve this, several forms of the Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) model were utilized. Both panel and individual stationarity, in addition to cointegration tests, were employed to highlight the interaction between these markets. The results suggest that Arab stock markets have weak linkages with the exception of those of the Gulf Cooperation Council (GCC). The authors also find out that the TARCH, EGARCH, PARCH, and Component GARCH (1,1) models are suitable in terms of passing the econometric analysis tests. Nevertheless, they conclude that the EGARCH model is the most appropriate for capturing the cross-market dynamic linkages, thereby outperforming the other GARCH specifications under study. The empirical findings bear special implications for economic literature regarding linkages of stock markets in the Arab world.
- Keywords
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JEL Classification (Paper profile tab)F36, G15, G32
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References42
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Tables9
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Figures1
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- Figure 1. Changes in monthly indices of Arab stock markets, 2014–2017
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- Table 1. Summary statistics of monthly prices of stock market indices, 2014M01–2017M12
- Table 2. Results: panel unit root test
- Table 3. Results: intermediate ADF unit root
- Table 4. Summary results of panel cointegration
- Table 5. Summary results of testing the cointegration
- Table 6. Summary results of model estimation, Model 1: TARCH
- Table 7. Summary results of model estimation, Model 2: EGARCH
- Table 8. Summary results of model estimation, Model 3: PARCH
- Table 9. Summary results of model estimation, Model 4: Component GARCH (1,1)
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- Abdmoulah, W. (2010). Testing the evolving efficiency of Arab stock markets. International Review of Financial Analysis, 19(1), 25-34.
- Al-Fayoumi, N. A., Khamees, B. A., & Al-Thuneibat, A. A. (2009). Information transmission among stock return indexes: Evidence from the Jordanian stock market. International Research Journal of Finance and Economics, 24(2), 194-208.
- Al-Najjar, D. M. (2016). Modelling and estimation of volatility using ARCH/GARCH models in Jordan’s stock market. Asian Journal of Finance & Accounting, 8(1), 152-167.
- Al-Nasser, O. M., & Hajilee, M. (2016). Integration of emerging stock markets with global stock markets. Research in International Business and Finance, 36, 1-12.
- Arab Monetary Fund (2018). Historical data of daily and monthly Arab stock markets index.
- Ardia, D., & Hoogerheide, L. F. (2014). GARCH models for daily stock returns: Impact of estimation frequency on Value-at-Risk and Expected Shortfall forecasts. Economics Letters, 123(2), 187-190.
- Assaf, A. (2003). Transmission of stock price movements: the case of GCC stock markets. Review of Middle East Economics and Finance, 1(2), 171-189.
- Basher, S. A., & Sadorsky, P. (2016). Hedging emerging market stock prices with oil, gold, VIX, and bonds: A comparison between DCC, ADCC and GO-GARCH. Energy Economics, 54, 235-247.
- Beirne, J., Caporale, G. M., Schulze-Ghattas, M., & Spagnolo, N. (2010). Global and regional spillovers in emerging stock markets: A multivariate GARCH-in-mean analysis. Emerging Markets Review, 11(3), 250-260.
- Bentes, S. R. (2015). On the integration of financial markets: How strong is the evidence from five international stock markets? Physica A: Statistical Mechanics and its Applications, 429, 205-214.
- Bhunia, A., & Yaman, D. (2017). Is there a causal relationship between financial markets in Asia and the US? The Lahore Journal of Economics, 22(1), 71-90.
- Chen, X., Tian, Y., & Zhao, R. (2017). Study of the cross-market effects of Brexit based on the improved symbolic transfer entropy GARCH model – An empirical analysis of stock-bond correlations. PloS one, 12(8), 183-194.
- Chinzara, Z., & Aziakpono, M. J. (2009). Dynamic returns linkages and volatility transmission between South African and world major stock markets. Studies in Economics and Econometrics, 33(3), 69-94.
- Click, R. W., & Plummer, M. G. (2005). Stock market integration in ASEAN after the Asian financial crisis. Journal of Asian Economics, 16(1), 5-28.
- Dasgupta, R. (2014). Integration and dynamic linkages of the Indian stock market with brick-an empirical study. Asian Economic and Financial Review, 4(6), 715.
- Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74, 427-431.
- Drachal, K. (2017). Volatility Clustering, Leverage Effects and Risk-Return Tradeoff in the Selected Stock Markets in the CEE Countries. Journal for Economic Forecasting, 3, 37-53.
- Floros, C. (2008). Modelling volatility using GARCH models: evidence from Egypt and Israel. Middle Eastern Finance and Economics, 2, 31-41.
- Gabriel, A. S. (2012). Evaluating the Forecasting Performance of GARCH Models. Evidence from Romania. Procedia-Social and Behavioral Sciences, 62, 1006-1010.
- Guo, H., & Neely, C. J. (2008). Investigating the intertemporal risk-return relation in international stock markets with the component GARCH model. Economics Letters, 99(2), 371-374.
- Guo, Z. Y. (2017). GARCH models with fat-tailed distributions and the Hong Kong stock market returns. International Journal of Business and Management, 12(9), 28.
- Hansen, P. R., & Huang, Z. (2016). Exponential GARCH modelling with realized measures of volatility. Journal of Business & Economic Statistics, 34(2), 269-287.
- Hatemi-J, A. (2012). Is the UAE stock market integrated with the USA stock market? New evidence from asymmetric causality testing. Research in International Business and Finance, 26(2), 273-280.
- Hendry, D., & Juselius, K. (2000). Explaining Cointegration Analysis: Part 1. The Energy Journal, 21(1), 1-42.
- Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115(1), 53-74.
- Iqbal, A., Khalid, N., & Rafiq, S. (2011). Dynamic Interrelationship among the Stock Markets of India, Pakistan and the United States. International Journal of Human and Social Sciences, 6(1), 31-37.
- Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica, 59(6), 1551-1580.
- Johansen, S. (1995). Identifying restrictions of linear equations with applications to simultaneous equations and cointegration. Journal of Econometrics, 69(1), 111-132.
- Joshi, P. (2011). Market integration and efficiency of Indian stock markets: A study of NSE. NSE Paper, 198, 1-29.
- Joy, M. (2011). Gold and the US dollar: Hedge or haven? Finance Research Letters, 8(3), 120-131.
- Kaur, P. (2018). Financial markets interdependence in India: An empirical analysis. International Journal of Economics and Business, 16(4), 513-533.
- Kenourgios, D., & Samitas, A. (2011). Equity market integration in emerging Balkan markets. Research in International Business and Finance, 25(3), 296-307.
- Jebran, K. (2014). Dynamic Linkages between Asian countries stock markets: Evidence from Karachi stock exchange. Research Journal of Management Sciences, 3(5), 6-13.
- Levin, A., Lin, C. F., & Chu, C. S. J. (2002). Unit root tests in panel data: asymptotic and finite-sample properties. Journal of Econometrics, 108(1), 1-24.
- Masih, A. M., & Masih, R. (1999). Are Asian stock market fluctuations due mainly to intra-regional contagion effects? Evidence-based on Asian emerging stock markets. Pacific-Basin Finance Journal, 7(3-4), 251-282.
- Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and statistics, 61(S1), 653-670.
- Pedroni, P. (2004). Panel cointegration: asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Econometric theory, 20(3), 597-625.
- Phillips, P. C. B., & Perron, P. (1988). Testing for Unit Roots in Time Series Regression. Biometrika, 75(2), 335-346.
- Rehman, M. Z., & Hazazi, M. A. (2014). Examining Linkages between Saudi Stock Market (TASI) and Selected Stock Markets Indices. International Journal of Financial Research, 5(4), 196.
- Samadder, S., & Bhunia, A. (2018). Linkages among selected Asian stock markets. International Journal of Computational Engineering & Management, 21(2), 26-34.
- Teresiene, D. (2009). Lithuanian stock market analysis using a set of GARCH models. Journal of Business Economics and Management, 10(4), 349-360.
- Valera, H. G. A., Holmes, M. J., & Hassan, G. (2017). Stock market uncertainty and interest rate behaviour: a panel GARCH approach. Applied Economics Letters, 24(11), 732-735.