Al Shugaa, Ameen and Masih, Mansur (2014): Uncertainty and Volatility in MENA Stock Markets During the Arab Spring.
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Abstract
This paper sheds light on the economic impacts of political uncertainty caused by the civil uprisings that swept across the Arab World and have been collectively known as the Arab Spring. Measuring documented effects of political uncertainty on regional stock market indices, we examine the impact of the Arab Spring on the volatility of stock markets in eight countries in the Middle East and North Africa (MENA) region: Egypt, Lebanon, Jordon, United Arab Emirate, Qatar, Bahrain, Oman and Kuwait. This analysis also permits testing the existence of financial contagion among equity markets in the MENA region during the Arab Spring. To capture the time-varying and multi-horizon nature of the evidence of volatility and contagion in the eight MENA stock markets, we apply two robust methodologies on data from November 2008 to March 2014: MGARCH-DCC, Continuous Wavelet Transforms (CWT). Our results tend to indicate two key findings. First, the discrepancies between the volatile stock markets of countries directly impacted by the Arab Spring and the countries that were not directly impacted indicate that international investors may still enjoy portfolio diversification and investment in MENA markets. Second, the lack of financial contagion during the Arab Spring suggests that there is little evidence of cointegration among MENA markets implying the opportunities of portfolio diversification. Providing a general analysis of the economic situation and the investment climate in the MENA region during and after the Arab Spring, this study bears significant importance for the policy makers, local and international investors, and market regulators.
Item Type: | MPRA Paper |
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Original Title: | Uncertainty and Volatility in MENA Stock Markets During the Arab Spring |
English Title: | Uncertainty and Volatility in MENA Stock Markets During the Arab Spring |
Language: | English |
Keywords: | Portfolio Diversification, MENA Region, Stock Market Indices, MGARCH-DCC, Wavelet Analysis, CWT |
Subjects: | C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C58 - Financial Econometrics G - Financial Economics > G1 - General Financial Markets > G11 - Portfolio Choice ; Investment Decisions G - Financial Economics > G1 - General Financial Markets > G15 - International Financial Markets |
Item ID: | 58867 |
Depositing User: | Professor Mansur Masih |
Date Deposited: | 25 Sep 2014 17:08 |
Last Modified: | 27 Sep 2019 05:34 |
References: | Aloui, Chaker and Besma Hkiri, 2014. Co-movements of GCC emerging stick markets: New Evidence From Wavelet Coherence Analysis. Economic Modelling.36, 421-431. Andersen T. and T. Bollerslev, 1997 Heterogeneous Information Arrivals and Return Volatility Dynamics, Journal of Finance, 52 , 975–1005. Arifovic, J. and R. Gencay, 2000 Statistical Properties of Genetic Learning in a Model of Exchange Rate. Journal of Economic Dynamics and Control, 24, 981– 100. Bollerslev, T. and Wooldridge, J., 1992. Quasi-Maximum Likelihood Estimation and Inference in Dynamic Models with Time-Varying Covariances. Econometric Reviews 11 (2), 143–172. Elie I. Bouri. 2014 Do Return and Volatility Traverse the Middle Eastern and North African(MENA) Stock Markets Borders?. Emerald Journal of Economic Studies Vol. 41 No. 2,317-344. Engle, R.F., 2002. ―Dynamic Conditional correlation a Aimple Class of Multivariate GARCH Models. Journal of Business and Economic Statistics, 20, 339–350. Erb, C., Harvey, C. and Viskanta, T., 1996. Expected Returns and Volatility in 135 Countries. Journal of Portfolio Management, 22, 46-58. Frankie Chau and R, Jun wand., 2014. Political Uncertainty and Stock Market Volatility in the (MENA) Countries. Mimeo, Durham University, 09 January, 2014. Gency, R.F.Selcuk and B.Whitcher, 2002. An Introduction to Wavelets and Other Filtering Methods in Finance and Economics. London: Academic Press. Gency, R.F.Selcuk and B.Whitcher, 2003. Systematic Risk and Time Scales. Quantitative Finance , 3,108-116. Gilpin, Robert and Jean M. Gilpin, 2001. Global Political Economy: Understanding the International Economic Order. English, Book edition. Hooi Hooi Lean and Kee Tuan eng,2013. Integration of World Seaders and Emerging powers into the Malaysian Market: A DCC-MGARCH Approach. Economic Modelling , 32, 333-342. In, F. and Kim, S. 2013. An introduction to wavelet theory in finance. World Scientific Publishing:Singapore. Kamil, N., Bacha, O., and Masih, M. 2012, Do Sin Stocks Deprive Islamic Stock Portfolios of Diversification? Some Insights from the Use of MGARCH-DCC, Capital Markets Review,20, No. 1 and 2, 1-20 Masih, R. and Masih, A. M. 2001, Long and Short Term Dynamic Causal Transmission Amongst International Stock Markets. Journal of International Money and Finance, 20, 563-587. Matthew, J and Box-Staffensmmeir, 2008. Dynamic Conditional Correlations in Political Science, American Journal of Political Science, 52, 3 , 688-704. Mohamed El Hedi Arouri, 2010, Time-Varying Characteristics of Cross-Market Linkages with Empirical Application to Gulf Stock Markets. Emerald Managerial Finance, 36, 1, 57-70 Najeeb, Faiq, Bacha, O. and Mansur Masih, 2015, Does Heterogeneity in Investment Horizons Affect Portfolio Diversification? Some insights using M-GARCH-DCC and Wavelet correlation analysis, Emerging Markets Finance and Trade (forthcoming). Saiti, B. (2012), Testing the contagion between conventional and Shari‘ah compliant stock indices, Evidence from wavelet analysis, PhD Dissertation, INCEIF, Kuala Lumpur: Malaysia. Torrence, C. and Webster, P.J., 1999. Inter-Decadal Changes in the ENSO-Monsoon System. J. Clim. 12, 2679–2690 |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/58867 |