Analysing Existence of Volatility Persistence in Sub-Sahara Africa Stock Markets
DOI:
https://doi.org/10.26905/afr.v2i1.3263Keywords:
Stock market returns, volatility persistence, GARCH model, the Sub-Sahara Africa stock marketsAbstract
The aim of this paper was to analyse volatility persistence in Sub-Sahara stock markets. The study concentrated on selected markets including Ghana, Nigeria and South Africa by analysing univariate GARCH (1,1) model using monthly data from January 2000 to December 2017. Estimates from descriptive statistics show that the mean monthly returns are positive for the Sub-Sahara stock markets, but the South Africa stock market generates more returns than Nigeria and Ghana within the study period. Skewness coefficients show that the stock returns distributions of the Sub-Sahara Africa stock markets are negatively skewed. Excess kurtosis is positive for all the stock markets returns. The Jarque-Bera statistics indicate the stock markets’ series are not normally distributed. Unit roots tests results indicate that the Sub-Sahara Africa stock markets series are integrated of order one. The results of the GARCH (1,1) model provide evidence to show that the Sub-Sahara Africa stock markets exhibit volatility clustering and persistence. The study therefore concludes that there is volatility persistence in Sub-Sahara Africa Stock Markets. The study therefore recommends that Sub-Sahara Africa portfolio managers watch movements in stock market volatility as part of their portfolio management strategy and formulate cushion policies to mitigate effects of volatility shocks.
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References
Ahmed E.M.A. & Suliman Z.S. (2011). Modeling Stock Market Volatility Using GARCH Models eEvidence from Sudan. International Journal of Business and Social Science 2(23) December: 114-128.
Anjikwi, A & Danjuma, J. (2018). Analysis of Nigeria Stock Market Using Bayesian Approach in Stochastic Volatility Model (2012 – 2016). International Journal of Statistics and Applications, 8(2), 53-58.
Bollerslev, T. (1986). A Generalized Autoregressive Conditional Heteroscedasticity. Journal of Econometrics, 31, 307-327.
Botha, F. & King, D. (2014). Modelling stock return Volatility Dynamics in Selected African Markets. Economic Research Southern Africa working paper 410, by the National Treasury of South Africa.
Brook, C. & Burke, S.P. (2003). Information criteria for GARCH Model Selection: An Application to High Frequency Data. European Journal of Finance, 9(6): 557-580.
Chowdhury, S.; Mollik, A. & Akhter, M. (2006). Does Predicted Macroeconomic Volatility Influence Stock Market Volatility? Evidence from the Bangladesh Capital Market. Department of Finance and Banking, University of Rajshahi Working Paper.
Dickey, D. A. & Fuller, W. A. (1981). Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root. Econometrica, 49 (4).
Emenike, K. O. (2010). Modelling stock returns volatility in Nigeria using GARCH models. African Journal of Management and Administration, 3(1): 8-15.
Emenike, K. O. (2016). Comparative Analysis of bureaux de Change and Official Exchange rates Volatility in Nigeria. Intellectual Economics,10(1): 28-37.
Emenike, K. O. & Aleke, S. F. (2012). Modeling Asymmetric Volatility in the Nigerian Stock Exchange. European Journal of Business and Management, 4(12): 52-59.
Emenike, K. O. & Ani, W. U. (2014). Volatility of the Banking Sector Stock Returns in Nigeria. Ruhuna Journal of Management and Finance, 1(1): 73-82.
Enders, W. (2004). Applied Econometric Time Series (2nd Ed.). Singapore: John Wiley & Sons (ASIA) Pte Ltd.
Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of the United Kingdom Inflation. Econometrics, 50: 987-1008.
Engle, R.F., & Paton A. J. (2001). What Good is a Volatility Model? Retrieved from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1296430.
Gujarati, D. N. (2003). Basic Econometrics (4th Ed.). Delhi: McGraw Hill Inc.
Makhwiting, M.R., Lesaoana, M. & Sigauke, C. (2012). Modelling Volatility and Financial Market Risk of Shares on the Johannesburg stock exchange. African Journal of Business Management, 6(27): 8065-8070.
Mwamba, J.M., Thaba, L. & Uwilingiye, J. (2014) Modelling the Short-term Interest Rate with Stochastic Differential Equation in Continuous time: linear and nonlinear models. MPRA Munich personal repec archive, Retrieved from http://mpra.ub.uni-muenchen.de/64386.
Nelson, D. (1991). Conditional heteroscedasticity in asset returns: A new approach. Econometrica, 59(2): 347-370.
Okpara, G.C. & Nwezeaku, N.C. (2009). Idiosyncratic Risk and the Cross-Section of Expected Stock Returns: Evidence from Nigeria. European Journal of Economics, Finance and Administrative Sciences, 17: 1-10.
Schwert, G. W., (1989). Why does stock market volatility change over Time? Journal of Finance, 44: 1115-1153.
Wilcox, R. R. & Keselman, H. J. (2003). Modern Robust Data Analysis Methods: Measures of central Tendency. Psychological Methods, 8(3): 254-274.
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