Stock Market Volatility Analysis using GARCH Family Models: Evidence from Zimbabwe Stock Exchange

Bonga, Wellington Garikai (2019): Stock Market Volatility Analysis using GARCH Family Models: Evidence from Zimbabwe Stock Exchange.

Abstract

Understanding the pattern of stock market volatility is important to investors as well as for investment policy. Volatility is directly associated with risks and returns, higher the volatility the more financial market is unstable. The volatility of the Zimbabwean stock market is modeled using monthly return series consisting of 109 observations from January 2010 to January 2019. ARCH effects test confirmed the use of GARCH family models. Symmetric and asymmetric models were used namely: GARCH(1,1), GARCH-M(1,1), IGARCH(1,1) and EGARCH(1,1). Post-estimation test for further ARCH effects were done for each model to confirm its efficiency for policy. EGARCH(1,1) turned to be the best model using both the AIC and SIC criterions; with the presence of asymmetry found to be significant. The study concludes that positive and negative shocks have different effects on the stock market returns series. Bad and good news will increase volatility of stock market returns in different magnitude. This simply imply that investors on the Zimbabwean stock exchange react differently to information depending be it positive or negative in making investment decisions.

• Item Type: MPRA Paper
• Language: English
• Keywords: Stock Market, Volatility, ARCH, GARCH, IGARCH, GARCH-M, EGARCH, Risk Premium, Zimbabwe
• 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; D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D81 - Criteria for Decision-Making under Risk and Uncertainty; D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D82 - Asymmetric and Private Information ; Mechanism Design; E - Macroeconomics and Monetary Economics > E2 - Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy > E22 - Investment ; Capital ; Intangible Capital ; Capacity; E - Macroeconomics and Monetary Economics > E4 - Money and Interest Rates > E44 - Financial Markets and the Macroeconomy; E - Macroeconomics and Monetary Economics > E4 - Money and Interest Rates > E47 - Forecasting and Simulation: Models and Applications; G - Financial Economics > G0 - General > G02 - Behavioral Finance: Underlying Principles; G - Financial Economics > G1 - General Financial Markets > G14 - Information and Market Efficiency ; Event Studies ; Insider Trading; G - Financial Economics > G1 - General Financial Markets > G15 - International Financial Markets; N - Economic History > N2 - Financial Markets and Institutions > N27 - Africa ; Oceania; O - Economic Development, Innovation, Technological Change, and Growth > O1 - Economic Development > O16 - Financial Markets ; Saving and Capital Investment ; Corporate Finance and Governance; R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R5 - Regional Government Analysis > R53 - Public Facility Location Analysis ; Public Investment and Capital Stock

References

[1]. Adelegan, O. J. (2009). “The Derivatives Market in South Africa: Lessons for sub-Saharan African Countries.” IMF Working Paper. WP/09/196.

[2]. Ahmed, E. M. A and Suliman, Z. S. (2011). “Modeling Stock Market Volatility Using GARCH Models Evidence from Sudan.” International Journal of Business and Social Science Vol. 2 No. 23, pp. 114-128.

[3]. Ananzeh, I. E. N., Jdaitawi, Q. M., and Al-Jayousi, A. M. (2013). “Relationship between Market Volatility and Trading Volume: Evidence from Amman Stock Exchange.” International Journal of Business and Social Science, Vol. 4 No. 16, pp. 188-198.

[4]. Banumathy, K and Azhagaiah, R. (2015). “Modelling Stock Market Volatility: Evidence from India.” Managing Global Transitions 13 (1): 27–42.

[5]. Bonga, W. G. (2014). “Empirical Analysis of Stock Returns and Volatility of the Zimbabwean Stock Markets.” IOSR Journal of Economics and Finance, Volume 5, Issue 6. Ver. III, PP 46-57.

[6]. Bonga, W. G., Chikeya, C. K., and Sithole, R. (2015). “The Need for Financial Stability in Zimbabwe: Use of Derivatives Securities.” IOSR Journal of Economics and Finance, Volume 6, Issue 4, Ver. III, PP. 06-14.

[7]. Cheteni, Priviledge (2016). “Stock market volatility using GARCH models: Evidence from South Africa and China stock markets.” MPRA Paper No. 77355.

[8]. Diebold, F.X. and Lopez, J.A. (1996). Modeling Volatility Dynamics. In K. Hoover, editor, Macroeconomics: Developments, Tensions and Prospects. Kluwer, Boston.

[9]. Edwards, Franklin. R. (2006). “Policies to Curb Stock Market Volatility.” Financial Analysts Journal, PP. 141-166.

[10]. Enders, Walter. (2014). “Applied Econometric Time Series.” (4th ed.). New Jersey: John Wiley & Sons Inc.

[11]. Engle, R. F. (1982). “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation”, Econometrica, Vol- 50, pp. 987–1007.

[12]. Engle, R. (2001). “GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics.” Journal of Economic Perspectives, Volume 15, No. 4, pp. 157–168.

[13]. Goyal, Amit (2000). “Predictability of Stock Return Volatility from GARCH Models.” Anderson Graduate School of Management, UCLA.

[14]. Hammoudeh, S & Li, H (2008). ‘Sudden changes in volatility in emerging markets: the case of Gulf Arab stock markets’, International Review of Financial Analysis, vol. 17, no. 1, pp. 47–63.

[15]. Hemanth, Kumar. P. and Basavaraj, Patil, S. (2016). “Volatility Forecasting - A Performance Measure of GARCH Techniques with Different Distribution Models.” International Journal of Soft Computing, Mathematics and Control (IJSCMC), Vol. 5, No. 2/3.

[16]. Iorember, P., Sokpo, J. and Usar, T. (2017). Inflation and Stock Market Returns Volatility: Evidence from the Nigerian Stock Exchange 1995Q1-2016Q4: An E-GARCH Approach." MPRA Paper No. 85656.

[17]. Jakobsen, Johan Stax (2018). “Modeling Financial Market Volatility: A Component Model Perspective.” PhD Thesis, Department of Economics and Business Economics, Aarhus University, Denmark.

[18]. Khositkulporn, Paramin (2013). “The Factors Affecting Stock Market Volatility and Contagion: Thailand and South-East Asia Evidence.” Doctorate Thesis, School of Business, Victoria University, Melbourne.

[19]. Koima, J. K., Mwita, P. N., and Nassiuma, D. K. (2015). “Volatility Estimation of Stock Prices using Garch Method.” European Journal of Business and Management, Vol.7, No.19, pp. 108-113.

[20]. Kupiec, Paul (1991). “Stock Market Volatility in OECD Countries: Recent Trends. Consequences for the Real Economy and Proposals for Reform.” Economic Studies No . 17.

[21]. Mittal, A. K., D. D. Arora, and N. Goyal.(2012). "Modelling the Volatility of Indian Stock Market." Gitam Journal of Management 10 (1): 224–43.

[22]. Murekachiro, Dennis (2016). “Time Series Volatility Forecasting of the Zimbabwean Stock Exchange.” The International Journal Of Business & Management, Vol. 4 (3), pp: 41-52.

[23]. Ndako, U. B (2012). ‘Financial liberalisation, structural breaks and stock market volatility: evidence from South Africa’, Applied Financial Economics, vol. 23, no. 19, pp. 1259–1273.

[24]. Sharaf, O. A, and Abdalla, S. M.(2013). “Estimating Stock Returns Volatility of Khartoum Stock Exchange through GARCH Models.” Journal of American Science, 9(11), pp: 132-144.

[25]. Shiller, Robert. J. (2006). “Causes of Changing Financial Market Volatility.” PP. 01-22.

[26]. Srinivasan, K. (2013). “Modeling the Symmetric and Asymmetric Volatility for Select Stock Futures in India: Evidence from GARCH Family Models.” Ushus JBMgt 12 (1), 61-82.

[27]. Surya Bahadur G.C. (2008). “Volatility Analysis of Nepalese Stock Market.” The Journal of Nepalese Business Studies, Vol. V, No. 1, pp. 76-84.

[28]. Surya, Bahadur G.C. (2008). “Volatility Analysis of Nepalese Stock Market.” The Journal of Nepalese Business Studies, Vol. V, No. 1, pp. 76-84.

[29]. Zimbabwe Stock Exchange, https://www.zse.co.zw/ . Accessed 27 May 2019.

[30]. Zivot, Eric (2008). “Practical Issues in the Analysis of Univariate GARCH Models.” Handbook of Financial Time Series, edited by T.G. Andersen, R.A. Davis, J-P Kreiss, and T. Mikosch.