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

MPRA Paper No. 94201, University Library of Munich, Germany.

13 Pages Posted: 20 Jun 2019

Date Written: May 30, 2019

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.

Keywords: Stock Market, Volatility, Investment, ARCH, GARCH, IGARCH, GARCH-M, EGARCH, Risk Premium, Zimbabwe

JEL Classification: C22, C58, D81, D82, E22, E44, E47, G02, G14, G15, N27, O16, R53

Suggested Citation

Bonga, Wellington Garikai, Stock Market Volatility Analysis using GARCH Family Models: Evidence from Zimbabwe Stock Exchange (May 30, 2019). MPRA Paper No. 94201, University Library of Munich, Germany., Available at SSRN: https://ssrn.com/abstract=3402342

Wellington Garikai Bonga (Contact Author)

Great Zimbabwe University ( email )

P. O. Box 1235
Masvingo
Masvingo, Masvingo 00263
Zimbabwe

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
135
Abstract Views
540
rank
239,442
PlumX Metrics