Article citationsMore >>

Ahmed, A.E.M. & Suliman, S.Z., 2011. Modeling Stock Market volatility using GARCH models evidence from Sudan. International Journal of Business and Social Science, 2(23), pp.114-128.

has been cited by the following article:

Article

Analysis of Asymmetric and Persistence in Stock Return Volatility in the Nairobi Securities Exchange Market Phases

1School of management and Commerce, Strathmore University, Nairobi, Kenya


Journal of Finance and Economics. 2016, Vol. 4 No. 3, 63-73
DOI: 10.12691/jfe-4-3-1
Copyright © 2016 Science and Education Publishing

Cite this paper:
Ogega Haggai Owidi, Freshia Mugo-Waweru. Analysis of Asymmetric and Persistence in Stock Return Volatility in the Nairobi Securities Exchange Market Phases. Journal of Finance and Economics. 2016; 4(3):63-73. doi: 10.12691/jfe-4-3-1.

Correspondence to: Ogega  Haggai Owidi, School of management and Commerce, Strathmore University, Nairobi, Kenya. Email: owidoxh@gmail.com

Abstract

Volatility persistence of stock returns has a major effect on future volatility of the market under the influence of shocks while asymmetric volatility increases market risk thus, fascinating features of stock market behaviour. This paper examines behaviour of stock return volatility in the Kenyan stock exchange market phases for the NSE20 share index and the 10 sampled stocks over 11 years. The asymmetric effect and volatility persistence were fitted by the Fractionally Integrated Exponential FIEGARCH (1,d,1). The study detected consistent peaks and troughs in the sampled series, obtaining in all cases two bear and three bull phases. The outcome shows persistent bullish phases than the bearish with bear phases much more frequent. Diagnostic tests and estimates shows volatility clustering, that is, shocks to the volatility process persist and react to news arrival asymmetrically with positive news impacting more during bullish and negative news during bearish. The results indicate non-systematic pattern across all stocks though a higher degree of dependence in both the level and volatility in the bull periods is detected. The empirical results would be beneficial to investors and surveillance regime as it provides indication of behaviour of stock market volatility during the market phases. Adopted FIEGARCH models have capability of modelling clusters of volatility and capturing its asymmetry taking into account the characteristic of long memory in the volatility. Findings robustness tested using two bear and three bull cycles. Few studies have examined the behaviour of stock returns volatility during bull and bear stock market phases with the majority of work done on developed markets.

Keywords