Journal of Finance and Economics
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Journal of Finance and Economics. 2016, 4(3), 63-73
DOI: 10.12691/jfe-4-3-1
Open AccessArticle

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

Ogega Haggai Owidi1, and Freshia Mugo-Waweru1

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

Pub. Date: May 21, 2016

Cite this paper:
Ogega Haggai Owidi and 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


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.

asymmetry bear behavior bull FIEGARCH persistence volatility

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[1]  Abdalla, S.Z.S. & Winker, P., 2012. Modelling Stock Market Volatility Using Univariate GARCH Models: Evidence from Sudan and Egypt. International Journal of Economics and Finance, 4(8), pp.161-176.
[2]  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.
[3]  Baloch, Q.B. et al., 2013. The impact of Interest Rate Volatility on Stock Returns Volatility: Empirical Evidence from Pakistani Markets. Life Science Journal, 10(7), pp.901-904.
[4]  Batra, A., 2004. Stock return volatility patterns in India, Indian Council for Research on International Economic Relations, New Delhi.
[5]  Biscarri, J.G. & de Gracia, F.P., 2004. Stock market cycles and stock market development in Spain. Spanish Economic Review, 6(2), pp.127-151.
[6]  Bollerslev, T. & Mikkelsen, H.O., 1996. Modeling and pricing long memory in stock market volatility. Journal of Econometrics, 73, pp.151-184.
[7]  Boubaker, A. & Makram, B., 2012. Modelling heavy tails and double long memory in North African stock market returns. The Journal of North African Studies, 17(2), pp.195-214.
[8]  Chordia, T., Roll, R. & Subrahmanyam, A., 2001. Market Liquidity and Trading Activity. The Journal of Finance, LVI(2).
[9]  Cont, R., 2010. Empirical properties of asset returns: stylized facts and statistical issues. Quantitative Finance, 1(2), pp.223-236.
[10]  Dukes, W.P., Bowlin, O.D. & MacDonald, S.S., 1987. The performance of beta in forecasting portfolio returns in bull and bear markets using alternative market proxies. Quarterly Journal of Business and Economics, 26(2), pp.89-103.
[11]  Fama, E.F., 1970. Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), pp.383-417.
[12]  Fama, E.F., 1965. The Behavior of Stock-Market Prices. The journal of Business, 38(1), pp.34-105.
[13]  Forgha, N., 2012. An Investigation into the Volatility and Stock Returns Efficiency in African Stock Exchange Markets. International Review of Business Research Papers, 8(5), pp.176-190.
[14]  Gandhi, D. et al., 2006. Testing for nonlinearity & Modeling volatility in emerging capital markets: The case of Tunisia. International Journal of Theoretical and Applied Finance, 9(7), pp.1021-1050.
[15]  Gliem, J. & Gliem, R., 2003. Calculating, interpreting, and reporting Cronbach’s alpha reliability coefficient for Likert-type scales. In 2003 Midwest Research to Practice Conference. pp. 82-88.
[16]  Gonzalez, L. et al., 2006. Defining and dating bull and bear markets: two centuries of evidence. Journal Multinational Finance, 10(1), pp.81-116.
[17]  Goudarzi, H. & Ramanarayanan, C., 2011. Modeling asymmetric volatility in the Indian stock market. International Journal of Business and Management, 6(3), pp.221-231.
[18]  Iyiegbuniwe, W., Ezike, J.E. & Amah, P.N., 2012. Heteroskedasticity of Market Return: A Look at the All Nigerian Stock Exchange Index Time Series. International Journal of Business and Management, 7(16), pp.13-30.
[19]  Jayasuriya, S., Shambora, W. & Rossiter, R., 2009. Asymmetric Volatility in Emerging and Mature Markets. Journal of Emerging Market Finance, 8(1), pp.25-43.
[20]  Julius, B. et al., 2011. Determinants of investor confidence for firms listed at the Nairobi stock exchange, Kenya. Annual Conference on Innovations in Business & Management, pp.1-20.
[21]  Kalu, E. & Friday, A.S., 2012. Modeling Asymmetric Volatility in the Nigerian Stock Exchange. European Journal of Business and Management, 4(12), pp.52-60.
[22]  Kang, S.H. & Yoon, S.-M., 2006. Asymmetric long memory feature in the volatility of Asian stock markets. Asia-Pacific Journal of Financial Studies, 35(5), pp.175-198.
[23]  Karolyi, G.A., 2001. Why Stock Return Volatility Really Matters. Institutional Investor Journals Series, (614), pp.1-16.
[24]  Koutmos, D., 2012. Time-Varying Behavior of Stock Prices, Volatility Dynamics and Beta Risk in Industry Sector Indices of the Shanghai Stock Exchange. Accounting and Finance Research, 1(2), pp.109-125.
[25]  Kumar, D. & Maheswaran, S., 2012. Modelling asymmetry and persistence under the impact of sudden changes in the volatility of the Indian stock market. IIMB Management Review, 24(3), pp.123-136.
[26]  Lopes, S.R.C. & Prass, T.S., 2013. Theoretical Results on Fractionally Integrated Exponential Generalized Autoregressive Conditional Heteroskedastic Processes,
[27]  Maheshchandra, J.P., 2012. Long Memory Property In Return and Volatility: Evidence from the Indian Stock Markets. Asian Journal of Finance & Accounting, 4(2), pp.218-230.
[28]  Maheu, J.M. & Mccurdy, T.H., 2000. Identifying Bull and Bear Markets in Stock Returns. Journal of Business &Economic Statistics, 18(1), pp.100-112.
[29]  Maheu, J.M., Mccurdy, T.H. & Song, Y., 2009. Extracting bull and bear markets from stock returns. Journal of Business & Economic Statistics, 18(1), pp.100-112.
[30]  Malkiel, B.G., 2003. The Efficient Market Hypothesis and Its Critics. Journal of Economic Perspectives, 17(1), pp.59-82.
[31]  Mandelbrot, B., 1963. The Variation of Certain Speculative Prices. Journal of Business, 36(4), pp.394-419. Available at:
[32]  Mittal, A.K. & Goyal, N., 2012. Modeling the volatility of India Stock Market. International Journal of Research in IT & Management, 2(1), pp.1-23.
[33]  Ndegwa, J. & Mboya, J., 2013. Are Good Companies Good Stocks? Evidence from Nairobi Stock Exchange. European Journal of Business and Management, 5(21), pp.16-27.
[34]  Nelson, D.B., 1991. Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 59(2), pp.347–370.
[35]  Nisar, S. & Hanif, M., 2012. Testing Weak Form of Efficient Market Hypothesis: Empirical Evidence from South-Asia. World Applied Sciences Journal, 17(4), pp.414-427.
[36]  Nyamongo, M.E. & Misati, R., 2010. Modelling the time-varying volatility of equities returns in Kenya. African Journal of Economic and Management Studies, 1(2), pp.183-196.
[37]  Ogilo, F., 2008. The bull and bear market at the Nairobi Securities Exchange. Aim Journal of Business.
[38]  Ogum, G., Beer, F. & Nouyrigat, G., 2005. Emerging equity market volatility. Journal of African Business, 6(1-2), pp.139-154.
[39]  Olweny, T. & Omondi, K., 2011. The effect of Macro-economic factors on stock return volatility in the Nairobi Stock Exchange, Kenya. Economics and Finance Review, 1(10), pp.34-48.
[40]  Oskooe, S.A.P. & Shamsavari, A., 2011. Asymmetric Effects in Emerging Stock Markets- The Case of Iran Stock Market. International Journal of Economics and Finance, 3(6), pp.16-24.
[41]  Owido, P.K., Onyuma, S.O. & Owuor, G., 2013. A Garch Approach to Measuring Efficiency: A Case Study of Nairobi Securities Exchange. Research Journal of Finance and Accounting, 4(4), pp.1-17.
[42]  Pagan, A. & Sossounov, K., 2003. A simple framework for analysing bull and bear markets. Journal of Applied Econometrics, (June).
[43]  Parvaresh, M. & Bavaghar, M., 2012. Forecasting Volatility in Tehran Stock Market with GARCH Models. Journal of Basic and Applied Scientific Research, 2(1), pp.150-155.
[44]  Pele, D.T. & Tepuș, A.-M., 2011. Information entropy and efficient market hypothesis. In International Conference On Applied Economics. pp. 463-472.
[45]  Ray, S., 2012. Revisiting the Strength of Dow Theory in Assessing Stock Price Movement. Advances in Applied Economics and Finance (AAEF), 3(3), pp.591-598.
[46]  Suleman, M.T., 2012. Stock Market Reaction to Good and Bad Political News. Asian Journal of Finance & Accounting, 4(1), pp.299-312.
[47]  Timmermann, A. & Granger, C.W.J., 2004. Efficient market hypothesis and forecasting. International Journal of Forecasting, 20(1), pp.15-27.
[48]  Tripathy, T. & Gil-Alana, L.A., 2010. Suitability of Volatility Models for Forecasting Stock Market Returns: A Study on the Indian National Stock Exchange. American Journal of Applied Sciences, 7(11), pp.1487-1494.
[49]  Vijayalakshmi, S. & Gaur, S., 2013. Modeling Volatility: Indian Stock and Foreign Exchange Markets. Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB), (1), pp.583-598.
[50]  Wagala, A. et al., 2012. Volatility Modelling of the Nairobi Securities Exchange Weekly Returns Using the Arch-Type Models. International Journal of Applied Science and Technology, 2(3), pp.165-174.
[51]  Woodward, G. & Anderson, H.M., 2009. Does beta react to market conditions? Estimates of “bull” and “bear” betas using a nonlinear market model with an endogenous threshold parameter. Quantitative Finance, 9(8), pp.913-924.
[52]  Yaya, O.S., Shittu, O.I. & Tumala, M.M., 2013. Estimates of Bull and Bear parameters in Smooth Threshold Parameter Nonlinear Market model: A Comparative study between Nigerian and Foreign Stock Markets. European Journal of Business and Management, 5(7), pp.107-123.