Asymmetry and Persistence of Stock Returns: A Case of the Ghana Stock Exchange
International Journal of Business and Economics Research
Volume 5, Issue 6, December 2016, Pages: 183-190
Received: May 19, 2016; Accepted: May 31, 2016; Published: Nov. 11, 2016
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Authors
Abonongo John, College of Science, Department of Mathematics, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
Oduro F. T., College of Science, Department of Mathematics, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
Ackora-Prah J., College of Science, Department of Mathematics, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
Luguterah Albert, Faculty of Mathematical Sciences, Department of Statistics, University for Development Studies, Navrongo, Ghana
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Abstract
Measuring and estimating volatility of asset return is bubbly for risk management, asset allocation, and option pricing. This paper investigated the asymmetry and persistence of the return of some stocks on the Ghana Stock Exchange using univariate TGARCH-M (1, 1) and half-life measure of the daily returns of eight stocks from 02/01/2004 to 20/12/2014. It was realized that, volatility was persistent (explosive process) in all the stocks. The persistence in volatility was extended in investigating the half-life measure of the stocks and it was realized that almost all the stocks had strong mean reversion and short half-life measure with the exception of Fan Milk Limited. Also all the returns series exhibited a positive leverage effect parameter indicating that bad news influenced volatility than good news of the same magnitude.
Keywords
Asymmetry, Persistent, Half-Life, Volatility, Leverage Effect
To cite this article
Abonongo John, Oduro F. T., Ackora-Prah J., Luguterah Albert, Asymmetry and Persistence of Stock Returns: A Case of the Ghana Stock Exchange, International Journal of Business and Economics Research. Vol. 5, No. 6, 2016, pp. 183-190. doi: 10.11648/j.ijber.20160506.11
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Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
References
[1]
Ahmed, E. and Suliman, Z. (2011). Stock market volatility using garch models: Evidence from sudan. International Journal of Business and Social Science, 2(23):114–128.
[2]
Banerjee, A. and Sarkar, S. (2006). Modelling daily volatility of the indian stock market using intra-day data. IIM CALCUTTA, Working paper.
[3]
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity, north holland. Journal of Econometrics, 31(3):307–327.
[4]
Cater, R., Hill, E., and William, C. (2007). Principles of Econometrics, 3rd Edition New York. John Wiley and Sons, Inc.
[5]
[5] Cheong, C. (2009). Modelling and forecasting crude oil markets using arch-type models. Energy Policy, 37(6):2346–2355.
[6]
Chinzara, Z. and Aziakpono, M. (2009). Dynamic rreturn linkages and volatility transmission between south aafrica and world major stock markets. Studies in Economics and Econometrics, 33(3):69–94.
[7]
Durbin, J. and Watson, G. (1950). Testing for serial correlation in least square regression. Econometrica, 37:409–428.
[8]
Engle, R. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of the united kingdom inflation. Econometrica, 50:987–1007.
[9]
Engle, R., Lilien, D., and Robins, R. (1987). Estimating time varying risk premia in the term structure, the arch-m model. Econometrica, 55(2):391–407.
[10]
Engle, R. and Ng, V. (1993). Measuring and testing the impact of news on volatility. Journal of Finance, 48:1749–1778.
[11]
Engle, R. and Patton, A. (2001). What good is a volatility model? Quantitative Finance, 1(2):237–245.
[12]
Eview, M. (2007). Help system.
[13]
Glosten, L., Jagannathan, R., and Runkle, D. (1993). On the relation between expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48:1779–1801.
[14]
Hasan, M., Selin, A., and Fazle, R. (2013). Asymmetry and persistence of energy price volatility. International Journal of Finance and Accounting, 2(7):373–378.
[15]
Jarque, C. and Bera, A. (1987). A test of nornormal of observations and regression residuals. International Statistical Review, 55:163–172.
[16]
Kalu, E. and Friday A. S (2012). Modelling Asymmetry Volatility in Nigerian Stock Exchange. Journal of Business and Management 4(12): 52-60.
[17]
Kang, S., Kang, M., and Yoon, M. (2009). Forecasting oil price volatility. Energy Economics, 31(1):119–125.
[18]
Kumar, D. and 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):123-136.
[19]
Kwiatkowsky, D., Phillips, P., P., and Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54(13):159–178.
[20]
Lee, J. and King, M. (1993). A locally most powerful based score test for arch and garch regression distrubances. Journal of Business and Economic Statistics, 7:259–279.
[21]
Ljung, G. and Box, G. (1978). On the measure of lack of fit in time series models. Econometrica, 65(2):297–303.
[22]
Magnus, F. and Fosu, O. (2006). Modelling and forecasting volatility of returns on the ghana stock exchange using garch models. American Journal of Applied Science, 3(10):2042–2048.
[23]
Narayan, P. and Narayan, S. (2007). Modelling oil price volatility. Energy Policy, 35(12):6549–6553.
[24]
Nelson, D. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2):347–370.
[25]
Ogega H. O and Freshia M. W (2016). Analysis of Asymmetric and Persistence in Stock Return Volatility in the Nairobi Security Exchange Market Phases. Journal of Finance and Economics, 4(3):63-73.
[26]
Pindyck, R. (2004). Volatility in natural gas and oil markets. Journal of Energy and Development, 30(1):1–19.
[27]
Poon and Granger (2003). Forecasting volatility in financial markets. Journal of Economic Literature, 41:2–478.
[28]
Salisu, A. and Fasanya, I. (2013). Modelling oil price volatility with structural breaks. Energy Policy, 52(2):554–562.
[29]
Serletis, A. and Andreadis, I. (2004). Random fractal structures in north american energy markets. Energy Economics, 26(3):389–399.
[30]
Tabak, B. and Cajueiro, D. (2007). Are markets oil markets becoming weakly efficient over time? a test for time-varying long-rang dependence in price and volatility. Energy Economics, 29(1):28–36.
[31]
Takaendesa, P., Tsheole, P., and Aziakpono, M. (2006). Real exchange rate volatility and its effect on trade flows. new evidence from south africa. Studies in Economics and Econometrics, 30(3):79–97.
[32]
Zakoian, J. (1994). Threshold heteroscedastic models. Journal of Economics Dynamics and Control, 18:931–944.
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