Modelling the Stock Price Volatility Using Asymmetry Garch and Ann-Asymmetry Garch Models
International Journal of Data Science and Analysis
Volume 4, Issue 4, August 2018, Pages: 46-52
Received: Sep. 19, 2018; Accepted: Oct. 9, 2018; Published: Oct. 23, 2018
Views 1150      Downloads 121
Henry Njagi, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Anthony Gichuhi Waititu, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Anthony Wanjoya, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Article Tools
Follow on us
The stock price in developing countries, especially in Kenya, has become one of the market that supports the economy growth of a country. Due to the political instabilities in the Kenyan contest, stock price markets have been affected. As a consequence of the instabilities in the financial markets, this paper model the volatility associated with the stock price for a one day ahead volatility forecast which will help in risk control in the market. This is accomplished by using the asymmetry GARCH and ANN-asymmetry GARCH models. The residuals obtained from artificial neural network are used when fitting ANN- asymmetry GARCH models. It was found that returns on the selected companies in NSE are categorized by volatility clustering, leptokurtosis and asymmetry. In the modelling, we further examine the performance of the leading alternatives with the daily log returns residuals of the leading companies in Kenyan stock market (PAFR, PORT and EGAD) from the period January 2006 to November 2017 for trading days excluding weekends and holidays. The root mean squared error indicated that among the available models i.e. ANN-EGARCH model, GJR-GARCH and EGARCH model, ANN-GJR-GARCH model performed better in modelling and forecasting the stock price volatility in Kenyan contest. The paper demonstrates that combined machine learning and statistical models can effectively model stock price volatility and make reliable forecasts.
Volatility, Rmse, Ann and Asymmetry Garch Models
To cite this article
Henry Njagi, Anthony Gichuhi Waititu, Anthony Wanjoya, Modelling the Stock Price Volatility Using Asymmetry Garch and Ann-Asymmetry Garch Models, International Journal of Data Science and Analysis. Vol. 4, No. 4, 2018, pp. 46-52. doi: 10.11648/j.ijdsa.20180404.11
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
S. N. N. &. S. K. Tayal, "Role of big data in make in India.," Advances in Intelligent Systems and Computing, pp. 564, 531- 437, 2018.
E. Siegel, "Predictive analytics: The power to predict who will click, buy, lie or die," Wiley India Pvt. ltd., New Delphi., pp. ISBN: 978-81-265-5977-0, 2016.
F. BLACK, "Studies of stock price volatility changes," Proceedings of the 1976 meetings of the American Statistical Association. Business and Economical Statistical section., pp. 177-181, 1976.
D. B. Nelson, "Conditional Heteroscedasticity in Asset Returns: A New Approach," Econometrica, vol. 59, no. 2, pp. 347-370, 1991.
Glosten, Jagannathan and Runkel, "The Relationship between the expected value and volatility of nominal excess returns," Journal of Finance, vol. 48, pp. 1779-1801, 1993.
C. BROOKS, "Introductory Econometrics for Finance (2e).," Cambridge University Press:, 2008.
M. M. M. R. a. S. C. LESAONA, "Modelling volatility and financial market risk of shares on the Johannesburg stock exchange.," African Journal of Business Management., pp. 6: 8065-8070., 2012.
P. Alagidede, "Return behaviour in Africa's emerging equity markets.," Quarterly Review of Economics and Finance, pp. 51(2): 133-140, 2011.
K. a. A. S. Emenik, "Modeling asymmetric volatility in the Negerian Stock Exchange.," European Journal of Business and Management, pp. 4(12): 52-61, 2012.
T. a. F. R. Brailsford, "An evaluation of volatility forecasting techniques.," Journal of Banking and Finance, pp. 20:419-438, 1996.
F. a. F. O. Magnus, "Modelling and Forecasting Volatility of Returns on the Ghana Stock Exchange Using GARCH Models.," American Journal of Applied Sciences., pp. 3(10): 2042-2048, 2006.
B. N. N. Siddikee M N, "Volatility of Dhaka stock exchange returns by GARCH models.," Asian Business Review,, pp. 8(5): 220-229, 2016.
G. C. W. Poon S H, "Forecasting Volatility in Financial markets:," A review journal of Economic Literature,, pp. 41(2):478-539, 2003.
D. A., "A study of the NSE's volatility for very small period using asymmetric GARCH models. Vilakshan:," The XIMB Journal of Management,, pp. 7(2): 107-120, 2010,.
H. J. C. Liu H C, "Forecasting S & P-100 stock index volatility: The role of volatility asymmetry and distributional assumption in GARCH models.," Expert Systems with Applications, pp. 37(7);4928-4934, 2010,.
B. B. E. E. I. I. O. Onwukwe C E, "On modelling the volatility of Nigerian stock returns using GARCH models.," Journal of Mathematics Research,, pp. 3(4):31-43, 2011,.
H. White, "Economic prediction using neural networks: The case of IBM stock prices.," Proceedings of the Second Annual IEEE Conference on Neural Networks, pp. Networks, pp. II: 451 458. New York: IEEE Press, 1988.
E. Schoeneburg, "Stock Price Prediction Using Neural Networks:" A Project Report, Neurocomputing,, pp. Vol. 2, pp. 17-27, 1990.
Bollerslev, "Generalized Autoregressive Condtional Heteroscedasticity," Journal of Econometrics, 1986.
T. L. B. F. Z. W. T. Q. Z. Guo, "Learning sentimental weights of mixedgram terms for classification and visualization.," PRICAI 2016, LNAI 9810, pp. pp. 116-124, 2016.
M. S. M. L. K. &. V. V. Sigo, "Forecasting the Stock Index Movements of India: Application of Neural Network.," International journal of Soft Computing, pp. 12(2), 120-131, 2017.
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
Tel: (001)347-983-5186