How to cite this paper
Lahmiri, S. (2012). An EGARCH-BPNN system for estimating and predicting stock market volatility in Morocco and Saudi Arabia: The effect of trading volume.Management Science Letters , 2(4), 1317-1324.
Refrences
Atsalakis, G.S., & Valavanis, K.P. (2009). Surveying stock market forecasting techniques – Part II: Soft computing methods. Expert Systems with Applications, 36 (3), 5932-5941.
Bildirici, M., & ?zgür, ?.E. (2009). Improving forecasts of GARCH family models with the artificial neural networks: An application to the daily returns in Istanbul Stock Exchange. Expert Systems with Applications, 36, 7355-7362.
Bohl, M.T., & Henke, H. (2003). Trading volume and stock market volatility: The Polish Case. International Review of Financial Analysis, 12, 513-525.
Bollerslev, T. (1986). Generalized Autoregressive Conditional Hetroscedasticity. Journal of Econometrics, 31, 307-327.
Brooks, C. (1998). Predicting stock index volatility: Can Market Volume help? Journal of Forecasting, 17, 59-98.
Clark, P.K. (1973). A subordinated stochastic process model with finite variance for speculative prices. Econometrica, 41, 135-156.
Davidson, R., & Mackinnon, J.G. (2008). Econometric Theory and Methods. Oxford University Press, International Edition.
Epps, T. & Epps, M. (1976). The stochastic dependence of stochastic price changes and transaction volume: Implications for the mixture of distribution hypothesis, Econometrica, 44, 305-321.
Girard, E., & Biswas, R. (2007). Trading volume and market volatility: developed versus emerging stock markets. The Financial Review, 42, 429-459.
Hajizadeh E., Seifi, A., Zarandi Fazel M.H., & Turksen, I.B. (2012). A hybrid modeling approach for forecasting the volatility of S & P 500 index return. Expert Systems with Applications, 39, 431-436.
Hamid, S.A., & Zahid, I. (2002). Using neural networks for forecasting volatility of S & P 500 index futures prices. Journal of Business Research, 5881, 1-10.
Hu, M.Y., & Tsoukalas, C. (1999). Combining conditional volatility forecasts using neural networks: An application to the EMS exchange rates. Journal of International Financial Markets, Institution and Money, 9, 407-422.
Huang, B.-N., & Yang, C.-W. (2001). An empirical investigation of trading volume and return volatility of the Taiwan stock market. Global Finance Journal, 12, 55-77.
Hung, J.-C. (2011). Applying a combined fuzzy systems and GARCH model to adaptively forecast stock market volatility. Applied Soft Computing, 11, 3938-3945.
Lamoureux, C.G., & Lastrapes, W.D. (1990). Heteroskedasticity in stock returns data: volume versus GARCH effects. The Journal of Finance, 45 (1), 221-229.
Lee, B.-S., & Rui, O.M. (2002). The dynamic of relationship between stock returns and trading volume: domestic and cross-country evidence. Journal of Banking and Finance, 26, 51-78.
McKenzie, E., & Omran, M.F. (2000). Heteroskedasticity in stock returns data revisited: volume versus GARCH effects. Applied Financial Economics, 10, 553-560.
Nelson, D.B. (1991). Conditional heteroskedasticity in asset returns: a new approach. Econometrica, 59, 347-370.
Nocedal, J., & Wright, S.J. (2000). Numerical optimization. Springer.
Roh, T.H. (2007). Forecasting the volatility of stock price index. Expert Systems with Applications, 33, 916-922.
Rumelhart, D.E., Hinton, G.E., & Williams, R.J. (1986). Learning internal representations by error propagation. In Rumelhart, D.E. and J.L. McClelland, eds. Parallel distributed processing:explorations in the microstructure of cognition. Cambridge, MA, MIT Press, 318-362.
Subbotin, M.T. (1923). On the law of frequency error. Matematicheskii Sbornik, 31, 296-301.
Tseng, C.-H., Cheng, S.-T., & Wang, Y.-H. (2009). New Hybrid Methodology for Stock Volatility Prediction. Expert Systems with Applications, 36 (2), 1833-1839.
Wang, C.P., Lin, S.H., Hung-Hsi Huang, H.H., & Wu, P.C. (2011). Using neural network for forecasting TXO price under different volatility models. Expert Systems with Applications, 39(5), 5025–5032.
Bildirici, M., & ?zgür, ?.E. (2009). Improving forecasts of GARCH family models with the artificial neural networks: An application to the daily returns in Istanbul Stock Exchange. Expert Systems with Applications, 36, 7355-7362.
Bohl, M.T., & Henke, H. (2003). Trading volume and stock market volatility: The Polish Case. International Review of Financial Analysis, 12, 513-525.
Bollerslev, T. (1986). Generalized Autoregressive Conditional Hetroscedasticity. Journal of Econometrics, 31, 307-327.
Brooks, C. (1998). Predicting stock index volatility: Can Market Volume help? Journal of Forecasting, 17, 59-98.
Clark, P.K. (1973). A subordinated stochastic process model with finite variance for speculative prices. Econometrica, 41, 135-156.
Davidson, R., & Mackinnon, J.G. (2008). Econometric Theory and Methods. Oxford University Press, International Edition.
Epps, T. & Epps, M. (1976). The stochastic dependence of stochastic price changes and transaction volume: Implications for the mixture of distribution hypothesis, Econometrica, 44, 305-321.
Girard, E., & Biswas, R. (2007). Trading volume and market volatility: developed versus emerging stock markets. The Financial Review, 42, 429-459.
Hajizadeh E., Seifi, A., Zarandi Fazel M.H., & Turksen, I.B. (2012). A hybrid modeling approach for forecasting the volatility of S & P 500 index return. Expert Systems with Applications, 39, 431-436.
Hamid, S.A., & Zahid, I. (2002). Using neural networks for forecasting volatility of S & P 500 index futures prices. Journal of Business Research, 5881, 1-10.
Hu, M.Y., & Tsoukalas, C. (1999). Combining conditional volatility forecasts using neural networks: An application to the EMS exchange rates. Journal of International Financial Markets, Institution and Money, 9, 407-422.
Huang, B.-N., & Yang, C.-W. (2001). An empirical investigation of trading volume and return volatility of the Taiwan stock market. Global Finance Journal, 12, 55-77.
Hung, J.-C. (2011). Applying a combined fuzzy systems and GARCH model to adaptively forecast stock market volatility. Applied Soft Computing, 11, 3938-3945.
Lamoureux, C.G., & Lastrapes, W.D. (1990). Heteroskedasticity in stock returns data: volume versus GARCH effects. The Journal of Finance, 45 (1), 221-229.
Lee, B.-S., & Rui, O.M. (2002). The dynamic of relationship between stock returns and trading volume: domestic and cross-country evidence. Journal of Banking and Finance, 26, 51-78.
McKenzie, E., & Omran, M.F. (2000). Heteroskedasticity in stock returns data revisited: volume versus GARCH effects. Applied Financial Economics, 10, 553-560.
Nelson, D.B. (1991). Conditional heteroskedasticity in asset returns: a new approach. Econometrica, 59, 347-370.
Nocedal, J., & Wright, S.J. (2000). Numerical optimization. Springer.
Roh, T.H. (2007). Forecasting the volatility of stock price index. Expert Systems with Applications, 33, 916-922.
Rumelhart, D.E., Hinton, G.E., & Williams, R.J. (1986). Learning internal representations by error propagation. In Rumelhart, D.E. and J.L. McClelland, eds. Parallel distributed processing:explorations in the microstructure of cognition. Cambridge, MA, MIT Press, 318-362.
Subbotin, M.T. (1923). On the law of frequency error. Matematicheskii Sbornik, 31, 296-301.
Tseng, C.-H., Cheng, S.-T., & Wang, Y.-H. (2009). New Hybrid Methodology for Stock Volatility Prediction. Expert Systems with Applications, 36 (2), 1833-1839.
Wang, C.P., Lin, S.H., Hung-Hsi Huang, H.H., & Wu, P.C. (2011). Using neural network for forecasting TXO price under different volatility models. Expert Systems with Applications, 39(5), 5025–5032.