An ensemble system based on hybrid EGARCH-ANN with different distributional assumptions to predict s&P 500 intraday volatility

Fluctuation and Noise Letters

Volumn 14, Issue 1, 2015, Pages

  Lahmiri, S ^a   Boukadoum, M ^b  

^a ESCA School of Management   (Morocco)

^b UNIVERSITÉ DU QUÉBEC À MONTRÉAL   (Canada)

Author keywords

EGARCH; ensemble; forecasting; neural networks; Stock market; volatility

Indexed keywords

EID: 84929168318     PISSN: 02194775     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0219477515500017     Document Type: Article

References (25)

1. R. F. Engle, Autoregressive conditional heteroscedasticity with estimates of the variance of UK inflation, Econometrica 50 (1982) 987-1008.
2. T. Bollerslev, Generalized heteroskedastic time series model for speculative prices and rates of return, Rev. Econom. Statist. 69 (1986) 542-547.
3. K. Chan, K. C. Chan and G. A. Karolyi, Intraday volatility in the stock index and stock index futures markets, Rev. Fin. Studies 4 (1991) 657-684.
4. Y.-H. Lee and T.-Y. Pai, REIT volatility prediction for skew-GED distribution of the GARCH model, Expert Syst. Appl. 37 (2010) 4737-4741.
5. T. Andersen and T. Bollerslev, Answering the skeptics: Yes, standard volatility models do provide accurate forecasts, Int. Econom. Rev. 39 (1998) 885-905.
6. T. Andersen, T. Bollerslev and S. Lange, Forecasting financial market volatility: Sample frequency vis-à-vis forecast horizon, J. Empirical Finan. 6 (1998) 457-477.
7. T. Andersen, T. Bollerslev, F. Diebold and H. Ebens, The distribution of stock return volatility, J. Fin. Econom. 61 (2001) 43-76.
8. E. Hajizadeh, A. Seifi, M. H. Fazel Zarandi and I. B. Turksen, A hybrid modeling approach for forecasting the volatility of S&P 500 index return, Expert Syst. Appl. 39 (2012) 431-436.
9. S. Haykin, Neural Networks and Learning Machines (Prentice Hall, USA, 2008).
10. R. Tae Hyup, Forecasting the volatility of stock price index, Expert Syst. Appl. 33 (2007) 916-922.
11. M. Bildirici and O. O. Ersin, Improving forecasts of GARCH family models with the artificial neural networks: An application to the daily returns in Istanbul stock exchange, Expert Syst. Appl. 36 (2009) 7355-7362.
12. W. Kristjanpoller, A. Fadic and M. C. Minutolo, Volatility forecast using hybrid neural network models, Expert Syst. Appl. 41 (2014) 2437-2442.
13. D. B. Nelson, Conditional heteroskedasticity in asset returns: A new approach, Econo-metrica 59 (1991) 347-370.
14. J. Lopez, Evaluating the predictive accuracy of variance models, J. Forecast. 20 (2001) 87-109.
15. J. Marcucci, Forecasting stock market volatility with regime-switching GARCH models, Studies Nonlinear Dynam. Economet. 9 (2005) 1-53.
16. B. M. A. Awartani and V. Corradi, Predicting the volatility of the S&P-500 stock index via GARCH models: The role of asymmetries, Int. J. Forecast. 21 (2005) 167-183.
17. T. Evans and D. G. McMillan, Volatility forecasts: The role of asymmetric and long-memory dynamics and regional evidence, Appl. Finan. Econom. 17 (2007) 1421-1430.
18. H.-C. Liu and J.-C. Hung, Forecasting S&P-100 stock index volatility: The role of volatility asymmetry and distributional assumption in GARCH models, Expert Syst. Appl. 37 (2010) 4928-4934.
19. C. W. Chong, M. I. Ahmad and M. Y. Abdullah, Performance of GARCH models in forecasting stock market volatility, J. Forecast. 18 (1999) 333-343.
20. G. F. Loudon, W. H. Watt and P. K. Yadav, An empirical analysis of alternative parametric ARCH models, J. Appl. Economet. 2 (2000) 117-136.
21. D. E. Rumelhart, G. E. Hinton and R. J. Williams, Learning representations by back-propagating errors, Nature 323 (1986) 533-536.
22. J. Nocedal and S. J. Wright, Numerical Optimization (Springer, Berlin, 2006).
23. U. Anders, O. Korn and C. Schmitt, Improving the pricing of options: A neural network approach, J. Forecast. 17 (1998) 369-388.
24. H. Amilon, A neural network versus Black-Scholes: A comparison of pricing and hedging performances, J. Forecast. 22 (2003) 317-335.
25. G. E. P. Box, G. M. Jenkins and G. C. Reinsel, Time Series Analysis: Forecasti ng and Control (Prentice Hall, Englewood Cliffs, NJ, 1994).