Volatility forecast using hybrid Neural Network models

Expert Systems with Applications

Volumn 41, Issue 5, 2014, Pages 2437-2442

  Kristjanpoller, Werner ^a   Fadic, Anton ^a   Minutolo, Marcel C ^b  

^a DEPARTAMENTO DE FÍSICA   (Chile)

^b ROBERT MORRIS UNIVERSITY   (United States)

Author keywords

American stock markets; Artificial Neural Networks; Emerging markets; GARCH models; Latin; Risk forecast

Indexed keywords

EMERGING MARKETS; GARCH MODELS; LATIN; RISK FORECASTS; STOCK MARKET;

COMMERCE; NEURAL NETWORKS;

FORECASTING;

EID: 84890119956     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2013.09.043     Document Type: Article

References (18)

1. M. Bildirici, and O. Ersin Improving forecasts of GARCH family models with the artificial neural networks: An application to the daily returns in Istanbul stock exchange Expert System with Applications 36 2009 7355 7362
2. T. Bollerslev Generalized autoregressive conditional heteroskedasticity Journal of Econometrics 31 1986 307 327
3. G. Donaldson, and M. Kamstra An artificial neural network-GARCH model for international stock return volatility Journal of Empirical Finance 4 1 1997 17 46 (Pubitemid 127341009)
4. R. Engle Garch 101: The use of ARCH/GARCH models in applied econometrics Journal of Economic Perspectives 15 4 1982 157 168
5. R. Gençay Non-linear prediction of security returns with moving average rules Journal of Forecasting 15 3 1996 165 174 (Pubitemid 126628171)
6. E. Hajizadeh, A. Seifi, H. Zarandi, and B. Turksen A hybrid modelling for forecasting the volatility of S&P 500 index return Expert Systems with Applications 39 2012 431 436
7. A. Hamid, and Z. Iqbal Using neural networks for forecasting volatility of S&P500 index future prices Journal of Business Research 57 10 2004 1116 1125 (Pubitemid 39167974)
8. K.M. Hornik, M. Stinchcombe, and H. White Multilayer feedforward networks are universal approximators Neural Networks 2 2 1989 359 366 (Pubitemid 20609008)
9. L. Kryzanowski, M. Galler, and W. Wright Using artificial neural networks to pick stocks Financial Analyst Journal 49 1993 21 27
10. H. Markowitz Portfolio selection Journal of Finance 7 1 1952 77 91
11. Ormoneit, D., & Neuneier, R. (1996). Experiments in predicting the German stock index DAX with density estimating neural networks. In Proceedings of the IEEE/IAFE 1996 conference on computational intelligence in financial engineering (pp. 66-71).
12. M. Qi Nonlinear predictability of stock returns using financial and economic variables Journal of Business & Economic Statistics 17 1999 419 429
13. M. Qi, and G.S. Maddala Economic factors and the stock market: A new perspective Journal of Forecasting 18 1999 151 166 (Pubitemid 129664173)
14. H. Roh Forecasting the volatility of stock price index Expert Systems with Applications 33 2007 916 922
15. C. Schittenkopf, G. Dorffner, and E. Dockner Forecasting time-dependent conditional densities: A semi-non-parametric neural network approach Journal of Forecasting 19 2000 355 374
16. S. Taylor Modelling financial time series 1986 Wiley New York
17. H. Tseng, T. Cheng, H. Wang, and T. Peng Artificial neural network of the hybrid EGARCH volatility of the Taiwan stock price index option prices Physica A 387 2008 3192 3200
18. Yoon, Y., & Swales, G (1991). Predicting stock price performance. A Neural Network approach. In 24th Hawaii International conference on system sciences.