Nazarian, Rafik and Gandali Alikhani, Nadiya and Naderi, Esmaeil and Amiri, Ashkan
(2013):
*Forecasting Stock Market Volatility: A Forecast Combination Approach.*

Preview |
PDF
MPRA_paper_46786.pdf Download (376kB) | Preview |

## Abstract

Recently, with the development of financial markets and due to the importance of these markets and their close relationship with other macroeconomic variables, using advanced mathematical models with complicated structures for forecasting these markets has become very popular. Besides, neural network models have gained a special position compared to other advanced models due to their high accuracy in forecasting different variables. Therefore, the main purpose of this study was to forecast the volatilities of TSE index by regressive models with long memory feature, feed forward neural network and hybrid models (based on forecast combination approach) using daily data. The results were indicative of the fact that based on the criteria for assessing forecasting error, i.e., MSE and RMSE, although forecasting errors of the feed forward neural network model were less than ARFIMA-FIGARCH model, the accuracy of the hybrid model of neural network and best GARCH was higher than each one of these models.

Item Type: | MPRA Paper |
---|---|

Original Title: | Forecasting Stock Market Volatility: A Forecast Combination Approach |

English Title: | Forecasting Stock Market Volatility: A Forecast Combination Approach |

Language: | English |

Keywords: | Stock Return, Long Memory, Neural Network, Hybrid Models. |

Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related Topics C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods |

Item ID: | 46786 |

Depositing User: | esmeil naderi |

Date Deposited: | 07 May 2013 05:35 |

Last Modified: | 26 Sep 2019 15:52 |

References: | [1] Abounoori, A. A., Naderi, E., Gandali Alikhani, N., Amiri, A. (2013). Financial Time Series Forecasting by Developing a Hybrid Intelligent System. European Journal of Scientific Research 4.98, PP. 529-541. [2] Baillie, R.T., Bollerslev, T., Mikkelsen, H.O. (1996). Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics 74, PP. 3–30. [3] Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics 31(3), PP. 307-327. [4] Bollerslev, T., Russell, J. R., Watson, M. W. (2010), Volatility and Time Series Econometrics: Essay in Honor of Robert F. Engel, Oxford University Press. [5] Chiang, Y. M., Chang, L. Ch., Chang, F.J. (2004). Comparison of static-feedforward and dynamic-feedback neural networks for rainfall–runoff modeling. Journal of Hydrology, 290(3–4), PP. 297-311. [6] Chuang, W.I., Liu, H.H., Susmel, R. (2012). The bivariate GARCH approach to investigating the relation between stock returns, trading volume, and return volatility. Global Finance Journal, In Press, Available online 13, Elsevier. [7] Cybenko, G. (1989). Approximations by superpositions of sigmoidal functions. Mathematics of Control. Signals and Systems 2(4), PP. 303-314. [8] Deng, J. (2013). Dynamic Neural Networks with Hybrid Structures for Nonlinear System Identification. Engineering Applications of Artificial Intelligence 26(1), PP. 281–292. [9] Ding, Z., Granger, C. W. J. (1996). Modeling Volatility Persistence of Speculative Returns: A New Approach. Journal of Econometrics 73, PP 185–215. [10] Engle, R. F. (1982). Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of UK Inflation. Econometrica 50, PP. 987–1008. [11] Farley, B., Clark, W.A. (1954). Simulation of Self-Organizing Systems by Digital Computer. IRE Transactions on Information Theory 4(4), PP. 76–84. [12] Funahashi, K. I. (1989). On the Approximate Realization of Continuous Mappings by Neural Networks. Neural Networks 2(3), PP. 183–192. [13] Georgescu, V., Dinucă, E. C. (2011). Evidence of Improvement in Neural-Network Based Predictability of Stock Market Indexes through Co-movement Entries. Recent Advances in Applied & Biomedical Informatics and Computational Engineering in Systems Applications, 11th WSEAS International Conference on Applied Informatics and Communications, Florence, Italy, PP.412-417. [14] Geweke, J., Porter-Hudak, S. (1983). The Estimation and Application of Long-Memory Time Series Models. Journal of Time Series Analysis 4, PP. 221–238. [15] Ghiassi, M., Zimbra, D. K., Saidane, H. (2006). Medium term system load forecasting with a dynamic artificial neural network model. Electric Power Systems Research 76(5), PP. 302-316. [16] Granger, C. W. J., Joyeux, R. (1980). An introduction to long memory time series models and fractional differencing. Journal of Time Series Analysis 1, PP. 15-29. [17] Guresen, E., Kayakutlu, G. (2008). Forecasting Stock Exchange Movements Using Artificial Neural Network Models and Hybrid Models. The International Federation for Information Processing 288, PP. 129-137. [18] Guresen, E., Kayakutlu, G., Daim, U. T. (2011). Using Artificial Neural Network Models in Stock Market Index Prediction. Expert Systems with Applications 38(8), PP. 10389-10397. [19] Hornik, K., Stinchcombe, M., White, H. (1990). Universal Approximation of an Unknown Mapping and its Derivatives Using Multilayer Feedforward Networks, Neural Networks 3(5), PP. 551-560. [20] Hosking, J.R.M. (1981). Fractional differencing, Biometrika 68, PP.165–176. [21] Hurst, H.R. (1951). Long-Term Storage in Reservoirs. Transactions of the American Society of Civil Engineers 116, PP. 770-799. [22] Kittiakarasakun, J., Tse, Y. (2011). Modeling the fat tails in Asian stock markets. International Review of Economics and Finance 20, PP. 430–440. [23] Krose, B., Smagt, P. (1996). An introduction to neural network. The University of Amsterdam. Eighth edition. [24] Kumar, K. J., Kailas, A. (2012). Prediction of Future Stock Close Price using Proposed Hybrid ANN Model of Functional Link Fuzzy Logic Neural Model. IAES International Journal of Artificial Intelligence, 1(1), PP. 25-30. [25] Lee, J.W., Lee, K.E., Rikvold, P.A. (2006). Multifractal Behavior of the Korean Stock Market Index KOSPI. Physica A: Statistical Mechanics and its Applications 364, PP. 355-361. [26] Lento, C. (2009). Long-term Dependencies and the Profitability of Technical Analysis. International Research Journal of Finance and Economics 269, PP. 126-133. [27] Los, C.A., Yalamova, R. (2004). Multifractal Spectral Analysis of the 1987 Stock Market Crash, Working Paper Kent State University, Department of Finance. [28] Majid D., Gandali Alikhani N. Esmaeil N. (2013). Do Dynamic Neural Networks Stand a Better Chance in Fractionally Integrated Process Forecasting?. International Journal of Economics and Financial Issues, Econjournals 3(2), PP. 466-475. [29] Mandelbrot, B.B. (1999). A Multifractal Walk Down Wall Street. Scientific American 280(2), PP. 70-73. [30] Mehrara, M., Moeini, A., Ahrari, M. Ghafari, A. (2010). Using Technical Analysis with Neural Network for Prediction Stock Price Index in Tehran Stock Exchange. Middle Eastern Finance and Economics 6(6), PP. 50-61. [31] Merh, N., Saxena, V. P., Pardasani, K. R. (2010). A Comparison between Hybrid Approaches of Ann and Arima for Indian Stock Trend Forecasting. Business Intelligence Journal 3(2), PP. 23-43. [32] Nielsen, H. (1987). Kolomogorr’s Mapping Neural Network Existence Theorem, In IEEE First Annual International Conference on Neural Networks 3, PP. 11-14. [33] Onali, E., Goddard, J. (2009). Unifractality and Multifractality in the Italian Stock Market. International Review of Financial Analysis 18(4), PP. 154-163. [34] Pai, P. F., Lin, Ch. Sh. (2005). A hybrid ARIMA and support vector machines model in stock price forecasting, Omega 33(6), PP. 497-505. [35] Poon, H., Granger, C. W. J. (2003). Forecasting Volatility in Financial Markets: A Review. Journal of Economic Literature XLI, PP. 478–539. [36] Ripley, B.D. (1996). Pattern Recognition and Neural Networks, Cambridge University Press. [37] Rochester, N., Holland, J.H., Habit, L.H., Duda, W.L. (1956). Tests on a cell assembly theory of the action of the brain, using a large digital computer. IRE Transactions on Information Theory 2(3), PP. 80–93. [38] Rosenblatt, F. (1958). The Perceptron: A Probalistic Model for Information Storage and Organization in the Brain. Psychological Review 65(6), PP. 386–408. [39] Sahin, S., Tolu, M.R., Hassanpour, R. (2012). Hybrid expert systems: A survey of current approaches and applications. Expert Systems with Applications 39(4), PP. 4609-4617. [40] Sheta, A. F., Jong, K. D. (2001). Time-series forecasting using GA-tuned radial basis functions. Information Sciences 133(3–4), PP. 221–228. [41] Soni, S. (2011). Applications of ANNs in Stock Market Prediction: A Survey. International Journal of Computer Science & Engineering Technology 2(3), pp. 71-83. [42] Sui, X., Hu, Q., Yu, D., Xie, Z., Qi, ZH. (2007). A Hybrid Method for Forecasting Stock Market Trend Using Soft-Thresholding De-noise Model and SVM. Lecture Notes in Computer Science 4482, PP. 387-394. [43] Swanson, D.A., Tayman, J., Bryan, T.M. (2011). MAPE-R: a Rescaled Measure of Accuracy for Cross-Sectional Subnational Population Forecasts. J Pop Research 28, PP. 225–243. [44] Tong-Seng Q. (2007). Using Neural Network for DJIA Stock Selection. Engineering Letters 15(1). PP. 15-31. [45] Tsoi, A. Ch., Back, A. (1995). Static and dynamic preprocessing methods in neural networks. Engineering Applications of Artificial Intelligence 8(6), PP. 633–642. [46] Ullah Khan, A., Gour, B. (2013). Stock Market Trends Prediction Using Neural Network Based Hybrid Model. International Journal of Computer Science Engineering and Information Technology Research (IJCSEITR), 3(1), PP. 11-18. [47] Xiu, J., Jin, Y. (2007). Empirical Study of ARFIMA Model Based on Fractional Differencing. Physica:A 377, PP. 137-184. [48] Wang, J. J., Wang, J. ZH., Zhang, ZH. G., Shu, SH. P. (2012). Stock index forecasting based on a hybrid model. Omega 40(6), PP. 758-766. [49] Wei, L. Y., Chen, T. L., Ho, T. H. (2011). A hybrid model based on adaptive-network-based fuzzy inference system to forecast Taiwan stock market. Expert Systems with Applications 38, PP. 13625–13631. [50] Werbos, P. J. (1975). Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. [51] White, H. (1992). Nonparametric Estimation of Conditional Quantiles Using Neural Networks. In Proceedings of the Symposium on the Interface. New York: Springer-Verlag, PP. 190-199. |

URI: | https://mpra.ub.uni-muenchen.de/id/eprint/46786 |