Ozun, Alper and Cifter, Atilla (2007): Nonlinear Combination of Financial Forecast with Genetic Algorithm.

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Abstract
Complexity in the financial markets requires intelligent forecasting models for return volatility. In this paper, historical simulation, GARCH, GARCH with skewed studentt distribution and asymmetric normal mixture GRJGARCH models are combined with Extreme Value Theory Hill by using artificial neural networks with genetic algorithm as the combination platform. By employing daily closing values of the Istanbul Stock Exchange from 01/10/1996 to 11/07/2006, Kupiec and Christoffersen tests as the backtesting mechanisms are performed for forecast comparison of the models. Empirical findings show that the fattails are more properly captured by the combination of GARCH with skewed studentt distribution and Extreme Value Theory Hill. Modeling return volatility in the emerging markets needs “intelligent” combinations of ValueatRisk models to capture the extreme movements in the markets rather than individual model forecast.
Item Type:  MPRA Paper 

Institution:  Marmara University 
Original Title:  Nonlinear Combination of Financial Forecast with Genetic Algorithm 
Language:  English 
Keywords:  Forecast combination; Artificial neural networks; GARCH models; Extreme value theory; Christoffersen test 
Subjects:  G  Financial Economics > G0  General C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C52  Model Evaluation, Validation, and Selection C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models ; Multiple Variables > C32  TimeSeries Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models 
Item ID:  2488 
Depositing User:  Atilla Cifter 
Date Deposited:  02 Apr 2007 
Last Modified:  30 Sep 2019 16:42 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/2488 