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Nonlinear Combination of Financial Forecast with Genetic Algorithm

Author

Listed:
  • Ozun, Alper
  • Cifter, Atilla

Abstract

Complexity in the financial markets requires intelligent forecasting models for return volatility. In this paper, historical simulation, GARCH, GARCH with skewed student-t distribution and asymmetric normal mixture GRJ-GARCH 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 back-testing mechanisms are performed for forecast comparison of the models. Empirical findings show that the fat-tails are more properly captured by the combination of GARCH with skewed student-t distribution and Extreme Value Theory Hill. Modeling return volatility in the emerging markets needs “intelligent” combinations of Value-at-Risk models to capture the extreme movements in the markets rather than individual model forecast.

Suggested Citation

  • Ozun, Alper & Cifter, Atilla, 2007. "Nonlinear Combination of Financial Forecast with Genetic Algorithm," MPRA Paper 2488, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:2488
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    File URL: https://mpra.ub.uni-muenchen.de/2488/1/MPRA_paper_2488.pdf
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    References listed on IDEAS

    as
    1. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    2. McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
    3. Peter Christoffersen & Kris Jacobs, 2004. "Which GARCH Model for Option Valuation?," Management Science, INFORMS, vol. 50(9), pages 1204-1221, September.
    4. Pagan, Adrian, 1996. "The econometrics of financial markets," Journal of Empirical Finance, Elsevier, vol. 3(1), pages 15-102, May.
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    Citations

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    Cited by:

    1. Christian Dunis & Jason Laws & Georgios Sermpinis, 2010. "Modelling commodity value at risk with higher order neural networks," Applied Financial Economics, Taylor & Francis Journals, vol. 20(7), pages 585-600.
    2. Cifter, Atilla, 2011. "Value-at-risk estimation with wavelet-based extreme value theory: Evidence from emerging markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(12), pages 2356-2367.

    More about this item

    Keywords

    Forecast combination; Artificial neural networks; GARCH models; Extreme value theory; Christoffersen test;

    JEL classification:

    • G0 - Financial Economics - - General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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