Volatility Forecasting Using Support Vector Regression and a Hybrid Genetic Algorithm
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DOI: 10.1007/s10614-013-9411-x
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References listed on IDEAS
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Cited by:
- Marcin Fałdziński & Piotr Fiszeder & Witold Orzeszko, 2020. "Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression," Energies, MDPI, vol. 14(1), pages 1-18, December.
- Manuel Rizzo & Francesco Battaglia, 2016. "On the Choice of a Genetic Algorithm for Estimating GARCH Models," Computational Economics, Springer;Society for Computational Economics, vol. 48(3), pages 473-485, October.
- Pedro Correia S. Bezerra & Pedro Henrique M. Albuquerque, 2017. "Volatility forecasting via SVR–GARCH with mixture of Gaussian kernels," Computational Management Science, Springer, vol. 14(2), pages 179-196, April.
- Hao Sun & Bo Yu, 2020. "Forecasting Financial Returns Volatility: A GARCH-SVR Model," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 451-471, February.
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Keywords
Support vector regression; Genetic algorithm; Boltzmann selection; Chaotic number generator; Parameter optimization; Volatility forecasting;All these keywords.
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