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http://onlinelibrary.wiley.com/doi/10.1002/for.1134/abstract
Jun 16, 2010 · Forecasting volatility with support vector machine-based GARCH model. Authors. Shiyi Chen, Corresponding author. ... This paper deals with the application of SVM in volatility forecasting under the GARCH framework, the performance of which is compared with simple moving average, standard GARCH, nonlinear EGARCH and traditional ANN-GARCH models ...
https://www.researchgate.net/publication/46559574_Forecasting_volatility_with_support_vector_machine-based_GARCH_model
In this paper, a new nonparametric volatility model based on support vector machine is introduced. Parametric volatility models based on GARCH type are also applied to …
https://onlinelibrary.wiley.com/doi/full/10.1002/for.1134
Forecasting volatility with support vector machine‐based GARCH model. Shiyi Chen. Corresponding Author. ... support vector machine (SVM), a novel artificial neural network (ANN), has been successfully used for financial forecasting. This paper deals with the application of SVM in volatility forecasting under the GARCH framework, the ...Cited by: 27
https://ideas.repec.org/a/jof/jforec/v29y2010i4p406-433.html
Downloadable! Recently, support vector machine (SVM), a novel artificial neural network (ANN), has been successfully used for financial forecasting. This paper deals with the application of SVM in volatility forecasting under the GARCH framework, the performance of which is compared with simple moving average, standard GARCH, nonlinear EGARCH and traditional ANN-GARCH models by using two ...
https://stats.stackexchange.com/questions/212840/understanding-recurrent-svm-in-volatility-estimation-of-garch-model
I read Chen et al. "Forecasting volatility with support vector machine-based GARCH model" (2010) where they implented a recurrent SVM procedure to estimate volatility by a GARCH based model. The m...
https://link.springer.com/article/10.1007%2Fs10614-019-09896-w
May 13, 2019 · Ou, P., & Wang, H. (2010). Financial volatility forecasting by least square support vector machine based on GARCH, EGARCH and GJR models: Evidence from ASEAN stock markets. International Journal of Economics and Finance, 2, 337–367. CrossRef Google ScholarAuthor: Hao Sun, Bo Yu
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.663.2881&rep=rep1&type=pdf
Financial Volatility Forecasting by Least Square Support Vector Machine Based on GARCH, EGARCH and GJR Models: Evidence from ASEAN Stock Markets Phichhang Ou (corresponding author) School of Business, University of Shanghai for Science and Technology Rm 101, International Exchange Center, No. 516, Jun Gong Road, Shanghai 200093, China
https://link.springer.com/article/10.1007%2Fs00521-011-0742-z
Oct 14, 2011 · The model delivers stronger performance both in- and out-of-sample than GARCH-type models in long-term forecasts. To enhance MSM’s short-term prediction accuracy, this paper proposes a support vector machine (SVM) based MSM approach which exploits MSM model to forecast volatility and SVM to model the innovations.Cited by: 16
https://file.scirp.org/pdf/JILSA20110400004_11998574.pdf
232 Recurrent Support and Relevance Vector Machines Based Model with Application to Forecasting Volatility of Financial Returns . examining the robustness properties of RSVM and RR- VM compared with GARCH type model, especially, in forecasting volatility in the presence of outliers. The remainder of the paper is organized as follows.
https://www.researchgate.net/publication/5101535_Support_Vector_Regression_Based_GARCH_Model_with_Application_to_Forecasting_Volatility_of_Financial_Returns
Support Vector Regression Based GARCH Model with Application to Forecasting Volatility of Financial Returns Article (PDF Available) in SSRN Electronic Journal · February 2008 with 172 Reads
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