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Support Vector Machine as an Efficient Framework for Stock Market Volatility Forecasting

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  • Valeriy Gavrishchaka
  • Supriya Banerjee

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  • Valeriy Gavrishchaka & Supriya Banerjee, 2006. "Support Vector Machine as an Efficient Framework for Stock Market Volatility Forecasting," Computational Management Science, Springer, vol. 3(2), pages 147-160, April.
    • Handle: RePEc:spr:comgts:v:3:y:2006:i:2:p:147-160
    DOI: 10.1007/s10287-005-0005-5
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    References listed on IDEAS

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    1. Donaldson, R. Glen & Kamstra, Mark, 1997. "An artificial neural network-GARCH model for international stock return volatility," Journal of Empirical Finance, Elsevier, vol. 4(1), pages 17-46, January.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
    4. Jaume Masoliver & Josep Perelló, 2002. "A Correlated Stochastic Volatility Model Measuring Leverage And Other Stylized Facts," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 5(05), pages 541-562.
    5. Olvi L. Mangasarian & W. Nick Street & William H. Wolberg, 1995. "Breast Cancer Diagnosis and Prognosis Via Linear Programming," Operations Research, INFORMS, vol. 43(4), pages 570-577, August.
    6. Gençay, Ramazan & Dacorogna, Michel & Muller, Ulrich A. & Pictet, Olivier & Olsen, Richard, 2001. "An Introduction to High-Frequency Finance," Elsevier Monographs, Elsevier, edition 1, number 9780122796715.
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    Cited by:

    1. Ming-Chi Tsai & Ching-Hsue Cheng & Meei-Ing Tsai & Huei-Yuan Shiu, 2018. "Forecasting leading industry stock prices based on a hybrid time-series forecast model," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-24, December.
    2. Baldomero-Naranjo, Marta & Martínez-Merino, Luisa I. & Rodríguez-Chía, Antonio M., 2020. "Tightening big Ms in integer programming formulations for support vector machines with ramp loss," European Journal of Operational Research, Elsevier, vol. 286(1), pages 84-100.
    3. Johan Hagenbjörk & Jörgen Blomvall, 2019. "Simulation and evaluation of the distribution of interest rate risk," Computational Management Science, Springer, vol. 16(1), pages 297-327, February.
    4. Tasnim Uddin Chowdhury & Md. Shahidul Islam, 2021. "ARIMA Time Series Analysis in Forecasting Daily Stock Price of Chittagong Stock Exchange (CSE)," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 5(6), pages 214-233, June.
    5. Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), 2008. "Handbook on Information Technology in Finance," International Handbooks on Information Systems, Springer, number 978-3-540-49487-4, November.
    6. Tiago E. Pratas & Filipe R. Ramos & Lihki Rubio, 2023. "Forecasting bitcoin volatility: exploring the potential of deep learning," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 13(2), pages 285-305, June.
    7. Ding, Shusheng & Cui, Tianxiang & Wu, Xiangling & Du, Min, 2022. "Supply chain management based on volatility clustering: The effect of CBDC volatility," Research in International Business and Finance, Elsevier, vol. 62(C).
    8. Liu, Jing & Chen, Zhonglu, 2023. "How do stock prices respond to the leading economic indicators? Analysis of large and small shocks," Finance Research Letters, Elsevier, vol. 51(C).
    9. Ronny Luss & Alexandre D'Aspremont, 2015. "Predicting abnormal returns from news using text classification," Quantitative Finance, Taylor & Francis Journals, vol. 15(6), pages 999-1012, June.
    10. 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.
    11. Jun Zhang & Lan Li & Wei Chen, 2021. "Predicting Stock Price Using Two-Stage Machine Learning Techniques," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1237-1261, April.
    12. 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.
    13. Thierry Warin & Aleksandar Stojkov, 2021. "Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature," JRFM, MDPI, vol. 14(7), pages 1-31, July.
    14. Duan, Wen-Qi & Stanley, H. Eugene, 2011. "Cross-correlation and the predictability of financial return series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(2), pages 290-296.

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