Support vector machine as an efficient framework for stock market volatility forecasting

DOI10.1007/s10287-005-0005-5zbMath1142.91718OpenAlexW2059135023MaRDI QIDQ2468372

Valeriy V. Gavrishchaka, Supriya Banerjee

Publication date: 22 January 2008

Published in: Computational Management Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10287-005-0005-5

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Economic time series analysis (91B84)

Related Items (7)

Supervised classification and mathematical optimization ⋮ Tightening big Ms in integer programming formulations for support vector machines with ramp loss ⋮ Time series adaptive online prediction method combined with modified LS-SVR and AGO ⋮ Simulation and evaluation of the distribution of interest rate risk ⋮ On conditional risk estimation considering model risk ⋮ Volatility forecasting via SVR-GARCH with mixture of Gaussian kernels ⋮ A derivative-free optimization approach for the autotuning of a forex trading strategy

Uses Software

• FinTS

Cites Work

• Fractionally integrated generalized autoregressive conditional heteroskedasticity
• Generalized autoregressive conditional heteroscedasticity
• Conditional Heteroskedasticity in Asset Returns: A New Approach
• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
• Breast Cancer Diagnosis and Prognosis Via Linear Programming
• Introduction to Econophysics
• Threshold heteroskedastic models
• Choosing multiple parameters for support vector machines
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