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Forecasting Financial Returns Volatility: A GARCH-SVR Model
16 references·4 citing·33 second hop
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References

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  • 1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation20,684 cited
  • 1993On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks9,041 cited
  • 1998Answering the Skeptics: Yes, Standard Volatility Models do Provide Accurate Forecasts3,380 cited
  • 1996Fractionally integrated generalized autoregressive conditional heteroskedasticity2,290 cited
  • 1994Chapter 49 Arch models880 cited
  • 1989Conditional Heteroscedasticity in Time Series of Stock Returns: Evidence and Forecasts856 cited
  • 2001Financial Forecasting Using Support Vector Machines392 cited
  • 2017The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with Support Vector Regression257 cited
  • 2005Statistical Tools for Finance and Insurance213 cited
  • 2011Statistical Tools for Finance and Insurance159 cited
  • 2003Estimating GARCH models using support vector machines*104 cited
  • 2009Forecasting volatility with support vector machine‐based GARCH model87 cited
  • 2016Volatility forecasting via SVR–GARCH with mixture of Gaussian kernels77 cited

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2008
Forecasting volatility based on wavelet support vector machine
66 cited
  • 2008Forecasting ability of GARCH vs Kalman filter method: evidence from daily UK time‐varying beta59 cited
  • Cited by

    • 2022Hybrid intelligent framework for carbon price prediction using improved variational mode decomposition and optimal extreme learning machine58 cited
    • 2022Supply chain management based on volatility clustering: The effect of CBDC volatility41 cited
    • 2021On improving GARCH volatility forecasts for Bitcoin via a meta-learning approach36 cited
    • 2021Carbon price forecasting using multiscale nonlinear integration model coupled optimal feature reconstruction with biphasic deep learning30 cited