Predicting Financial Market Volatility with Modern Model and Traditional Model

The major topic investigates how classical methods (ARCH and GARCH) and well-known machine learning algorithms, support vector regression, and hybrid methods. This paper aims to predict and forecast volatility to develop a two-stage forecasting approach the volatility of the Amman Stock Exchange Index (ASE) effectively. Additionally, the effectiveness of the machine learning techniques’ selection and utilization of information in stock data is evaluated. Methods the semiparametric estimating technique known as support vector regression (SVR) has been widely used for the prediction of volatility in financial time series. By integrating SVR with the GARCH model (GARCH-SVR) application with various kernels (Radial Basis Kernel Function (RBF), Polynomial Kernel Function (PF), and linear Kernel Function (lF)). The suggested learning approaches are compared to two well-known statistical time series models, Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH), in order to assess the assertion that they can properly anticipate ASE volatility. To compare their results, RMSE is employed as an error metric. There is evidence that the GARCH-SVR model performs best for predicting volatility time series, and classical volatility model techniques have an enormous predictive performance better than machine learning models.

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