Comparison of the performance of different machine learning methods in predicting vix volatility

HIGHLIGHTS

• What: With this in mind the purpose of this study is to investigate the process of Random Forest Support Vector Regression as well as in predicting VIX and to evaluate their performance. Based on the evaluations experiments in this study show that performs optimally for smaller low-dimensional time series data. The aim of SVR is to minimize the following objective function: 1 2 T ‖𝑤‖ 2 + C ∑𝑁 𝑖=1 Here, w is the normal vector, and b is the displacement term, C is the regularization parameter. From the results of the STL time series decomposition and autocorrelation test . . .
  

   

  

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