Predicting VIX Futures Public
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This project used machine learning techniques to try and approximate the values of the VIX and VIX futures based on S\&P 500 options. A number of feedforward neural networks were trained using various network architectures and feature representations. LASSO regression was used to select a subset of the available features that appear to be more important for predictions. This subset was then used as a feature set for several neural networks. All neural networks were then compared on basis of accuracy to set what effects changes in the number of features had on the accuracy of the resulting model.
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Permanent link to this page: https://digital.wpi.edu/show/rv042w827