This paper performs a comprehensive analysis of Vix Index data with Heikin Ashi Transformation of stock index Neural Network Learning. It has been demonstrated that Heikin Ashi Transformation can improve the learning effect of Neural Network and the effect can also be filter out if volume weights are also considered. This paper introduces another improvement beside using volume-weighted data. Instead volatility index is used as an input and its effect for neural network learning process is analyzed.
This research paper explores the usefulness of a neural network in portfolio management using gamma scalping. Since gamma scalping requires both stock options position as well as the underlying stock to be added or removed frequently. It becomes important to optimize such transactions. The underlying portfolio provides enough data points that may be used to calculate precise points of time to initiate stock addition or removal.
This paper analyses the Index Option Greek with respect to a transformed data set of Index that has been Heikin Ashi Transformed. It has been noted that Heikin Ashi Transformation can provide better prediction than normal data and the noise effect can also be used to filter out if volume weights are also considered. This paper tries to predict option greeks for index option with the help of a Neural Network setup. Since option greeks play a very important role in understanding the correct pricing of index option, the paper provides some useful insights in such models.
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