The VIX under scrutiny of Machine Learning

For those interested in machine learning applications to financial markets, I strongly recommend this interesting paper discussing several approaches to replicate the VIX index as well as‧‧‧

For those interested in machine learning applications to financial markets, I strongly recommend this interesting paper discussing several approaches to replicate the VIX index as well as VIX futures by using ONLY a subset of relevant options as well as neural networks that are trained to automatically learn the underlying formula. Despite its theoretical foundation in option price theory, the VIX index value, which quotes the expected annualized change in the S&P 500 index over the following 30 days, is prone to errors mainly caused by the illiquidity of some of its components. Using subset selection approaches on top of the original CBOE methodology, as well as building machine learning and neural network models including Random Forests, Support Vector Machines, feed-forward neural networks, and long short-term memory (LSTM) models, the authors show that a small number of options is sufficient to replicate the VIX index and they are able to exploit potential arbitrage opportunities between the VIX index and its underlying derivatives.


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    As an Investment Consultant and Specialist, Pompeo Pontone is a Professional Investor with 25 years’ experience in the fields of Investment Management, Quantitative Finance & Derivatives Trading and Data Science.

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