Computations in Option Pricing Engines Public
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As computers increase their power, machine learning gains an important role in various industries. We consider how to apply this method of analysis and pattern identification to complement extant financial models, specifically option pricing methods. We first prove the discussed model is arbitrage-free to confirm it will yield appropriate results. Next, we apply a neural network algorithm and study its ability to approximate option prices from existing models. The results show great potential for applying machine learning where traditional methods fail. As an example, we study the implied volatility surface of highly liquid stocks using real data, which is computationally intensive, to justify the practical impact of the methods proposed.
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Permanent link to this page: https://digital.wpi.edu/show/nc580q54p