Computational Intelligence Approach to Capturing the Implied Volatility

Abstract : In this paper, a Computational Intelligence Approach and more particularly a neural network is used to learn from data on the Black-Scholes implied volatility. The implied volatility forecasts, generated from the Neural Net, are converted to option price using the Black-Scholes formula. The neural network option pricing capabilities are shown to be superior to the Black-Scholes and the GARCH option-pricing model. The neural network has also shown that it is able to reproduce the implied volatility well into the future whereas the GARCH option-pricing model shows deterioration in the implied volatility with time.
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Tharam Dillon. 4th IFIP International Conference on Artificial Intelligence in Theory and Practice (AI 2015), Oct 2015, Daejeon, South Korea. IFIP Advances in Information and Communication Technology, AICT-465, pp.85-97, 2015, Artificial Intelligence in Theory and Practice IV. 〈10.1007/978-3-319-25261-2_8〉
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Fahed Mostafa, Tharam Dillon, Elizabeth Chang. Computational Intelligence Approach to Capturing the Implied Volatility. Tharam Dillon. 4th IFIP International Conference on Artificial Intelligence in Theory and Practice (AI 2015), Oct 2015, Daejeon, South Korea. IFIP Advances in Information and Communication Technology, AICT-465, pp.85-97, 2015, Artificial Intelligence in Theory and Practice IV. 〈10.1007/978-3-319-25261-2_8〉. 〈hal-01383948〉

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