A CNN Based System for Predicting the Implied Volatility and Option Prices
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Date
2020-01-07
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Abstract
The evaluations of option prices and implied volatility are critical for option risk management and trading. Common strategies in existing studies relied on the parametric models. However, these models are based on several idealistic assumptions. In addition, previous research of option pricing mainly depends on the historical transaction records without considering the performance of other concurrent options. To address these challenges, we proposed a convolutional neural network (CNN) based system for predicting the implied volatility and the option prices. Specifically, the customized non-parametric learning approach is first used to estimate the implied volatility. Second, several traditional parametric models are also implemented to estimate these prices as well. The convolutional neural network is utilized to obtain the predictions based on the estimation of the implied volatility. Our experiments based on Chinese SSE 50ETF options demonstrate that the proposed framework outperforms the traditional methods with at least 40.12% performance enhancement in terms of RMSE.
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Machine Learning and Predictive Analytics in Accounting, Finance and Management, cnn, implied volatility, option pricing, ensembling learning.
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8 pages
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Proceedings of the 53rd Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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