Predicting a distribution of implied volatilities for option pricing

Authors:
Seung-Ho Yang;Jaewook Lee
Affiliations:
Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, Kyungbuk 790-784, Republic of Korea;Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, Kyungbuk 790-784, Republic of Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

In this paper, we propose a method that predicts a distribution of the implied volatility functions and that provides confidence intervals for the option prices from it. The proposed method, based on a Bayesian approach, employs a Bayesian kernel machine, so-called Gaussian process regression. To verify the performance of the proposed method, we conducted simulations on some model-generated option prices data and real option market data. The simulation results show that the proposed method performs well with practically meaningful option ranges as well as overcomes the problem of containing negative prices in their predicted confidence intervals by the previous works.