Forecasting nonnegative option price distributions using Bayesian kernel methods

Authors:
Hyejin Park;Jaewook Lee
Affiliations:
Department of Industrial and Management Engineering, Pohang University of Science and Technology (POSTECH), San 31, Hyoja Pohang 790-784, South Korea;Department of Industrial Engineering, Seoul National University, 599 Gwanak-ro, Gwanak-gu, Seoul 151-744, South Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

This paper proposes a novel Bayesian kernel model that can forecast the non-negative distribution of target option prices, which are constrained to be positive. The method utilizes a new transform measure that guarantees the non-negativity of option prices, and can be applied to Bayesian kernel models to provide predictive distributions of option prices. Simulations conducted on the model-generated option data and KOSPI 200 index option data show that the proposed method not only provide a predictive distribution of non-negative option prices, but also preserves the probabilistic distribution of large deviations. We also perform a very extensive empirical study on a large-scale time series of option prices to assess the prediction performance of the proposed method. We find that the method outperforms other state of the arts non-parametric methods in prediction accuracy and is statistically different.