Forecasting nonnegative option price distributions using Bayesian kernel methods

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.

► The proposed Bayesian learning model predicts positive option price distribution.
► A new transform measure preserves distributions of large deviations.
► Comprehensive empirical study using the KOSPI200 index options is given.
► Simulation verifies better performance of the proposed method.