Condence interval estimation of option prices by using the predicted distribution of implied volatility
Kazem Nouri, Maryam Elahi · 2013
Many option pricing formulas have been developed to overcome the restrictive assumptions of Black and Scholes models and to give more accurate prices. Most of the methods are focused on a point prediction of option price. In this paper, we propose a method that predicts a distribution of the implied volatility functions by applying a Gaussian process regression and estimating condence intervals of option prices using the predicted volatility distributions. 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 condence intervals by the previous works.