Research On Option Pricing Of Black Scholes Model And Nonparametric Method

At first, this paper reviews the history of option pricing. since the birth of the Black-Scholes model, scholars from various countries have constantly make great efforts in terms of option pricing, some choose to change the assumptions of the Black-Scholes model which are not consistent with the real situation, Some choose another totally new way to understand option pricing and then to build option pricing model. During this process, we will use a variety of mathematical models, because these mathematical models capture the characteristics of price change of the underlying asset. After all, these mathematical models are often highly idealized, which is usually not consistent with the actual situation. So we should consider improving the mathematical model. However, this effect of improvement is sometimes very poor, which can’t satisfy our requirement. For this reason, we combined mathematical models with nonparametric learning techniques. Compared to other pricing models, the effect of option pricing can be greatly improved. This improvement is achieved by estimating the state price distribution function. In this article, we recommend a new semi-parametric method to estimate the distribution function of the state price. This method estimate the standardized state price function by a combination of mathematical models and nonparametric techniques, it is called automatic correction of error pricing model(ACE). This method is very easy to implement, widely applied, and can be combined with any mathematical model. it improves the effect of pricing model by the pricing error learning technique. Empirical study of Hong Kong’s Heng Sheng stock index options shows that compared with ad black scholes model, the direct non-parametric model, and the semi-parametric black scholes model, its effect of pricing is excellent.