Simulation Analysis of American Style Option Pricing Incorporating Monte Carlo Simulation Models

Abstract

Because American options generally do not have analytical solutions, their pricing has always been a hot issue in the theoretical and industry circles. American options have always dominated the market in terms of ownership because they give buyers more freedom to trade, but their early tradable characteristics make it difficult to simulate their pricing, and traditional methods are not adaptable and accurate enough to be effectively applied to multiple Actual environmental requirements for American-style options for variables and parameters. In recent years, with the development of option pricing theory, some American options pricing methods based on Monte Carlo simulation method have appeared. To solve the problem of inefficiency of reverse substitution, this paper proposes a Monte Carlo method to improve the efficiency of pricing. Based on this, this paper firstly analyzes the characteristics and simulation methods of the Monte Carlo model in depth, especially for the shortcomings of the model, and gives a strategy to reduce its variance. Secondly, the optimization strategy and optimization steps of the stochastic process of option pricing are designed, and the variable control strategy is analyzed from the importance sampling simulation and stratified sampling simulation of American options. Finally, this paper verifies the effectiveness of the Monte Carlo model based on multiple sets of option data, and further compares the differences between the models before and after optimization in the American option pricing simulation. The results of this paper show that the fusion of the Monte Carlo simulation model can greatly improve the pricing of American options.