Parameter Estimation Of Option Pricing Model Based On Intelligent Optimization Algorithms

Derivative security is one of the main research objects of finance engineering. Option is one of the most important derivative securities. An option is an agreement giving the holder the right to purchase or sell the underlying at a particular price in the future. The option pricing theory is an important part of modern finance engineering. The Black-Scholes option pricing model is the foundation of the modern option pricing theory. This paper made an intensive study of the Black-Scholes model. Because of the nonlinear of the Black-Scholes option pricing model, r andσare not easy to be solved by analytic method. Intelligent optimization algorithms have the ability of random search, so the paper use intelligent optimization algorithms to estimate the parameters of the Black-Scholes option pricing model.The paper includes five parts. The introduction part has summarizes the research significance of this article and the main research work. The second part introduces the concept and category of option, the value of option and it's composition, the influence factors of the option price, the Black-Scholes option pricing model and the deduction of the Black-Scholes differential equation. The third part mainly introduces Particle Swarm Optimization (PSO), Quantum-behaved Particle Swarm Optimization (QPSO), Deferential Evolution (DE) and Evolution Strategies (ES). At the forth part, using the finite difference method to solve the option price firstly, and then using the intelligent optimization algorithms to estimate the parameters r andσof the Black-Scholes option pricing model. Experimental results show that QPSO algorithm is more effectively than the other three algorithms in parameter estimation of option pricing. The fifth part is the conclusion of the paper.