Generalized Cox-Ross-Rubinstein Binomial Models
在经典CRR二项式模型中加入一个拉伸参数，形成广义模型，用于更高效地为障碍期权等各类期权定价，并分析了该模型向Black-Scholes公式收敛的精细结构。
This paper generalizes the seminal Cox-Ross-Rubinstein (CRR) binomial model by adding a stretch parameter. The generalized CRR (GCRR) model allows us to fine-tune (via the stretch parameter) the lattice structure so as to efficiently price a range of options, such as barrier options. Our analysis provides insights into the fine structure of convergence of the general binomial model to the Black-Scholes formula. We also discuss how to improve the rate of convergence or the oscillatory behavior of the GCRR model. The numerical results suggest that the GCRR models with various modifications are efficient for pricing a range of options.