基于多层次蒙特卡罗方法的巴黎期权定价

巴黎期权是由障碍期权发展起来的一种复杂的路径依赖期权,其允许期权持有者在标的资产价格满足在某个给定的价格水平(障碍价格)之上或者之下连续或累计停留预先设定的一段时间的条件下,以预先约定的价格(执行价格)买入或卖出某种标的资产。目前巴黎期权定价的主流数值方法有二叉树方法、有限差分法和蒙特卡罗方法。论文的研究结果表明,在给定的精度条件下,与标准蒙特卡罗方法相比,多层蒙特卡罗方法能够将运算成本从O(ε^-3)减少到O(ε^-2(logε)²);反之,在给定的计算成本条件下,相对于标准蒙特卡罗方法,多层蒙特卡罗方法能够更快地收敛到真值附近。本文将其应用于巴黎期权的定价计算中,增加了巴黎期权的数值算法选择范围,并提高了巴黎期权定价的精度。

Pricing Parisian Option by Multi-level Monte Carlo Method

Parisian option is a complex path-dependent option extended from the barrier options, which allows the holder buy or sell a certain underlying asset at a pre-specified price under the condition that underlying asset price above or below a given level of a continuous or cumulative occupation time before maturity. The numerical methods for pricing Parisian option include binomial tree method, finite difference method and Monte Carlo method. Compared with other numerical methods, Monte Carlo method is more flexible and easy to implement and improve; moreover, its estimation error and convergence speed has stronger independence with the dimensions of the problem to be solved, and thus can solve the target variable of high-dimensional derivative securities pricing better.In this paper the Parisian option is priced using the Monte Carlo method, and improves the standard Monte Carlo algorithm is improved to multi-level Monte Carlo algorithm. Our research results show that under the given accuracy, multi-level Monte Carlo algorithm can reduce the calculation costs from O(ε^-3) to O(ε^-2(logε)²) comparing with the standard Monte Carlo method. On the other hand, under given calculation cost, multi-level Monte Carlo method can converge to the true value faster comparing with standard Monte Carlo method. Applying this method to Parisian option pricing not only expanses the choice scope of Parisian options' numerical algorithms, but also improves the precision of Parisian option pricing, and lays a certain foundation for Parisian options' application in the domestic market.