亚式期权的MCMC回归模拟定价

Pricing Asian Options Based on Markov Chain Monte Carlo Regression Method

在Boyle将几何平均亚式期权定价作为算术平均亚式期权定价的控制变量基础上,提出将马尔科夫链蒙特卡洛算法融入到方差减少方法的控制变量技术中,对回归方程系数进行有效估计,实现算术平均亚式期权的更加合理定价.并以对无红利算术平均亚式看涨股票期权定价为例,进行大量实证模拟,从期权价格、期权价格标准差等方面与蒙特卡洛模拟方法、最小二乘控制变量方法进行对比分析,结果表明,基于MCMC回归模拟的定价方法具有可行性与有效性.

Based on Boyle＇s regarding the geometric Asian option price as the control variable for computing arithmetic Asian options, the paper proposed to estimate the regression equations efficiently by integrating the Markov Chain Monte Carlo （MCMC） method with the variance reduction, realizing the reasonable positioning of Asian option. Citing, for example, no bonus Arithmetic Average Asian bullish stock option pricing, the methodology is extensively tested on simulated data, and a comparative analysis is made from the price of an option, the option price standard deviation and the Monte Carlo simulation method, least squares control variable. It is concluded that MCMC regression method for pricing Asian options is very feasible and effective.