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首页> 外文期刊>Journal of Mathematics and Statistics >Efficient Monte Carlo Algorithm Using Antithetic Variate and Brownian Bridge Techniques for Pricing the Barrier Options with Rebate Payments | Science Publications
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Efficient Monte Carlo Algorithm Using Antithetic Variate and Brownian Bridge Techniques for Pricing the Barrier Options with Rebate Payments | Science Publications

机译:对数变量和布朗桥技术的有效蒙特卡洛算法,用于带回扣支付的障碍期权定价科学出版物

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> >The down-and-out call barrier option with rebate payment on dividend-paying stock is simulated using a new version of the Monte Carlo algorithm. The standard Monte Carlo method for simulating such an option suffers from two sources of errors: Hitting time error inherent from time stepping and the Monte Carlo statistical error. We present a modified version of Monte Carlo method that can reduce these errors efficiently using the Brownian bridge technique for the hitting time error and the antithetic variate approach for the statistical error. We found that the Brownian bridge technique is responsible for improving the order of convergence in hitting time from one half to one and the antithetic variate technique can speed up the Monte Carlo simulation by reducing the variance of the computed payoff, giving almost twice as much accuracy. The standard error and the coefficient of variation are applied in order to measure the effectiveness of the volatility of the underlying option.
机译: > >使用新版本的蒙特卡洛算法对带有股息支付股票返利的下回呼叫障碍期权进行模拟。用于模拟此选项的标准蒙特卡洛方法有两个误差源:时间步长固有的击中时间误差和蒙特卡洛统计误差。我们提出了蒙特卡罗方法的改进版本,该方法可以使用Brownian桥技术有效地减少击中时间误差,而对数变异方法则可以使用统计误差有效地减少这些误差。我们发现布朗桥技术可将击球时间的收敛顺序从一半提高到一半,而对立变量技术可通过减少计算收益的方差来加快蒙特卡洛模拟,从而提供几乎两倍的精度。应用标准误差和变异系数以衡量基础期权波动的有效性。

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