论文标题
被炒的重要性：增压准蒙特卡洛
The importance of being scrambled: supercharged Quasi Monte Carlo
论文作者
论文摘要
在许多财务应用中，基于SOBOL的低单层序列（LDS）的准蒙特卡洛（QMC）优于蒙特卡洛的表现更快，更稳定。但是，与MC QMC不同，缺乏实际的错误估计。随机QMC（RQMC）方法结合了两种方法中最好的。加扰的LD的应用允许在估计值围绕置信区间计算置信区间，从而提供了实际错误。通过两种方法将Sobol'LD随机化：使用双曲线局部波动率模型对亚洲选项和希腊人进行计算进行比较。 RQMC证明了比标准QMC出色的性能，显示收敛率增加并提供实际误差范围。
In many financial applications Quasi Monte Carlo (QMC) based on Sobol low-discrepancy sequences (LDS) outperforms Monte Carlo showing faster and more stable convergence. However, unlike MC QMC lacks a practical error estimate. Randomized QMC (RQMC) method combines the best of two methods. Application of scrambled LDS allow to compute confidence intervals around the estimated value, providing a practical error bound. Randomization of Sobol' LDS by two methods: Owen's scrambling and digital shift are compared considering computation of Asian options and Greeks using hyperbolic local volatility model. RQMC demonstrated the superior performance over standard QMC showing increased convergence rates and providing practical error bounds.
