  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.
