A central limit theorem and improved error bounds for a hybrid-Monte Carlo sequence with applications in computational finance

@article{kten2006ACL,
  title={A central limit theorem and improved error bounds for a hybrid-Monte Carlo sequence with applications in computational finance},
  author={Giray {\"O}kten and Bruno Tuffin and Vadim Burago},
  journal={J. Complexity},
  year={2006},
  volume={22},
  pages={435-458}
}
In problems of moderate dimensions, the quasi-Monte Carlo method usually provides better estimates than the Monte Carlo method. However, as the dimension of the problem increases, the advantages of the quasi-Monte Carlo method diminish quickly. A remedy for this problem is to use hybrid sequences; sequences that combine pseudorandom and low-discrepancy vectors. In this paper we discuss a particular hybrid sequence called the mixed sequence. We will provide improved discrepancy bounds for this… CONTINUE READING