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Reports (Research Report) Year : 2005

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

Abstract

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 sequence and prove a central limit theorem for the corresponding estimator. We will also provide numerical results that compare the mixed sequence with the Monte Carlo and randomized quasi-Monte Carlo methods.

Domains

Other [cs.OH]
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Dates and versions

inria-00070407 , version 1 (19-05-2006)

Identifiers

  • HAL Id : inria-00070407 , version 1

Cite

Giray Ökten, Bruno Tuffin, Vadim Burago. A central limit theorem and improved error bounds for a hybrid-Monte Carlo sequence with applications in computational finance. [Research Report] RR-5600, INRIA. 2005, pp.28. ⟨inria-00070407⟩
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