Quasi-Monte Carlo for finance applications

Abstract

Monte Carlo methods are used extensively in computational finance to estimate the price of financial derivative options. We review the use of quasi-Monte Carlo methods to obtain the same accuracy at a much lower computational cost, and focus on three key ingredients: the generation of Sobol' and lattice points, reduction of effective dimension using the principal component analysis approach at full potential, and randomization by shifting or digital shifting to give an unbiased estimator with a confidence interval. Our aim is to provide a starting point for finance practitioners new to quasi-Monte Carlo methods. References

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