Low Discrepancy Sequences

Computation

Low discrepancy sequences function as deterministic alternatives to pseudo-random numbers in quasi-Monte Carlo methods for financial modeling. These sequences aim to fill multidimensional space more uniformly than standard random sampling, which reduces the number of simulations required to achieve convergence in complex pricing models. By minimizing the clustering effects common in typical computational approaches, they significantly enhance the speed and accuracy of evaluating path-dependent digital assets.